update

update

.github/workflows/benchmark.yml

@@ -11,17 +11,18 @@ env:
   HF_HOME: /mnt/cache
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
+  BASE_PATH: benchmark_outputs
 
 jobs:
-  torch_pipelines_cuda_benchmark_tests:
+  torch_models_cuda_benchmark_tests:
     env:
       SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_BENCHMARK }}
-    name: Torch Core Pipelines CUDA Benchmarking Tests
+    name: Torch Core Models CUDA Benchmarking Tests
     strategy:
       fail-fast: false
       max-parallel: 1
     runs-on:
-      group: aws-g6-4xlarge-plus
+      group: aws-g6e-4xlarge
     container:
       image: diffusers/diffusers-pytorch-cuda
       options: --shm-size "16gb" --ipc host --gpus 0
@@ -35,27 +36,47 @@ jobs:
           nvidia-smi
       - name: Install dependencies
         run: |
+          apt update
+          apt install -y libpq-dev postgresql-client
           python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
           python -m uv pip install -e [quality,test]
-          python -m uv pip install pandas peft
-          python -m uv pip uninstall transformers && python -m uv pip install transformers==4.48.0
+          python -m uv pip install -r benchmarks/requirements.txt
       - name: Environment
         run: |
           python utils/print_env.py
       - name: Diffusers Benchmarking
         env:
-            HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
-            BASE_PATH: benchmark_outputs
+          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         run: |
-          export TOTAL_GPU_MEMORY=$(python -c "import torch; print(torch.cuda.get_device_properties(0).total_memory / (1024**3))")
-          cd benchmarks && mkdir ${BASE_PATH} && python run_all.py && python push_results.py
+          cd benchmarks && python run_all.py
+
+      - name: Push results to the Hub
+        env: 
+          HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
+        run: |
+          cd benchmarks && python push_results.py
+          mkdir $BASE_PATH && cp *.csv $BASE_PATH
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
           name: benchmark_test_reports
-          path: benchmarks/benchmark_outputs
+          path: benchmarks/${{ env.BASE_PATH }}
+      
+      # TODO: enable this once the connection problem has been resolved.
+      - name: Update benchmarking results to DB
+        env:
+          PGDATABASE: metrics
+          PGHOST: ${{ secrets.DIFFUSERS_BENCHMARKS_PGHOST }}
+          PGUSER: transformers_benchmarks
+          PGPASSWORD: ${{ secrets.DIFFUSERS_BENCHMARKS_PGPASSWORD }}
+          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+        run: |
+          git config --global --add safe.directory /__w/diffusers/diffusers
+          commit_id=$GITHUB_SHA
+          commit_msg=$(git show -s --format=%s "$commit_id" | cut -c1-70)
+          cd benchmarks && python populate_into_db.py "$BRANCH_NAME" "$commit_id" "$commit_msg"
 
       - name: Report success status
         if: ${{ success() }}

.github/workflows/nightly_tests.yml

@@ -248,7 +248,7 @@ jobs:
           BIG_GPU_MEMORY: 40
         run: |
           python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-            -m "big_gpu_with_torch_cuda" \
+            -m "big_accelerator" \
             --make-reports=tests_big_gpu_torch_cuda \
             --report-log=tests_big_gpu_torch_cuda.log \
             tests/

.github/workflows/pr_modular_tests.yml

@@ -0,0 +1,141 @@
+name: Fast PR tests for Modular
+
+on:
+  pull_request:
+    branches: [main]
+    paths:
+      - "src/diffusers/modular_pipelines/**.py"
+      - "src/diffusers/models/modeling_utils.py"
+      - "src/diffusers/models/model_loading_utils.py"
+      - "src/diffusers/pipelines/pipeline_utils.py"
+      - "src/diffusers/pipeline_loading_utils.py"
+      - "src/diffusers/loaders/lora_base.py"
+      - "src/diffusers/loaders/lora_pipeline.py"
+      - "src/diffusers/loaders/peft.py"
+      - "tests/modular_pipelines/**.py"
+      - ".github/**.yml"
+      - "utils/**.py"
+      - "setup.py"
+  push:
+    branches:
+      - ci-*
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  DIFFUSERS_IS_CI: yes
+  HF_HUB_ENABLE_HF_TRANSFER: 1
+  OMP_NUM_THREADS: 4
+  MKL_NUM_THREADS: 4
+  PYTEST_TIMEOUT: 60
+
+jobs:
+  check_code_quality:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check quality
+        run: make quality
+      - name: Check if failure
+        if: ${{ failure() }}
+        run: |
+          echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
+
+  check_repository_consistency:
+    needs: check_code_quality
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check repo consistency
+        run: |
+          python utils/check_copies.py
+          python utils/check_dummies.py
+          python utils/check_support_list.py
+          make deps_table_check_updated
+      - name: Check if failure
+        if: ${{ failure() }}
+        run: |
+          echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
+
+  run_fast_tests:
+    needs: [check_code_quality, check_repository_consistency]
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - name: Fast PyTorch Modular Pipeline CPU tests
+            framework: pytorch_pipelines
+            runner: aws-highmemory-32-plus
+            image: diffusers/diffusers-pytorch-cpu
+            report: torch_cpu_modular_pipelines
+
+    name: ${{ matrix.config.name }}
+
+    runs-on:
+      group: ${{ matrix.config.runner }}
+
+    container:
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m uv pip install -e [quality,test]
+        pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+        pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
+
+    - name: Environment
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python utils/print_env.py
+
+    - name: Run fast PyTorch Pipeline CPU tests
+      if: ${{ matrix.config.framework == 'pytorch_pipelines' }}
+      run: |
+        python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
+        python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/modular_pipelines
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: pr_${{ matrix.config.framework }}_${{ matrix.config.report }}_test_reports
+        path: reports
+
+

.github/workflows/pr_tests_gpu.yml

@@ -13,6 +13,7 @@ on:
       - "src/diffusers/loaders/peft.py"
       - "tests/pipelines/test_pipelines_common.py"
       - "tests/models/test_modeling_common.py"
+      - "examples/**/*.py"
   workflow_dispatch:
 
 concurrency:
@@ -188,7 +189,7 @@ jobs:
         shell: bash
     strategy:
       fail-fast: false
-      max-parallel: 2
+      max-parallel: 4
       matrix:
         module: [models, schedulers, lora, others]
     steps:

benchmarks/README.md

@@ -0,0 +1,69 @@
+# Diffusers Benchmarks
+
+Welcome to Diffusers Benchmarks. These benchmarks are use to obtain latency and memory information of the most popular models across different scenarios such as:
+
+* Base case i.e., when using `torch.bfloat16` and `torch.nn.functional.scaled_dot_product_attention`.
+* Base + `torch.compile()`
+* NF4 quantization
+* Layerwise upcasting
+
+Instead of full diffusion pipelines, only the forward pass of the respective model classes (such as `FluxTransformer2DModel`) is tested with the real checkpoints (such as `"black-forest-labs/FLUX.1-dev"`). 
+
+The entrypoint to running all the currently available benchmarks is in `run_all.py`. However, one can run the individual benchmarks, too, e.g., `python benchmarking_flux.py`. It should produce a CSV file containing various information about the benchmarks run.
+
+The benchmarks are run on a weekly basis and the CI is defined in [benchmark.yml](../.github/workflows/benchmark.yml).
+
+## Running the benchmarks manually
+
+First set up `torch` and install `diffusers` from the root of the directory:
+
+```py
+pip install -e ".[quality,test]"
+```
+
+Then make sure the other dependencies are installed:
+
+```sh
+cd benchmarks/
+pip install -r requirements.txt
+```
+
+We need to be authenticated to access some of the checkpoints used during benchmarking:
+
+```sh
+huggingface-cli login
+```
+
+We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).
+
+Then you can either launch the entire benchmarking suite by running:
+
+```sh
+python run_all.py
+```
+
+Or, you can run the individual benchmarks.
+
+## Customizing the benchmarks
+
+We define "scenarios" to cover the most common ways in which these models are used. You can
+define a new scenario, modifying an existing benchmark file:
+
+```py
+BenchmarkScenario(
+    name=f"{CKPT_ID}-bnb-8bit",
+    model_cls=FluxTransformer2DModel,
+    model_init_kwargs={
+        "pretrained_model_name_or_path": CKPT_ID,
+        "torch_dtype": torch.bfloat16,
+        "subfolder": "transformer",
+        "quantization_config": BitsAndBytesConfig(load_in_8bit=True),
+    },
+    get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+    model_init_fn=model_init_fn,
+)
+```
+
+You can also configure a new model-level benchmark and add it to the existing suite. To do so, just defining a valid benchmarking file like `benchmarking_flux.py` should be enough.
+
+Happy benchmarking 🧨

benchmarks/base_classes.py

@@ -1,346 +0,0 @@
-import os
-import sys
-
-import torch
-
-from diffusers import (
-    AutoPipelineForImage2Image,
-    AutoPipelineForInpainting,
-    AutoPipelineForText2Image,
-    ControlNetModel,
-    LCMScheduler,
-    StableDiffusionAdapterPipeline,
-    StableDiffusionControlNetPipeline,
-    StableDiffusionXLAdapterPipeline,
-    StableDiffusionXLControlNetPipeline,
-    T2IAdapter,
-    WuerstchenCombinedPipeline,
-)
-from diffusers.utils import load_image
-
-
-sys.path.append(".")
-
-from utils import (  # noqa: E402
-    BASE_PATH,
-    PROMPT,
-    BenchmarkInfo,
-    benchmark_fn,
-    bytes_to_giga_bytes,
-    flush,
-    generate_csv_dict,
-    write_to_csv,
-)
-
-
-RESOLUTION_MAPPING = {
-    "Lykon/DreamShaper": (512, 512),
-    "lllyasviel/sd-controlnet-canny": (512, 512),
-    "diffusers/controlnet-canny-sdxl-1.0": (1024, 1024),
-    "TencentARC/t2iadapter_canny_sd14v1": (512, 512),
-    "TencentARC/t2i-adapter-canny-sdxl-1.0": (1024, 1024),
-    "stabilityai/stable-diffusion-2-1": (768, 768),
-    "stabilityai/stable-diffusion-xl-base-1.0": (1024, 1024),
-    "stabilityai/stable-diffusion-xl-refiner-1.0": (1024, 1024),
-    "stabilityai/sdxl-turbo": (512, 512),
-}
-
-
-class BaseBenchmak:
-    pipeline_class = None
-
-    def __init__(self, args):
-        super().__init__()
-
-    def run_inference(self, args):
-        raise NotImplementedError
-
-    def benchmark(self, args):
-        raise NotImplementedError
-
-    def get_result_filepath(self, args):
-        pipeline_class_name = str(self.pipe.__class__.__name__)
-        name = (
-            args.ckpt.replace("/", "_")
-            + "_"
-            + pipeline_class_name
-            + f"-bs@{args.batch_size}-steps@{args.num_inference_steps}-mco@{args.model_cpu_offload}-compile@{args.run_compile}.csv"
-        )
-        filepath = os.path.join(BASE_PATH, name)
-        return filepath
-
-
-class TextToImageBenchmark(BaseBenchmak):
-    pipeline_class = AutoPipelineForText2Image
-
-    def __init__(self, args):
-        pipe = self.pipeline_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
-        pipe = pipe.to("cuda")
-
-        if args.run_compile:
-            if not isinstance(pipe, WuerstchenCombinedPipeline):
-                pipe.unet.to(memory_format=torch.channels_last)
-                print("Run torch compile")
-                pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-                if hasattr(pipe, "movq") and getattr(pipe, "movq", None) is not None:
-                    pipe.movq.to(memory_format=torch.channels_last)
-                    pipe.movq = torch.compile(pipe.movq, mode="reduce-overhead", fullgraph=True)
-            else:
-                print("Run torch compile")
-                pipe.decoder = torch.compile(pipe.decoder, mode="reduce-overhead", fullgraph=True)
-                pipe.vqgan = torch.compile(pipe.vqgan, mode="reduce-overhead", fullgraph=True)
-
-        pipe.set_progress_bar_config(disable=True)
-        self.pipe = pipe
-
-    def run_inference(self, pipe, args):
-        _ = pipe(
-            prompt=PROMPT,
-            num_inference_steps=args.num_inference_steps,
-            num_images_per_prompt=args.batch_size,
-        )
-
-    def benchmark(self, args):
-        flush()
-
-        print(f"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\n")
-
-        time = benchmark_fn(self.run_inference, self.pipe, args)  # in seconds.
-        memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated())  # in GBs.
-        benchmark_info = BenchmarkInfo(time=time, memory=memory)
-
-        pipeline_class_name = str(self.pipe.__class__.__name__)
-        flush()
-        csv_dict = generate_csv_dict(
-            pipeline_cls=pipeline_class_name, ckpt=args.ckpt, args=args, benchmark_info=benchmark_info
-        )
-        filepath = self.get_result_filepath(args)
-        write_to_csv(filepath, csv_dict)
-        print(f"Logs written to: {filepath}")
-        flush()
-
-
-class TurboTextToImageBenchmark(TextToImageBenchmark):
-    def __init__(self, args):
-        super().__init__(args)
-
-    def run_inference(self, pipe, args):
-        _ = pipe(
-            prompt=PROMPT,
-            num_inference_steps=args.num_inference_steps,
-            num_images_per_prompt=args.batch_size,
-            guidance_scale=0.0,
-        )
-
-
-class LCMLoRATextToImageBenchmark(TextToImageBenchmark):
-    lora_id = "latent-consistency/lcm-lora-sdxl"
-
-    def __init__(self, args):
-        super().__init__(args)
-        self.pipe.load_lora_weights(self.lora_id)
-        self.pipe.fuse_lora()
-        self.pipe.unload_lora_weights()
-        self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)
-
-    def get_result_filepath(self, args):
-        pipeline_class_name = str(self.pipe.__class__.__name__)
-        name = (
-            self.lora_id.replace("/", "_")
-            + "_"
-            + pipeline_class_name
-            + f"-bs@{args.batch_size}-steps@{args.num_inference_steps}-mco@{args.model_cpu_offload}-compile@{args.run_compile}.csv"
-        )
-        filepath = os.path.join(BASE_PATH, name)
-        return filepath
-
-    def run_inference(self, pipe, args):
-        _ = pipe(
-            prompt=PROMPT,
-            num_inference_steps=args.num_inference_steps,
-            num_images_per_prompt=args.batch_size,
-            guidance_scale=1.0,
-        )
-
-    def benchmark(self, args):
-        flush()
-
-        print(f"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\n")
-
-        time = benchmark_fn(self.run_inference, self.pipe, args)  # in seconds.
-        memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated())  # in GBs.
-        benchmark_info = BenchmarkInfo(time=time, memory=memory)
-
-        pipeline_class_name = str(self.pipe.__class__.__name__)
-        flush()
-        csv_dict = generate_csv_dict(
-            pipeline_cls=pipeline_class_name, ckpt=self.lora_id, args=args, benchmark_info=benchmark_info
-        )
-        filepath = self.get_result_filepath(args)
-        write_to_csv(filepath, csv_dict)
-        print(f"Logs written to: {filepath}")
-        flush()
-
-
-class ImageToImageBenchmark(TextToImageBenchmark):
-    pipeline_class = AutoPipelineForImage2Image
-    url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/1665_Girl_with_a_Pearl_Earring.jpg"
-    image = load_image(url).convert("RGB")
-
-    def __init__(self, args):
-        super().__init__(args)
-        self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
-
-    def run_inference(self, pipe, args):
-        _ = pipe(
-            prompt=PROMPT,
-            image=self.image,
-            num_inference_steps=args.num_inference_steps,
-            num_images_per_prompt=args.batch_size,
-        )
-
-
-class TurboImageToImageBenchmark(ImageToImageBenchmark):
-    def __init__(self, args):
-        super().__init__(args)
-
-    def run_inference(self, pipe, args):
-        _ = pipe(
-            prompt=PROMPT,
-            image=self.image,
-            num_inference_steps=args.num_inference_steps,
-            num_images_per_prompt=args.batch_size,
-            guidance_scale=0.0,
-            strength=0.5,
-        )
-
-
-class InpaintingBenchmark(ImageToImageBenchmark):
-    pipeline_class = AutoPipelineForInpainting
-    mask_url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/overture-creations-5sI6fQgYIuo_mask.png"
-    mask = load_image(mask_url).convert("RGB")
-
-    def __init__(self, args):
-        super().__init__(args)
-        self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
-        self.mask = self.mask.resize(RESOLUTION_MAPPING[args.ckpt])
-
-    def run_inference(self, pipe, args):
-        _ = pipe(
-            prompt=PROMPT,
-            image=self.image,
-            mask_image=self.mask,
-            num_inference_steps=args.num_inference_steps,
-            num_images_per_prompt=args.batch_size,
-        )
-
-
-class IPAdapterTextToImageBenchmark(TextToImageBenchmark):
-    url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png"
-    image = load_image(url)
-
-    def __init__(self, args):
-        pipe = self.pipeline_class.from_pretrained(args.ckpt, torch_dtype=torch.float16).to("cuda")
-        pipe.load_ip_adapter(
-            args.ip_adapter_id[0],
-            subfolder="models" if "sdxl" not in args.ip_adapter_id[1] else "sdxl_models",
-            weight_name=args.ip_adapter_id[1],
-        )
-
-        if args.run_compile:
-            pipe.unet.to(memory_format=torch.channels_last)
-            print("Run torch compile")
-            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-        pipe.set_progress_bar_config(disable=True)
-        self.pipe = pipe
-
-    def run_inference(self, pipe, args):
-        _ = pipe(
-            prompt=PROMPT,
-            ip_adapter_image=self.image,
-            num_inference_steps=args.num_inference_steps,
-            num_images_per_prompt=args.batch_size,
-        )
-
-
-class ControlNetBenchmark(TextToImageBenchmark):
-    pipeline_class = StableDiffusionControlNetPipeline
-    aux_network_class = ControlNetModel
-    root_ckpt = "Lykon/DreamShaper"
-
-    url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_image_condition.png"
-    image = load_image(url).convert("RGB")
-
-    def __init__(self, args):
-        aux_network = self.aux_network_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
-        pipe = self.pipeline_class.from_pretrained(self.root_ckpt, controlnet=aux_network, torch_dtype=torch.float16)
-        pipe = pipe.to("cuda")
-
-        pipe.set_progress_bar_config(disable=True)
-        self.pipe = pipe
-
-        if args.run_compile:
-            pipe.unet.to(memory_format=torch.channels_last)
-            pipe.controlnet.to(memory_format=torch.channels_last)
-
-            print("Run torch compile")
-            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-            pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True)
-
-        self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
-
-    def run_inference(self, pipe, args):
-        _ = pipe(
-            prompt=PROMPT,
-            image=self.image,
-            num_inference_steps=args.num_inference_steps,
-            num_images_per_prompt=args.batch_size,
-        )
-
-
-class ControlNetSDXLBenchmark(ControlNetBenchmark):
-    pipeline_class = StableDiffusionXLControlNetPipeline
-    root_ckpt = "stabilityai/stable-diffusion-xl-base-1.0"
-
-    def __init__(self, args):
-        super().__init__(args)
-
-
-class T2IAdapterBenchmark(ControlNetBenchmark):
-    pipeline_class = StableDiffusionAdapterPipeline
-    aux_network_class = T2IAdapter
-    root_ckpt = "Lykon/DreamShaper"
-
-    url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter.png"
-    image = load_image(url).convert("L")
-
-    def __init__(self, args):
-        aux_network = self.aux_network_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)
-        pipe = self.pipeline_class.from_pretrained(self.root_ckpt, adapter=aux_network, torch_dtype=torch.float16)
-        pipe = pipe.to("cuda")
-
-        pipe.set_progress_bar_config(disable=True)
-        self.pipe = pipe
-
-        if args.run_compile:
-            pipe.unet.to(memory_format=torch.channels_last)
-            pipe.adapter.to(memory_format=torch.channels_last)
-
-            print("Run torch compile")
-            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-            pipe.adapter = torch.compile(pipe.adapter, mode="reduce-overhead", fullgraph=True)
-
-        self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])
-
-
-class T2IAdapterSDXLBenchmark(T2IAdapterBenchmark):
-    pipeline_class = StableDiffusionXLAdapterPipeline
-    root_ckpt = "stabilityai/stable-diffusion-xl-base-1.0"
-
-    url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter_sdxl.png"
-    image = load_image(url)
-
-    def __init__(self, args):
-        super().__init__(args)

benchmarks/benchmark_controlnet.py

@@ -1,26 +0,0 @@
-import argparse
-import sys
-
-
-sys.path.append(".")
-from base_classes import ControlNetBenchmark, ControlNetSDXLBenchmark  # noqa: E402
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        default="lllyasviel/sd-controlnet-canny",
-        choices=["lllyasviel/sd-controlnet-canny", "diffusers/controlnet-canny-sdxl-1.0"],
-    )
-    parser.add_argument("--batch_size", type=int, default=1)
-    parser.add_argument("--num_inference_steps", type=int, default=50)
-    parser.add_argument("--model_cpu_offload", action="store_true")
-    parser.add_argument("--run_compile", action="store_true")
-    args = parser.parse_args()
-
-    benchmark_pipe = (
-        ControlNetBenchmark(args) if args.ckpt == "lllyasviel/sd-controlnet-canny" else ControlNetSDXLBenchmark(args)
-    )
-    benchmark_pipe.benchmark(args)

benchmarks/benchmark_ip_adapters.py

@@ -1,33 +0,0 @@
-import argparse
-import sys
-
-
-sys.path.append(".")
-from base_classes import IPAdapterTextToImageBenchmark  # noqa: E402
-
-
-IP_ADAPTER_CKPTS = {
-    # because original SD v1.5 has been taken down.
-    "Lykon/DreamShaper": ("h94/IP-Adapter", "ip-adapter_sd15.bin"),
-    "stabilityai/stable-diffusion-xl-base-1.0": ("h94/IP-Adapter", "ip-adapter_sdxl.bin"),
-}
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        default="rstabilityai/stable-diffusion-xl-base-1.0",
-        choices=list(IP_ADAPTER_CKPTS.keys()),
-    )
-    parser.add_argument("--batch_size", type=int, default=1)
-    parser.add_argument("--num_inference_steps", type=int, default=50)
-    parser.add_argument("--model_cpu_offload", action="store_true")
-    parser.add_argument("--run_compile", action="store_true")
-    args = parser.parse_args()
-
-    args.ip_adapter_id = IP_ADAPTER_CKPTS[args.ckpt]
-    benchmark_pipe = IPAdapterTextToImageBenchmark(args)
-    args.ckpt = f"{args.ckpt} (IP-Adapter)"
-    benchmark_pipe.benchmark(args)

benchmarks/benchmark_sd_img.py

@@ -1,29 +0,0 @@
-import argparse
-import sys
-
-
-sys.path.append(".")
-from base_classes import ImageToImageBenchmark, TurboImageToImageBenchmark  # noqa: E402
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        default="Lykon/DreamShaper",
-        choices=[
-            "Lykon/DreamShaper",
-            "stabilityai/stable-diffusion-2-1",
-            "stabilityai/stable-diffusion-xl-refiner-1.0",
-            "stabilityai/sdxl-turbo",
-        ],
-    )
-    parser.add_argument("--batch_size", type=int, default=1)
-    parser.add_argument("--num_inference_steps", type=int, default=50)
-    parser.add_argument("--model_cpu_offload", action="store_true")
-    parser.add_argument("--run_compile", action="store_true")
-    args = parser.parse_args()
-
-    benchmark_pipe = ImageToImageBenchmark(args) if "turbo" not in args.ckpt else TurboImageToImageBenchmark(args)
-    benchmark_pipe.benchmark(args)

benchmarks/benchmark_sd_inpainting.py

@@ -1,28 +0,0 @@
-import argparse
-import sys
-
-
-sys.path.append(".")
-from base_classes import InpaintingBenchmark  # noqa: E402
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        default="Lykon/DreamShaper",
-        choices=[
-            "Lykon/DreamShaper",
-            "stabilityai/stable-diffusion-2-1",
-            "stabilityai/stable-diffusion-xl-base-1.0",
-        ],
-    )
-    parser.add_argument("--batch_size", type=int, default=1)
-    parser.add_argument("--num_inference_steps", type=int, default=50)
-    parser.add_argument("--model_cpu_offload", action="store_true")
-    parser.add_argument("--run_compile", action="store_true")
-    args = parser.parse_args()
-
-    benchmark_pipe = InpaintingBenchmark(args)
-    benchmark_pipe.benchmark(args)

benchmarks/benchmark_t2i_adapter.py

@@ -1,28 +0,0 @@
-import argparse
-import sys
-
-
-sys.path.append(".")
-from base_classes import T2IAdapterBenchmark, T2IAdapterSDXLBenchmark  # noqa: E402
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        default="TencentARC/t2iadapter_canny_sd14v1",
-        choices=["TencentARC/t2iadapter_canny_sd14v1", "TencentARC/t2i-adapter-canny-sdxl-1.0"],
-    )
-    parser.add_argument("--batch_size", type=int, default=1)
-    parser.add_argument("--num_inference_steps", type=int, default=50)
-    parser.add_argument("--model_cpu_offload", action="store_true")
-    parser.add_argument("--run_compile", action="store_true")
-    args = parser.parse_args()
-
-    benchmark_pipe = (
-        T2IAdapterBenchmark(args)
-        if args.ckpt == "TencentARC/t2iadapter_canny_sd14v1"
-        else T2IAdapterSDXLBenchmark(args)
-    )
-    benchmark_pipe.benchmark(args)

benchmarks/benchmark_t2i_lcm_lora.py

@@ -1,23 +0,0 @@
-import argparse
-import sys
-
-
-sys.path.append(".")
-from base_classes import LCMLoRATextToImageBenchmark  # noqa: E402
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        default="stabilityai/stable-diffusion-xl-base-1.0",
-    )
-    parser.add_argument("--batch_size", type=int, default=1)
-    parser.add_argument("--num_inference_steps", type=int, default=4)
-    parser.add_argument("--model_cpu_offload", action="store_true")
-    parser.add_argument("--run_compile", action="store_true")
-    args = parser.parse_args()
-
-    benchmark_pipe = LCMLoRATextToImageBenchmark(args)
-    benchmark_pipe.benchmark(args)

benchmarks/benchmark_text_to_image.py

@@ -1,40 +0,0 @@
-import argparse
-import sys
-
-
-sys.path.append(".")
-from base_classes import TextToImageBenchmark, TurboTextToImageBenchmark  # noqa: E402
-
-
-ALL_T2I_CKPTS = [
-    "Lykon/DreamShaper",
-    "segmind/SSD-1B",
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    "kandinsky-community/kandinsky-2-2-decoder",
-    "warp-ai/wuerstchen",
-    "stabilityai/sdxl-turbo",
-]
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--ckpt",
-        type=str,
-        default="Lykon/DreamShaper",
-        choices=ALL_T2I_CKPTS,
-    )
-    parser.add_argument("--batch_size", type=int, default=1)
-    parser.add_argument("--num_inference_steps", type=int, default=50)
-    parser.add_argument("--model_cpu_offload", action="store_true")
-    parser.add_argument("--run_compile", action="store_true")
-    args = parser.parse_args()
-
-    benchmark_cls = None
-    if "turbo" in args.ckpt:
-        benchmark_cls = TurboTextToImageBenchmark
-    else:
-        benchmark_cls = TextToImageBenchmark
-
-    benchmark_pipe = benchmark_cls(args)
-    benchmark_pipe.benchmark(args)

benchmarks/benchmarking_flux.py

@@ -0,0 +1,98 @@
+from functools import partial
+
+import torch
+from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
+
+from diffusers import BitsAndBytesConfig, FluxTransformer2DModel
+from diffusers.utils.testing_utils import torch_device
+
+
+CKPT_ID = "black-forest-labs/FLUX.1-dev"
+RESULT_FILENAME = "flux.csv"
+
+
+def get_input_dict(**device_dtype_kwargs):
+    # resolution: 1024x1024
+    # maximum sequence length 512
+    hidden_states = torch.randn(1, 4096, 64, **device_dtype_kwargs)
+    encoder_hidden_states = torch.randn(1, 512, 4096, **device_dtype_kwargs)
+    pooled_prompt_embeds = torch.randn(1, 768, **device_dtype_kwargs)
+    image_ids = torch.ones(512, 3, **device_dtype_kwargs)
+    text_ids = torch.ones(4096, 3, **device_dtype_kwargs)
+    timestep = torch.tensor([1.0], **device_dtype_kwargs)
+    guidance = torch.tensor([1.0], **device_dtype_kwargs)
+
+    return {
+        "hidden_states": hidden_states,
+        "encoder_hidden_states": encoder_hidden_states,
+        "img_ids": image_ids,
+        "txt_ids": text_ids,
+        "pooled_projections": pooled_prompt_embeds,
+        "timestep": timestep,
+        "guidance": guidance,
+    }
+
+
+if __name__ == "__main__":
+    scenarios = [
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-bf16",
+            model_cls=FluxTransformer2DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=model_init_fn,
+            compile_kwargs={"fullgraph": True},
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-bnb-nf4",
+            model_cls=FluxTransformer2DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+                "quantization_config": BitsAndBytesConfig(
+                    load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4"
+                ),
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=model_init_fn,
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-layerwise-upcasting",
+            model_cls=FluxTransformer2DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-group-offload-leaf",
+            model_cls=FluxTransformer2DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(
+                model_init_fn,
+                group_offload_kwargs={
+                    "onload_device": torch_device,
+                    "offload_device": torch.device("cpu"),
+                    "offload_type": "leaf_level",
+                    "use_stream": True,
+                    "non_blocking": True,
+                },
+            ),
+        ),
+    ]
+
+    runner = BenchmarkMixin()
+    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

benchmarks/benchmarking_ltx.py

@@ -0,0 +1,80 @@
+from functools import partial
+
+import torch
+from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
+
+from diffusers import LTXVideoTransformer3DModel
+from diffusers.utils.testing_utils import torch_device
+
+
+CKPT_ID = "Lightricks/LTX-Video-0.9.7-dev"
+RESULT_FILENAME = "ltx.csv"
+
+
+def get_input_dict(**device_dtype_kwargs):
+    # 512x704 (161 frames)
+    # `max_sequence_length`: 256
+    hidden_states = torch.randn(1, 7392, 128, **device_dtype_kwargs)
+    encoder_hidden_states = torch.randn(1, 256, 4096, **device_dtype_kwargs)
+    encoder_attention_mask = torch.ones(1, 256, **device_dtype_kwargs)
+    timestep = torch.tensor([1.0], **device_dtype_kwargs)
+    video_coords = torch.randn(1, 3, 7392, **device_dtype_kwargs)
+
+    return {
+        "hidden_states": hidden_states,
+        "encoder_hidden_states": encoder_hidden_states,
+        "encoder_attention_mask": encoder_attention_mask,
+        "timestep": timestep,
+        "video_coords": video_coords,
+    }
+
+
+if __name__ == "__main__":
+    scenarios = [
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-bf16",
+            model_cls=LTXVideoTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=model_init_fn,
+            compile_kwargs={"fullgraph": True},
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-layerwise-upcasting",
+            model_cls=LTXVideoTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-group-offload-leaf",
+            model_cls=LTXVideoTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(
+                model_init_fn,
+                group_offload_kwargs={
+                    "onload_device": torch_device,
+                    "offload_device": torch.device("cpu"),
+                    "offload_type": "leaf_level",
+                    "use_stream": True,
+                    "non_blocking": True,
+                },
+            ),
+        ),
+    ]
+
+    runner = BenchmarkMixin()
+    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

benchmarks/benchmarking_sdxl.py

@@ -0,0 +1,82 @@
+from functools import partial
+
+import torch
+from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
+
+from diffusers import UNet2DConditionModel
+from diffusers.utils.testing_utils import torch_device
+
+
+CKPT_ID = "stabilityai/stable-diffusion-xl-base-1.0"
+RESULT_FILENAME = "sdxl.csv"
+
+
+def get_input_dict(**device_dtype_kwargs):
+    # height: 1024
+    # width: 1024
+    # max_sequence_length: 77
+    hidden_states = torch.randn(1, 4, 128, 128, **device_dtype_kwargs)
+    encoder_hidden_states = torch.randn(1, 77, 2048, **device_dtype_kwargs)
+    timestep = torch.tensor([1.0], **device_dtype_kwargs)
+    added_cond_kwargs = {
+        "text_embeds": torch.randn(1, 1280, **device_dtype_kwargs),
+        "time_ids": torch.ones(1, 6, **device_dtype_kwargs),
+    }
+
+    return {
+        "sample": hidden_states,
+        "encoder_hidden_states": encoder_hidden_states,
+        "timestep": timestep,
+        "added_cond_kwargs": added_cond_kwargs,
+    }
+
+
+if __name__ == "__main__":
+    scenarios = [
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-bf16",
+            model_cls=UNet2DConditionModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "unet",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=model_init_fn,
+            compile_kwargs={"fullgraph": True},
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-layerwise-upcasting",
+            model_cls=UNet2DConditionModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "unet",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-group-offload-leaf",
+            model_cls=UNet2DConditionModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "unet",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(
+                model_init_fn,
+                group_offload_kwargs={
+                    "onload_device": torch_device,
+                    "offload_device": torch.device("cpu"),
+                    "offload_type": "leaf_level",
+                    "use_stream": True,
+                    "non_blocking": True,
+                },
+            ),
+        ),
+    ]
+
+    runner = BenchmarkMixin()
+    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

benchmarks/benchmarking_utils.py

@@ -0,0 +1,244 @@
+import gc
+import inspect
+import logging
+import os
+import queue
+import threading
+from contextlib import nullcontext
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, Optional, Union
+
+import pandas as pd
+import torch
+import torch.utils.benchmark as benchmark
+
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils.testing_utils import require_torch_gpu, torch_device
+
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+logger = logging.getLogger(__name__)
+
+NUM_WARMUP_ROUNDS = 5
+
+
+def benchmark_fn(f, *args, **kwargs):
+    t0 = benchmark.Timer(
+        stmt="f(*args, **kwargs)",
+        globals={"args": args, "kwargs": kwargs, "f": f},
+        num_threads=1,
+    )
+    return float(f"{(t0.blocked_autorange().mean):.3f}")
+
+
+def flush():
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.reset_max_memory_allocated()
+    torch.cuda.reset_peak_memory_stats()
+
+
+# Adapted from https://github.com/lucasb-eyer/cnn_vit_benchmarks/blob/15b665ff758e8062131353076153905cae00a71f/main.py
+def calculate_flops(model, input_dict):
+    try:
+        from torchprofile import profile_macs
+    except ModuleNotFoundError:
+        raise
+
+    # This is a hacky way to convert the kwargs to args as `profile_macs` cries about kwargs.
+    sig = inspect.signature(model.forward)
+    param_names = [
+        p.name
+        for p in sig.parameters.values()
+        if p.kind
+        in (
+            inspect.Parameter.POSITIONAL_ONLY,
+            inspect.Parameter.POSITIONAL_OR_KEYWORD,
+        )
+        and p.name != "self"
+    ]
+    bound = sig.bind_partial(**input_dict)
+    bound.apply_defaults()
+    args = tuple(bound.arguments[name] for name in param_names)
+
+    model.eval()
+    with torch.no_grad():
+        macs = profile_macs(model, args)
+    flops = 2 * macs  # 1 MAC operation = 2 FLOPs (1 multiplication + 1 addition)
+    return flops
+
+
+def calculate_params(model):
+    return sum(p.numel() for p in model.parameters())
+
+
+# Users can define their own in case this doesn't suffice. For most cases,
+# it should be sufficient.
+def model_init_fn(model_cls, group_offload_kwargs=None, layerwise_upcasting=False, **init_kwargs):
+    model = model_cls.from_pretrained(**init_kwargs).eval()
+    if group_offload_kwargs and isinstance(group_offload_kwargs, dict):
+        model.enable_group_offload(**group_offload_kwargs)
+    else:
+        model.to(torch_device)
+    if layerwise_upcasting:
+        model.enable_layerwise_casting(
+            storage_dtype=torch.float8_e4m3fn, compute_dtype=init_kwargs.get("torch_dtype", torch.bfloat16)
+        )
+    return model
+
+
+@dataclass
+class BenchmarkScenario:
+    name: str
+    model_cls: ModelMixin
+    model_init_kwargs: Dict[str, Any]
+    model_init_fn: Callable
+    get_model_input_dict: Callable
+    compile_kwargs: Optional[Dict[str, Any]] = None
+
+
+@require_torch_gpu
+class BenchmarkMixin:
+    def pre_benchmark(self):
+        flush()
+        torch.compiler.reset()
+
+    def post_benchmark(self, model):
+        model.cpu()
+        flush()
+        torch.compiler.reset()
+
+    @torch.no_grad()
+    def run_benchmark(self, scenario: BenchmarkScenario):
+        # 0) Basic stats
+        logger.info(f"Running scenario: {scenario.name}.")
+        try:
+            model = model_init_fn(scenario.model_cls, **scenario.model_init_kwargs)
+            num_params = round(calculate_params(model) / 1e9, 2)
+            try:
+                flops = round(calculate_flops(model, input_dict=scenario.get_model_input_dict()) / 1e9, 2)
+            except Exception as e:
+                logger.info(f"Problem in calculating FLOPs:\n{e}")
+                flops = None
+            model.cpu()
+            del model
+        except Exception as e:
+            logger.info(f"Error while initializing the model and calculating FLOPs:\n{e}")
+            return {}
+        self.pre_benchmark()
+
+        # 1) plain stats
+        results = {}
+        plain = None
+        try:
+            plain = self._run_phase(
+                model_cls=scenario.model_cls,
+                init_fn=scenario.model_init_fn,
+                init_kwargs=scenario.model_init_kwargs,
+                get_input_fn=scenario.get_model_input_dict,
+                compile_kwargs=None,
+            )
+        except Exception as e:
+            logger.info(f"Benchmark could not be run with the following error:\n{e}")
+            return results
+
+        # 2) compiled stats (if any)
+        compiled = {"time": None, "memory": None}
+        if scenario.compile_kwargs:
+            try:
+                compiled = self._run_phase(
+                    model_cls=scenario.model_cls,
+                    init_fn=scenario.model_init_fn,
+                    init_kwargs=scenario.model_init_kwargs,
+                    get_input_fn=scenario.get_model_input_dict,
+                    compile_kwargs=scenario.compile_kwargs,
+                )
+            except Exception as e:
+                logger.info(f"Compilation benchmark could not be run with the following error\n: {e}")
+                if plain is None:
+                    return results
+
+        # 3) merge
+        result = {
+            "scenario": scenario.name,
+            "model_cls": scenario.model_cls.__name__,
+            "num_params_B": num_params,
+            "flops_G": flops,
+            "time_plain_s": plain["time"],
+            "mem_plain_GB": plain["memory"],
+            "time_compile_s": compiled["time"],
+            "mem_compile_GB": compiled["memory"],
+        }
+        if scenario.compile_kwargs:
+            result["fullgraph"] = scenario.compile_kwargs.get("fullgraph", False)
+            result["mode"] = scenario.compile_kwargs.get("mode", "default")
+        else:
+            result["fullgraph"], result["mode"] = None, None
+        return result
+
+    def run_bencmarks_and_collate(self, scenarios: Union[BenchmarkScenario, list[BenchmarkScenario]], filename: str):
+        if not isinstance(scenarios, list):
+            scenarios = [scenarios]
+        record_queue = queue.Queue()
+        stop_signal = object()
+
+        def _writer_thread():
+            while True:
+                item = record_queue.get()
+                if item is stop_signal:
+                    break
+                df_row = pd.DataFrame([item])
+                write_header = not os.path.exists(filename)
+                df_row.to_csv(filename, mode="a", header=write_header, index=False)
+                record_queue.task_done()
+
+            record_queue.task_done()
+
+        writer = threading.Thread(target=_writer_thread, daemon=True)
+        writer.start()
+
+        for s in scenarios:
+            try:
+                record = self.run_benchmark(s)
+                if record:
+                    record_queue.put(record)
+                else:
+                    logger.info(f"Record empty from scenario: {s.name}.")
+            except Exception as e:
+                logger.info(f"Running scenario ({s.name}) led to error:\n{e}")
+        record_queue.put(stop_signal)
+        logger.info(f"Results serialized to {filename=}.")
+
+    def _run_phase(
+        self,
+        *,
+        model_cls: ModelMixin,
+        init_fn: Callable,
+        init_kwargs: Dict[str, Any],
+        get_input_fn: Callable,
+        compile_kwargs: Optional[Dict[str, Any]],
+    ) -> Dict[str, float]:
+        # setup
+        self.pre_benchmark()
+
+        # init & (optional) compile
+        model = init_fn(model_cls, **init_kwargs)
+        if compile_kwargs:
+            model.compile(**compile_kwargs)
+
+        # build inputs
+        inp = get_input_fn()
+
+        # measure
+        run_ctx = torch._inductor.utils.fresh_inductor_cache() if compile_kwargs else nullcontext()
+        with run_ctx:
+            for _ in range(NUM_WARMUP_ROUNDS):
+                _ = model(**inp)
+            time_s = benchmark_fn(lambda m, d: m(**d), model, inp)
+        mem_gb = torch.cuda.max_memory_allocated() / (1024**3)
+        mem_gb = round(mem_gb, 2)
+
+        # teardown
+        self.post_benchmark(model)
+        del model
+        return {"time": time_s, "memory": mem_gb}

benchmarks/benchmarking_wan.py

@@ -0,0 +1,74 @@
+from functools import partial
+
+import torch
+from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
+
+from diffusers import WanTransformer3DModel
+from diffusers.utils.testing_utils import torch_device
+
+
+CKPT_ID = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
+RESULT_FILENAME = "wan.csv"
+
+
+def get_input_dict(**device_dtype_kwargs):
+    # height: 480
+    # width: 832
+    # num_frames: 81
+    # max_sequence_length: 512
+    hidden_states = torch.randn(1, 16, 21, 60, 104, **device_dtype_kwargs)
+    encoder_hidden_states = torch.randn(1, 512, 4096, **device_dtype_kwargs)
+    timestep = torch.tensor([1.0], **device_dtype_kwargs)
+
+    return {"hidden_states": hidden_states, "encoder_hidden_states": encoder_hidden_states, "timestep": timestep}
+
+
+if __name__ == "__main__":
+    scenarios = [
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-bf16",
+            model_cls=WanTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=model_init_fn,
+            compile_kwargs={"fullgraph": True},
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-layerwise-upcasting",
+            model_cls=WanTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-group-offload-leaf",
+            model_cls=WanTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(
+                model_init_fn,
+                group_offload_kwargs={
+                    "onload_device": torch_device,
+                    "offload_device": torch.device("cpu"),
+                    "offload_type": "leaf_level",
+                    "use_stream": True,
+                    "non_blocking": True,
+                },
+            ),
+        ),
+    ]
+
+    runner = BenchmarkMixin()
+    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

benchmarks/populate_into_db.py

@@ -0,0 +1,166 @@
+import argparse
+import os
+import sys
+
+import gpustat
+import pandas as pd
+import psycopg2
+import psycopg2.extras
+from psycopg2.extensions import register_adapter
+from psycopg2.extras import Json
+
+
+register_adapter(dict, Json)
+
+FINAL_CSV_FILENAME = "collated_results.csv"
+# https://github.com/huggingface/transformers/blob/593e29c5e2a9b17baec010e8dc7c1431fed6e841/benchmark/init_db.sql#L27
+BENCHMARKS_TABLE_NAME = "benchmarks"
+MEASUREMENTS_TABLE_NAME = "model_measurements"
+
+
+def _init_benchmark(conn, branch, commit_id, commit_msg):
+    gpu_stats = gpustat.GPUStatCollection.new_query()
+    metadata = {"gpu_name": gpu_stats[0]["name"]}
+    repository = "huggingface/diffusers"
+    with conn.cursor() as cur:
+        cur.execute(
+            f"INSERT INTO {BENCHMARKS_TABLE_NAME} (repository, branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s, %s) RETURNING benchmark_id",
+            (repository, branch, commit_id, commit_msg, metadata),
+        )
+        benchmark_id = cur.fetchone()[0]
+        print(f"Initialised benchmark #{benchmark_id}")
+        return benchmark_id
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "branch",
+        type=str,
+        help="The branch name on which the benchmarking is performed.",
+    )
+
+    parser.add_argument(
+        "commit_id",
+        type=str,
+        help="The commit hash on which the benchmarking is performed.",
+    )
+
+    parser.add_argument(
+        "commit_msg",
+        type=str,
+        help="The commit message associated with the commit, truncated to 70 characters.",
+    )
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    try:
+        conn = psycopg2.connect(
+            host=os.getenv("PGHOST"),
+            database=os.getenv("PGDATABASE"),
+            user=os.getenv("PGUSER"),
+            password=os.getenv("PGPASSWORD"),
+        )
+        print("DB connection established successfully.")
+    except Exception as e:
+        print(f"Problem during DB init: {e}")
+        sys.exit(1)
+
+    try:
+        benchmark_id = _init_benchmark(
+            conn=conn,
+            branch=args.branch,
+            commit_id=args.commit_id,
+            commit_msg=args.commit_msg,
+        )
+    except Exception as e:
+        print(f"Problem during initializing benchmark: {e}")
+        sys.exit(1)
+
+    cur = conn.cursor()
+
+    df = pd.read_csv(FINAL_CSV_FILENAME)
+
+    # Helper to cast values (or None) given a dtype
+    def _cast_value(val, dtype: str):
+        if pd.isna(val):
+            return None
+
+        if dtype == "text":
+            return str(val).strip()
+
+        if dtype == "float":
+            try:
+                return float(val)
+            except ValueError:
+                return None
+
+        if dtype == "bool":
+            s = str(val).strip().lower()
+            if s in ("true", "t", "yes", "1"):
+                return True
+            if s in ("false", "f", "no", "0"):
+                return False
+            if val in (1, 1.0):
+                return True
+            if val in (0, 0.0):
+                return False
+            return None
+
+        return val
+
+    try:
+        rows_to_insert = []
+        for _, row in df.iterrows():
+            scenario = _cast_value(row.get("scenario"), "text")
+            model_cls = _cast_value(row.get("model_cls"), "text")
+            num_params_B = _cast_value(row.get("num_params_B"), "float")
+            flops_G = _cast_value(row.get("flops_G"), "float")
+            time_plain_s = _cast_value(row.get("time_plain_s"), "float")
+            mem_plain_GB = _cast_value(row.get("mem_plain_GB"), "float")
+            time_compile_s = _cast_value(row.get("time_compile_s"), "float")
+            mem_compile_GB = _cast_value(row.get("mem_compile_GB"), "float")
+            fullgraph = _cast_value(row.get("fullgraph"), "bool")
+            mode = _cast_value(row.get("mode"), "text")
+
+            # If "github_sha" column exists in the CSV, cast it; else default to None
+            if "github_sha" in df.columns:
+                github_sha = _cast_value(row.get("github_sha"), "text")
+            else:
+                github_sha = None
+
+            measurements = {
+                "scenario": scenario,
+                "model_cls": model_cls,
+                "num_params_B": num_params_B,
+                "flops_G": flops_G,
+                "time_plain_s": time_plain_s,
+                "mem_plain_GB": mem_plain_GB,
+                "time_compile_s": time_compile_s,
+                "mem_compile_GB": mem_compile_GB,
+                "fullgraph": fullgraph,
+                "mode": mode,
+                "github_sha": github_sha,
+            }
+            rows_to_insert.append((benchmark_id, measurements))
+
+        # Batch-insert all rows
+        insert_sql = f"""
+        INSERT INTO {MEASUREMENTS_TABLE_NAME} (
+            benchmark_id,
+            measurements
+        )
+        VALUES (%s, %s);
+        """
+
+        psycopg2.extras.execute_batch(cur, insert_sql, rows_to_insert)
+        conn.commit()
+
+        cur.close()
+        conn.close()
+    except Exception as e:
+        print(f"Exception: {e}")
+        sys.exit(1)

benchmarks/push_results.py

@@ -1,19 +1,19 @@
-import glob
-import sys
+import os
 
 import pandas as pd
 from huggingface_hub import hf_hub_download, upload_file
 from huggingface_hub.utils import EntryNotFoundError
 
 
-sys.path.append(".")
-from utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv  # noqa: E402
+REPO_ID = "diffusers/benchmarks"
 
 
 def has_previous_benchmark() -> str:
+    from run_all import FINAL_CSV_FILENAME
+
     csv_path = None
     try:
-        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILE)
+        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILENAME)
     except EntryNotFoundError:
         csv_path = None
     return csv_path
@@ -26,46 +26,50 @@ def filter_float(value):
 
 
 def push_to_hf_dataset():
-    all_csvs = sorted(glob.glob(f"{BASE_PATH}/*.csv"))
-    collate_csv(all_csvs, FINAL_CSV_FILE)
+    from run_all import FINAL_CSV_FILENAME, GITHUB_SHA
 
-    # If there's an existing benchmark file, we should report the changes.
     csv_path = has_previous_benchmark()
     if csv_path is not None:
-        current_results = pd.read_csv(FINAL_CSV_FILE)
+        current_results = pd.read_csv(FINAL_CSV_FILENAME)
         previous_results = pd.read_csv(csv_path)
 
         numeric_columns = current_results.select_dtypes(include=["float64", "int64"]).columns
-        numeric_columns = [
-            c for c in numeric_columns if c not in ["batch_size", "num_inference_steps", "actual_gpu_memory (gbs)"]
-        ]
 
         for column in numeric_columns:
-            previous_results[column] = previous_results[column].map(lambda x: filter_float(x))
+            # get previous values as floats, aligned to current index
+            prev_vals = previous_results[column].map(filter_float).reindex(current_results.index)
 
-            # Calculate the percentage change
-            current_results[column] = current_results[column].astype(float)
-            previous_results[column] = previous_results[column].astype(float)
-            percent_change = ((current_results[column] - previous_results[column]) / previous_results[column]) * 100
+            # get current values as floats
+            curr_vals = current_results[column].astype(float)
 
-            # Format the values with '+' or '-' sign and append to original values
-            current_results[column] = current_results[column].map(str) + percent_change.map(
-                lambda x: f" ({'+' if x > 0 else ''}{x:.2f}%)"
+            # stringify the current values
+            curr_str = curr_vals.map(str)
+
+            # build an appendage only when prev exists and differs
+            append_str = prev_vals.where(prev_vals.notnull() & (prev_vals != curr_vals), other=pd.NA).map(
+                lambda x: f" ({x})" if pd.notnull(x) else ""
             )
-            # There might be newly added rows. So, filter out the NaNs.
-            current_results[column] = current_results[column].map(lambda x: x.replace(" (nan%)", ""))
 
-        # Overwrite the current result file.
-        current_results.to_csv(FINAL_CSV_FILE, index=False)
+            # combine
+            current_results[column] = curr_str + append_str
+        os.remove(FINAL_CSV_FILENAME)
+        current_results.to_csv(FINAL_CSV_FILENAME, index=False)
 
     commit_message = f"upload from sha: {GITHUB_SHA}" if GITHUB_SHA is not None else "upload benchmark results"
     upload_file(
         repo_id=REPO_ID,
-        path_in_repo=FINAL_CSV_FILE,
-        path_or_fileobj=FINAL_CSV_FILE,
+        path_in_repo=FINAL_CSV_FILENAME,
+        path_or_fileobj=FINAL_CSV_FILENAME,
         repo_type="dataset",
         commit_message=commit_message,
     )
+    upload_file(
+        repo_id="diffusers/benchmark-analyzer",
+        path_in_repo=FINAL_CSV_FILENAME,
+        path_or_fileobj=FINAL_CSV_FILENAME,
+        repo_type="space",
+        commit_message=commit_message,
+    )
 
 
 if __name__ == "__main__":

benchmarks/requirements.txt

@@ -0,0 +1,6 @@
+pandas 
+psutil
+gpustat
+torchprofile
+bitsandbytes
+psycopg2==2.9.9

benchmarks/run_all.py

@@ -1,101 +1,84 @@
 import glob
+import logging
+import os
 import subprocess
-import sys
-from typing import List
+
+import pandas as pd
 
 
-sys.path.append(".")
-from benchmark_text_to_image import ALL_T2I_CKPTS  # noqa: E402
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+logger = logging.getLogger(__name__)
 
-
-PATTERN = "benchmark_*.py"
+PATTERN = "benchmarking_*.py"
+FINAL_CSV_FILENAME = "collated_results.csv"
+GITHUB_SHA = os.getenv("GITHUB_SHA", None)
 
 
 class SubprocessCallException(Exception):
     pass
 
 
-# Taken from `test_examples_utils.py`
-def run_command(command: List[str], return_stdout=False):
-    """
-    Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
-    if an error occurred while running `command`
-    """
+def run_command(command: list[str], return_stdout=False):
     try:
         output = subprocess.check_output(command, stderr=subprocess.STDOUT)
-        if return_stdout:
-            if hasattr(output, "decode"):
-                output = output.decode("utf-8")
-            return output
+        if return_stdout and hasattr(output, "decode"):
+            return output.decode("utf-8")
     except subprocess.CalledProcessError as e:
-        raise SubprocessCallException(
-            f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
-        ) from e
+        raise SubprocessCallException(f"Command `{' '.join(command)}` failed with:\n{e.output.decode()}") from e
 
 
-def main():
-    python_files = glob.glob(PATTERN)
+def merge_csvs(final_csv: str = "collated_results.csv"):
+    all_csvs = glob.glob("*.csv")
+    all_csvs = [f for f in all_csvs if f != final_csv]
+    if not all_csvs:
+        logger.info("No result CSVs found to merge.")
+        return
 
-    for file in python_files:
-        print(f"****** Running file: {file} ******")
-
-        # Run with canonical settings.
-        if file != "benchmark_text_to_image.py" and file != "benchmark_ip_adapters.py":
-            command = f"python {file}"
-            run_command(command.split())
-
-            command += " --run_compile"
-            run_command(command.split())
-
-    # Run variants.
-    for file in python_files:
-        # See: https://github.com/pytorch/pytorch/issues/129637
-        if file == "benchmark_ip_adapters.py":
+    df_list = []
+    for f in all_csvs:
+        try:
+            d = pd.read_csv(f)
+        except pd.errors.EmptyDataError:
+            # If a file existed but was zero‐bytes or corrupted, skip it
             continue
+        df_list.append(d)
 
-        if file == "benchmark_text_to_image.py":
-            for ckpt in ALL_T2I_CKPTS:
-                command = f"python {file} --ckpt {ckpt}"
+    if not df_list:
+        logger.info("All result CSVs were empty or invalid; nothing to merge.")
+        return
 
-                if "turbo" in ckpt:
-                    command += " --num_inference_steps 1"
+    final_df = pd.concat(df_list, ignore_index=True)
+    if GITHUB_SHA is not None:
+        final_df["github_sha"] = GITHUB_SHA
+    final_df.to_csv(final_csv, index=False)
+    logger.info(f"Merged {len(all_csvs)} partial CSVs → {final_csv}.")
 
-                run_command(command.split())
 
-                command += " --run_compile"
-                run_command(command.split())
+def run_scripts():
+    python_files = sorted(glob.glob(PATTERN))
+    python_files = [f for f in python_files if f != "benchmarking_utils.py"]
 
-        elif file == "benchmark_sd_img.py":
-            for ckpt in ["stabilityai/stable-diffusion-xl-refiner-1.0", "stabilityai/sdxl-turbo"]:
-                command = f"python {file} --ckpt {ckpt}"
+    for file in python_files:
+        script_name = file.split(".py")[0].split("_")[-1]  # example: benchmarking_foo.py -> foo
+        logger.info(f"\n****** Running file: {file} ******")
 
-                if ckpt == "stabilityai/sdxl-turbo":
-                    command += " --num_inference_steps 2"
+        partial_csv = f"{script_name}.csv"
+        if os.path.exists(partial_csv):
+            logger.info(f"Found {partial_csv}. Removing for safer numbers and duplication.")
+            os.remove(partial_csv)
 
-                run_command(command.split())
-                command += " --run_compile"
-                run_command(command.split())
+        command = ["python", file]
+        try:
+            run_command(command)
+            logger.info(f"→ {file} finished normally.")
+        except SubprocessCallException as e:
+            logger.info(f"Error running {file}:\n{e}")
+        finally:
+            logger.info(f"→ Merging partial CSVs after {file} …")
+            merge_csvs(final_csv=FINAL_CSV_FILENAME)
 
-        elif file in ["benchmark_sd_inpainting.py", "benchmark_ip_adapters.py"]:
-            sdxl_ckpt = "stabilityai/stable-diffusion-xl-base-1.0"
-            command = f"python {file} --ckpt {sdxl_ckpt}"
-            run_command(command.split())
-
-            command += " --run_compile"
-            run_command(command.split())
-
-        elif file in ["benchmark_controlnet.py", "benchmark_t2i_adapter.py"]:
-            sdxl_ckpt = (
-                "diffusers/controlnet-canny-sdxl-1.0"
-                if "controlnet" in file
-                else "TencentARC/t2i-adapter-canny-sdxl-1.0"
-            )
-            command = f"python {file} --ckpt {sdxl_ckpt}"
-            run_command(command.split())
-
-            command += " --run_compile"
-            run_command(command.split())
+    logger.info(f"\nAll scripts attempted. Final collated CSV: {FINAL_CSV_FILENAME}")
 
 
 if __name__ == "__main__":
-    main()
+    run_scripts()

benchmarks/utils.py

@@ -1,98 +0,0 @@
-import argparse
-import csv
-import gc
-import os
-from dataclasses import dataclass
-from typing import Dict, List, Union
-
-import torch
-import torch.utils.benchmark as benchmark
-
-
-GITHUB_SHA = os.getenv("GITHUB_SHA", None)
-BENCHMARK_FIELDS = [
-    "pipeline_cls",
-    "ckpt_id",
-    "batch_size",
-    "num_inference_steps",
-    "model_cpu_offload",
-    "run_compile",
-    "time (secs)",
-    "memory (gbs)",
-    "actual_gpu_memory (gbs)",
-    "github_sha",
-]
-
-PROMPT = "ghibli style, a fantasy landscape with castles"
-BASE_PATH = os.getenv("BASE_PATH", ".")
-TOTAL_GPU_MEMORY = float(os.getenv("TOTAL_GPU_MEMORY", torch.cuda.get_device_properties(0).total_memory / (1024**3)))
-
-REPO_ID = "diffusers/benchmarks"
-FINAL_CSV_FILE = "collated_results.csv"
-
-
-@dataclass
-class BenchmarkInfo:
-    time: float
-    memory: float
-
-
-def flush():
-    """Wipes off memory."""
-    gc.collect()
-    torch.cuda.empty_cache()
-    torch.cuda.reset_max_memory_allocated()
-    torch.cuda.reset_peak_memory_stats()
-
-
-def bytes_to_giga_bytes(bytes):
-    return f"{(bytes / 1024 / 1024 / 1024):.3f}"
-
-
-def benchmark_fn(f, *args, **kwargs):
-    t0 = benchmark.Timer(
-        stmt="f(*args, **kwargs)",
-        globals={"args": args, "kwargs": kwargs, "f": f},
-        num_threads=torch.get_num_threads(),
-    )
-    return f"{(t0.blocked_autorange().mean):.3f}"
-
-
-def generate_csv_dict(
-    pipeline_cls: str, ckpt: str, args: argparse.Namespace, benchmark_info: BenchmarkInfo
-) -> Dict[str, Union[str, bool, float]]:
-    """Packs benchmarking data into a dictionary for latter serialization."""
-    data_dict = {
-        "pipeline_cls": pipeline_cls,
-        "ckpt_id": ckpt,
-        "batch_size": args.batch_size,
-        "num_inference_steps": args.num_inference_steps,
-        "model_cpu_offload": args.model_cpu_offload,
-        "run_compile": args.run_compile,
-        "time (secs)": benchmark_info.time,
-        "memory (gbs)": benchmark_info.memory,
-        "actual_gpu_memory (gbs)": f"{(TOTAL_GPU_MEMORY):.3f}",
-        "github_sha": GITHUB_SHA,
-    }
-    return data_dict
-
-
-def write_to_csv(file_name: str, data_dict: Dict[str, Union[str, bool, float]]):
-    """Serializes a dictionary into a CSV file."""
-    with open(file_name, mode="w", newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=BENCHMARK_FIELDS)
-        writer.writeheader()
-        writer.writerow(data_dict)
-
-
-def collate_csv(input_files: List[str], output_file: str):
-    """Collates multiple identically structured CSVs into a single CSV file."""
-    with open(output_file, mode="w", newline="") as outfile:
-        writer = csv.DictWriter(outfile, fieldnames=BENCHMARK_FIELDS)
-        writer.writeheader()
-
-        for file in input_files:
-            with open(file, mode="r") as infile:
-                reader = csv.DictReader(infile)
-                for row in reader:
-                    writer.writerow(row)

docker/diffusers-doc-builder/Dockerfile

@@ -47,6 +47,10 @@ RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
         tensorboard \
         transformers \
         matplotlib \
-        setuptools==69.5.1
+        setuptools==69.5.1 \
+        bitsandbytes \
+        torchao \
+        gguf \
+        optimum-quanto
 
 CMD ["/bin/bash"]

docs/source/en/_toctree.yml

@@ -1,36 +1,39 @@
-- sections:
+- title: Get started
+  sections:
   - local: index
-    title: 🧨 Diffusers
+    title: Diffusers
+  - local: installation
+    title: Installation
   - local: quicktour
     title: Quicktour
   - local: stable_diffusion
     title: Effective and efficient diffusion
-  - local: installation
-    title: Installation
-  title: Get started
-- sections:
-  - local: tutorials/tutorial_overview
-    title: Overview
-  - local: using-diffusers/write_own_pipeline
-    title: Understanding pipelines, models and schedulers
-  - local: tutorials/autopipeline
-    title: AutoPipeline
-  - local: tutorials/basic_training
-    title: Train a diffusion model
-  title: Tutorials
-- sections:
+
+- title: DiffusionPipeline
+  isExpanded: false
+  sections:
   - local: using-diffusers/loading
     title: Load pipelines
+  - local: tutorials/autopipeline
+    title: AutoPipeline
   - local: using-diffusers/custom_pipeline_overview
     title: Load community pipelines and components
+  - local: using-diffusers/callback
+    title: Pipeline callbacks
+  - local: using-diffusers/reusing_seeds
+    title: Reproducible pipelines
   - local: using-diffusers/schedulers
     title: Load schedulers and models
+  - local: using-diffusers/scheduler_features
+    title: Scheduler features
   - local: using-diffusers/other-formats
     title: Model files and layouts
   - local: using-diffusers/push_to_hub
     title: Push files to the Hub
-  title: Load pipelines and adapters
-- sections:
+
+- title: Adapters
+  isExpanded: false
+  sections:
   - local: tutorials/using_peft_for_inference
     title: LoRA
   - local: using-diffusers/ip_adapter
@@ -43,27 +46,16 @@
     title: DreamBooth
   - local: using-diffusers/textual_inversion_inference
     title: Textual inversion
-  title: Adapters
+
+- title: Inference
   isExpanded: false
-- sections:
-  - local: using-diffusers/unconditional_image_generation
-    title: Unconditional image generation
-  - local: using-diffusers/conditional_image_generation
-    title: Text-to-image
-  - local: using-diffusers/img2img
-    title: Image-to-image
-  - local: using-diffusers/inpaint
-    title: Inpainting
-  - local: using-diffusers/text-img2vid
-    title: Video generation
-  - local: using-diffusers/depth2img
-    title: Depth-to-image
-  title: Generative tasks
-- sections:
-  - local: using-diffusers/overview_techniques
-    title: Overview
+  sections:
+  - local: using-diffusers/weighted_prompts
+    title: Prompt techniques
   - local: using-diffusers/create_a_server
     title: Create a server
+  - local: using-diffusers/batched_inference
+    title: Batch inference
   - local: training/distributed_inference
     title: Distributed inference
   - local: using-diffusers/scheduler_features
@@ -74,14 +66,38 @@
     title: Reproducible pipelines
   - local: using-diffusers/image_quality
     title: Controlling image quality
-  - local: using-diffusers/weighted_prompts
-    title: Prompt techniques
-  title: Inference techniques
-- sections:
-  - local: advanced_inference/outpaint
-    title: Outpainting
-  title: Advanced inference
-- sections:
+
+- title: Inference optimization
+  isExpanded: false
+  sections:
+  - local: optimization/fp16
+    title: Accelerate inference
+  - local: optimization/cache
+    title: Caching
+  - local: optimization/memory
+    title: Reduce memory usage
+  - local: optimization/speed-memory-optims
+    title: Compile and offloading quantized models
+  - title: Community optimizations
+    sections:
+    - local: optimization/pruna
+      title: Pruna
+    - local: optimization/xformers
+      title: xFormers
+    - local: optimization/tome
+      title: Token merging
+    - local: optimization/deepcache
+      title: DeepCache
+    - local: optimization/tgate
+      title: TGATE
+    - local: optimization/xdit
+      title: xDiT
+    - local: optimization/para_attn
+      title: ParaAttention
+
+- title: Hybrid Inference
+  isExpanded: false
+  sections:
   - local: hybrid_inference/overview
     title: Overview
   - local: hybrid_inference/vae_decode
@@ -90,8 +106,110 @@
     title: VAE Encode
   - local: hybrid_inference/api_reference
     title: API Reference
-  title: Hybrid Inference
-- sections:
+
+- title: Modular Diffusers
+  isExpanded: false
+  sections:
+  - local: modular_diffusers/overview
+    title: Overview
+  - local: modular_diffusers/modular_pipeline
+    title: Modular Pipeline
+  - local: modular_diffusers/components_manager
+    title: Components Manager
+  - local: modular_diffusers/modular_diffusers_states
+    title: Modular Diffusers States
+  - local: modular_diffusers/pipeline_block
+    title: Pipeline Block
+  - local: modular_diffusers/sequential_pipeline_blocks
+    title: Sequential Pipeline Blocks
+  - local: modular_diffusers/loop_sequential_pipeline_blocks
+    title: Loop Sequential Pipeline Blocks
+  - local: modular_diffusers/auto_pipeline_blocks
+    title: Auto Pipeline Blocks
+  - local: modular_diffusers/end_to_end_guide
+    title: End-to-End Example
+
+- title: Training
+  isExpanded: false
+  sections:
+  - local: training/overview
+    title: Overview
+  - local: training/create_dataset
+    title: Create a dataset for training
+  - local: training/adapt_a_model
+    title: Adapt a model to a new task
+  - local: tutorials/basic_training
+    title: Train a diffusion model
+  - title: Models
+    sections:
+    - local: training/unconditional_training
+      title: Unconditional image generation
+    - local: training/text2image
+      title: Text-to-image
+    - local: training/sdxl
+      title: Stable Diffusion XL
+    - local: training/kandinsky
+      title: Kandinsky 2.2
+    - local: training/wuerstchen
+      title: Wuerstchen
+    - local: training/controlnet
+      title: ControlNet
+    - local: training/t2i_adapters
+      title: T2I-Adapters
+    - local: training/instructpix2pix
+      title: InstructPix2Pix
+    - local: training/cogvideox
+      title: CogVideoX
+  - title: Methods
+    sections:
+    - local: training/text_inversion
+      title: Textual Inversion
+    - local: training/dreambooth
+      title: DreamBooth
+    - local: training/lora
+      title: LoRA
+    - local: training/custom_diffusion
+      title: Custom Diffusion
+    - local: training/lcm_distill
+      title: Latent Consistency Distillation
+    - local: training/ddpo
+      title: Reinforcement learning training with DDPO
+
+- title: Quantization
+  isExpanded: false
+  sections:
+  - local: quantization/overview
+    title: Getting Started
+  - local: quantization/bitsandbytes
+    title: bitsandbytes
+  - local: quantization/gguf
+    title: gguf
+  - local: quantization/torchao
+    title: torchao
+  - local: quantization/quanto
+    title: quanto
+
+- title: Model accelerators and hardware
+  isExpanded: false
+  sections:
+  - local: using-diffusers/stable_diffusion_jax_how_to
+    title: JAX/Flax
+  - local: optimization/onnx
+    title: ONNX
+  - local: optimization/open_vino
+    title: OpenVINO
+  - local: optimization/coreml
+    title: Core ML
+  - local: optimization/mps
+    title: Metal Performance Shaders (MPS)
+  - local: optimization/habana
+    title: Intel Gaudi
+  - local: optimization/neuron
+    title: AWS Neuron
+
+- title: Specific pipeline examples
+  isExpanded: false
+  sections:
   - local: using-diffusers/consisid
     title: ConsisID
   - local: using-diffusers/sdxl
@@ -116,106 +234,30 @@
     title: Stable Video Diffusion
   - local: using-diffusers/marigold_usage
     title: Marigold Computer Vision
-  title: Specific pipeline examples
-- sections:
-  - local: training/overview
-    title: Overview
-  - local: training/create_dataset
-    title: Create a dataset for training
-  - local: training/adapt_a_model
-    title: Adapt a model to a new task
-  - isExpanded: false
+
+- title: Resources
+  isExpanded: false
+  sections:
+  - title: Task recipes
     sections:
-    - local: training/unconditional_training
+    - local: using-diffusers/unconditional_image_generation
       title: Unconditional image generation
-    - local: training/text2image
+    - local: using-diffusers/conditional_image_generation
       title: Text-to-image
-    - local: training/sdxl
-      title: Stable Diffusion XL
-    - local: training/kandinsky
-      title: Kandinsky 2.2
-    - local: training/wuerstchen
-      title: Wuerstchen
-    - local: training/controlnet
-      title: ControlNet
-    - local: training/t2i_adapters
-      title: T2I-Adapters
-    - local: training/instructpix2pix
-      title: InstructPix2Pix
-    - local: training/cogvideox
-      title: CogVideoX
-    title: Models
-  - isExpanded: false
-    sections:
-    - local: training/text_inversion
-      title: Textual Inversion
-    - local: training/dreambooth
-      title: DreamBooth
-    - local: training/lora
-      title: LoRA
-    - local: training/custom_diffusion
-      title: Custom Diffusion
-    - local: training/lcm_distill
-      title: Latent Consistency Distillation
-    - local: training/ddpo
-      title: Reinforcement learning training with DDPO
-    title: Methods
-  title: Training
-- sections:
-  - local: quantization/overview
-    title: Getting Started
-  - local: quantization/bitsandbytes
-    title: bitsandbytes
-  - local: quantization/gguf
-    title: gguf
-  - local: quantization/torchao
-    title: torchao
-  - local: quantization/quanto
-    title: quanto
-  title: Quantization Methods
-- sections:
-  - local: optimization/fp16
-    title: Accelerate inference
-  - local: optimization/cache
-    title: Caching
-  - local: optimization/memory
-    title: Reduce memory usage
-  - local: optimization/speed-memory-optims
-    title: Compile and offloading quantized models
-  - local: optimization/pruna
-    title: Pruna
-  - local: optimization/xformers
-    title: xFormers
-  - local: optimization/tome
-    title: Token merging
-  - local: optimization/deepcache
-    title: DeepCache
-  - local: optimization/tgate
-    title: TGATE
-  - local: optimization/xdit
-    title: xDiT
-  - local: optimization/para_attn
-    title: ParaAttention
-  - sections:
-    - local: using-diffusers/stable_diffusion_jax_how_to
-      title: JAX/Flax
-    - local: optimization/onnx
-      title: ONNX
-    - local: optimization/open_vino
-      title: OpenVINO
-    - local: optimization/coreml
-      title: Core ML
-    title: Optimized model formats
-  - sections:
-    - local: optimization/mps
-      title: Metal Performance Shaders (MPS)
-    - local: optimization/habana
-      title: Intel Gaudi
-    - local: optimization/neuron
-      title: AWS Neuron
-    title: Optimized hardware
-  title: Accelerate inference and reduce memory
-- sections:
+    - local: using-diffusers/img2img
+      title: Image-to-image
+    - local: using-diffusers/inpaint
+      title: Inpainting
+    - local: advanced_inference/outpaint
+      title: Outpainting
+    - local: using-diffusers/text-img2vid
+      title: Video generation
+    - local: using-diffusers/depth2img
+      title: Depth-to-image
+  - local: using-diffusers/write_own_pipeline
+    title: Understanding pipelines, models and schedulers
+  - local: community_projects
+    title: Projects built with Diffusers
   - local: conceptual/philosophy
     title: Philosophy
   - local: using-diffusers/controlling_generation
@@ -226,13 +268,11 @@
     title: Diffusers' Ethical Guidelines
   - local: conceptual/evaluation
     title: Evaluating Diffusion Models
-  title: Conceptual Guides
-- sections:
-  - local: community_projects
-    title: Projects built with Diffusers
-  title: Community Projects
-- sections:
-  - isExpanded: false
+
+- title: API
+  isExpanded: false
+  sections:
+  - title: Main Classes
     sections:
     - local: api/configuration
       title: Configuration
@@ -242,8 +282,7 @@
       title: Outputs
     - local: api/quantization
       title: Quantization
-    title: Main Classes
-  - isExpanded: false
+  - title: Loaders
     sections:
     - local: api/loaders/ip_adapter
       title: IP-Adapter
@@ -259,14 +298,14 @@
       title: SD3Transformer2D
     - local: api/loaders/peft
       title: PEFT
-    title: Loaders
-  - isExpanded: false
+  - title: Models
     sections:
     - local: api/models/overview
       title: Overview
     - local: api/models/auto_model
       title: AutoModel
-    - sections:
+    - title: ControlNets
+      sections:
       - local: api/models/controlnet
         title: ControlNetModel
       - local: api/models/controlnet_union
@@ -281,8 +320,8 @@
         title: SD3ControlNetModel
       - local: api/models/controlnet_sparsectrl
         title: SparseControlNetModel
-      title: ControlNets
-    - sections:
+    - title: Transformers
+      sections:
       - local: api/models/allegro_transformer3d
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
@@ -331,6 +370,8 @@
         title: SanaTransformer2DModel
       - local: api/models/sd3_transformer2d
         title: SD3Transformer2DModel
+      - local: api/models/skyreels_v2_transformer_3d
+        title: SkyReelsV2Transformer3DModel
       - local: api/models/stable_audio_transformer
         title: StableAudioDiTModel
       - local: api/models/transformer2d
@@ -339,8 +380,8 @@
         title: TransformerTemporalModel
       - local: api/models/wan_transformer_3d
         title: WanTransformer3DModel
-      title: Transformers
-    - sections:
+    - title: UNets
+      sections:
       - local: api/models/stable_cascade_unet
         title: StableCascadeUNet
       - local: api/models/unet
@@ -355,8 +396,8 @@
         title: UNetMotionModel
       - local: api/models/uvit2d
         title: UViT2DModel
-      title: UNets
-    - sections:
+    - title: VAEs
+      sections:
       - local: api/models/asymmetricautoencoderkl
         title: AsymmetricAutoencoderKL
       - local: api/models/autoencoder_dc
@@ -387,9 +428,7 @@
         title: Tiny AutoEncoder
       - local: api/models/vq
         title: VQModel
-      title: VAEs
-    title: Models
-  - isExpanded: false
+  - title: Pipelines
     sections:
     - local: api/pipelines/overview
       title: Overview
@@ -525,11 +564,14 @@
       title: Semantic Guidance
     - local: api/pipelines/shap_e
       title: Shap-E
+    - local: api/pipelines/skyreels_v2
+      title: SkyReels-V2
     - local: api/pipelines/stable_audio
       title: Stable Audio
     - local: api/pipelines/stable_cascade
       title: Stable Cascade
-    - sections:
+    - title: Stable Diffusion
+      sections:
       - local: api/pipelines/stable_diffusion/overview
         title: Overview
       - local: api/pipelines/stable_diffusion/depth2img
@@ -566,7 +608,6 @@
         title: T2I-Adapter
       - local: api/pipelines/stable_diffusion/text2img
         title: Text-to-image
-      title: Stable Diffusion
     - local: api/pipelines/stable_unclip
       title: Stable unCLIP
     - local: api/pipelines/text_to_video
@@ -585,8 +626,7 @@
       title: Wan
     - local: api/pipelines/wuerstchen
       title: Wuerstchen
-    title: Pipelines
-  - isExpanded: false
+  - title: Schedulers
     sections:
     - local: api/schedulers/overview
       title: Overview
@@ -656,8 +696,7 @@
       title: UniPCMultistepScheduler
     - local: api/schedulers/vq_diffusion
       title: VQDiffusionScheduler
-    title: Schedulers
-  - isExpanded: false
+  - title: Internal classes
     sections:
     - local: api/internal_classes_overview
       title: Overview
@@ -675,5 +714,3 @@
       title: VAE Image Processor
     - local: api/video_processor
       title: Video Processor
-    title: Internal classes
-  title: API

docs/source/en/api/cache.md

@@ -28,3 +28,9 @@ Cache methods speedup diffusion transformers by storing and reusing intermediate
 [[autodoc]] FasterCacheConfig
 
 [[autodoc]] apply_faster_cache
+
+### FirstBlockCacheConfig
+
+[[autodoc]] FirstBlockCacheConfig
+
+[[autodoc]] apply_first_block_cache

docs/source/en/api/loaders/lora.md

@@ -26,6 +26,7 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
 - [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
+- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
 - [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
@@ -92,6 +93,10 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
 
+## SkyReelsV2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
+
 ## AmusedLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
@@ -100,6 +105,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
 
-## WanLoraLoaderMixin
+## LoraBaseMixin
 
-[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
+[[autodoc]] loaders.lora_base.LoraBaseMixin

docs/source/en/api/models/skyreels_v2_transformer_3d.md

@@ -0,0 +1,30 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# SkyReelsV2Transformer3DModel
+
+A Diffusion Transformer model for 3D video-like data was introduced in [SkyReels-V2](https://github.com/SkyworkAI/SkyReels-V2) by the Skywork AI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import SkyReelsV2Transformer3DModel
+
+transformer = SkyReelsV2Transformer3DModel.from_pretrained("Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## SkyReelsV2Transformer3DModel
+
+[[autodoc]] SkyReelsV2Transformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/pipelines/amused.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # aMUSEd
 
 aMUSEd was introduced in [aMUSEd: An Open MUSE Reproduction](https://huggingface.co/papers/2401.01808) by Suraj Patil, William Berman, Robin Rombach, and Patrick von Platen.

docs/source/en/api/pipelines/attend_and_excite.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Attend-and-Excite
 
 Attend-and-Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over image generation.

docs/source/en/api/pipelines/audioldm.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # AudioLDM
 
 AudioLDM was proposed in [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://huggingface.co/papers/2301.12503) by Haohe Liu et al. Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM

docs/source/en/api/pipelines/blip_diffusion.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # BLIP-Diffusion
 
 BLIP-Diffusion was proposed in [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://huggingface.co/papers/2305.14720). It enables zero-shot subject-driven generation and control-guided zero-shot generation.

docs/source/en/api/pipelines/chroma.md

@@ -36,7 +36,7 @@ import torch
 from diffusers import ChromaPipeline
 
 pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
-pipe.enabe_model_cpu_offload()
+pipe.enable_model_cpu_offload()
 
 prompt = [
     "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."

docs/source/en/api/pipelines/controlnetxs.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # ControlNet-XS
 
 <div class="flex flex-wrap space-x-1">

docs/source/en/api/pipelines/controlnetxs_sdxl.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # ControlNet-XS with Stable Diffusion XL
 
 ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results.

docs/source/en/api/pipelines/dance_diffusion.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Dance Diffusion
 
 [Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is by Zach Evans.

docs/source/en/api/pipelines/diffedit.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # DiffEdit
 
 [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://huggingface.co/papers/2210.11427) is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.

docs/source/en/api/pipelines/i2vgenxl.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # I2VGen-XL
 
 [I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models](https://hf.co/papers/2311.04145.pdf) by Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, and Jingren Zhou.

docs/source/en/api/pipelines/musicldm.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # MusicLDM
 
 MusicLDM was proposed in [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://huggingface.co/papers/2308.01546) by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov.

docs/source/en/api/pipelines/paint_by_example.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Paint by Example
 
 [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://huggingface.co/papers/2211.13227) is by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen.

docs/source/en/api/pipelines/panorama.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # MultiDiffusion
 
 <div class="flex flex-wrap space-x-1">

docs/source/en/api/pipelines/pia.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Image-to-Video Generation with PIA (Personalized Image Animator)
 
 <div class="flex flex-wrap space-x-1">

docs/source/en/api/pipelines/self_attention_guidance.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Self-Attention Guidance
 
 [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://huggingface.co/papers/2210.00939) is by Susung Hong et al.

docs/source/en/api/pipelines/semantic_stable_diffusion.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Semantic Guidance
 
 Semantic Guidance for Diffusion Models was proposed in [SEGA: Instructing Text-to-Image Models using Semantic Guidance](https://huggingface.co/papers/2301.12247) and provides strong semantic control over image generation.

docs/source/en/api/pipelines/skyreels_v2.md

@@ -0,0 +1,367 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License. -->
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+    <a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
+      <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+    </a>
+  </div>
+</div>
+
+# SkyReels-V2: Infinite-length Film Generative model
+
+[SkyReels-V2](https://huggingface.co/papers/2504.13074) by the SkyReels Team.
+
+*Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion dynamics, and duration: compromises in motion dynamics to enhance temporal visual quality, constrained video duration (5-10 seconds) to prioritize resolution, and inadequate shot-aware generation stemming from general-purpose MLLMs' inability to interpret cinematic grammar, such as shot composition, actor expressions, and camera motions. These intertwined limitations hinder realistic long-form synthesis and professional film-style generation. To address these limitations, we propose SkyReels-V2, an Infinite-length Film Generative Model, that synergizes Multi-modal Large Language Model (MLLM), Multi-stage Pretraining, Reinforcement Learning, and Diffusion Forcing Framework. Firstly, we design a comprehensive structural representation of video that combines the general descriptions by the Multi-modal LLM and the detailed shot language by sub-expert models. Aided with human annotation, we then train a unified Video Captioner, named SkyCaptioner-V1, to efficiently label the video data. Secondly, we establish progressive-resolution pretraining for the fundamental video generation, followed by a four-stage post-training enhancement: Initial concept-balanced Supervised Fine-Tuning (SFT) improves baseline quality; Motion-specific Reinforcement Learning (RL) training with human-annotated and synthetic distortion data addresses dynamic artifacts; Our diffusion forcing framework with non-decreasing noise schedules enables long-video synthesis in an efficient search space; Final high-quality SFT refines visual fidelity. All the code and models are available at [this https URL](https://github.com/SkyworkAI/SkyReels-V2).*
+
+You can find all the original SkyReels-V2 checkpoints under the [Skywork](https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9) organization.
+
+The following SkyReels-V2 models are supported in Diffusers:
+- [SkyReels-V2 DF 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers)
+- [SkyReels-V2 DF 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P-Diffusers)
+- [SkyReels-V2 DF 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-720P-Diffusers)
+- [SkyReels-V2 T2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-540P-Diffusers)
+- [SkyReels-V2 T2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-720P-Diffusers)
+- [SkyReels-V2 I2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P-Diffusers)
+- [SkyReels-V2 I2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P-Diffusers)
+- [SkyReels-V2 I2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-720P-Diffusers)
+- [SkyReels-V2 FLF2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-FLF2V-1.3B-540P-Diffusers)
+
+> [!TIP]
+> Click on the SkyReels-V2 models in the right sidebar for more examples of video generation.
+
+### A _Visual_ Demonstration
+
+        An example with these parameters:
+        base_num_frames=97, num_frames=97, num_inference_steps=30, ar_step=5, causal_block_size=5
+
+        vae_scale_factor_temporal -> 4
+        num_latent_frames: (97-1)//vae_scale_factor_temporal+1 = 25 frames -> 5 blocks of 5 frames each
+
+        base_num_latent_frames = (97-1)//vae_scale_factor_temporal+1 = 25 → blocks = 25//5 = 5 blocks
+        This 5 blocks means the maximum context length of the model is 25 frames in the latent space.
+
+        Asynchronous Processing Timeline:
+        ┌─────────────────────────────────────────────────────────────────┐
+        │ Steps:    1    6   11   16   21   26   31   36   41   46   50   │
+        │ Block 1: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]                       │
+        │ Block 2:      [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]                  │
+        │ Block 3:           [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]             │
+        │ Block 4:                [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]        │
+        │ Block 5:                     [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■]   │
+        └─────────────────────────────────────────────────────────────────┘
+
+        For Long Videos (num_frames > base_num_frames):
+        base_num_frames acts as the "sliding window size" for processing long videos.
+
+        Example: 257-frame video with base_num_frames=97, overlap_history=17
+        ┌──── Iteration 1 (frames 1-97) ────┐
+        │ Processing window: 97 frames      │ → 5 blocks, async processing
+        │ Generates: frames 1-97            │
+        └───────────────────────────────────┘
+                    ┌────── Iteration 2 (frames 81-177) ──────┐
+                    │ Processing window: 97 frames            │
+                    │ Overlap: 17 frames (81-97) from prev    │ → 5 blocks, async processing
+                    │ Generates: frames 98-177                │
+                    └─────────────────────────────────────────┘
+                                ┌────── Iteration 3 (frames 161-257) ──────┐
+                                │ Processing window: 97 frames             │
+                                │ Overlap: 17 frames (161-177) from prev   │ → 5 blocks, async processing
+                                │ Generates: frames 178-257                │
+                                └──────────────────────────────────────────┘
+
+        Each iteration independently runs the asynchronous processing with its own 5 blocks.
+        base_num_frames controls:
+        1. Memory usage (larger window = more VRAM)
+        2. Model context length (must match training constraints)
+        3. Number of blocks per iteration (base_num_latent_frames // causal_block_size)
+
+        Each block takes 30 steps to complete denoising.
+        Block N starts at step: 1 + (N-1) x ar_step
+        Total steps: 30 + (5-1) x 5 = 50 steps
+
+
+        Synchronous mode (ar_step=0) would process all blocks/frames simultaneously:
+        ┌──────────────────────────────────────────────┐
+        │ Steps:       1            ...            30  │
+        │ All blocks: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
+        └──────────────────────────────────────────────┘
+        Total steps: 30 steps
+
+
+        An example on how the step matrix is constructed for asynchronous processing:
+        Given the parameters: (num_inference_steps=30, flow_shift=8, num_frames=97, ar_step=5, causal_block_size=5)
+        - num_latent_frames = (97 frames - 1) // (4 temporal downsampling) + 1 = 25
+        - step_template = [999, 995, 991, 986, 980, 975, 969, 963, 956, 948,
+                           941, 932, 922, 912, 901, 888, 874, 859, 841, 822,
+                           799, 773, 743, 708, 666, 615, 551, 470, 363, 216]
+
+        The algorithm creates a 50x25 step_matrix where:
+        - Row 1:  [999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]
+        - Row 2:  [995, 995, 995, 995, 995, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]
+        - Row 3:  [991, 991, 991, 991, 991, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]
+        - ...
+        - Row 7:  [969, 969, 969, 969, 969, 995, 995, 995, 995, 995, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 999]
+        - ...
+        - Row 21: [799, 799, 799, 799, 799, 888, 888, 888, 888, 888, 941, 941, 941, 941, 941, 975, 975, 975, 975, 975, 999, 999, 999, 999, 999]
+        - ...
+        - Row 35: [  0,   0,   0,   0,   0, 216, 216, 216, 216, 216, 666, 666, 666, 666, 666, 822, 822, 822, 822, 822, 901, 901, 901, 901, 901]
+        - ...
+        - Row 42: [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 551, 551, 551, 551, 551, 773, 773, 773, 773, 773]
+        - ...
+        - Row 50: [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 216, 216, 216, 216, 216]
+
+        Detailed Row 6 Analysis:
+        - step_matrix[5]:       [ 975, 975, 975, 975, 975, 999, 999, 999, 999, 999, 999,  ...,  999]
+        - step_index[5]:        [   6,   6,   6,   6,   6,   1,   1,   1,   1,   1,   0,  ...,    0]
+        - step_update_mask[5]:  [True,True,True,True,True,True,True,True,True,True,False, ...,False]
+        - valid_interval[5]:    (0, 25)
+
+        Key Pattern: Block i lags behind Block i-1 by exactly ar_step=5 timesteps, creating the
+        staggered "diffusion forcing" effect where later blocks condition on cleaner earlier blocks.
+
+### Text-to-Video Generation
+
+The example below demonstrates how to generate a video from text.
+
+<hfoptions id="T2V usage">
+<hfoption id="T2V memory">
+
+Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
+
+From the original repo:
+>You can use --ar_step 5 to enable asynchronous inference. When asynchronous inference, --causal_block_size 5 is recommended while it is not supposed to be set for synchronous generation... Asynchronous inference will take more steps to diffuse the whole sequence which means it will be SLOWER than synchronous mode. In our experiments, asynchronous inference may improve the instruction following and visual consistent performance.
+
+```py
+# pip install ftfy
+import torch
+from diffusers import AutoModel, SkyReelsV2DiffusionForcingPipeline, UniPCMultistepScheduler
+from diffusers.utils import export_to_video
+
+vae = AutoModel.from_pretrained("Skywork/SkyReels-V2-DF-14B-540P-Diffusers", subfolder="vae", torch_dtype=torch.float32)
+transformer = AutoModel.from_pretrained("Skywork/SkyReels-V2-DF-14B-540P-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+
+pipeline = SkyReelsV2DiffusionForcingPipeline.from_pretrained(
+    "Skywork/SkyReels-V2-DF-14B-540P-Diffusers",
+    vae=vae,
+    transformer=transformer,
+    torch_dtype=torch.bfloat16
+)
+flow_shift = 8.0  # 8.0 for T2V, 5.0 for I2V
+pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
+pipeline = pipeline.to("cuda")
+
+prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
+
+output = pipeline(
+    prompt=prompt,
+    num_inference_steps=30,
+    height=544,  # 720 for 720P
+    width=960,   # 1280 for 720P
+    num_frames=97,
+    base_num_frames=97,  # 121 for 720P
+    ar_step=5,  # Controls asynchronous inference (0 for synchronous mode)
+    causal_block_size=5,  # Number of frames in each block for asynchronous processing
+    overlap_history=None,  # Number of frames to overlap for smooth transitions in long videos; 17 for long video generations
+    addnoise_condition=20,  # Improves consistency in long video generation
+).frames[0]
+export_to_video(output, "T2V.mp4", fps=24, quality=8)
+```
+
+</hfoption>
+</hfoptions>
+
+### First-Last-Frame-to-Video Generation
+
+The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description, a starting frame, and an ending frame.
+
+<hfoptions id="FLF2V usage">
+<hfoption id="usage">
+
+```python
+import numpy as np
+import torch
+import torchvision.transforms.functional as TF
+from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingImageToVideoPipeline, UniPCMultistepScheduler
+from diffusers.utils import export_to_video, load_image
+
+
+model_id = "Skywork/SkyReels-V2-DF-14B-720P-Diffusers"
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+pipeline = SkyReelsV2DiffusionForcingImageToVideoPipeline.from_pretrained(
+    model_id, vae=vae, torch_dtype=torch.bfloat16
+)
+flow_shift = 5.0  # 8.0 for T2V, 5.0 for I2V
+pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
+pipeline.to("cuda")
+
+first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png")
+last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png")
+
+def aspect_ratio_resize(image, pipeline, max_area=720 * 1280):
+    aspect_ratio = image.height / image.width
+    mod_value = pipeline.vae_scale_factor_spatial * pipeline.transformer.config.patch_size[1]
+    height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+    width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+    image = image.resize((width, height))
+    return image, height, width
+
+def center_crop_resize(image, height, width):
+    # Calculate resize ratio to match first frame dimensions
+    resize_ratio = max(width / image.width, height / image.height)
+
+    # Resize the image
+    width = round(image.width * resize_ratio)
+    height = round(image.height * resize_ratio)
+    size = [width, height]
+    image = TF.center_crop(image, size)
+
+    return image, height, width
+
+first_frame, height, width = aspect_ratio_resize(first_frame, pipeline)
+if last_frame.size != first_frame.size:
+    last_frame, _, _ = center_crop_resize(last_frame, height, width)
+
+prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
+
+output = pipeline(
+    image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.0
+).frames[0]
+export_to_video(output, "output.mp4", fps=24, quality=8)
+```
+
+</hfoption>
+</hfoptions>
+
+
+### Video-to-Video Generation
+
+<hfoptions id="V2V usage">
+<hfoption id="usage">
+
+`SkyReelsV2DiffusionForcingVideoToVideoPipeline` extends a given video.
+
+```python
+import numpy as np
+import torch
+import torchvision.transforms.functional as TF
+from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingVideoToVideoPipeline, UniPCMultistepScheduler
+from diffusers.utils import export_to_video, load_video
+
+
+model_id = "Skywork/SkyReels-V2-DF-14B-540P-Diffusers"
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+pipeline = SkyReelsV2DiffusionForcingVideoToVideoPipeline.from_pretrained(
+    model_id, vae=vae, torch_dtype=torch.bfloat16
+)
+flow_shift = 5.0  # 8.0 for T2V, 5.0 for I2V
+pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
+pipeline.to("cuda")
+
+video = load_video("input_video.mp4")
+
+prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
+
+output = pipeline(
+    video=video, prompt=prompt, height=544, width=960, guidance_scale=5.0,
+    num_inference_steps=30, num_frames=257, base_num_frames=97#, ar_step=5, causal_block_size=5,
+).frames[0]
+export_to_video(output, "output.mp4", fps=24, quality=8)
+# Total frames will be the number of frames of given video + 257
+```
+
+</hfoption>
+</hfoptions>
+
+
+## Notes
+
+- SkyReels-V2 supports LoRAs with [`~loaders.SkyReelsV2LoraLoaderMixin.load_lora_weights`].
+
+  <details>
+  <summary>Show example code</summary>
+
+  ```py
+  # pip install ftfy
+  import torch
+  from diffusers import AutoModel, SkyReelsV2DiffusionForcingPipeline
+  from diffusers.utils import export_to_video
+
+  vae = AutoModel.from_pretrained(
+      "Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", subfolder="vae", torch_dtype=torch.float32
+  )
+  pipeline = SkyReelsV2DiffusionForcingPipeline.from_pretrained(
+      "Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", vae=vae, torch_dtype=torch.bfloat16
+  )
+  pipeline.to("cuda")
+
+  pipeline.load_lora_weights("benjamin-paine/steamboat-willie-1.3b", adapter_name="steamboat-willie")
+  pipeline.set_adapters("steamboat-willie")
+
+  pipeline.enable_model_cpu_offload()
+
+  # use "steamboat willie style" to trigger the LoRA
+  prompt = """
+  steamboat willie style, golden era animation, The camera rushes from far to near in a low-angle shot,
+  revealing a white ferret on a log. It plays, leaps into the water, and emerges, as the camera zooms in
+  for a close-up. Water splashes berry bushes nearby, while moss, snow, and leaves blanket the ground.
+  Birch trees and a light blue sky frame the scene, with ferns in the foreground. Side lighting casts dynamic
+  shadows and warm highlights. Medium composition, front view, low angle, with depth of field.
+  """
+
+  output = pipeline(
+      prompt=prompt,
+      num_frames=97,
+      guidance_scale=6.0,
+  ).frames[0]
+  export_to_video(output, "output.mp4", fps=24)
+  ```
+
+  </details>
+
+
+## SkyReelsV2DiffusionForcingPipeline
+
+[[autodoc]] SkyReelsV2DiffusionForcingPipeline
+  - all
+  - __call__
+
+## SkyReelsV2DiffusionForcingImageToVideoPipeline
+
+[[autodoc]] SkyReelsV2DiffusionForcingImageToVideoPipeline
+  - all
+  - __call__
+
+## SkyReelsV2DiffusionForcingVideoToVideoPipeline
+
+[[autodoc]] SkyReelsV2DiffusionForcingVideoToVideoPipeline
+  - all
+  - __call__
+
+## SkyReelsV2Pipeline
+
+[[autodoc]] SkyReelsV2Pipeline
+  - all
+  - __call__
+
+## SkyReelsV2ImageToVideoPipeline
+
+[[autodoc]] SkyReelsV2ImageToVideoPipeline
+  - all
+  - __call__
+
+## SkyReelsV2PipelineOutput
+
+[[autodoc]] pipelines.skyreels_v2.pipeline_output.SkyReelsV2PipelineOutput

docs/source/en/api/pipelines/stable_diffusion/gligen.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # GLIGEN (Grounded Language-to-Image Generation)
 
 The GLIGEN model was created by researchers and engineers from [University of Wisconsin-Madison, Columbia University, and Microsoft](https://github.com/gligen/GLIGEN). The [`StableDiffusionGLIGENPipeline`] and [`StableDiffusionGLIGENTextImagePipeline`] can generate photorealistic images conditioned on grounding inputs. Along with text and bounding boxes with [`StableDiffusionGLIGENPipeline`], if input images are given, [`StableDiffusionGLIGENTextImagePipeline`] can insert objects described by text at the region defined by bounding boxes. Otherwise, it'll generate an image described by the caption/prompt and insert objects described by text at the region defined by bounding boxes. It's trained on COCO2014D and COCO2014CD datasets, and the model uses a frozen CLIP ViT-L/14 text encoder to condition itself on grounding inputs.

docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # K-Diffusion
 
 [k-diffusion](https://github.com/crowsonkb/k-diffusion) is a popular library created by [Katherine Crowson](https://github.com/crowsonkb/). We provide `StableDiffusionKDiffusionPipeline` and `StableDiffusionXLKDiffusionPipeline` that allow you to run Stable DIffusion with samplers from k-diffusion.

docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Text-to-(RGB, depth)
 
 <div class="flex flex-wrap space-x-1">

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Safe Stable Diffusion
 
 Safe Stable Diffusion was proposed in [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://huggingface.co/papers/2211.05105) and mitigates inappropriate degeneration from Stable Diffusion models because they're trained on unfiltered web-crawled datasets. For instance Stable Diffusion may unexpectedly generate nudity, violence, images depicting self-harm, and otherwise offensive content. Safe Stable Diffusion is an extension of Stable Diffusion that drastically reduces this type of content.

docs/source/en/api/pipelines/text_to_video.md

@@ -10,11 +10,8 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-<Tip warning={true}>
-
-🧪 This pipeline is for research purposes only.
-
-</Tip>
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
 
 # Text-to-video
 

docs/source/en/api/pipelines/text_to_video_zero.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # Text2Video-Zero
 
 <div class="flex flex-wrap space-x-1">

docs/source/en/api/pipelines/unclip.md

@@ -7,6 +7,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # unCLIP
 
 [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) is by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen. The unCLIP model in 🤗 Diffusers comes from kakaobrain's [karlo](https://github.com/kakaobrain/karlo).

docs/source/en/api/pipelines/unidiffuser.md

@@ -10,6 +10,9 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 # UniDiffuser
 
 <div class="flex flex-wrap space-x-1">

docs/source/en/api/pipelines/wuerstchen.md

@@ -12,6 +12,9 @@ specific language governing permissions and limitations under the License.
 
 # Würstchen
 
+> [!WARNING]
+> This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
+
 <div class="flex flex-wrap space-x-1">
   <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
 </div>

docs/source/en/modular_diffusers/auto_pipeline_blocks.md

@@ -0,0 +1,316 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# AutoPipelineBlocks
+
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
+`AutoPipelineBlocks` is a subclass of `ModularPipelineBlocks`. It is a multi-block that automatically selects which sub-blocks to run based on the inputs provided at runtime, creating conditional workflows that adapt to different scenarios. The main purpose is convenience and portability - for developers, you can package everything into one workflow, making it easier to share and use.
+
+In this tutorial, we will show you how to create an `AutoPipelineBlocks` and learn more about how the conditional selection works.
+
+<Tip>
+
+Other types of multi-blocks include [SequentialPipelineBlocks](sequential_pipeline_blocks.md) (for linear workflows) and [LoopSequentialPipelineBlocks](loop_sequential_pipeline_blocks.md) (for iterative workflows). For information on creating individual blocks, see the [PipelineBlock guide](pipeline_block.md).
+
+Additionally, like all `ModularPipelineBlocks`, `AutoPipelineBlocks` are definitions/specifications, not runnable pipelines. You need to convert them into a `ModularPipeline` to actually execute them. For information on creating and running pipelines, see the [Modular Pipeline guide](modular_pipeline.md).
+
+</Tip>
+
+For example, you might want to support text-to-image and image-to-image tasks. Instead of creating two separate pipelines, you can create an `AutoPipelineBlocks` that automatically chooses the workflow based on whether an `image` input is provided.
+
+Let's see an example. We'll use the helper function from the [PipelineBlock guide](./pipeline_block.md) to create our blocks:
+
+**Helper Function**
+
+```py
+from diffusers.modular_pipelines import PipelineBlock, InputParam, OutputParam
+import torch
+
+def make_block(inputs=[], intermediate_inputs=[], intermediate_outputs=[], block_fn=None, description=None):
+    class TestBlock(PipelineBlock):
+        model_name = "test"
+        
+        @property
+        def inputs(self):
+            return inputs
+            
+        @property
+        def intermediate_inputs(self):
+            return intermediate_inputs
+            
+        @property
+        def intermediate_outputs(self):
+            return intermediate_outputs
+            
+        @property
+        def description(self):
+            return description if description is not None else ""
+            
+        def __call__(self, components, state):
+            block_state = self.get_block_state(state)
+            if block_fn is not None:
+                block_state = block_fn(block_state, state)
+            self.set_block_state(state, block_state)
+            return components, state
+    
+    return TestBlock
+```
+
+Now let's create a dummy `AutoPipelineBlocks` that includes dummy text-to-image, image-to-image, and inpaint pipelines.
+
+
+```py
+from diffusers.modular_pipelines import AutoPipelineBlocks 
+
+# These are dummy blocks and we only focus on "inputs" for our purpose
+inputs = [InputParam(name="prompt")]
+# block_fn prints out which workflow is running so we can see the execution order at runtime
+block_fn = lambda x, y: print("running the text-to-image workflow")
+block_t2i_cls = make_block(inputs=inputs, block_fn=block_fn, description="I'm a text-to-image workflow!")
+
+inputs = [InputParam(name="prompt"), InputParam(name="image")]
+block_fn = lambda x, y: print("running the image-to-image workflow")
+block_i2i_cls = make_block(inputs=inputs, block_fn=block_fn, description="I'm a image-to-image workflow!")
+
+inputs = [InputParam(name="prompt"), InputParam(name="image"), InputParam(name="mask")]
+block_fn = lambda x, y: print("running the inpaint workflow")
+block_inpaint_cls = make_block(inputs=inputs, block_fn=block_fn, description="I'm a inpaint workflow!")
+
+class AutoImageBlocks(AutoPipelineBlocks):
+    # List of sub-block classes to choose from
+    block_classes = [block_inpaint_cls, block_i2i_cls, block_t2i_cls]
+    # Names for each block in the same order
+    block_names = ["inpaint", "img2img", "text2img"]
+    # Trigger inputs that determine which block to run
+    # - "mask" triggers inpaint workflow
+    # - "image" triggers img2img workflow (but only if mask is not provided) 
+    # - if none of above, runs the text2img workflow (default)
+    block_trigger_inputs = ["mask", "image", None]
+    # Description is extremely important for AutoPipelineBlocks
+    @property
+    def description(self):
+        return (
+            "Pipeline generates images given different types of conditions!\n"
+            + "This is an auto pipeline block that works for text2img, img2img and inpainting tasks.\n"
+            + " - inpaint workflow is run when `mask` is provided.\n"
+            + " - img2img workflow is run when `image` is provided (but only when `mask` is not provided).\n"
+            + " - text2img workflow is run when neither `image` nor `mask` is provided.\n"
+        )
+
+# Create the blocks
+auto_blocks = AutoImageBlocks()
+# convert to pipeline
+auto_pipeline = auto_blocks.init_pipeline()
+```
+
+Now we have created an `AutoPipelineBlocks` that contains 3 sub-blocks. Notice the warning message at the top - this automatically appears in every `ModularPipelineBlocks` that contains `AutoPipelineBlocks` to remind end users that dynamic block selection happens at runtime. 
+
+```py
+AutoImageBlocks(
+  Class: AutoPipelineBlocks
+
+  ====================================================================================================
+  This pipeline contains blocks that are selected at runtime based on inputs.
+  Trigger Inputs: ['mask', 'image']
+  ====================================================================================================
+
+
+  Description: Pipeline generates images given different types of conditions!
+      This is an auto pipeline block that works for text2img, img2img and inpainting tasks.
+       - inpaint workflow is run when `mask` is provided.
+       - img2img workflow is run when `image` is provided (but only when `mask` is not provided).
+       - text2img workflow is run when neither `image` nor `mask` is provided.
+      
+
+
+  Sub-Blocks:
+    • inpaint [trigger: mask] (TestBlock)
+       Description: I'm a inpaint workflow!
+
+    • img2img [trigger: image] (TestBlock)
+       Description: I'm a image-to-image workflow!
+
+    • text2img [default] (TestBlock)
+       Description: I'm a text-to-image workflow!
+
+)
+```
+
+Check out the documentation with `print(auto_pipeline.doc)`:
+
+```py
+>>> print(auto_pipeline.doc)
+class AutoImageBlocks
+
+  Pipeline generates images given different types of conditions!
+  This is an auto pipeline block that works for text2img, img2img and inpainting tasks.
+   - inpaint workflow is run when `mask` is provided.
+   - img2img workflow is run when `image` is provided (but only when `mask` is not provided).
+   - text2img workflow is run when neither `image` nor `mask` is provided.
+
+  Inputs:
+
+      prompt (`None`, *optional*):
+
+      image (`None`, *optional*):
+
+      mask (`None`, *optional*):
+```
+
+There is a fundamental trade-off of AutoPipelineBlocks: it trades clarity for convenience. While it is really easy for packaging multiple workflows, it can become confusing without proper documentation. e.g. if we just throw a pipeline at you and tell you that it contains 3 sub-blocks and takes 3 inputs `prompt`, `image` and `mask`, and ask you to run an image-to-image workflow: if you don't have any prior knowledge on how these pipelines work, you would be pretty clueless, right?
+
+This pipeline we just made though, has a docstring that shows all available inputs and workflows and explains how to use each with different inputs. So it's really helpful for users. For example, it's clear that you need to pass `image` to run img2img. This is why the description field is absolutely critical for AutoPipelineBlocks. We highly recommend you to explain the conditional logic very well for each `AutoPipelineBlocks` you would make. We also recommend to always test individual pipelines first before packaging them into AutoPipelineBlocks. 
+
+Let's run this auto pipeline with different inputs to see if the conditional logic works as described. Remember that we have added `print` in each `PipelineBlock`'s `__call__` method to print out its workflow name, so it should be easy to tell which one is running:
+
+```py
+>>> _ = auto_pipeline(image="image", mask="mask")
+running the inpaint workflow
+>>> _ = auto_pipeline(image="image")
+running the image-to-image workflow
+>>> _ = auto_pipeline(prompt="prompt")
+running the text-to-image workflow
+>>> _ = auto_pipeline(image="prompt", mask="mask")
+running the inpaint workflow
+```
+
+However, even with documentation, it can become very confusing when AutoPipelineBlocks are combined with other blocks. The complexity grows quickly when you have nested AutoPipelineBlocks or use them as sub-blocks in larger pipelines.
+
+Let's make another `AutoPipelineBlocks` - this one only contains one block, and it does not include `None` in its `block_trigger_inputs` (which corresponds to the default block to run when none of the trigger inputs are provided). This means this block will be skipped if the trigger input (`ip_adapter_image`) is not provided at runtime.
+
+```py
+from diffusers.modular_pipelines import SequentialPipelineBlocks, InsertableDict
+inputs = [InputParam(name="ip_adapter_image")]
+block_fn = lambda x, y: print("running the ip-adapter workflow")
+block_ipa_cls = make_block(inputs=inputs, block_fn=block_fn, description="I'm a IP-adapter workflow!")
+
+class AutoIPAdapter(AutoPipelineBlocks):
+    block_classes = [block_ipa_cls]
+    block_names = ["ip-adapter"]
+    block_trigger_inputs = ["ip_adapter_image"]
+    @property
+    def description(self):
+        return "Run IP Adapter step if `ip_adapter_image` is provided."
+```
+
+Now let's combine these 2 auto blocks together into a `SequentialPipelineBlocks`:
+
+```py
+auto_ipa_blocks = AutoIPAdapter()
+blocks_dict = InsertableDict()
+blocks_dict["ip-adapter"] = auto_ipa_blocks
+blocks_dict["image-generation"] = auto_blocks
+all_blocks = SequentialPipelineBlocks.from_blocks_dict(blocks_dict)
+pipeline = all_blocks.init_pipeline()
+```
+
+Let's take a look: now things get more confusing. In this particular example, you could still try to explain the conditional logic in the `description` field here - there are only 4 possible execution paths so it's doable. However, since this is a `SequentialPipelineBlocks` that could contain many more blocks, the complexity can quickly get out of hand as the number of blocks increases.
+
+```py
+>>> all_blocks
+SequentialPipelineBlocks(
+  Class: ModularPipelineBlocks
+
+  ====================================================================================================
+  This pipeline contains blocks that are selected at runtime based on inputs.
+  Trigger Inputs: ['image', 'mask', 'ip_adapter_image']
+  Use `get_execution_blocks()` with input names to see selected blocks (e.g. `get_execution_blocks('image')`).
+  ====================================================================================================
+
+
+  Description: 
+
+
+  Sub-Blocks:
+    [0] ip-adapter (AutoIPAdapter)
+       Description: Run IP Adapter step if `ip_adapter_image` is provided.
+                   
+
+    [1] image-generation (AutoImageBlocks)
+       Description: Pipeline generates images given different types of conditions!
+                   This is an auto pipeline block that works for text2img, img2img and inpainting tasks.
+                    - inpaint workflow is run when `mask` is provided.
+                    - img2img workflow is run when `image` is provided (but only when `mask` is not provided).
+                    - text2img workflow is run when neither `image` nor `mask` is provided.
+                   
+
+)
+
+```
+
+This is when the `get_execution_blocks()` method comes in handy - it basically extracts a `SequentialPipelineBlocks` that only contains the blocks that are actually run based on your inputs.
+
+Let's try some examples:
+
+`mask`: we expect it to skip the first ip-adapter since `ip_adapter_image` is not provided, and then run the inpaint for the second block.
+
+```py
+>>> all_blocks.get_execution_blocks('mask')
+SequentialPipelineBlocks(
+  Class: ModularPipelineBlocks
+
+  Description: 
+
+
+  Sub-Blocks:
+    [0] image-generation (TestBlock)
+       Description: I'm a inpaint workflow!
+
+)
+```
+
+Let's also actually run the pipeline to confirm:
+
+```py
+>>> _ = pipeline(mask="mask")
+skipping auto block: AutoIPAdapter
+running the inpaint workflow
+```
+
+Try a few more:
+
+```py
+print(f"inputs: ip_adapter_image:")
+blocks_select = all_blocks.get_execution_blocks('ip_adapter_image')
+print(f"expected_execution_blocks: {blocks_select}")
+print(f"actual execution blocks:")
+_ = pipeline(ip_adapter_image="ip_adapter_image", prompt="prompt")
+# expect to see ip-adapter + text2img
+
+print(f"inputs: image:")
+blocks_select = all_blocks.get_execution_blocks('image')
+print(f"expected_execution_blocks: {blocks_select}")
+print(f"actual execution blocks:")
+_ = pipeline(image="image", prompt="prompt")
+# expect to see img2img
+
+print(f"inputs: prompt:")
+blocks_select = all_blocks.get_execution_blocks('prompt')
+print(f"expected_execution_blocks: {blocks_select}")
+print(f"actual execution blocks:")
+_ = pipeline(prompt="prompt")
+# expect to see text2img (prompt is not a trigger input so fallback to default)
+
+print(f"inputs: mask + ip_adapter_image:")
+blocks_select = all_blocks.get_execution_blocks('mask','ip_adapter_image')
+print(f"expected_execution_blocks: {blocks_select}")
+print(f"actual execution blocks:")
+_ = pipeline(mask="mask", ip_adapter_image="ip_adapter_image")
+# expect to see ip-adapter + inpaint
+```
+
+In summary, `AutoPipelineBlocks` is a good tool for packaging multiple workflows into a single, convenient interface and it can greatly simplify the user experience. However, always provide clear descriptions explaining the conditional logic, test individual pipelines first before combining them, and use `get_execution_blocks()` to understand runtime behavior in complex compositions.

docs/source/en/modular_diffusers/components_manager.md

@@ -0,0 +1,514 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Components Manager
+
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: This is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
+The Components Manager is a central model registry and management system in diffusers. It lets you add models then reuse them across multiple pipelines and workflows. It tracks all models in one place with useful metadata such as model size, device placement and loaded adapters (LoRA, IP-Adapter). It has mechanisms in place to prevent duplicate model instances, enables memory-efficient sharing. Most significantly, it offers offloading that works across pipelines — unlike regular DiffusionPipeline offloading (i.e. `enable_model_cpu_offload` and `enable_sequential_cpu_offload`) which is limited to one pipeline with predefined sequences, the Components Manager automatically manages your device memory across all your models and workflows. 
+
+
+## Basic Operations
+
+Let's start with the most basic operations. First, create a Components Manager:
+
+```py
+from diffusers import ComponentsManager
+comp = ComponentsManager()
+```
+
+Use the `add(name, component)` method to register a component. It returns a unique ID that combines the component name with the object's unique identifier (using Python's `id()` function):
+
+```py
+from diffusers import AutoModel
+text_encoder = AutoModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder")
+# Returns component_id like 'text_encoder_139917733042864'
+component_id = comp.add("text_encoder", text_encoder)
+```
+
+You can view all registered components and their metadata:
+
+```py
+>>> comp
+Components:
+===============================================================================================================================================
+Models:
+-----------------------------------------------------------------------------------------------------------------------------------------------
+Name_ID                      | Class                     | Device: act(exec)    | Dtype           | Size (GB)  | Load ID         | Collection
+-----------------------------------------------------------------------------------------------------------------------------------------------
+text_encoder_139917733042864 | CLIPTextModel             | cpu                  | torch.float32   | 0.46       | N/A             | N/A
+-----------------------------------------------------------------------------------------------------------------------------------------------
+
+Additional Component Info:
+==================================================
+```
+
+And remove components using their unique ID:
+
+```py
+comp.remove("text_encoder_139917733042864")
+```
+
+## Duplicate Detection
+
+The Components Manager automatically detects and prevents duplicate model instances to save memory and avoid confusion. Let's walk through how this works in practice.
+
+When you try to add the same object twice, the manager will warn you and return the existing ID:
+
+```py
+>>> comp.add("text_encoder", text_encoder)
+'text_encoder_139917733042864'
+>>> comp.add("text_encoder", text_encoder)
+ComponentsManager: component 'text_encoder' already exists as 'text_encoder_139917733042864'
+'text_encoder_139917733042864'
+```
+
+Even if you add the same object under a different name, it will still be detected as a duplicate:
+
+```py
+>>> comp.add("clip", text_encoder)
+ComponentsManager: adding component 'clip' as 'clip_139917733042864', but it is duplicate of 'text_encoder_139917733042864'
+To remove a duplicate, call `components_manager.remove('<component_id>')`.
+'clip_139917733042864'
+```
+
+However, there's a more subtle case where duplicate detection becomes tricky. When you load the same model into different objects, the manager can't detect duplicates unless you use `ComponentSpec`. For example:
+
+```py
+>>> text_encoder_2 = AutoModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder")
+>>> comp.add("text_encoder", text_encoder_2)
+'text_encoder_139917732983664'
+```
+
+This creates a problem - you now have two copies of the same model consuming double the memory:
+
+```py
+>>> comp
+Components:
+===============================================================================================================================================
+Models:
+-----------------------------------------------------------------------------------------------------------------------------------------------
+Name_ID                      | Class                     | Device: act(exec)    | Dtype           | Size (GB)  | Load ID         | Collection
+-----------------------------------------------------------------------------------------------------------------------------------------------
+text_encoder_139917733042864 | CLIPTextModel             | cpu                  | torch.float32   | 0.46       | N/A             | N/A
+clip_139917733042864         | CLIPTextModel             | cpu                  | torch.float32   | 0.46       | N/A             | N/A
+text_encoder_139917732983664 | CLIPTextModel             | cpu                  | torch.float32   | 0.46       | N/A             | N/A
+-----------------------------------------------------------------------------------------------------------------------------------------------
+
+Additional Component Info:
+==================================================
+```
+
+We recommend using `ComponentSpec` to load your models. Models loaded with `ComponentSpec` get tagged with a unique ID that encodes their loading parameters, allowing the Components Manager to detect when different objects represent the same underlying checkpoint:
+
+```py
+from diffusers import ComponentSpec, ComponentsManager
+from transformers import CLIPTextModel
+comp = ComponentsManager()
+
+# Create ComponentSpec for the first text encoder
+spec = ComponentSpec(name="text_encoder", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", type_hint=AutoModel)
+# Create ComponentSpec for a duplicate text encoder (it is same checkpoint, from same repo/subfolder)
+spec_duplicated = ComponentSpec(name="text_encoder_duplicated", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="text_encoder", type_hint=CLIPTextModel)
+
+# Load and add both components - the manager will detect they're the same model
+comp.add("text_encoder", spec.load())
+comp.add("text_encoder_duplicated", spec_duplicated.load())
+```
+
+Now the manager detects the duplicate and warns you:
+
+```out
+ComponentsManager: adding component 'text_encoder_duplicated_139917580682672', but it has duplicate load_id 'stabilityai/stable-diffusion-xl-base-1.0|text_encoder|null|null' with existing components: text_encoder_139918506246832. To remove a duplicate, call `components_manager.remove('<component_id>')`.
+'text_encoder_duplicated_139917580682672'
+```
+
+Both models now show the same `load_id`, making it clear they're the same model:
+
+```py
+>>> comp
+Components:
+======================================================================================================================================================================================================
+Models:
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+Name_ID                                 | Class                     | Device: act(exec)    | Dtype           | Size (GB)  | Load ID                                                         | Collection
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+text_encoder_139918506246832            | CLIPTextModel             | cpu                  | torch.float32   | 0.46       | stabilityai/stable-diffusion-xl-base-1.0|text_encoder|null|null | N/A
+text_encoder_duplicated_139917580682672 | CLIPTextModel             | cpu                  | torch.float32   | 0.46       | stabilityai/stable-diffusion-xl-base-1.0|text_encoder|null|null | N/A
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+Additional Component Info:
+==================================================
+```
+
+## Collections
+
+Collections are labels you can assign to components for better organization and management. You add a component under a collection by passing the `collection=` parameter when you add the component to the manager, i.e. `add(name, component, collection=...)`. Within each collection, only one component per name is allowed - if you add a second component with the same name, the first one is automatically removed.
+
+Here's how collections work in practice:
+
+```py
+comp = ComponentsManager()
+# Create ComponentSpec for the first UNet (SDXL base)
+spec = ComponentSpec(name="unet", repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", type_hint=AutoModel)
+# Create ComponentSpec for a different UNet (Juggernaut-XL)
+spec2 = ComponentSpec(name="unet", repo="RunDiffusion/Juggernaut-XL-v9", subfolder="unet", type_hint=AutoModel, variant="fp16")
+
+# Add both UNets to the same collection - the second one will replace the first
+comp.add("unet", spec.load(), collection="sdxl")
+comp.add("unet", spec2.load(), collection="sdxl")
+```
+
+The manager automatically removes the old UNet and adds the new one:
+
+```out
+ComponentsManager: removing existing unet from collection 'sdxl': unet_139917723891888
+'unet_139917723893136'
+```
+
+Only one UNet remains in the collection:
+
+```py
+>>> comp
+Components:
+====================================================================================================================================================================
+Models:
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------
+Name_ID              | Class                     | Device: act(exec)    | Dtype           | Size (GB)  | Load ID                                      | Collection
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------
+unet_139917723893136 | UNet2DConditionModel      | cpu                  | torch.float32   | 9.56       | RunDiffusion/Juggernaut-XL-v9|unet|fp16|null | sdxl
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+Additional Component Info:
+==================================================
+```
+
+For example, in node-based systems, you can mark all models loaded from one node with the same collection label, automatically replace models when user loads new checkpoints under same name, batch delete all models in a collection when a node is removed.
+
+## Retrieving Components
+
+The Components Manager provides several methods to retrieve registered components.
+
+The `get_one()` method returns a single component and supports pattern matching for the `name` parameter. You can use:
+- exact matches like `comp.get_one(name="unet")`
+- wildcards like `comp.get_one(name="unet*")` for components starting with "unet"
+- exclusion patterns like `comp.get_one(name="!unet")` to exclude components named "unet"
+- OR patterns like `comp.get_one(name="unet|vae")` to match either "unet" OR "vae". 
+
+Optionally, You can add collection and load_id as filters e.g. `comp.get_one(name="unet", collection="sdxl")`. If multiple components match, `get_one()` throws an error.
+
+Another useful method is `get_components_by_names()`, which takes a list of names and returns a dictionary mapping names to components. This is particularly helpful with modular pipelines since they provide lists of required component names, and the returned dictionary can be directly passed to `pipeline.update_components()`.
+
+```py
+# Get components by name list
+component_dict = comp.get_components_by_names(names=["text_encoder", "unet", "vae"])
+# Returns: {"text_encoder": component1, "unet": component2, "vae": component3}
+```
+
+## Using Components Manager with Modular Pipelines
+
+The Components Manager integrates seamlessly with Modular Pipelines. All you need to do is pass a Components Manager instance to `from_pretrained()` or `init_pipeline()` with an optional `collection` parameter:
+
+```py
+from diffusers import ModularPipeline, ComponentsManager
+comp = ComponentsManager()
+pipe = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test1")
+```
+
+By default, modular pipelines don't load components immediately, so both the pipeline and Components Manager start empty:
+
+```py
+>>> comp
+Components:
+==================================================
+No components registered.
+==================================================
+```
+
+When you load components on the pipeline, they are automatically registered in the Components Manager:
+
+```py
+>>> pipe.load_components(names="unet")
+>>> comp
+Components:
+==============================================================================================================================================================
+Models:
+--------------------------------------------------------------------------------------------------------------------------------------------------------------
+Name_ID              | Class                     | Device: act(exec)    | Dtype           | Size (GB)  | Load ID                                | Collection
+--------------------------------------------------------------------------------------------------------------------------------------------------------------
+unet_139917726686304 | UNet2DConditionModel      | cpu                  | torch.float32   | 9.56       | SG161222/RealVisXL_V4.0|unet|null|null | test1
+--------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+Additional Component Info:
+==================================================
+```
+
+Now let's load all default components and then create a second pipeline that reuses all components from the first one. We pass the same Components Manager to the second pipeline but with a different collection:
+
+```py
+# Load all default components 
+>>> pipe.load_default_components()
+
+# Create a second pipeline using the same Components Manager but with a different collection
+>>> pipe2 = ModularPipeline.from_pretrained("YiYiXu/modular-demo-auto", components_manager=comp, collection="test2")
+```
+
+As mentioned earlier, `ModularPipeline` has a property `null_component_names` that returns a list of component names it needs to load. We can conveniently use this list with the `get_components_by_names` method on the Components Manager:
+
+```py
+# Get the list of components that pipe2 needs to load
+>>> pipe2.null_component_names 
+['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'image_encoder', 'unet', 'vae', 'scheduler', 'controlnet']
+
+# Retrieve all required components from the Components Manager
+>>> comp_dict = comp.get_components_by_names(names=pipe2.null_component_names)
+
+# Update the pipeline with the retrieved components
+>>> pipe2.update_components(**comp_dict)
+```
+
+The warnings that follow are expected and indicate that the Components Manager is correctly identifying that these components already exist and will be reused rather than creating duplicates:
+
+```out
+ComponentsManager: component 'text_encoder' already exists as 'text_encoder_139917586016400'
+ComponentsManager: component 'text_encoder_2' already exists as 'text_encoder_2_139917699973424'
+ComponentsManager: component 'tokenizer' already exists as 'tokenizer_139917580599504'
+ComponentsManager: component 'tokenizer_2' already exists as 'tokenizer_2_139915763443904'
+ComponentsManager: component 'image_encoder' already exists as 'image_encoder_139917722468304'
+ComponentsManager: component 'unet' already exists as 'unet_139917580609632'
+ComponentsManager: component 'vae' already exists as 'vae_139917722459040'
+ComponentsManager: component 'scheduler' already exists as 'scheduler_139916266559408'
+ComponentsManager: component 'controlnet' already exists as 'controlnet_139917722454432'
+```
+
+
+The pipeline is now fully loaded:
+
+```py
+# null_component_names return empty list, meaning everything are loaded
+>>> pipe2.null_component_names
+[]
+```
+
+No new components were added to the Components Manager - we're reusing everything. All models are now associated with both `test1` and `test2` collections, showing that these components are shared across multiple pipelines:
+```py
+>>> comp
+Components:
+========================================================================================================================================================================================
+Models:
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+Name_ID                        | Class                         | Device: act(exec)    | Dtype           | Size (GB)  | Load ID                                            | Collection
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+text_encoder_139917586016400   | CLIPTextModel                 | cpu                  | torch.float32   | 0.46       | SG161222/RealVisXL_V4.0|text_encoder|null|null     | test1
+                               |                               |                      |                 |            |                                                    | test2
+text_encoder_2_139917699973424 | CLIPTextModelWithProjection   | cpu                  | torch.float32   | 2.59       | SG161222/RealVisXL_V4.0|text_encoder_2|null|null   | test1
+                               |                               |                      |                 |            |                                                    | test2
+unet_139917580609632           | UNet2DConditionModel          | cpu                  | torch.float32   | 9.56       | SG161222/RealVisXL_V4.0|unet|null|null             | test1
+                               |                               |                      |                 |            |                                                    | test2
+controlnet_139917722454432     | ControlNetModel               | cpu                  | torch.float32   | 4.66       | diffusers/controlnet-canny-sdxl-1.0|null|null|null | test1
+                               |                               |                      |                 |            |                                                    | test2
+vae_139917722459040            | AutoencoderKL                 | cpu                  | torch.float32   | 0.31       | SG161222/RealVisXL_V4.0|vae|null|null              | test1
+                               |                               |                      |                 |            |                                                    | test2
+image_encoder_139917722468304  | CLIPVisionModelWithProjection | cpu                  | torch.float32   | 6.87       | h94/IP-Adapter|sdxl_models/image_encoder|null|null | test1
+                               |                               |                      |                 |            |                                                    | test2
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+Other Components:
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+ID                             | Class                         | Collection
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+tokenizer_139917580599504      | CLIPTokenizer                 | test1
+                               |                               | test2
+scheduler_139916266559408      | EulerDiscreteScheduler        | test1
+                               |                               | test2
+tokenizer_2_139915763443904    | CLIPTokenizer                 | test1
+                               |                               | test2
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+Additional Component Info:
+==================================================
+```
+
+
+## Automatic Memory Management
+
+The Components Manager provides a global offloading strategy across all models, regardless of which pipeline is using them:
+
+```py
+comp.enable_auto_cpu_offload(device="cuda")
+```
+
+When enabled, all models start on CPU. The manager moves models to the device right before they're used and moves other models back to CPU when GPU memory runs low. You can set your own rules for which models to offload first. This works smoothly as you add or remove components. Once it's on, you don't need to worry about device placement - you can focus on your workflow.
+
+
+
+## Practical Example: Building Modular Workflows with Component Reuse
+
+Now that we've covered the basics of the Components Manager, let's walk through a practical example that shows how to build workflows in a modular setting and use the Components Manager to reuse components across multiple pipelines. This example demonstrates the true power of Modular Diffusers by working with multiple pipelines that can share components. 
+
+In this example, we'll generate latents from a text-to-image pipeline, then refine them with an image-to-image pipeline.
+
+Let's create a modular text-to-image workflow by separating it into three workflows: `text_blocks` for encoding prompts, `t2i_blocks` for generating latents, and `decoder_blocks` for creating final images.
+
+```py
+import torch
+from diffusers.modular_pipelines import SequentialPipelineBlocks
+from diffusers.modular_pipelines.stable_diffusion_xl import ALL_BLOCKS
+
+# Create modular blocks and separate text encoding and decoding steps
+t2i_blocks = SequentialPipelineBlocks.from_blocks_dict(ALL_BLOCKS["text2img"])
+text_blocks = t2i_blocks.sub_blocks.pop("text_encoder")
+decoder_blocks = t2i_blocks.sub_blocks.pop("decode")
+```
+
+Now we will convert them into runnalbe pipelines and set up the Components Manager with auto offloading and organize components under a "t2i" collection
+
+Since we now have 3 different workflows that share components, we create a separate pipeline that serves as a dedicated loader to load all the components, register them to the component manager, and then reuse them across different workflows.
+
+```py
+from diffusers import ComponentsManager, ModularPipeline
+
+# Set up Components Manager with auto offloading
+components = ComponentsManager()
+components.enable_auto_cpu_offload(device="cuda")
+
+# Create a new pipeline to load the components
+t2i_repo = "YiYiXu/modular-demo-auto"
+t2i_loader_pipe = ModularPipeline.from_pretrained(t2i_repo, components_manager=components, collection="t2i")
+
+# convert the 3 blocks into pipelines and attach the same components manager to all 3
+text_node = text_blocks.init_pipeline(t2i_repo, components_manager=components)
+decoder_node = decoder_blocks.init_pipeline(t2i_repo, components_manager=components)
+t2i_pipe = t2i_blocks.init_pipeline(t2i_repo, components_manager=components)
+```
+
+Load all components into the loader pipeline, they should all be automatically registered to Components Manager under the "t2i" collection:
+
+```py
+# Load all components (including IP-Adapter and ControlNet for later use)
+t2i_loader_pipe.load_default_components(torch_dtype=torch.float16)
+```
+
+Now distribute the loaded components to each pipeline:
+
+```py
+# Get VAE for decoder (using get_one since there's only one)
+vae = components.get_one(load_id="SG161222/RealVisXL_V4.0|vae|null|null")
+decoder_node.update_components(vae=vae)
+
+# Get text components for text node (using get_components_by_names for multiple components)
+text_components = components.get_components_by_names(text_node.null_component_names)
+text_node.update_components(**text_components)
+
+# Get remaining components for t2i pipeline
+t2i_components = components.get_components_by_names(t2i_pipe.null_component_names)
+t2i_pipe.update_components(**t2i_components)
+```
+
+Now we can generate images using our modular workflow:
+
+```py
+# Generate text embeddings
+prompt = "an astronaut"
+text_embeddings = text_node(prompt=prompt, output=["prompt_embeds","negative_prompt_embeds", "pooled_prompt_embeds", "negative_pooled_prompt_embeds"])
+
+# Generate latents and decode to image
+generator = torch.Generator(device="cuda").manual_seed(0)
+latents_t2i = t2i_pipe(**text_embeddings, num_inference_steps=25, generator=generator, output="latents")
+image = decoder_node(latents=latents_t2i, output="images")[0]
+image.save("modular_part2_t2i.png")
+```
+
+Let's add a LoRA:
+
+```py
+# Load LoRA weights 
+>>> t2i_loader_pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy_face")
+>>> components
+Components:
+============================================================================================================================================================
+...
+Additional Component Info:
+==================================================
+
+unet:
+  Adapters: ['toy_face']
+```
+
+You can see that the Components Manager tracks adapters metadata for all models it manages, and in our case, only Unet has lora loaded. This means we can reuse existing text embeddings. 
+
+```py
+# Generate with LoRA (reusing existing text embeddings)
+generator = torch.Generator(device="cuda").manual_seed(0)
+latents_lora = t2i_pipe(**text_embeddings, num_inference_steps=25, generator=generator, output="latents")
+image = decoder_node(latents=latents_lora, output="images")[0]
+image.save("modular_part2_lora.png")
+```
+
+
+Now let's create a refiner pipeline that reuses components from our text-to-image workflow:
+
+```py
+# Create refiner blocks (removing image_encoder and decode since we work with latents)
+refiner_blocks = SequentialPipelineBlocks.from_blocks_dict(ALL_BLOCKS["img2img"])
+refiner_blocks.sub_blocks.pop("image_encoder")
+refiner_blocks.sub_blocks.pop("decode")
+
+# Create refiner pipeline with different repo and collection,
+# Attach the same component manager to it
+refiner_repo = "YiYiXu/modular_refiner"
+refiner_pipe = refiner_blocks.init_pipeline(refiner_repo, components_manager=components, collection="refiner")
+```
+
+We pass the **same Components Manager** (`components`) to the refiner pipeline, but with a **different collection** (`"refiner"`). This allows the refiner to access and reuse components from the "t2i" collection while organizing its own components (like the refiner UNet) under the "refiner" collection. 
+
+```py
+# Load only the refiner UNet (different from t2i UNet)
+refiner_pipe.load_components(names="unet", torch_dtype=torch.float16)
+
+# Reuse components from t2i pipeline using pattern matching
+reuse_components = components.search_components("text_encoder_2|scheduler|vae|tokenizer_2")
+refiner_pipe.update_components(**reuse_components)
+```
+
+When we reuse components from the "t2i" collection, they automatically get added to the "refiner" collection as well. You can verify this by checking the Components Manager - you'll see components like `vae`, `scheduler`, etc. listed under both collections, indicating they're shared between workflows.
+
+Now we can refine any of our generated latents:
+
+```py
+# Refine all our different latents
+refined_latents = refiner_pipe(image_latents=latents_t2i, prompt=prompt, num_inference_steps=10, output="latents")
+refined_image = decoder_node(latents=refined_latents, output="images")[0]
+refined_image.save("modular_part2_t2i_refine_out.png")
+
+refined_latents = refiner_pipe(image_latents=latents_lora, prompt=prompt, num_inference_steps=10, output="latents")
+refined_image = decoder_node(latents=refined_latents, output="images")[0]
+refined_image.save("modular_part2_lora_refine_out.png")
+```
+
+
+Here are the results from our modular pipeline examples.
+
+#### Base Text-to-Image Generation
+| Base Text-to-Image | Base Text-to-Image (Refined) |
+|-------------------|------------------------------|
+| ![Base T2I](https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/modular_quicktour/modular_part2_t2i.png) | ![Base T2I Refined](https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/modular_quicktour/modular_part2_t2i_refine_out.png) |
+
+#### LoRA
+| LoRA              | LoRA               (Refined) |
+|-------------------|------------------------------|
+| ![LoRA](https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/modular_quicktour/modular_part2_lora.png) | ![LoRA Refined](https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/modular_quicktour/modular_part2_lora_refine_out.png) |
+

docs/source/en/modular_diffusers/end_to_end_guide.md

@@ -0,0 +1,648 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# End-to-End Developer Guide: Building with Modular Diffusers
+
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
+
+In this tutorial we will walk through the process of adding a new pipeline to the modular framework using differential diffusion as our example. We'll cover the complete workflow from implementation to deployment: implementing the new pipeline, ensuring compatibility with existing tools, sharing the code on Hugging Face Hub, and deploying it as a UI node. 
+
+We'll also demonstrate the 4-step framework process we use for implementing new basic pipelines in the modular system.
+
+1. **Start with an existing pipeline as a base**
+   - Identify which existing pipeline is most similar to the one you want to implement
+   - Determine what part of the pipeline needs modification
+
+2. **Build a working pipeline structure first**
+   - Assemble the complete pipeline structure
+   - Use existing blocks wherever possible
+   - For new blocks, create placeholders (e.g. you can copy from similar blocks and change the name) without implementing custom logic just yet
+
+3. **Set up an example**
+   - Create a simple inference script with expected inputs/outputs
+
+4. **Implement your custom logic and test incrementally**
+   - Add the custom logics the blocks you want to change
+   - Test incrementally, and inspect pipeline states and debug as needed
+
+Let's see how this works with the Differential Diffusion example.
+
+
+## Differential Diffusion Pipeline
+
+### Start with an existing pipeline
+
+Differential diffusion (https://differential-diffusion.github.io/) is an image-to-image workflow, so it makes sense for us to start with the preset of pipeline blocks used to build img2img pipeline (`IMAGE2IMAGE_BLOCKS`) and see how we can build this new pipeline with them. 
+
+```py
+>>> from diffusers.modular_pipelines.stable_diffusion_xl import IMAGE2IMAGE_BLOCKS
+>>> IMAGE2IMAGE_BLOCKS = InsertableDict([
+...     ("text_encoder", StableDiffusionXLTextEncoderStep),
+...     ("image_encoder", StableDiffusionXLVaeEncoderStep),
+...     ("input", StableDiffusionXLInputStep),
+...     ("set_timesteps", StableDiffusionXLImg2ImgSetTimestepsStep),
+...     ("prepare_latents", StableDiffusionXLImg2ImgPrepareLatentsStep),
+...     ("prepare_add_cond", StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep),
+...     ("denoise", StableDiffusionXLDenoiseStep),
+...     ("decode", StableDiffusionXLDecodeStep)
+... ])
+```
+
+Note that "denoise" (`StableDiffusionXLDenoiseStep`) is a `LoopSequentialPipelineBlocks` that contains 3 loop blocks (more on LoopSequentialPipelineBlocks [here](https://huggingface.co/docs/diffusers/modular_diffusers/write_own_pipeline_block#loopsequentialpipelineblocks))
+
+```py
+>>> denoise_blocks = IMAGE2IMAGE_BLOCKS["denoise"]()
+>>> print(denoise_blocks)
+```
+
+```out
+StableDiffusionXLDenoiseStep(
+  Class: StableDiffusionXLDenoiseLoopWrapper
+
+  Description: Denoise step that iteratively denoise the latents. 
+      Its loop logic is defined in `StableDiffusionXLDenoiseLoopWrapper.__call__` method 
+      At each iteration, it runs blocks defined in `sub_blocks` sequencially:
+       - `StableDiffusionXLLoopBeforeDenoiser`
+       - `StableDiffusionXLLoopDenoiser`
+       - `StableDiffusionXLLoopAfterDenoiser`
+      This block supports both text2img and img2img tasks.
+
+
+  Components:
+      scheduler (`EulerDiscreteScheduler`)
+      guider (`ClassifierFreeGuidance`)
+      unet (`UNet2DConditionModel`)
+
+  Sub-Blocks:
+    [0] before_denoiser (StableDiffusionXLLoopBeforeDenoiser)
+       Description: step within the denoising loop that prepare the latent input for the denoiser. This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` object (e.g. `StableDiffusionXLDenoiseLoopWrapper`)
+
+    [1] denoiser (StableDiffusionXLLoopDenoiser)
+       Description: Step within the denoising loop that denoise the latents with guidance. This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` object (e.g. `StableDiffusionXLDenoiseLoopWrapper`)
+
+    [2] after_denoiser (StableDiffusionXLLoopAfterDenoiser)
+       Description: step within the denoising loop that update the latents. This block should be used to compose the `sub_blocks` attribute of a `LoopSequentialPipelineBlocks` object (e.g. `StableDiffusionXLDenoiseLoopWrapper`)
+
+)
+```
+
+Let's compare standard image-to-image and differential diffusion! The key difference in algorithm is that standard image-to-image diffusion applies uniform noise across all pixels based on a single `strength` parameter, but differential diffusion uses a change map where each pixel value determines when that region starts denoising. Regions with lower values get "frozen" earlier by replacing them with noised original latents, preserving more of the original image.
+
+Therefore, the key differences when it comes to pipeline implementation would be:
+1. The `prepare_latents` step (which prepares the change map and pre-computes noised latents for all timesteps) 
+2. The `denoise` step (which selectively applies denoising based on the change map)
+3. Since differential diffusion doesn't use the `strength` parameter, we'll use the text-to-image `set_timesteps` step instead of the image-to-image version
+
+To implement differntial diffusion, we can reuse most blocks from image-to-image and text-to-image workflows, only modifying the `prepare_latents` step and the first part of the `denoise` step (i.e. `before_denoiser (StableDiffusionXLLoopBeforeDenoiser)`). 
+
+Here's a flowchart showing the pipeline structure and the changes we need to make:
+
+
+![DiffDiff Pipeline Structure](https://mermaid.ink/img/pako:eNqVVO9r4kAQ_VeWLQWFKEk00eRDwZpa7Q-ucPfpYpE1mdWlcTdsVmpb-7_fZk1tTCl3J0Sy8968N5kZ9g0nIgUc4pUk-Rr9iuYc6d_Ibs14vlXoQYpNrtqo07lAo1jBTi2AlynysWIa6DJmG7KCBnZpsHHMSqkqNjaxKC5ALRTbQKEgLyosMthVnEvIiYRFRhRwVaBoNpmUT0W7MrTJkUbSdJEInlbwxMDXcQpcsAKq6OH_2mDTODIY4yt0J0ReUaYGnLXiJVChdSsB-enfPhBnhnjT-rCQj-1K_8Ygt62YUAVy8Ykf4FvU6XYu9rpuIGqPpvXSzs_RVEj2KrgiGUp02zNQTHBEM_FcK3BfQbBHd7qAst-PxvW-9WOrypnNylG0G9oRUMYBFeolg-IQTTJSFDqOUkZp-fwsQURZloVnlPpLf2kVSoonCM-SwCUuqY6dZ5aqddjLd1YiMiFLNrWorrxj9EOmP4El37lsl_9p5PzFqIqwVwgdN981fDM94bphH5I06R8NXZ_4QcPQPTFs6JltPrS6JssFhw9N817l27bdyM-lSKAo6iVBAAnQY0n9wLO9wbcluY7ruUFDtdguH74K0yENKDkK-8nAG6TfNrfy_bf-HjdrlOfZS7VYSAlU5JAwyhLE9WrWVw1dWdPTXauDsy8LUkdHtnX_pfMnBOvSGluRNbGurbuTHtdZN9Zts1MljC19_7EUh0puwcIbkBtSHvFbic6xWsMG5jjUrymRT3M85-86Jyf8txCbjzQptqs1DinJCn3a5qm-viJG9M26OUYlcH0_jsWWKxwGttHA4Rve4dD1el3H8_yh49hD3_X7roVfcNhx-l3b14PxvGHQ0xMa9t4t_Gp8na7tDvu-4w08HXecweD9D4X54ZI)
+
+
+### Build a Working Pipeline Structure
+
+ok now we've identified the blocks to modify, let's build the pipeline skeleton first - at this stage, our goal is to get the pipeline struture working end-to-end (even though it's just doing the img2img behavior). I would simply create placeholder blocks by copying from existing ones:
+
+```py
+>>> # Copy existing blocks as placeholders
+>>> class SDXLDiffDiffPrepareLatentsStep(PipelineBlock):
+...     """Copied from StableDiffusionXLImg2ImgPrepareLatentsStep - will modify later"""
+...     # ... same implementation as StableDiffusionXLImg2ImgPrepareLatentsStep
+... 
+>>> class SDXLDiffDiffLoopBeforeDenoiser(PipelineBlock):
+...     """Copied from StableDiffusionXLLoopBeforeDenoiser - will modify later"""
+...     # ... same implementation as StableDiffusionXLLoopBeforeDenoiser
+```
+
+`SDXLDiffDiffLoopBeforeDenoiser` is the be part of the denoise loop we need to change. Let's use it to assemble a `SDXLDiffDiffDenoiseStep`.
+
+```py
+>>> class SDXLDiffDiffDenoiseStep(StableDiffusionXLDenoiseLoopWrapper):
+...     block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLLoopDenoiser, StableDiffusionXLLoopAfterDenoiser]
+...     block_names = ["before_denoiser", "denoiser", "after_denoiser"]
+```
+
+Now we can put together our differential diffusion pipeline.
+
+```py
+>>> DIFFDIFF_BLOCKS = IMAGE2IMAGE_BLOCKS.copy()
+>>> DIFFDIFF_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"]
+>>> DIFFDIFF_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep
+>>> DIFFDIFF_BLOCKS["denoise"] = SDXLDiffDiffDenoiseStep
+>>> 
+>>> dd_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_BLOCKS)
+>>> print(dd_blocks)
+>>> # At this point, the pipeline works exactly like img2img since our blocks are just copies
+```
+
+### Set up an example
+
+ok, so now our blocks should be able to compile without an error, we can move on to the next step. Let's setup a simple example so we can run the pipeline as we build it. diff-diff use same model checkpoints as SDXL so we can fetch the models from a regular SDXL repo.
+
+```py
+>>> dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
+>>> dd_pipeline.load_default_componenets(torch_dtype=torch.float16)
+>>> dd_pipeline.to("cuda")
+```
+
+We will use this example script:
+
+```py
+>>> image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true")
+>>> mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true") 
+>>> 
+>>> prompt = "a green pear"
+>>> negative_prompt = "blurry"
+>>> 
+>>> image = dd_pipeline(
+...     prompt=prompt,
+...     negative_prompt=negative_prompt,
+...     num_inference_steps=25,
+...     diffdiff_map=mask,
+...     image=image,
+...     output="images"
+... )[0]
+>>> 
+>>> image.save("diffdiff_out.png")
+```
+
+If you run the script right now, you will get a complaint about unexpected input `diffdiff_map`. 
+and you would get the same result as the original img2img pipeline.
+
+### implement your custom logic and test incrementally
+
+Let's modify the pipeline so that we can get expected result with this example script.
+
+We'll start with the `prepare_latents` step. The main changes are:
+- Requires a new user input `diffdiff_map`
+- Requires new component `mask_processor` to process the `diffdiff_map`
+- Requires new intermediate inputs: 
+  - Need `timestep` instead of `latent_timestep` to precompute all the latents
+  - Need `num_inference_steps` to create the `diffdiff_masks`
+- create a new output `diffdiff_masks` and `original_latents`
+
+<Tip>
+
+💡  use `print(dd_pipeline.doc)` to check compiled inputs and outputs of the built piepline. 
+
+e.g. after we added `diffdiff_map` as an input in this step, we can run `print(dd_pipeline.doc)` to verify that it shows up in the docstring as a user input. 
+
+</Tip>
+
+Once we make sure all the variables we need are available in the block state, we can implement the diff-diff logic inside `__call__`. We created 2 new variables: the change map `diffdiff_mask` and the pre-computed noised latents for all timesteps `original_latents`.
+
+<Tip>
+
+💡  Implement incrementally! Run the example script as you go, and insert `print(state)` and `print(block_state)` everywhere inside the `__call__` method to inspect the intermediate results. This helps you understand what's going on and what each line you just added does.
+
+</Tip>
+
+Here are the key changes we made to implement differential diffusion:
+
+**1. Modified `prepare_latents` step:**
+```diff
+class SDXLDiffDiffPrepareLatentsStep(PipelineBlock):
+    @property
+    def expected_components(self) -> List[ComponentSpec]:
+        return [
+            ComponentSpec("vae", AutoencoderKL),
+            ComponentSpec("scheduler", EulerDiscreteScheduler),
++           ComponentSpec("mask_processor", VaeImageProcessor, config=FrozenDict({"do_normalize": False, "do_convert_grayscale": True}))
+        ]
+
+    @property
+    def inputs(self) -> List[Tuple[str, Any]]:
+        return [
++           InputParam("diffdiff_map", required=True),
+        ]
+
+    @property
+    def intermediate_inputs(self) -> List[InputParam]:
+        return [
+            InputParam("generator"),
+-           InputParam("latent_timestep", required=True, type_hint=torch.Tensor),
++           InputParam("timesteps", type_hint=torch.Tensor),
++           InputParam("num_inference_steps", type_hint=int),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
++           OutputParam("original_latents", type_hint=torch.Tensor),
++           OutputParam("diffdiff_masks", type_hint=torch.Tensor),
+        ]
+
+    def __call__(self, components, state: PipelineState):
+        # ... existing logic ...
++       # Process change map and create masks
++       diffdiff_map = components.mask_processor.preprocess(block_state.diffdiff_map, height=latent_height, width=latent_width)
++       thresholds = torch.arange(block_state.num_inference_steps, dtype=diffdiff_map.dtype) / block_state.num_inference_steps
++       block_state.diffdiff_masks = diffdiff_map > (thresholds + (block_state.denoising_start or 0))
++       block_state.original_latents = block_state.latents
+```
+
+**2. Modified `before_denoiser` step:**
+```diff
+class SDXLDiffDiffLoopBeforeDenoiser(PipelineBlock):
+    @property
+    def description(self) -> str:
+        return (
+            "Step within the denoising loop for differential diffusion that prepare the latent input for the denoiser"
+        )
+
++   @property
++   def inputs(self) -> List[Tuple[str, Any]]:
++       return [
++           InputParam("denoising_start"),
++       ]
+
+    @property
+    def intermediate_inputs(self) -> List[str]:
+        return [
+            InputParam("latents", required=True, type_hint=torch.Tensor),
++           InputParam("original_latents", type_hint=torch.Tensor),
++           InputParam("diffdiff_masks", type_hint=torch.Tensor),
+        ]
+
+    def __call__(self, components, block_state, i, t):
++       # Apply differential diffusion logic
++       if i == 0 and block_state.denoising_start is None:
++           block_state.latents = block_state.original_latents[:1]
++       else:
++           block_state.mask = block_state.diffdiff_masks[i].unsqueeze(0).unsqueeze(1)
++           block_state.latents = block_state.original_latents[i] * block_state.mask + block_state.latents * (1 - block_state.mask)
+        
+        # ... rest of existing logic ...
+```
+
+That's all there is to it! We've just created a simple sequential pipeline by mix-and-match some existing and new pipeline blocks.
+
+Now we use the process we've prepred in step2 to build the pipeline and inspect it.
+
+
+```py
+>> dd_pipeline
+SequentialPipelineBlocks(
+  Class: ModularPipelineBlocks
+
+  Description: 
+
+
+  Components:
+      text_encoder (`CLIPTextModel`)
+      text_encoder_2 (`CLIPTextModelWithProjection`)
+      tokenizer (`CLIPTokenizer`)
+      tokenizer_2 (`CLIPTokenizer`)
+      guider (`ClassifierFreeGuidance`)
+      vae (`AutoencoderKL`)
+      image_processor (`VaeImageProcessor`)
+      scheduler (`EulerDiscreteScheduler`)
+      mask_processor (`VaeImageProcessor`)
+      unet (`UNet2DConditionModel`)
+
+  Configs:
+      force_zeros_for_empty_prompt (default: True)
+      requires_aesthetics_score (default: False)
+
+  Blocks:
+    [0] text_encoder (StableDiffusionXLTextEncoderStep)
+       Description: Text Encoder step that generate text_embeddings to guide the image generation
+
+    [1] image_encoder (StableDiffusionXLVaeEncoderStep)
+       Description: Vae Encoder step that encode the input image into a latent representation
+
+    [2] input (StableDiffusionXLInputStep)
+       Description: Input processing step that:
+                     1. Determines `batch_size` and `dtype` based on `prompt_embeds`
+                     2. Adjusts input tensor shapes based on `batch_size` (number of prompts) and `num_images_per_prompt`
+                   
+                   All input tensors are expected to have either batch_size=1 or match the batch_size
+                   of prompt_embeds. The tensors will be duplicated across the batch dimension to
+                   have a final batch_size of batch_size * num_images_per_prompt.
+
+    [3] set_timesteps (StableDiffusionXLSetTimestepsStep)
+       Description: Step that sets the scheduler's timesteps for inference
+
+    [4] prepare_latents (SDXLDiffDiffPrepareLatentsStep)
+       Description: Step that prepares the latents for the differential diffusion generation process
+
+    [5] prepare_add_cond (StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep)
+       Description: Step that prepares the additional conditioning for the image-to-image/inpainting generation process
+
+    [6] denoise (SDXLDiffDiffDenoiseStep)
+       Description: Pipeline block that iteratively denoise the latents over `timesteps`. The specific steps with each iteration can be customized with `sub_blocks` attributes
+
+    [7] decode (StableDiffusionXLDecodeStep)
+       Description: Step that decodes the denoised latents into images
+
+)
+```
+
+Run the example now, you should see an apple with its right half transformed into a green pear.
+
+![Image description](https://cdn-uploads.huggingface.co/production/uploads/624ef9ba9d608e459387b34e/4zqJOz-35Q0i6jyUW3liL.png)
+
+
+## Adding IP-adapter
+
+We provide an auto IP-adapter block that you can plug-and-play into your modular workflow. It's an `AutoPipelineBlocks`, so it will only run when the user passes an IP adapter image. In this tutorial, we'll focus on how to package it into your differential diffusion workflow. To learn more about `AutoPipelineBlocks`, see [here](./auto_pipeline_blocks.md)
+
+We talked about how to add IP-adapter into your workflow in the [Modular Pipeline Guide](./modular_pipeline.md). Let's just go ahead to create the IP-adapter block.
+
+```py
+>>> from diffusers.modular_pipelines.stable_diffusion_xl.encoders import StableDiffusionXLAutoIPAdapterStep
+>>> ip_adapter_block = StableDiffusionXLAutoIPAdapterStep()
+```
+
+We can directly add the ip-adapter block instance to the `diffdiff_blocks` that we created before. The `sub_blocks` attribute is a `InsertableDict`, so we're able to insert the it at specific position (index `0` here).
+
+```py
+>>> dd_blocks.sub_blocks.insert("ip_adapter", ip_adapter_block, 0)
+```
+
+Take a look at the new diff-diff pipeline with ip-adapter! 
+
+```py
+>>> print(dd_blocks)
+```
+
+The pipeline now lists ip-adapter as its first block, and tells you that it will run only if `ip_adapter_image` is provided. It also includes the two new components from ip-adpater: `image_encoder` and `feature_extractor`
+
+```out
+SequentialPipelineBlocks(
+  Class: ModularPipelineBlocks
+
+  ====================================================================================================
+  This pipeline contains blocks that are selected at runtime based on inputs.
+  Trigger Inputs: {'ip_adapter_image'}
+  Use `get_execution_blocks()` with input names to see selected blocks (e.g. `get_execution_blocks('ip_adapter_image')`).
+  ====================================================================================================
+
+
+  Description: 
+
+
+  Components:
+      image_encoder (`CLIPVisionModelWithProjection`)
+      feature_extractor (`CLIPImageProcessor`)
+      unet (`UNet2DConditionModel`)
+      guider (`ClassifierFreeGuidance`)
+      text_encoder (`CLIPTextModel`)
+      text_encoder_2 (`CLIPTextModelWithProjection`)
+      tokenizer (`CLIPTokenizer`)
+      tokenizer_2 (`CLIPTokenizer`)
+      vae (`AutoencoderKL`)
+      image_processor (`VaeImageProcessor`)
+      scheduler (`EulerDiscreteScheduler`)
+      mask_processor (`VaeImageProcessor`)
+
+  Configs:
+      force_zeros_for_empty_prompt (default: True)
+      requires_aesthetics_score (default: False)
+
+  Blocks:
+    [0] ip_adapter (StableDiffusionXLAutoIPAdapterStep)
+       Description: Run IP Adapter step if `ip_adapter_image` is provided.
+
+    [1] text_encoder (StableDiffusionXLTextEncoderStep)
+       Description: Text Encoder step that generate text_embeddings to guide the image generation
+
+    [2] image_encoder (StableDiffusionXLVaeEncoderStep)
+       Description: Vae Encoder step that encode the input image into a latent representation
+
+    [3] input (StableDiffusionXLInputStep)
+       Description: Input processing step that:
+                     1. Determines `batch_size` and `dtype` based on `prompt_embeds`
+                     2. Adjusts input tensor shapes based on `batch_size` (number of prompts) and `num_images_per_prompt`
+                   
+                   All input tensors are expected to have either batch_size=1 or match the batch_size
+                   of prompt_embeds. The tensors will be duplicated across the batch dimension to
+                   have a final batch_size of batch_size * num_images_per_prompt.
+
+    [4] set_timesteps (StableDiffusionXLSetTimestepsStep)
+       Description: Step that sets the scheduler's timesteps for inference
+
+    [5] prepare_latents (SDXLDiffDiffPrepareLatentsStep)
+       Description: Step that prepares the latents for the differential diffusion generation process
+
+    [6] prepare_add_cond (StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep)
+       Description: Step that prepares the additional conditioning for the image-to-image/inpainting generation process
+
+    [7] denoise (SDXLDiffDiffDenoiseStep)
+       Description: Pipeline block that iteratively denoise the latents over `timesteps`. The specific steps with each iteration can be customized with `sub_blocks` attributes
+
+    [8] decode (StableDiffusionXLDecodeStep)
+       Description: Step that decodes the denoised latents into images
+
+)
+```
+
+Let's test it out. We used an orange image to condition the generation via ip-addapter and we can see a slight orange color and texture in the final output.
+
+
+```py
+>>> ip_adapter_block = StableDiffusionXLAutoIPAdapterStep()
+>>> dd_blocks.sub_blocks.insert("ip_adapter", ip_adapter_block, 0)
+>>> 
+>>> dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
+>>> dd_pipeline.load_default_components(torch_dtype=torch.float16)
+>>> dd_pipeline.loader.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+>>> dd_pipeline.loader.set_ip_adapter_scale(0.6)
+>>> dd_pipeline = dd_pipeline.to(device)
+>>> 
+>>> ip_adapter_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_orange.jpeg")
+>>> image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true")
+>>> mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true") 
+>>> 
+>>> prompt = "a green pear"
+>>> negative_prompt = "blurry"
+>>> generator = torch.Generator(device=device).manual_seed(42)
+>>> 
+>>> image = dd_pipeline(
+...     prompt=prompt,
+...     negative_prompt=negative_prompt,
+...     num_inference_steps=25,
+...     generator=generator,
+...     ip_adapter_image=ip_adapter_image,
+...     diffdiff_map=mask,
+...     image=image,
+...     output="images"
+... )[0]
+```
+
+## Working with ControlNets
+
+What about controlnet? Can differential diffusion work with controlnet? The key differences between a regular pipeline and a ControlNet pipeline are:
+1. A ControlNet input step that prepares the control condition
+2. Inside the denoising loop, a modified denoiser step where the control image is first processed through ControlNet, then control information is injected into the UNet
+
+From looking at the code workflow: differential diffusion only modifies the "before denoiser" step, while ControlNet operates within the "denoiser" itself. Since they intervene at different points in the pipeline, they should work together without conflicts.
+
+Intuitively, these two techniques are orthogonal and should combine naturally: differential diffusion controls how much the inference process can deviate from the original in each region, while ControlNet controls in what direction that change occurs.
+
+With this understanding, let's assemble the diffdiff-controlnet loop by combining the diffdiff before-denoiser step and controlnet denoiser step.
+
+```py
+>>> class SDXLDiffDiffControlNetDenoiseStep(StableDiffusionXLDenoiseLoopWrapper):
+...     block_classes = [SDXLDiffDiffLoopBeforeDenoiser, StableDiffusionXLControlNetLoopDenoiser, StableDiffusionXLDenoiseLoopAfterDenoiser]
+...     block_names = ["before_denoiser", "denoiser", "after_denoiser"]
+>>> 
+>>> controlnet_denoise_block = SDXLDiffDiffControlNetDenoiseStep()
+>>> # print(controlnet_denoise)
+```
+
+We provide a auto controlnet input block that you can directly put into your workflow to proceess the `control_image`: similar to auto ip-adapter block, this step will only run if `control_image` input is passed from user. It work with both controlnet and controlnet union.
+
+
+```py
+>>> from diffusers.modular_pipelines.stable_diffusion_xl.modular_blocks import StableDiffusionXLAutoControlNetInputStep
+>>> control_input_block = StableDiffusionXLAutoControlNetInputStep()
+>>> print(control_input_block)
+```
+
+```out
+StableDiffusionXLAutoControlNetInputStep(
+  Class: AutoPipelineBlocks
+
+  ====================================================================================================
+  This pipeline contains blocks that are selected at runtime based on inputs.
+  Trigger Inputs: ['control_image', 'control_mode']
+  ====================================================================================================
+
+
+  Description: Controlnet Input step that prepare the controlnet input.
+      This is an auto pipeline block that works for both controlnet and controlnet_union.
+       (it should be called right before the denoise step) - `StableDiffusionXLControlNetUnionInputStep` is called to prepare the controlnet input when `control_mode` and `control_image` are provided.
+       - `StableDiffusionXLControlNetInputStep` is called to prepare the controlnet input when `control_image` is provided. - if neither `control_mode` nor `control_image` is provided, step will be skipped.
+
+
+  Components:
+      controlnet (`ControlNetUnionModel`)
+      control_image_processor (`VaeImageProcessor`)
+
+  Sub-Blocks:
+    • controlnet_union [trigger: control_mode] (StableDiffusionXLControlNetUnionInputStep)
+       Description: step that prepares inputs for the ControlNetUnion model
+
+    • controlnet [trigger: control_image] (StableDiffusionXLControlNetInputStep)
+       Description: step that prepare inputs for controlnet
+
+)
+
+```
+
+Let's assemble the blocks and run an example using controlnet + differential diffusion. We used a tomato as `control_image`, so you can see that in the output, the right half that transformed into a pear had a tomato-like shape.
+
+```py
+>>> dd_blocks.sub_blocks.insert("controlnet_input", control_input_block, 7)
+>>> dd_blocks.sub_blocks["denoise"] = controlnet_denoise_block
+>>> 
+>>> dd_pipeline = dd_blocks.init_pipeline("YiYiXu/modular-demo-auto", collection="diffdiff")
+>>> dd_pipeline.load_default_components(torch_dtype=torch.float16)
+>>> dd_pipeline = dd_pipeline.to(device)
+>>> 
+>>> control_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/diffdiff_tomato_canny.jpeg")
+>>> image = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true")
+>>> mask = load_image("https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true") 
+>>> 
+>>> prompt = "a green pear"
+>>> negative_prompt = "blurry"
+>>> generator = torch.Generator(device=device).manual_seed(42)
+>>> 
+>>> image = dd_pipeline(
+...     prompt=prompt,
+...     negative_prompt=negative_prompt,
+...     num_inference_steps=25,
+...     generator=generator,
+...     control_image=control_image,
+...     controlnet_conditioning_scale=0.5,
+...     diffdiff_map=mask,
+...     image=image,
+...     output="images"
+... )[0]
+```
+
+Optionally, We can combine `SDXLDiffDiffControlNetDenoiseStep` and `SDXLDiffDiffDenoiseStep` into a `AutoPipelineBlocks` so that same workflow can work with or without controlnet. 
+
+
+```py
+>>> class SDXLDiffDiffAutoDenoiseStep(AutoPipelineBlocks):
+...     block_classes = [SDXLDiffDiffControlNetDenoiseStep, SDXLDiffDiffDenoiseStep]
+...     block_names = ["controlnet_denoise", "denoise"]
+...     block_trigger_inputs = ["controlnet_cond", None]
+```
+
+`SDXLDiffDiffAutoDenoiseStep` will run the ControlNet denoise step if `control_image` input is provided, otherwise it will run the regular denoise step.
+
+<Tip>
+
+ Note that it's perfectly fine not to use `AutoPipelineBlocks`. In fact, we recommend only using `AutoPipelineBlocks` to package your workflow at the end once you've verified all your pipelines work as expected.
+
+</Tip>
+
+Now you can create the differential diffusion preset that works with ip-adapter & controlnet.
+
+```py
+>>> DIFFDIFF_AUTO_BLOCKS = IMAGE2IMAGE_BLOCKS.copy()
+>>> DIFFDIFF_AUTO_BLOCKS["prepare_latents"] = SDXLDiffDiffPrepareLatentsStep
+>>> DIFFDIFF_AUTO_BLOCKS["set_timesteps"] = TEXT2IMAGE_BLOCKS["set_timesteps"]
+>>> DIFFDIFF_AUTO_BLOCKS["denoise"] = SDXLDiffDiffAutoDenoiseStep
+>>> DIFFDIFF_AUTO_BLOCKS.insert("ip_adapter", StableDiffusionXLAutoIPAdapterStep, 0)
+>>> DIFFDIFF_AUTO_BLOCKS.insert("controlnet_input",StableDiffusionXLControlNetAutoInput, 7)
+>>> 
+>>> print(DIFFDIFF_AUTO_BLOCKS)
+```
+
+to use
+
+```py
+>>> dd_auto_blocks = SequentialPipelineBlocks.from_blocks_dict(DIFFDIFF_AUTO_BLOCKS)
+>>> dd_pipeline = dd_auto_blocks.init_pipeline(...)
+```
+## Creating a Modular Repo
+
+You can easily share your differential diffusion workflow on the Hub by creating a modular repo. This is one created using the code we just wrote together: https://huggingface.co/YiYiXu/modular-diffdiff
+
+To create a Modular Repo and share on hub, you just need to run `save_pretrained()` along with the `push_to_hub=True` flag. Note that if your pipeline contains custom block, you need to manually upload the code to the hub. But we are working on a command line tool to help you upload it very easily.
+
+```py
+dd_pipeline.save_pretrained("YiYiXu/test_modular_doc", push_to_hub=True)
+```
+
+With a modular repo, it is very easy for the community to use the workflow you just created! Here is an example to use the differential-diffusion pipeline we just created and shared.
+
+```py
+>>> from diffusers.modular_pipelines import ModularPipeline, ComponentsManager
+>>> import torch
+>>> from diffusers.utils import load_image
+>>> 
+>>> repo_id = "YiYiXu/modular-diffdiff-0704"
+>>> 
+>>> components = ComponentsManager()
+>>> 
+>>> diffdiff_pipeline = ModularPipeline.from_pretrained(repo_id, trust_remote_code=True, components_manager=components, collection="diffdiff")
+>>> diffdiff_pipeline.load_default_components(torch_dtype=torch.float16)
+>>> components.enable_auto_cpu_offload()
+```
+
+see more usage example on model card.
+
+## deploy a mellon node
+
+[YIYI TODO: for now, here is an example of mellon node https://huggingface.co/YiYiXu/diff-diff-mellon]

docs/source/en/modular_diffusers/loop_sequential_pipeline_blocks.md

@@ -0,0 +1,194 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# LoopSequentialPipelineBlocks
+
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
+`LoopSequentialPipelineBlocks` is a subclass of `ModularPipelineBlocks`. It is a multi-block that composes other blocks together in a loop, creating iterative workflows where blocks run multiple times with evolving state. It's particularly useful for denoising loops requiring repeated execution of the same blocks.
+
+<Tip>
+
+Other types of multi-blocks include [SequentialPipelineBlocks](./sequential_pipeline_blocks.md) (for linear workflows) and [AutoPipelineBlocks](./auto_pipeline_blocks.md) (for conditional block selection). For information on creating individual blocks, see the [PipelineBlock guide](./pipeline_block.md).
+
+Additionally, like all `ModularPipelineBlocks`, `LoopSequentialPipelineBlocks` are definitions/specifications, not runnable pipelines. You need to convert them into a `ModularPipeline` to actually execute them. For information on creating and running pipelines, see the [Modular Pipeline guide](modular_pipeline.md).
+
+</Tip>
+
+You could create a loop using `PipelineBlock` like this:
+
+```python
+class DenoiseLoop(PipelineBlock):
+    def __call__(self, components, state):
+        block_state = self.get_block_state(state)
+        for t in range(block_state.num_inference_steps):
+            # ... loop logic here
+            pass
+        self.set_block_state(state, block_state)
+        return components, state
+```
+
+But in this tutorial, we will focus on how to use `LoopSequentialPipelineBlocks` to create a "composable" denoising loop where you can add or remove blocks within the loop or reuse the same loop structure with different block combinations.
+
+It involves two parts: a **loop wrapper** and **loop blocks**
+
+* The **loop wrapper** (`LoopSequentialPipelineBlocks`) defines the loop structure, e.g. it defines the iteration variables, and loop configurations such as progress bar.
+
+* The **loop blocks** are basically standard pipeline blocks you add to the loop wrapper.
+  - they run sequentially for each iteration of the loop
+  - they receive the current iteration index as an additional parameter
+  - they share the same block_state throughout the entire loop
+
+Unlike regular `SequentialPipelineBlocks` where each block gets its own state, loop blocks share a single state that persists and evolves across iterations.
+
+We will build a simple loop block to demonstrate these concepts. Creating a loop block involves three steps:
+1. defining the loop wrapper class
+2. creating the loop blocks
+3. adding the loop blocks to the loop wrapper class to create the loop wrapper instance
+
+**Step 1: Define the Loop Wrapper**
+
+To create a `LoopSequentialPipelineBlocks` class, you need to define:
+
+* `loop_inputs`: User input variables (equivalent to `PipelineBlock.inputs`)
+* `loop_intermediate_inputs`: Intermediate variables needed from the mutable pipeline state (equivalent to `PipelineBlock.intermediates_inputs`)
+* `loop_intermediate_outputs`: New intermediate variables this block will add to the mutable pipeline state (equivalent to `PipelineBlock.intermediates_outputs`)
+* `__call__` method: Defines the loop structure and iteration logic
+
+Here is an example of a loop wrapper:
+
+```py
+import torch
+from diffusers.modular_pipelines import LoopSequentialPipelineBlocks, PipelineBlock, InputParam, OutputParam
+
+class LoopWrapper(LoopSequentialPipelineBlocks):
+    model_name = "test"
+    @property
+    def description(self):
+        return "I'm a loop!!"
+    @property
+    def loop_inputs(self):
+        return [InputParam(name="num_steps")]
+    @torch.no_grad()
+    def __call__(self, components, state):
+        block_state = self.get_block_state(state)
+        # Loop structure - can be customized to your needs
+        for i in range(block_state.num_steps):
+            # loop_step executes all registered blocks in sequence
+            components, block_state = self.loop_step(components, block_state, i=i)
+        self.set_block_state(state, block_state)
+        return components, state
+```
+
+**Step 2: Create Loop Blocks**
+
+Loop blocks are standard `PipelineBlock`s, but their `__call__` method works differently:
+* It receives the iteration variable (e.g., `i`) passed by the loop wrapper
+* It works directly with `block_state` instead of pipeline state
+* No need to call `self.get_block_state()` or `self.set_block_state()`
+
+```py
+class LoopBlock(PipelineBlock):
+    # this is used to identify the model family, we won't worry about it in this example
+    model_name = "test"
+    @property
+    def inputs(self):
+        return [InputParam(name="x")]
+    @property
+    def intermediate_outputs(self):
+        # outputs produced by this block
+        return [OutputParam(name="x")]
+    @property
+    def description(self):
+        return "I'm a block used inside the `LoopWrapper` class"
+    def __call__(self, components, block_state, i: int):
+        block_state.x += 1
+        return components, block_state
+```
+
+**Step 3: Combine Everything**
+
+Finally, assemble your loop by adding the block(s) to the wrapper:
+
+```py
+loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock})
+```
+
+Now you've created a loop with one step:
+
+```py
+>>> loop
+LoopWrapper(
+  Class: LoopSequentialPipelineBlocks
+
+  Description: I'm a loop!!
+
+  Sub-Blocks:
+    [0] block1 (LoopBlock)
+       Description: I'm a block used inside the `LoopWrapper` class
+
+)
+```
+
+It has two inputs: `x` (used at each step within the loop) and `num_steps` used to define the loop.
+
+```py
+>>> print(loop.doc)
+class LoopWrapper
+
+  I'm a loop!!
+
+  Inputs:
+
+      x (`None`, *optional*):
+
+      num_steps (`None`, *optional*):
+
+  Outputs:
+
+      x (`None`):
+```
+
+**Running the Loop:**
+
+```py
+# run the loop
+loop_pipeline = loop.init_pipeline()
+x = loop_pipeline(num_steps=10, x=0, output="x")
+assert x == 10
+```
+
+**Adding Multiple Blocks:**
+
+We can add multiple blocks to run within each iteration. Let's run the loop block twice within each iteration:
+
+```py
+loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock(), "block2": LoopBlock})
+loop_pipeline = loop.init_pipeline()
+x = loop_pipeline(num_steps=10, x=0, output="x")
+assert x == 20  # Each iteration runs 2 blocks, so 10 iterations * 2 = 20
+```
+
+**Key Differences from SequentialPipelineBlocks:**
+
+The main difference is that loop blocks share the same `block_state` across all iterations, allowing values to accumulate and evolve throughout the loop. Loop blocks could receive additional arguments (like the current iteration index) depending on the loop wrapper's implementation, since the wrapper defines how loop blocks are called. You can easily add, remove, or reorder blocks within the loop without changing the loop logic itself.
+
+The officially supported denoising loops in Modular Diffusers are implemented using `LoopSequentialPipelineBlocks`. You can explore the actual implementation to see how these concepts work in practice:
+
+```py
+from diffusers.modular_pipelines.stable_diffusion_xl.denoise import StableDiffusionXLDenoiseStep
+StableDiffusionXLDenoiseStep()
+```

docs/source/en/modular_diffusers/modular_diffusers_states.md

@@ -0,0 +1,59 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# PipelineState and BlockState
+
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
+In Modular Diffusers, `PipelineState` and `BlockState` are the core data structures that enable blocks to communicate and share data. The concept is fundamental to understand how blocks interact with each other and the pipeline system.
+
+In the modular diffusers system, `PipelineState` acts as the global state container that all pipeline blocks operate on. It maintains the complete runtime state of the pipeline and provides a structured way for blocks to read from and write to shared data.
+
+A `PipelineState` consists of two distinct states:
+
+- **The immutable state** (i.e. the `inputs` dict) contains a copy of values provided by users. Once a value is added to the immutable state, it cannot be changed. Blocks can read from the immutable state but cannot write to it.
+
+- **The mutable state** (i.e. the `intermediates` dict) contains variables that are passed between blocks and can be modified by them.
+
+Here's an example of what a `PipelineState` looks like:
+
+```py
+PipelineState(
+  inputs={
+    'prompt': 'a cat'
+    'guidance_scale': 7.0
+    'num_inference_steps': 25
+  },
+  intermediates={
+    'prompt_embeds': Tensor(dtype=torch.float32, shape=torch.Size([1, 1, 1, 1]))
+    'negative_prompt_embeds': None
+  },
+)
+```
+
+Each pipeline blocks define what parts of that state they can read from and write to through their `inputs`, `intermediate_inputs`, and `intermediate_outputs` properties. At run time, they gets a local view (`BlockState`) of the relevant variables it needs from `PipelineState`, performs its operations, and then updates `PipelineState` with any changes.
+
+For example, if a block defines an input `image`, inside the block's `__call__` method, the `BlockState` would contain:
+
+```py
+BlockState(
+    image: <PIL.Image.Image image mode=RGB size=512x512 at 0x7F3ECC494640>
+)
+```
+
+You can access the variables directly as attributes: `block_state.image`.
+
+We will explore more on how blocks interact with pipeline state through their `inputs`, `intermediate_inputs`, and `intermediate_outputs` properties, see the [PipelineBlock guide](./pipeline_block.md).

docs/source/en/modular_diffusers/overview.md

@@ -0,0 +1,42 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Getting Started with Modular Diffusers
+
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
+With Modular Diffusers, we introduce a unified pipeline system that simplifies how you work with diffusion models. Instead of creating separate pipelines for each task, Modular Diffusers lets you:
+
+**Write Only What's New**: You won't need to write an entire pipeline from scratch every time you have a new use case. You can create pipeline blocks just for your new workflow's unique aspects and reuse existing blocks for existing functionalities. 
+
+**Assemble Like LEGO®**: You can mix and match between blocks in flexible ways. This allows you to write dedicated blocks unique to specific workflows, and then assemble different blocks into a pipeline that can be used more conveniently for multiple workflows. 
+
+
+Here's how our guides are organized to help you navigate the Modular Diffusers documentation:
+
+### 🚀 Running Pipelines
+- **[Modular Pipeline Guide](./modular_pipeline.md)** - How to use predefined blocks to build a pipeline and run it
+- **[Components Manager Guide](./components_manager.md)** - How to manage and reuse components across multiple pipelines
+
+### 📚 Creating PipelineBlocks
+- **[Pipeline and Block States](./modular_diffusers_states.md)** - Understanding PipelineState and BlockState
+- **[Pipeline Block](./pipeline_block.md)** - How to write custom PipelineBlocks
+- **[SequentialPipelineBlocks](sequential_pipeline_blocks.md)** - Connecting blocks in sequence
+- **[LoopSequentialPipelineBlocks](./loop_sequential_pipeline_blocks.md)** - Creating iterative workflows
+- **[AutoPipelineBlocks](./auto_pipeline_blocks.md)** - Conditional block selection
+
+### 🎯 Practical Examples
+- **[End-to-End Example](./end_to_end_guide.md)** - Complete end-to-end examples including sharing your workflow in huggingface hub and deplying UI nodes

docs/source/en/modular_diffusers/pipeline_block.md

@@ -0,0 +1,292 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# PipelineBlock
+
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
+In Modular Diffusers, you build your workflow using `ModularPipelineBlocks`. We support 4 different types of blocks: `PipelineBlock`, `SequentialPipelineBlocks`, `LoopSequentialPipelineBlocks`, and `AutoPipelineBlocks`. Among them, `PipelineBlock` is the most fundamental building block of the whole system - it's like a brick in a Lego system. These blocks are designed to easily connect with each other, allowing for modular construction of creative and potentially very complex workflows.
+
+<Tip>
+
+**Important**: `PipelineBlock`s are definitions/specifications, not runnable pipelines. They define what a block should do and what data it needs, but you need to convert them into a `ModularPipeline` to actually execute them. For information on creating and running pipelines, see the [Modular Pipeline guide](./modular_pipeline.md).
+
+</Tip>
+
+In this tutorial, we will focus on how to write a basic `PipelineBlock` and how it interacts with the pipeline state.
+
+## PipelineState
+
+Before we dive into creating `PipelineBlock`s, make sure you have a basic understanding of `PipelineState`. It acts as the global state container that all blocks operate on - each block gets a local view (`BlockState`) of the relevant variables it needs from `PipelineState`, performs its operations, and then updates `PipelineState` with any changes. See the [PipelineState and BlockState guide](./modular_diffusers_states.md) for more details.
+
+## Define a `PipelineBlock`
+
+To write a `PipelineBlock` class, you need to define a few properties that determine how your block interacts with the pipeline state. Understanding these properties is crucial - they define what data your block can access and what it can produce.
+
+The three main properties you need to define are:
+- `inputs`: Immutable values from the user that cannot be modified
+- `intermediate_inputs`: Mutable values from previous blocks that can be read and modified  
+- `intermediate_outputs`: New values your block creates for subsequent blocks and user access
+
+Let's explore each one and understand how they work with the pipeline state.
+
+**Inputs: Immutable User Values**
+
+Inputs are variables your block needs from the immutable pipeline state - these are user-provided values that cannot be modified by any block. You define them using `InputParam`:
+
+```py
+user_inputs = [
+    InputParam(name="image", type_hint="PIL.Image", description="raw input image to process")
+]
+```
+
+When you list something as an input, you're saying "I need this value directly from the end user, and I will talk to them directly, telling them what I need in the 'description' field. They will provide it and it will come to me unchanged."
+
+This is especially useful for raw values that serve as the "source of truth" in your workflow. For example, with a raw image, many workflows require preprocessing steps like resizing that a previous block might have performed. But in many cases, you also want the raw PIL image. In some inpainting workflows, you need the original image to overlay with the generated result for better control and consistency.
+
+**Intermediate Inputs: Mutable Values from Previous Blocks, or Users**
+
+Intermediate inputs are variables your block needs from the mutable pipeline state - these are values that can be read and modified. They're typically created by previous blocks, but could also be directly provided by the user if not the case:
+
+```py
+user_intermediate_inputs = [
+    InputParam(name="processed_image", type_hint="torch.Tensor", description="image that has been preprocessed and normalized"),
+]
+```
+
+When you list something as an intermediate input, you're saying "I need this value, but I want to work with a different block that has already created it. I already know for sure that I can get it from this other block, but it's okay if other developers want use something different."
+
+**Intermediate Outputs: New Values for Subsequent Blocks and User Access**
+
+Intermediate outputs are new variables your block creates and adds to the mutable pipeline state. They serve two purposes:
+
+1. **For subsequent blocks**: They can be used as intermediate inputs by other blocks in the pipeline
+2. **For users**: They become available as final outputs that users can access when running the pipeline
+
+```py
+user_intermediate_outputs = [
+    OutputParam(name="image_latents", description="latents representing the image")
+]
+```
+
+Intermediate inputs and intermediate outputs work together like Lego studs and anti-studs - they're the connection points that make blocks modular. When one block produces an intermediate output, it becomes available as an intermediate input for subsequent blocks. This is where the "modular" nature of the system really shines - blocks can be connected and reconnected in different ways as long as their inputs and outputs match.
+
+Additionally, all intermediate outputs are accessible to users when they run the pipeline, typically you would only need the final images, but they are also able to access intermediate results like latents, embeddings, or other processing steps.
+
+**The `__call__` Method Structure**
+
+Your `PipelineBlock`'s `__call__` method should follow this structure:
+
+```py
+def __call__(self, components, state):
+    # Get a local view of the state variables this block needs
+    block_state = self.get_block_state(state)
+    
+    # Your computation logic here
+    # block_state contains all your inputs and intermediate_inputs
+    # You can access them like: block_state.image, block_state.processed_image
+    
+    # Update the pipeline state with your updated block_states
+    self.set_block_state(state, block_state)
+    return components, state
+```
+
+The `block_state` object contains all the variables you defined in `inputs` and `intermediate_inputs`, making them easily accessible for your computation.
+
+**Components and Configs**
+
+You can define the components and pipeline-level configs your block needs using `ComponentSpec` and `ConfigSpec`:
+
+```py
+from diffusers import ComponentSpec, ConfigSpec
+
+# Define components your block needs
+expected_components = [
+    ComponentSpec(name="unet", type_hint=UNet2DConditionModel),
+    ComponentSpec(name="scheduler", type_hint=EulerDiscreteScheduler)
+]
+
+# Define pipeline-level configs
+expected_config = [
+    ConfigSpec("force_zeros_for_empty_prompt", True)
+]
+```
+
+**Components**: In the `ComponentSpec`, you must provide a `name` and ideally a `type_hint`. You can also specify a `default_creation_method` to indicate whether the component should be loaded from a pretrained model or created with default configurations. The actual loading details (`repo`, `subfolder`, `variant` and `revision` fields) are typically specified when creating the pipeline, as we covered in the [Modular Pipeline Guide](./modular_pipeline.md).
+
+**Configs**: Pipeline-level settings that control behavior across all blocks.
+
+When you convert your blocks into a pipeline using `blocks.init_pipeline()`, the pipeline collects all component requirements from the blocks and fetches the loading specs from the modular repository. The components are then made available to your block as the first argument of the `__call__` method. You can access any component you need using dot notation:
+
+```py
+def __call__(self, components, state):
+    # Access components using dot notation
+    unet = components.unet
+    vae = components.vae
+    scheduler = components.scheduler
+```
+
+That's all you need to define in order to create a `PipelineBlock`. There is no hidden complexity. In fact we are going to create a helper function that take exactly these variables as input and return a pipeline block. We will use this helper function through out the tutorial to create test blocks
+
+Note that for `__call__` method, the only part you should implement differently is the part between `self.get_block_state()` and `self.set_block_state()`, which can be abstracted into a simple function that takes `block_state` and returns the updated state. Our helper function accepts a `block_fn` that does exactly that.
+
+**Helper Function**
+
+```py
+from diffusers.modular_pipelines import PipelineBlock, InputParam, OutputParam
+import torch
+
+def make_block(inputs=[], intermediate_inputs=[], intermediate_outputs=[], block_fn=None, description=None):
+    class TestBlock(PipelineBlock):
+        model_name = "test"
+        
+        @property
+        def inputs(self):
+            return inputs
+            
+        @property
+        def intermediate_inputs(self):
+            return intermediate_inputs
+            
+        @property
+        def intermediate_outputs(self):
+            return intermediate_outputs
+            
+        @property
+        def description(self):
+            return description if description is not None else ""
+            
+        def __call__(self, components, state):
+            block_state = self.get_block_state(state)
+            if block_fn is not None:
+                block_state = block_fn(block_state, state)
+            self.set_block_state(state, block_state)
+            return components, state
+    
+    return TestBlock
+```
+
+## Example: Creating a Simple Pipeline Block
+
+Let's create a simple block to see how these definitions interact with the pipeline state. To better understand what's happening, we'll print out the states before and after updates to inspect them:
+
+```py
+inputs = [
+    InputParam(name="image", type_hint="PIL.Image", description="raw input image to process")
+]
+
+intermediate_inputs = [InputParam(name="batch_size", type_hint=int)]
+
+intermediate_outputs = [
+    OutputParam(name="image_latents", description="latents representing the image")
+]
+
+def image_encoder_block_fn(block_state, pipeline_state):
+    print(f"pipeline_state (before update): {pipeline_state}")
+    print(f"block_state (before update): {block_state}")
+    
+    # Simulate processing the image
+    block_state.image = torch.randn(1, 3, 512, 512)
+    block_state.batch_size = block_state.batch_size * 2
+    block_state.processed_image = [torch.randn(1, 3, 512, 512)] * block_state.batch_size
+    block_state.image_latents = torch.randn(1, 4, 64, 64)
+    
+    print(f"block_state (after update): {block_state}")
+    return block_state
+
+# Create a block with our definitions
+image_encoder_block_cls = make_block(
+    inputs=inputs, 
+    intermediate_inputs=intermediate_inputs,
+    intermediate_outputs=intermediate_outputs, 
+    block_fn=image_encoder_block_fn,
+    description="Encode raw image into its latent presentation"
+)
+image_encoder_block = image_encoder_block_cls()
+pipe = image_encoder_block.init_pipeline()
+```
+
+Let's check the pipeline's docstring to see what inputs it expects:
+```py
+>>> print(pipe.doc)
+class TestBlock
+
+  Encode raw image into its latent presentation
+
+  Inputs:
+
+      image (`PIL.Image`, *optional*):
+          raw input image to process
+
+      batch_size (`int`, *optional*):
+
+  Outputs:
+
+      image_latents (`None`):
+          latents representing the image
+```
+
+Notice that `batch_size` appears as an input even though we defined it as an intermediate input. This happens because no previous block provided it, so the pipeline makes it available as a user input. However, unlike regular inputs, this value goes directly into the mutable intermediate state.
+
+Now let's run the pipeline:
+
+```py
+from diffusers.utils import load_image
+
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/image_of_squirrel_painting.png")
+state = pipe(image=image, batch_size=2)
+print(f"pipeline_state (after update): {state}")
+```
+```out
+pipeline_state (before update): PipelineState(
+  inputs={
+    image: <PIL.Image.Image image mode=RGB size=512x512 at 0x7F3ECC494550>
+  },
+  intermediates={
+    batch_size: 2
+  },
+)
+block_state (before update): BlockState(
+    image: <PIL.Image.Image image mode=RGB size=512x512 at 0x7F3ECC494640>
+    batch_size: 2
+)
+
+block_state (after update): BlockState(
+    image: Tensor(dtype=torch.float32, shape=torch.Size([1, 3, 512, 512]))
+    batch_size: 4
+    processed_image: List[4] of Tensors with shapes [torch.Size([1, 3, 512, 512]), torch.Size([1, 3, 512, 512]), torch.Size([1, 3, 512, 512]), torch.Size([1, 3, 512, 512])]
+    image_latents: Tensor(dtype=torch.float32, shape=torch.Size([1, 4, 64, 64]))
+)
+pipeline_state (after update): PipelineState(
+  inputs={
+    image: <PIL.Image.Image image mode=RGB size=512x512 at 0x7F3ECC494550>
+  },
+  intermediates={
+    batch_size: 4
+    image_latents: Tensor(dtype=torch.float32, shape=torch.Size([1, 4, 64, 64]))
+  },
+)
+```
+
+**Key Observations:**
+
+1. **Before the update**: `image` (the input) goes to the immutable inputs dict, while `batch_size` (the intermediate_input) goes to the mutable intermediates dict, and both are available in `block_state`.
+
+2. **After the update**:
+   - **`image` (inputs)** changed in `block_state` but not in `pipeline_state` - this change is local to the block only. 
+   - **`batch_size (intermediate_inputs)`** was updated in both `block_state` and `pipeline_state` - this change affects subsequent blocks (we didn't need to declare it as an intermediate output since it was already in the intermediates dict)
+   - **`image_latents (intermediate_outputs)`** was added to `pipeline_state` because it was declared as an intermediate output
+   - **`processed_image`** was not added to `pipeline_state` because it wasn't declared as an intermediate output

docs/source/en/modular_diffusers/sequential_pipeline_blocks.md

@@ -0,0 +1,189 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# SequentialPipelineBlocks
+
+<Tip warning={true}>
+
+🧪 **Experimental Feature**: Modular Diffusers is an experimental feature we are actively developing. The API may be subject to breaking changes.
+
+</Tip>
+
+`SequentialPipelineBlocks` is a subclass of `ModularPipelineBlocks`. Unlike `PipelineBlock`, it is a multi-block that composes other blocks together in sequence, creating modular workflows where data flows from one block to the next. It's one of the most common ways to build complex pipelines by combining simpler building blocks.
+
+<Tip>
+
+Other types of multi-blocks include [AutoPipelineBlocks](auto_pipeline_blocks.md) (for conditional block selection) and [LoopSequentialPipelineBlocks](loop_sequential_pipeline_blocks.md) (for iterative workflows). For information on creating individual blocks, see the [PipelineBlock guide](pipeline_block.md).
+
+Additionally, like all `ModularPipelineBlocks`, `SequentialPipelineBlocks` are definitions/specifications, not runnable pipelines. You need to convert them into a `ModularPipeline` to actually execute them. For information on creating and running pipelines, see the [Modular Pipeline guide](modular_pipeline.md).
+
+</Tip>
+
+In this tutorial, we will focus on how to create `SequentialPipelineBlocks` and how blocks connect and work together.
+
+The key insight is that blocks connect through their intermediate inputs and outputs - the "studs and anti-studs" we discussed in the [PipelineBlock guide](pipeline_block.md). When one block produces an intermediate output, it becomes available as an intermediate input for subsequent blocks.
+
+Let's explore this through an example. We will use the same helper function from the PipelineBlock guide to create blocks.
+
+```py
+from diffusers.modular_pipelines import PipelineBlock, InputParam, OutputParam
+import torch
+
+def make_block(inputs=[], intermediate_inputs=[], intermediate_outputs=[], block_fn=None, description=None):
+    class TestBlock(PipelineBlock):
+        model_name = "test"
+        
+        @property
+        def inputs(self):
+            return inputs
+            
+        @property
+        def intermediate_inputs(self):
+            return intermediate_inputs
+            
+        @property
+        def intermediate_outputs(self):
+            return intermediate_outputs
+            
+        @property
+        def description(self):
+            return description if description is not None else ""
+            
+        def __call__(self, components, state):
+            block_state = self.get_block_state(state)
+            if block_fn is not None:
+                block_state = block_fn(block_state, state)
+            self.set_block_state(state, block_state)
+            return components, state
+    
+    return TestBlock
+```
+
+Let's create a block that produces `batch_size`, which we'll call "input_block":
+
+```py
+def input_block_fn(block_state, pipeline_state):
+    
+    batch_size = len(block_state.prompt)
+    block_state.batch_size = batch_size * block_state.num_images_per_prompt
+    
+    return block_state
+
+input_block_cls = make_block(
+    inputs=[
+        InputParam(name="prompt", type_hint=list, description="list of text prompts"),
+        InputParam(name="num_images_per_prompt", type_hint=int, description="number of images per prompt")
+    ],
+    intermediate_outputs=[
+        OutputParam(name="batch_size", description="calculated batch size")
+    ],
+    block_fn=input_block_fn,
+    description="A block that determines batch_size based on the number of prompts and num_images_per_prompt argument."
+)
+input_block = input_block_cls()
+```
+
+Now let's create a second block that uses the `batch_size` from the first block:
+
+```py
+def image_encoder_block_fn(block_state, pipeline_state):
+    # Simulate processing the image
+    block_state.image = torch.randn(1, 3, 512, 512)
+    block_state.batch_size = block_state.batch_size * 2
+    block_state.image_latents = torch.randn(1, 4, 64, 64)
+    return block_state
+
+image_encoder_block_cls = make_block(
+    inputs=[
+        InputParam(name="image", type_hint="PIL.Image", description="raw input image to process")
+    ],
+    intermediate_inputs=[
+        InputParam(name="batch_size", type_hint=int)
+    ],
+    intermediate_outputs=[
+        OutputParam(name="image_latents", description="latents representing the image")
+    ],
+    block_fn=image_encoder_block_fn,
+    description="Encode raw image into its latent presentation"
+)
+image_encoder_block = image_encoder_block_cls()
+```
+
+Now let's connect these blocks to create a `SequentialPipelineBlocks`:
+
+```py
+from diffusers.modular_pipelines import SequentialPipelineBlocks, InsertableDict
+
+# Define a dict mapping block names to block instances
+blocks_dict = InsertableDict()
+blocks_dict["input"] = input_block
+blocks_dict["image_encoder"] = image_encoder_block
+
+# Create the SequentialPipelineBlocks
+blocks = SequentialPipelineBlocks.from_blocks_dict(blocks_dict)
+```
+
+Now you have a `SequentialPipelineBlocks` with 2 blocks:
+
+```py
+>>> blocks
+SequentialPipelineBlocks(
+  Class: ModularPipelineBlocks
+
+  Description: 
+
+
+  Sub-Blocks:
+    [0] input (TestBlock)
+       Description: A block that determines batch_size based on the number of prompts and num_images_per_prompt argument.
+
+    [1] image_encoder (TestBlock)
+       Description: Encode raw image into its latent presentation
+
+)
+```
+
+When you inspect `blocks.doc`, you can see that `batch_size` is not listed as an input. The pipeline automatically detects that the `input_block` can produce `batch_size` for the `image_encoder_block`, so it doesn't ask the user to provide it.
+
+```py
+>>> print(blocks.doc)
+class SequentialPipelineBlocks
+
+  Inputs:
+
+      prompt (`None`, *optional*):
+
+      num_images_per_prompt (`None`, *optional*):
+
+      image (`PIL.Image`, *optional*):
+          raw input image to process
+
+  Outputs:
+
+      batch_size (`None`):
+
+      image_latents (`None`):
+          latents representing the image
+```
+
+At runtime, you have data flow like this:
+
+![Data Flow Diagram](https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/modular_quicktour/Editor%20_%20Mermaid%20Chart-2025-06-30-092631.png)
+
+**How SequentialPipelineBlocks Works:**
+
+1. Blocks are executed in the order they're registered in the `blocks_dict`
+2. Outputs from one block become available as intermediate inputs to all subsequent blocks
+3. The pipeline automatically figures out which values need to be provided by the user and which will be generated by previous blocks
+4. Each block maintains its own behavior and operates through its defined interface, while collectively these interfaces determine what the entire pipeline accepts and produces
+
+What happens within each block follows the same pattern we described earlier: each block gets its own `block_state` with the relevant inputs and intermediate inputs, performs its computation, and updates the pipeline state with its intermediate outputs.

docs/source/en/optimization/fp16.md

@@ -174,39 +174,36 @@ Feel free to open an issue if dynamic compilation doesn't work as expected for a
 
 ### Regional compilation
 
+[Regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) trims cold-start latency by only compiling the *small and frequently-repeated block(s)* of a model - typically a transformer layer - and enables reusing compiled artifacts for every subsequent occurrence.
+For many diffusion architectures, this delivers the same runtime speedups as full-graph compilation and reduces compile time by 8–10x.
 
-[Regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) trims cold-start latency by compiling **only the small, frequently-repeated block(s)** of a model, typically a Transformer layer, enabling reuse of compiled artifacts for every subsequent occurrence.
-For many diffusion architectures this delivers the *same* runtime speed-ups as full-graph compilation yet cuts compile time by **8–10 ×**.
-
-To make this effortless, [`ModelMixin`] exposes [`ModelMixin.compile_repeated_blocks`] API, a helper that wraps `torch.compile` around any sub-modules you designate as repeatable:
+Use the [`~ModelMixin.compile_repeated_blocks`] method, a helper that wraps `torch.compile`, on any component such as the transformer model as shown below.
 
 ```py
 # pip install -U diffusers
 import torch
 from diffusers import StableDiffusionXLPipeline
 
-pipe = StableDiffusionXLPipeline.from_pretrained(
+pipeline = StableDiffusionXLPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
     torch_dtype=torch.float16,
 ).to("cuda")
 
-# Compile only the repeated Transformer layers inside the UNet
-pipe.unet.compile_repeated_blocks(fullgraph=True)
+# compile only the repeated transformer layers inside the UNet
+pipeline.unet.compile_repeated_blocks(fullgraph=True)
 ```
 
-To enable a new model with regional compilation, add a `_repeated_blocks` attribute to your model class containing the class names (as strings) of the blocks you want compiled:
-
+To enable regional compilation for a new model, add a `_repeated_blocks` attribute to a model class containing the class names (as strings) of the blocks you want to compile.
 
 ```py
 class MyUNet(ModelMixin):
     _repeated_blocks = ("Transformer2DModel",)  # ← compiled by default
 ```
 
-For more examples, see the reference [PR](https://github.com/huggingface/diffusers/pull/11705).
-
-**Relation to Accelerate compile_regions** There is also a separate API in [accelerate](https://huggingface.co/docs/accelerate/index) - [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78). It takes a fully automatic approach: it walks the module, picks candidate blocks, then compiles the remaining graph separately. That hands-off experience is handy for quick experiments, but it also leaves fewer knobs when you want to fine-tune which blocks are compiled or adjust compilation flags.
-
+> [!TIP]
+> For more regional compilation examples, see the reference [PR](https://github.com/huggingface/diffusers/pull/11705).
 
+There is also a [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78) method in [Accelerate](https://huggingface.co/docs/accelerate/index) that automatically selects candidate blocks in a model to compile. The remaining graph is compiled separately. This is useful for quick experiments because there aren't as many options for you to set which blocks to compile or adjust compilation flags.
 
 ```py
 # pip install -U accelerate
@@ -219,8 +216,8 @@ pipeline = StableDiffusionXLPipeline.from_pretrained(
 ).to("cuda")
 pipeline.unet = compile_regions(pipeline.unet, mode="reduce-overhead", fullgraph=True)
 ```
-`compile_repeated_blocks`, by contrast, is intentionally explicit. You list the repeated blocks once (via `_repeated_blocks`) and the helper compiles exactly those, nothing more. In practice this small dose of control hits a sweet spot for diffusion models: predictable behavior, easy reasoning about cache reuse, and still a one-liner for users.
 
+[`~ModelMixin.compile_repeated_blocks`] is intentionally explicit. List the blocks to repeat in `_repeated_blocks` and the helper only compiles those blocks. It offers predictable behavior and easy reasoning about cache reuse in one line of code.
 
 ### Graph breaks
 
@@ -242,6 +239,12 @@ The `step()` function is [called](https://github.com/huggingface/diffusers/blob/
 
 In general, the `sigmas` should [stay on the CPU](https://github.com/huggingface/diffusers/blob/35a969d297cba69110d175ee79c59312b9f49e1e/src/diffusers/schedulers/scheduling_euler_discrete.py#L240) to avoid the communication sync and latency.
 
+<Tip>
+
+Refer to the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post for maximizing performance with `torch.compile` for diffusion models.
+
+</Tip>
+
 ### Benchmarks
 
 Refer to the [diffusers/benchmarks](https://huggingface.co/datasets/diffusers/benchmarks) dataset to see inference latency and memory usage data for compiled pipelines.
@@ -296,3 +299,11 @@ An input is projected into three subspaces, represented by the projection matric
 ```py
 pipeline.fuse_qkv_projections()
 ```
+
+## Resources
+
+- Read the [Presenting Flux Fast: Making Flux go brrr on H100s](https://pytorch.org/blog/presenting-flux-fast-making-flux-go-brrr-on-h100s/) blog post to learn more about how you can combine all of these optimizations with [TorchInductor](https://docs.pytorch.org/docs/stable/torch.compiler.html) and [AOTInductor](https://docs.pytorch.org/docs/stable/torch.compiler_aot_inductor.html) for a ~2.5x speedup using recipes from [flux-fast](https://github.com/huggingface/flux-fast).
+
+    These recipes support AMD hardware and [Flux.1 Kontext Dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
+- Read the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post
+to maximize performance when using `torch.compile`.

docs/source/en/optimization/speed-memory-optims.md

@@ -14,6 +14,9 @@ specific language governing permissions and limitations under the License.
 
 Optimizing models often involves trade-offs between [inference speed](./fp16) and [memory-usage](./memory). For instance, while [caching](./cache) can boost inference speed, it also increases memory consumption since it needs to store the outputs of intermediate attention layers. A more balanced optimization strategy combines quantizing a model, [torch.compile](./fp16#torchcompile) and various [offloading methods](./memory#offloading).
 
+> [!TIP]
+> Check the [torch.compile](./fp16#torchcompile) guide to learn more about compilation and how they can be applied here. For example, regional compilation can significantly reduce compilation time without giving up any speedups. 
+
 For image generation, combining quantization and [model offloading](./memory#model-offloading) can often give the best trade-off between quality, speed, and memory. Group offloading is not as effective for image generation because it is usually not possible to *fully* overlap data transfer if the compute kernel finishes faster. This results in some communication overhead between the CPU and GPU.
 
 For video generation, combining quantization and [group-offloading](./memory#group-offloading) tends to be better because video models are more compute-bound. 
@@ -25,7 +28,7 @@ The table below provides a comparison of optimization strategy combinations and
 | quantization  | 32.602 | 14.9453 |
 | quantization, torch.compile  | 25.847 | 14.9448 |
 | quantization, torch.compile, model CPU offloading | 32.312 | 12.2369 |
-<small>These results are benchmarked on Flux with a RTX 4090. The transformer and text_encoder components are quantized. Refer to the <a href="https://gist.github.com/sayakpaul/0db9d8eeeb3d2a0e5ed7cf0d9ca19b7d" benchmarking script</a> if you're interested in evaluating your own model.</small>
+<small>These results are benchmarked on Flux with a RTX 4090. The transformer and text_encoder components are quantized. Refer to the [benchmarking script](https://gist.github.com/sayakpaul/0db9d8eeeb3d2a0e5ed7cf0d9ca19b7d) if you're interested in evaluating your own model.</small>
 
 This guide will show you how to compile and offload a quantized model with [bitsandbytes](../quantization/bitsandbytes#torchcompile). Make sure you are using [PyTorch nightly](https://pytorch.org/get-started/locally/) and the latest version of bitsandbytes.
 

docs/source/en/tutorials/tutorial_overview.md

@@ -1,23 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Overview
-
-Welcome to 🧨 Diffusers! If you're new to diffusion models and generative AI, and want to learn more, then you've come to the right place. These beginner-friendly tutorials are designed to provide a gentle introduction to diffusion models and help you understand the library fundamentals - the core components and how 🧨 Diffusers is meant to be used.
-
-You'll learn how to use a pipeline for inference to rapidly generate things, and then deconstruct that pipeline to really understand how to use the library as a modular toolbox for building your own diffusion systems. In the next lesson, you'll learn how to train your own diffusion model to generate what you want.
-
-After completing the tutorials, you'll have gained the necessary skills to start exploring the library on your own and see how to use it for your own projects and applications.
-
-Feel free to join our community on [Discord](https://discord.com/invite/JfAtkvEtRb) or the [forums](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) to connect and collaborate with other users and developers!
-
-Let's start diffusing! 🧨

docs/source/en/using-diffusers/batched_inference.md

@@ -0,0 +1,264 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Batch inference
+
+Batch inference processes multiple prompts at a time to increase throughput. It is more efficient because processing multiple prompts at once maximizes GPU usage versus processing a single prompt and underutilizing the GPU.
+
+The downside is increased latency because you must wait for the entire batch to complete, and more GPU memory is required for large batches.
+
+<hfoptions id="usage">
+<hfoption id="text-to-image">
+
+For text-to-image, pass a list of prompts to the pipeline.
+
+```py
+import torch
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16
+).to("cuda")
+
+prompts = [
+    "cinematic photo of A beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
+    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+    "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+images = pipeline(
+    prompt=prompts,
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+To generate multiple variations of one prompt, use the `num_images_per_prompt` argument.
+
+```py
+import torch
+import matplotlib.pyplot as plt
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16
+).to("cuda")
+
+images = pipeline(
+    prompt="pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics",
+    num_images_per_prompt=4
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+Combine both approaches to generate different variations of different prompts.
+
+```py
+images = pipeline(
+    prompt=prompts,
+    num_images_per_prompt=2,
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+</hfoption>
+<hfoption id="image-to-image">
+
+For image-to-image, pass a list of input images and prompts to the pipeline.
+
+```py
+import torch
+from diffusers.utils import load_image
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16
+).to("cuda")
+
+input_images = [
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png"),
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"),
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+]
+
+prompts = [
+    "cinematic photo of a beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
+    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+    "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+images = pipeline(
+    prompt=prompts,
+    image=input_images,
+    guidance_scale=8.0,
+    strength=0.5
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+To generate multiple variations of one prompt, use the `num_images_per_prompt` argument.
+
+```py
+import torch
+import matplotlib.pyplot as plt
+from diffusers.utils import load_image
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16
+).to("cuda")
+
+input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+
+images = pipeline(
+    prompt="pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics",
+    image=input_image,
+    num_images_per_prompt=4
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+Combine both approaches to generate different variations of different prompts.
+
+```py
+input_images = [
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"),
+    load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/detail-prompt.png")
+]
+
+prompts = [
+    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+    "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+images = pipeline(
+    prompt=prompts,
+    image=input_images,
+    num_images_per_prompt=2,
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+</hfoption>
+</hfoptions>
+
+## Deterministic generation
+
+Enable reproducible batch generation by passing a list of [Generator’s](https://pytorch.org/docs/stable/generated/torch.Generator.html) to the pipeline and tie each `Generator` to a seed to reuse it.
+
+Use a list comprehension to iterate over the batch size specified in `range()` to create a unique `Generator` object for each image in the batch.
+
+Don't multiply the `Generator` by the batch size because that only creates one `Generator` object that is used sequentially for each image in the batch.
+
+```py
+generator = [torch.Generator(device="cuda").manual_seed(0)] * 3
+```
+
+Pass the `generator` to the pipeline.
+
+```py
+import torch
+from diffusers import DiffusionPipeline
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16
+).to("cuda")
+
+generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(3)]
+prompts = [
+    "cinematic photo of A beautiful sunset over mountains, 35mm photograph, film, professional, 4k, highly detailed",
+    "cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain",
+    "pixel-art a cozy coffee shop interior, low-res, blocky, pixel art style, 8-bit graphics"
+]
+
+images = pipeline(
+    prompt=prompts,
+    generator=generator
+).images
+
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+axes = axes.flatten()
+
+for i, image in enumerate(images):
+    axes[i].imshow(image)
+    axes[i].set_title(f"Image {i+1}")
+    axes[i].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+You can use this to iteratively select an image associated with a seed and then improve on it by crafting a more detailed prompt.

docs/source/en/using-diffusers/other-formats.md

@@ -70,41 +70,32 @@ pipeline = StableDiffusionPipeline.from_single_file(
 </hfoption>
 </hfoptions>
 
-#### LoRA files
+#### LoRAs
 
-[LoRA](https://hf.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) is a lightweight adapter that is fast and easy to train, making them especially popular for generating images in a certain way or style. These adapters are commonly stored in a safetensors file, and are widely popular on model sharing platforms like [civitai](https://civitai.com/).
+[LoRAs](../tutorials/using_peft_for_inference) are lightweight checkpoints fine-tuned to generate images or video in a specific style. If you are using a checkpoint trained with a Diffusers training script, the LoRA configuration is automatically saved as metadata in a safetensors file. When the safetensors file is loaded, the metadata is parsed to correctly configure the LoRA and avoids missing or incorrect LoRA configurations.
 
-LoRAs are loaded into a base model with the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] method.
-
-```py
-from diffusers import StableDiffusionXLPipeline
-import torch
-
-# base model
-pipeline = StableDiffusionXLPipeline.from_pretrained(
-    "Lykon/dreamshaper-xl-1-0", torch_dtype=torch.float16, variant="fp16"
-).to("cuda")
-
-# download LoRA weights
-!wget https://civitai.com/api/download/models/168776 -O blueprintify.safetensors
-
-# load LoRA weights
-pipeline.load_lora_weights(".", weight_name="blueprintify.safetensors")
-prompt = "bl3uprint, a highly detailed blueprint of the empire state building, explaining how to build all parts, many txt, blueprint grid backdrop"
-negative_prompt = "lowres, cropped, worst quality, low quality, normal quality, artifacts, signature, watermark, username, blurry, more than one bridge, bad architecture"
-
-image = pipeline(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    generator=torch.manual_seed(0),
-).images[0]
-image
-```
+The easiest way to inspect the metadata, if available, is by clicking on the Safetensors logo next to the weights.
 
 <div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/blueprint-lora.png"/>
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/safetensors_lora.png"/>
 </div>
 
+For LoRAs that aren't trained with Diffusers, you can still save metadata with the `transformer_lora_adapter_metadata` and `text_encoder_lora_adapter_metadata` arguments in [`~loaders.FluxLoraLoaderMixin.save_lora_weights`] as long as it is a safetensors file.
+
+```py
+import torch
+from diffusers import FluxPipeline
+
+pipeline = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
+).to("cuda")
+pipeline.load_lora_weights("linoyts/yarn_art_Flux_LoRA")
+pipeline.save_lora_weights(
+    transformer_lora_adapter_metadata={"r": 16, "lora_alpha": 16},
+    text_encoder_lora_adapter_metadata={"r": 8, "lora_alpha": 8}
+)
+```
+
 ### ckpt
 
 > [!WARNING]

docs/source/en/using-diffusers/overview_techniques.md

@@ -1,18 +0,0 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Overview
-
-The inference pipeline supports and enables a wide range of techniques that are divided into two categories:
-
-* Pipeline functionality: these techniques modify the pipeline or extend it for other applications. For example, pipeline callbacks add new features to a pipeline and a pipeline can also be extended for distributed inference.
-* Improve inference quality: these techniques increase the visual quality of the generated images. For example, you can enhance your prompts with GPT2 to create better images with lower effort.

docs/source/en/using-diffusers/reusing_seeds.md

@@ -136,53 +136,3 @@ result2 = pipe(prompt=prompt, num_inference_steps=50, generator=g, output_type="
 print("L_inf dist =", abs(result1 - result2).max())
 "L_inf dist = tensor(0., device='cuda:0')"
 ```
-
-## Deterministic batch generation
-
-A practical application of creating reproducible pipelines is *deterministic batch generation*. You generate a batch of images and select one image to improve with a more detailed prompt. The main idea is to pass a list of [Generator's](https://pytorch.org/docs/stable/generated/torch.Generator.html) to the pipeline and tie each `Generator` to a seed so you can reuse it.
-
-Let's use the [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) checkpoint and generate a batch of images.
-
-```py
-import torch
-from diffusers import DiffusionPipeline
-from diffusers.utils import make_image_grid
-
-pipeline = DiffusionPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
-)
-pipeline = pipeline.to("cuda")
-```
-
-Define four different `Generator`s and assign each `Generator` a seed (`0` to `3`). Then generate a batch of images and pick one to iterate on.
-
-> [!WARNING]
-> Use a list comprehension that iterates over the batch size specified in `range()` to create a unique `Generator` object for each image in the batch. If you multiply the `Generator` by the batch size integer, it only creates *one* `Generator` object that is used sequentially for each image in the batch.
->
-> ```py
-> [torch.Generator().manual_seed(seed)] * 4
-> ```
-
-```python
-generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(4)]
-prompt = "Labrador in the style of Vermeer"
-images = pipeline(prompt, generator=generator, num_images_per_prompt=4).images[0]
-make_image_grid(images, rows=2, cols=2)
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds.jpg"/>
-</div>
-
-Let's improve the first image (you can choose any image you want) which corresponds to the `Generator` with seed `0`. Add some additional text to your prompt and then make sure you reuse the same `Generator` with seed `0`. All the generated images should resemble the first image.
-
-```python
-prompt = [prompt + t for t in [", highly realistic", ", artsy", ", trending", ", colorful"]]
-generator = [torch.Generator(device="cuda").manual_seed(0) for i in range(4)]
-images = pipeline(prompt, generator=generator).images
-make_image_grid(images, rows=2, cols=2)
-```
-
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds_2.jpg"/>
-</div>

docs/source/en/using-diffusers/schedulers.md

@@ -242,3 +242,15 @@ unet = UNet2DConditionModel.from_pretrained(
 )
 unet.save_pretrained("./local-unet", variant="non_ema")
 ```
+
+Use the `torch_dtype` argument in [`~ModelMixin.from_pretrained`] to specify the dtype to load a model in.
+
+```py
+from diffusers import AutoModel
+
+unet = AutoModel.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", torch_dtype=torch.float16
+)
+```
+
+You can also use the [torch.Tensor.to](https://docs.pytorch.org/docs/stable/generated/torch.Tensor.to.html) method to convert to the specified dtype on the fly. It converts *all* weights unlike the `torch_dtype` argument that respects the `_keep_in_fp32_modules`. This is important for models whose layers must remain in fp32 for numerical stability and best generation quality (see example [here](https://github.com/huggingface/diffusers/blob/f864a9a352fa4a220d860bfdd1782e3e5af96382/src/diffusers/models/transformers/transformer_wan.py#L374)).

examples/community/README.md

@@ -87,6 +87,7 @@ PIXART-α Controlnet pipeline | Implementation of the controlnet model for pixar
 | CogVideoX DDIM Inversion Pipeline | Implementation of DDIM inversion and guided attention-based editing denoising process on CogVideoX. | [CogVideoX DDIM Inversion Pipeline](#cogvideox-ddim-inversion-pipeline) | - | [LittleNyima](https://github.com/LittleNyima) |
 | FaithDiff Stable Diffusion XL Pipeline | Implementation of [(CVPR 2025) FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolutionUnleashing Diffusion Priors for Faithful Image Super-resolution](https://huggingface.co/papers/2411.18824) - FaithDiff is a faithful image super-resolution method that leverages latent diffusion models by actively adapting the diffusion prior and jointly fine-tuning its components (encoder and diffusion model) with an alignment module to ensure high fidelity and structural consistency. | [FaithDiff Stable Diffusion XL Pipeline](#faithdiff-stable-diffusion-xl-pipeline) | [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/jychen9811/FaithDiff) | [Junyang Chen, Jinshan Pan, Jiangxin Dong, IMAG Lab, (Adapted by Eliseu Silva)](https://github.com/JyChen9811/FaithDiff) |
 | Stable Diffusion 3 InstructPix2Pix Pipeline | Implementation of Stable Diffusion 3 InstructPix2Pix Pipeline | [Stable Diffusion 3 InstructPix2Pix Pipeline](#stable-diffusion-3-instructpix2pix-pipeline) | [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/BleachNick/SD3_UltraEdit_freeform) [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/CaptainZZZ/sd3-instructpix2pix) | [Jiayu Zhang](https://github.com/xduzhangjiayu) and [Haozhe Zhao](https://github.com/HaozheZhao)|
+| Flux Kontext multiple images | A modified version of the `FluxKontextPipeline` that supports calling Flux Kontext with multiple reference images.| [Flux Kontext multiple input Pipeline](#flux-kontext-multiple-images) | - |  [Net-Mist](https://github.com/Net-Mist) |
 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
 
 ```py
@@ -5479,4 +5480,48 @@ edited_image.save("edited_image.png")
 ### Note
 This model is trained on 512x512, so input size is better on 512x512.
 For better editing performance, please refer to this powerful model https://huggingface.co/BleachNick/SD3_UltraEdit_freeform and Paper "UltraEdit: Instruction-based Fine-Grained Image
-Editing at Scale", many thanks to their contribution!
+Editing at Scale", many thanks to their contribution!
+
+# Flux Kontext multiple images
+
+This implementation of Flux Kontext allows users to pass multiple reference images. Each image is encoded separately, and the resulting latent vectors are concatenated.
+
+As explained in Section 3 of [the paper](https://arxiv.org/pdf/2506.15742), the model's sequence concatenation mechanism can extend its capabilities to handle multiple reference images. However, note that the current version of Flux Kontext was not trained for this use case. In practice, stacking along the first axis does not yield correct results, while stacking along the other two axes appears to work.
+
+## Example Usage
+
+This pipeline loads two reference images and generates a new image based on them.
+
+```python
+import torch
+
+from diffusers import FluxKontextPipeline
+from diffusers.utils import load_image
+
+
+pipe = FluxKontextPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev",
+    torch_dtype=torch.bfloat16,
+    custom_pipeline="pipeline_flux_kontext_multiple_images",
+)
+pipe.to("cuda")
+
+pikachu_image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yarn-art-pikachu.png"
+).convert("RGB")
+cat_image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"
+).convert("RGB")
+
+
+prompts = [
+    "Pikachu and the cat are sitting together at a pizzeria table, enjoying a delicious pizza.",
+]
+images = pipe(
+    multiple_images=[(pikachu_image, cat_image)],
+    prompt=prompts,
+    guidance_scale=2.5,
+    generator=torch.Generator().manual_seed(42),
+).images
+images[0].save("pizzeria.png")
+```

examples/controlnet/train_controlnet_sd3.py

@@ -1330,7 +1330,7 @@ def main(args):
                 # controlnet(s) inference
                 controlnet_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
                 controlnet_image = vae.encode(controlnet_image).latent_dist.sample()
-                controlnet_image = controlnet_image * vae.config.scaling_factor
+                controlnet_image = (controlnet_image - vae.config.shift_factor) * vae.config.scaling_factor
 
                 control_block_res_samples = controlnet(
                     hidden_states=noisy_model_input,

examples/dreambooth/README_flux.md

@@ -263,9 +263,19 @@ This reduces memory requirements significantly w/o a significant quality loss. N
 ## Training Kontext
 
 [Kontext](https://bfl.ai/announcements/flux-1-kontext) lets us perform image editing as well as image generation. Even though it can accept both image and text as inputs, one can use it for text-to-image (T2I) generation, too. We
-provide a simple script for LoRA fine-tuning Kontext in [train_dreambooth_lora_flux_kontext.py](./train_dreambooth_lora_flux_kontext.py) for T2I. The optimizations discussed above apply this script, too.
+provide a simple script for LoRA fine-tuning Kontext in [train_dreambooth_lora_flux_kontext.py](./train_dreambooth_lora_flux_kontext.py) for both T2I and I2I. The optimizations discussed above apply this script, too.
 
-Make sure to follow the [instructions to set up your environment](#running-locally-with-pytorch) before proceeding to the rest of the section.
+**important**
+
+> [!NOTE] 
+> To make sure you can successfully run the latest version of the kontext example script, we highly recommend installing from source, specifically from the commit mentioned below.
+> To do this, execute the following steps in a new virtual environment:
+> ```
+> git clone https://github.com/huggingface/diffusers
+> cd diffusers
+> git checkout 05e7a854d0a5661f5b433f6dd5954c224b104f0b
+> pip install -e .
+> ```
 
 Below is an example training command:
 
@@ -294,6 +304,42 @@ accelerate launch train_dreambooth_lora_flux_kontext.py \
 Fine-tuning Kontext on the T2I task can be useful when working with specific styles/subjects where it may not
 perform as expected.
 
+Image-guided fine-tuning (I2I) is also supported. To start, you must have a dataset containing triplets:
+
+* Condition image
+* Target image
+* Instruction
+
+[kontext-community/relighting](https://huggingface.co/datasets/kontext-community/relighting) is a good example of such a dataset. If you are using such a dataset, you can use the command below to launch training:
+
+```bash
+accelerate launch train_dreambooth_lora_flux_kontext.py \
+  --pretrained_model_name_or_path=black-forest-labs/FLUX.1-Kontext-dev  \
+  --output_dir="kontext-i2i" \
+  --dataset_name="kontext-community/relighting" \
+  --image_column="output" --cond_image_column="file_name" --caption_column="instruction" \
+  --mixed_precision="bf16" \
+  --resolution=1024 \
+  --train_batch_size=1 \
+  --guidance_scale=1 \
+  --gradient_accumulation_steps=4 \
+  --gradient_checkpointing \
+  --optimizer="adamw" \
+  --use_8bit_adam \
+  --cache_latents \
+  --learning_rate=1e-4 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=200 \
+  --max_train_steps=1000 \
+  --rank=16\
+  --seed="0" 
+```
+
+More generally, when performing I2I fine-tuning, we expect you to:
+
+* Have a dataset `kontext-community/relighting`
+* Supply `image_column`, `cond_image_column`, and `caption_column` values when launching training
+
 ### Misc notes
 
 * By default, we use `mode` as the value of `--vae_encode_mode` argument. This is because Kontext uses `mode()` of the distribution predicted by the VAE instead of sampling from it.
@@ -307,4 +353,4 @@ To enable aspect ratio bucketing, pass `--aspect_ratio_buckets` argument with a
 Since Flux Kontext finetuning is still an experimental phase, we encourage you to explore different settings and share your insights! 🤗 
 
 ## Other notes
-Thanks to `bghira` and `ostris` for their help with reviewing & insight sharing ♥️
+Thanks to `bghira` and `ostris` for their help with reviewing & insight sharing ♥️

examples/dreambooth/train_dreambooth_lora_flux_kontext.py

@@ -40,7 +40,7 @@ from PIL.ImageOps import exif_transpose
 from torch.utils.data import Dataset
 from torch.utils.data.sampler import BatchSampler
 from torchvision import transforms
-from torchvision.transforms.functional import crop
+from torchvision.transforms import functional as TF
 from tqdm.auto import tqdm
 from transformers import CLIPTokenizer, PretrainedConfig, T5TokenizerFast
 
@@ -62,11 +62,7 @@ from diffusers.training_utils import (
     free_memory,
     parse_buckets_string,
 )
-from diffusers.utils import (
-    check_min_version,
-    convert_unet_state_dict_to_peft,
-    is_wandb_available,
-)
+from diffusers.utils import check_min_version, convert_unet_state_dict_to_peft, is_wandb_available, load_image
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.import_utils import is_torch_npu_available
 from diffusers.utils.torch_utils import is_compiled_module
@@ -186,6 +182,7 @@ def log_validation(
     )
     pipeline = pipeline.to(accelerator.device, dtype=torch_dtype)
     pipeline.set_progress_bar_config(disable=True)
+    pipeline_args_cp = pipeline_args.copy()
 
     # run inference
     generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
@@ -193,14 +190,16 @@ def log_validation(
 
     # pre-calculate  prompt embeds, pooled prompt embeds, text ids because t5 does not support autocast
     with torch.no_grad():
-        prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
-            pipeline_args["prompt"], prompt_2=pipeline_args["prompt"]
-        )
+        prompt = pipeline_args_cp.pop("prompt")
+        prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(prompt, prompt_2=None)
     images = []
     for _ in range(args.num_validation_images):
         with autocast_ctx:
             image = pipeline(
-                prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, generator=generator
+                **pipeline_args_cp,
+                prompt_embeds=prompt_embeds,
+                pooled_prompt_embeds=pooled_prompt_embeds,
+                generator=generator,
             ).images[0]
             images.append(image)
 
@@ -310,6 +309,12 @@ def parse_args(input_args=None):
         "default, the standard Image Dataset maps out 'file_name' "
         "to 'image'.",
     )
+    parser.add_argument(
+        "--cond_image_column",
+        type=str,
+        default=None,
+        help="Column in the dataset containing the condition image. Must be specified when performing I2I fine-tuning",
+    )
     parser.add_argument(
         "--caption_column",
         type=str,
@@ -330,7 +335,6 @@ def parse_args(input_args=None):
         "--instance_prompt",
         type=str,
         default=None,
-        required=True,
         help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'",
     )
     parser.add_argument(
@@ -351,6 +355,12 @@ def parse_args(input_args=None):
         default=None,
         help="A prompt that is used during validation to verify that the model is learning.",
     )
+    parser.add_argument(
+        "--validation_image",
+        type=str,
+        default=None,
+        help="Validation image to use (during I2I fine-tuning) to verify that the model is learning.",
+    )
     parser.add_argument(
         "--num_validation_images",
         type=int,
@@ -399,7 +409,7 @@ def parse_args(input_args=None):
     parser.add_argument(
         "--output_dir",
         type=str,
-        default="flux-dreambooth-lora",
+        default="flux-kontext-lora",
         help="The output directory where the model predictions and checkpoints will be written.",
     )
     parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
@@ -716,6 +726,8 @@ def parse_args(input_args=None):
             raise ValueError("You must specify a data directory for class images.")
         if args.class_prompt is None:
             raise ValueError("You must specify prompt for class images.")
+        if args.cond_image_column is not None:
+            raise ValueError("Prior preservation isn't supported with I2I training.")
     else:
         # logger is not available yet
         if args.class_data_dir is not None:
@@ -723,6 +735,14 @@ def parse_args(input_args=None):
         if args.class_prompt is not None:
             warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
 
+    if args.cond_image_column is not None:
+        assert args.image_column is not None
+        assert args.caption_column is not None
+        assert args.dataset_name is not None
+        assert not args.train_text_encoder
+        if args.validation_prompt is not None:
+            assert args.validation_image is None and os.path.exists(args.validation_image)
+
     return args
 
 
@@ -742,6 +762,7 @@ class DreamBoothDataset(Dataset):
         repeats=1,
         center_crop=False,
         buckets=None,
+        args=None,
     ):
         self.center_crop = center_crop
 
@@ -774,6 +795,10 @@ class DreamBoothDataset(Dataset):
             column_names = dataset["train"].column_names
 
             # 6. Get the column names for input/target.
+            if args.cond_image_column is not None and args.cond_image_column not in column_names:
+                raise ValueError(
+                    f"`--cond_image_column` value '{args.cond_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
+                )
             if args.image_column is None:
                 image_column = column_names[0]
                 logger.info(f"image column defaulting to {image_column}")
@@ -783,7 +808,12 @@ class DreamBoothDataset(Dataset):
                     raise ValueError(
                         f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
                     )
-            instance_images = dataset["train"][image_column]
+            instance_images = [dataset["train"][i][image_column] for i in range(len(dataset["train"]))]
+            cond_images = None
+            cond_image_column = args.cond_image_column
+            if cond_image_column is not None:
+                cond_images = [dataset["train"][i][cond_image_column] for i in range(len(dataset["train"]))]
+                assert len(instance_images) == len(cond_images)
 
             if args.caption_column is None:
                 logger.info(
@@ -811,14 +841,23 @@ class DreamBoothDataset(Dataset):
             self.custom_instance_prompts = None
 
         self.instance_images = []
-        for img in instance_images:
+        self.cond_images = []
+        for i, img in enumerate(instance_images):
             self.instance_images.extend(itertools.repeat(img, repeats))
+            if args.dataset_name is not None and cond_images is not None:
+                self.cond_images.extend(itertools.repeat(cond_images[i], repeats))
 
         self.pixel_values = []
-        for image in self.instance_images:
+        self.cond_pixel_values = []
+        for i, image in enumerate(self.instance_images):
             image = exif_transpose(image)
             if not image.mode == "RGB":
                 image = image.convert("RGB")
+            dest_image = None
+            if self.cond_images:
+                dest_image = exif_transpose(self.cond_images[i])
+                if not dest_image.mode == "RGB":
+                    dest_image = dest_image.convert("RGB")
 
             width, height = image.size
 
@@ -828,25 +867,16 @@ class DreamBoothDataset(Dataset):
             self.size = (target_height, target_width)
 
             # based on the bucket assignment, define the transformations
-            train_resize = transforms.Resize(self.size, interpolation=transforms.InterpolationMode.BILINEAR)
-            train_crop = transforms.CenterCrop(self.size) if center_crop else transforms.RandomCrop(self.size)
-            train_flip = transforms.RandomHorizontalFlip(p=1.0)
-            train_transforms = transforms.Compose(
-                [
-                    transforms.ToTensor(),
-                    transforms.Normalize([0.5], [0.5]),
-                ]
+            image, dest_image = self.paired_transform(
+                image,
+                dest_image=dest_image,
+                size=self.size,
+                center_crop=args.center_crop,
+                random_flip=args.random_flip,
             )
-            image = train_resize(image)
-            if args.center_crop:
-                image = train_crop(image)
-            else:
-                y1, x1, h, w = train_crop.get_params(image, self.size)
-                image = crop(image, y1, x1, h, w)
-            if args.random_flip and random.random() < 0.5:
-                image = train_flip(image)
-            image = train_transforms(image)
             self.pixel_values.append((image, bucket_idx))
+            if dest_image is not None:
+                self.cond_pixel_values.append((dest_image, bucket_idx))
 
         self.num_instance_images = len(self.instance_images)
         self._length = self.num_instance_images
@@ -880,6 +910,9 @@ class DreamBoothDataset(Dataset):
         instance_image, bucket_idx = self.pixel_values[index % self.num_instance_images]
         example["instance_images"] = instance_image
         example["bucket_idx"] = bucket_idx
+        if self.cond_pixel_values:
+            dest_image, _ = self.cond_pixel_values[index % self.num_instance_images]
+            example["cond_images"] = dest_image
 
         if self.custom_instance_prompts:
             caption = self.custom_instance_prompts[index % self.num_instance_images]
@@ -902,6 +935,43 @@ class DreamBoothDataset(Dataset):
 
         return example
 
+    def paired_transform(self, image, dest_image=None, size=(224, 224), center_crop=False, random_flip=False):
+        # 1. Resize (deterministic)
+        resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)
+        image = resize(image)
+        if dest_image is not None:
+            dest_image = resize(dest_image)
+
+        # 2. Crop: either center or SAME random crop
+        if center_crop:
+            crop = transforms.CenterCrop(size)
+            image = crop(image)
+            if dest_image is not None:
+                dest_image = crop(dest_image)
+        else:
+            # get_params returns (i, j, h, w)
+            i, j, h, w = transforms.RandomCrop.get_params(image, output_size=size)
+            image = TF.crop(image, i, j, h, w)
+            if dest_image is not None:
+                dest_image = TF.crop(dest_image, i, j, h, w)
+
+        # 3. Random horizontal flip with the SAME coin flip
+        if random_flip:
+            do_flip = random.random() < 0.5
+            if do_flip:
+                image = TF.hflip(image)
+                if dest_image is not None:
+                    dest_image = TF.hflip(dest_image)
+
+        # 4. ToTensor + Normalize (deterministic)
+        to_tensor = transforms.ToTensor()
+        normalize = transforms.Normalize([0.5], [0.5])
+        image = normalize(to_tensor(image))
+        if dest_image is not None:
+            dest_image = normalize(to_tensor(dest_image))
+
+        return (image, dest_image) if dest_image is not None else (image, None)
+
 
 def collate_fn(examples, with_prior_preservation=False):
     pixel_values = [example["instance_images"] for example in examples]
@@ -917,6 +987,11 @@ def collate_fn(examples, with_prior_preservation=False):
     pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
 
     batch = {"pixel_values": pixel_values, "prompts": prompts}
+    if any("cond_images" in example for example in examples):
+        cond_pixel_values = [example["cond_images"] for example in examples]
+        cond_pixel_values = torch.stack(cond_pixel_values)
+        cond_pixel_values = cond_pixel_values.to(memory_format=torch.contiguous_format).float()
+        batch.update({"cond_pixel_values": cond_pixel_values})
     return batch
 
 
@@ -1318,6 +1393,7 @@ def main(args):
             "ff.net.2",
             "ff_context.net.0.proj",
             "ff_context.net.2",
+            "proj_mlp",
         ]
 
     # now we will add new LoRA weights the transformer layers
@@ -1534,8 +1610,11 @@ def main(args):
         buckets=buckets,
         repeats=args.repeats,
         center_crop=args.center_crop,
+        args=args,
     )
-    batch_sampler = BucketBatchSampler(train_dataset, batch_size=args.train_batch_size, drop_last=False)
+    if args.cond_image_column is not None:
+        logger.info("I2I fine-tuning enabled.")
+    batch_sampler = BucketBatchSampler(train_dataset, batch_size=args.train_batch_size, drop_last=True)
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset,
         batch_sampler=batch_sampler,
@@ -1574,6 +1653,7 @@ def main(args):
 
     # Clear the memory here
     if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
+        text_encoder_one.cpu(), text_encoder_two.cpu()
         del text_encoder_one, text_encoder_two, tokenizer_one, tokenizer_two
         free_memory()
 
@@ -1605,19 +1685,41 @@ def main(args):
                 tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
                 tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
 
+    elif train_dataset.custom_instance_prompts and not args.train_text_encoder:
+        cached_text_embeddings = []
+        for batch in tqdm(train_dataloader, desc="Embedding prompts"):
+            batch_prompts = batch["prompts"]
+            prompt_embeds, pooled_prompt_embeds, text_ids = compute_text_embeddings(
+                batch_prompts, text_encoders, tokenizers
+            )
+            cached_text_embeddings.append((prompt_embeds, pooled_prompt_embeds, text_ids))
+
+        if args.validation_prompt is None:
+            text_encoder_one.cpu(), text_encoder_two.cpu()
+            del text_encoder_one, text_encoder_two, tokenizer_one, tokenizer_two
+            free_memory()
+
     vae_config_shift_factor = vae.config.shift_factor
     vae_config_scaling_factor = vae.config.scaling_factor
     vae_config_block_out_channels = vae.config.block_out_channels
+    has_image_input = args.cond_image_column is not None
     if args.cache_latents:
         latents_cache = []
+        cond_latents_cache = []
         for batch in tqdm(train_dataloader, desc="Caching latents"):
             with torch.no_grad():
                 batch["pixel_values"] = batch["pixel_values"].to(
                     accelerator.device, non_blocking=True, dtype=weight_dtype
                 )
                 latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
+                if has_image_input:
+                    batch["cond_pixel_values"] = batch["cond_pixel_values"].to(
+                        accelerator.device, non_blocking=True, dtype=weight_dtype
+                    )
+                    cond_latents_cache.append(vae.encode(batch["cond_pixel_values"]).latent_dist)
 
         if args.validation_prompt is None:
+            vae.cpu()
             del vae
             free_memory()
 
@@ -1678,7 +1780,7 @@ def main(args):
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.
     if accelerator.is_main_process:
-        tracker_name = "dreambooth-flux-dev-lora"
+        tracker_name = "dreambooth-flux-kontext-lora"
         accelerator.init_trackers(tracker_name, config=vars(args))
 
     # Train!
@@ -1742,6 +1844,7 @@ def main(args):
             sigma = sigma.unsqueeze(-1)
         return sigma
 
+    has_guidance = unwrap_model(transformer).config.guidance_embeds
     for epoch in range(first_epoch, args.num_train_epochs):
         transformer.train()
         if args.train_text_encoder:
@@ -1759,9 +1862,7 @@ def main(args):
                 # encode batch prompts when custom prompts are provided for each image -
                 if train_dataset.custom_instance_prompts:
                     if not args.train_text_encoder:
-                        prompt_embeds, pooled_prompt_embeds, text_ids = compute_text_embeddings(
-                            prompts, text_encoders, tokenizers
-                        )
+                        prompt_embeds, pooled_prompt_embeds, text_ids = cached_text_embeddings[step]
                     else:
                         tokens_one = tokenize_prompt(tokenizer_one, prompts, max_sequence_length=77)
                         tokens_two = tokenize_prompt(
@@ -1794,16 +1895,29 @@ def main(args):
                 if args.cache_latents:
                     if args.vae_encode_mode == "sample":
                         model_input = latents_cache[step].sample()
+                        if has_image_input:
+                            cond_model_input = cond_latents_cache[step].sample()
                     else:
                         model_input = latents_cache[step].mode()
+                        if has_image_input:
+                            cond_model_input = cond_latents_cache[step].mode()
                 else:
                     pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
+                    if has_image_input:
+                        cond_pixel_values = batch["cond_pixel_values"].to(dtype=vae.dtype)
                     if args.vae_encode_mode == "sample":
                         model_input = vae.encode(pixel_values).latent_dist.sample()
+                        if has_image_input:
+                            cond_model_input = vae.encode(cond_pixel_values).latent_dist.sample()
                     else:
                         model_input = vae.encode(pixel_values).latent_dist.mode()
+                        if has_image_input:
+                            cond_model_input = vae.encode(cond_pixel_values).latent_dist.mode()
                 model_input = (model_input - vae_config_shift_factor) * vae_config_scaling_factor
                 model_input = model_input.to(dtype=weight_dtype)
+                if has_image_input:
+                    cond_model_input = (cond_model_input - vae_config_shift_factor) * vae_config_scaling_factor
+                    cond_model_input = cond_model_input.to(dtype=weight_dtype)
 
                 vae_scale_factor = 2 ** (len(vae_config_block_out_channels) - 1)
 
@@ -1814,6 +1928,17 @@ def main(args):
                     accelerator.device,
                     weight_dtype,
                 )
+                if has_image_input:
+                    cond_latents_ids = FluxKontextPipeline._prepare_latent_image_ids(
+                        cond_model_input.shape[0],
+                        cond_model_input.shape[2] // 2,
+                        cond_model_input.shape[3] // 2,
+                        accelerator.device,
+                        weight_dtype,
+                    )
+                    cond_latents_ids[..., 0] = 1
+                    latent_image_ids = torch.cat([latent_image_ids, cond_latents_ids], dim=0)
+
                 # Sample noise that we'll add to the latents
                 noise = torch.randn_like(model_input)
                 bsz = model_input.shape[0]
@@ -1834,7 +1959,6 @@ def main(args):
                 # zt = (1 - texp) * x + texp * z1
                 sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
                 noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise
-
                 packed_noisy_model_input = FluxKontextPipeline._pack_latents(
                     noisy_model_input,
                     batch_size=model_input.shape[0],
@@ -1842,13 +1966,22 @@ def main(args):
                     height=model_input.shape[2],
                     width=model_input.shape[3],
                 )
+                orig_inp_shape = packed_noisy_model_input.shape
+                if has_image_input:
+                    packed_cond_input = FluxKontextPipeline._pack_latents(
+                        cond_model_input,
+                        batch_size=cond_model_input.shape[0],
+                        num_channels_latents=cond_model_input.shape[1],
+                        height=cond_model_input.shape[2],
+                        width=cond_model_input.shape[3],
+                    )
+                    packed_noisy_model_input = torch.cat([packed_noisy_model_input, packed_cond_input], dim=1)
 
-                # handle guidance
-                if unwrap_model(transformer).config.guidance_embeds:
+                # Kontext always has guidance
+                guidance = None
+                if has_guidance:
                     guidance = torch.tensor([args.guidance_scale], device=accelerator.device)
                     guidance = guidance.expand(model_input.shape[0])
-                else:
-                    guidance = None
 
                 # Predict the noise residual
                 model_pred = transformer(
@@ -1862,6 +1995,8 @@ def main(args):
                     img_ids=latent_image_ids,
                     return_dict=False,
                 )[0]
+                if has_image_input:
+                    model_pred = model_pred[:, : orig_inp_shape[1]]
                 model_pred = FluxKontextPipeline._unpack_latents(
                     model_pred,
                     height=model_input.shape[2] * vae_scale_factor,
@@ -1970,6 +2105,8 @@ def main(args):
                     torch_dtype=weight_dtype,
                 )
                 pipeline_args = {"prompt": args.validation_prompt}
+                if has_image_input and args.validation_image:
+                    pipeline_args.update({"image": load_image(args.validation_image)})
                 images = log_validation(
                     pipeline=pipeline,
                     args=args,
@@ -2030,6 +2167,8 @@ def main(args):
         images = []
         if args.validation_prompt and args.num_validation_images > 0:
             pipeline_args = {"prompt": args.validation_prompt}
+            if has_image_input and args.validation_image:
+                pipeline_args.update({"image": load_image(args.validation_image)})
             images = log_validation(
                 pipeline=pipeline,
                 args=args,

examples/dreambooth/train_dreambooth_lora_hidream.py

@@ -58,6 +58,7 @@ from diffusers.training_utils import (
     compute_density_for_timestep_sampling,
     compute_loss_weighting_for_sd3,
     free_memory,
+    offload_models,
 )
 from diffusers.utils import (
     check_min_version,
@@ -1364,43 +1365,34 @@ def main(args):
     # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
     # the redundant encoding.
     if not train_dataset.custom_instance_prompts:
-        if args.offload:
-            text_encoding_pipeline = text_encoding_pipeline.to(accelerator.device)
-        (
-            instance_prompt_hidden_states_t5,
-            instance_prompt_hidden_states_llama3,
-            instance_pooled_prompt_embeds,
-            _,
-            _,
-            _,
-        ) = compute_text_embeddings(args.instance_prompt, text_encoding_pipeline)
-        if args.offload:
-            text_encoding_pipeline = text_encoding_pipeline.to("cpu")
+        with offload_models(text_encoding_pipeline, device=accelerator.device, offload=args.offload):
+            (
+                instance_prompt_hidden_states_t5,
+                instance_prompt_hidden_states_llama3,
+                instance_pooled_prompt_embeds,
+                _,
+                _,
+                _,
+            ) = compute_text_embeddings(args.instance_prompt, text_encoding_pipeline)
 
     # Handle class prompt for prior-preservation.
     if args.with_prior_preservation:
-        if args.offload:
-            text_encoding_pipeline = text_encoding_pipeline.to(accelerator.device)
-        (class_prompt_hidden_states_t5, class_prompt_hidden_states_llama3, class_pooled_prompt_embeds, _, _, _) = (
-            compute_text_embeddings(args.class_prompt, text_encoding_pipeline)
-        )
-        if args.offload:
-            text_encoding_pipeline = text_encoding_pipeline.to("cpu")
+        with offload_models(text_encoding_pipeline, device=accelerator.device, offload=args.offload):
+            (class_prompt_hidden_states_t5, class_prompt_hidden_states_llama3, class_pooled_prompt_embeds, _, _, _) = (
+                compute_text_embeddings(args.class_prompt, text_encoding_pipeline)
+            )
 
     validation_embeddings = {}
     if args.validation_prompt is not None:
-        if args.offload:
-            text_encoding_pipeline = text_encoding_pipeline.to(accelerator.device)
-        (
-            validation_embeddings["prompt_embeds_t5"],
-            validation_embeddings["prompt_embeds_llama3"],
-            validation_embeddings["pooled_prompt_embeds"],
-            validation_embeddings["negative_prompt_embeds_t5"],
-            validation_embeddings["negative_prompt_embeds_llama3"],
-            validation_embeddings["negative_pooled_prompt_embeds"],
-        ) = compute_text_embeddings(args.validation_prompt, text_encoding_pipeline)
-        if args.offload:
-            text_encoding_pipeline = text_encoding_pipeline.to("cpu")
+        with offload_models(text_encoding_pipeline, device=accelerator.device, offload=args.offload):
+            (
+                validation_embeddings["prompt_embeds_t5"],
+                validation_embeddings["prompt_embeds_llama3"],
+                validation_embeddings["pooled_prompt_embeds"],
+                validation_embeddings["negative_prompt_embeds_t5"],
+                validation_embeddings["negative_prompt_embeds_llama3"],
+                validation_embeddings["negative_pooled_prompt_embeds"],
+            ) = compute_text_embeddings(args.validation_prompt, text_encoding_pipeline)
 
     # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
     # pack the statically computed variables appropriately here. This is so that we don't
@@ -1581,12 +1573,10 @@ def main(args):
                 if args.cache_latents:
                     model_input = latents_cache[step].sample()
                 else:
-                    if args.offload:
-                        vae = vae.to(accelerator.device)
-                    pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
+                    with offload_models(vae, device=accelerator.device, offload=args.offload):
+                        pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
                     model_input = vae.encode(pixel_values).latent_dist.sample()
-                    if args.offload:
-                        vae = vae.to("cpu")
+
                 model_input = (model_input - vae_config_shift_factor) * vae_config_scaling_factor
                 model_input = model_input.to(dtype=weight_dtype)
 

examples/flux-control/train_control_lora_flux.py

@@ -837,11 +837,6 @@ def main(args):
     assert torch.all(flux_transformer.x_embedder.weight[:, initial_input_channels:].data == 0)
     flux_transformer.register_to_config(in_channels=initial_input_channels * 2, out_channels=initial_input_channels)
 
-    if args.train_norm_layers:
-        for name, param in flux_transformer.named_parameters():
-            if any(k in name for k in NORM_LAYER_PREFIXES):
-                param.requires_grad = True
-
     if args.lora_layers is not None:
         if args.lora_layers != "all-linear":
             target_modules = [layer.strip() for layer in args.lora_layers.split(",")]
@@ -879,6 +874,11 @@ def main(args):
     )
     flux_transformer.add_adapter(transformer_lora_config)
 
+    if args.train_norm_layers:
+        for name, param in flux_transformer.named_parameters():
+            if any(k in name for k in NORM_LAYER_PREFIXES):
+                param.requires_grad = True
+
     def unwrap_model(model):
         model = accelerator.unwrap_model(model)
         model = model._orig_mod if is_compiled_module(model) else model

examples/server/requirements.in

@@ -1,4 +1,4 @@
-torch~=2.4.0
+torch~=2.7.0
 transformers==4.46.1
 sentencepiece
 aiohttp

examples/server/requirements.txt

@@ -1,10 +1,10 @@
 # This file was autogenerated by uv via the following command:
 #    uv pip compile requirements.in -o requirements.txt
-aiohappyeyeballs==2.4.3
+aiohappyeyeballs==2.6.1
     # via aiohttp
-aiohttp==3.10.10
+aiohttp==3.12.14
     # via -r requirements.in
-aiosignal==1.3.1
+aiosignal==1.4.0
     # via aiohttp
 annotated-types==0.7.0
     # via pydantic
@@ -29,7 +29,6 @@ filelock==3.16.1
     #   huggingface-hub
     #   torch
     #   transformers
-    #   triton
 frozenlist==1.5.0
     # via
     #   aiohttp
@@ -63,36 +62,42 @@ networkx==3.2.1
     # via torch
 numpy==2.0.2
     # via transformers
-nvidia-cublas-cu12==12.1.3.1
+nvidia-cublas-cu12==12.6.4.1
     # via
     #   nvidia-cudnn-cu12
     #   nvidia-cusolver-cu12
     #   torch
-nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-cupti-cu12==12.6.80
     # via torch
-nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.6.77
     # via torch
-nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.6.77
     # via torch
-nvidia-cudnn-cu12==9.1.0.70
+nvidia-cudnn-cu12==9.5.1.17
     # via torch
-nvidia-cufft-cu12==11.0.2.54
+nvidia-cufft-cu12==11.3.0.4
     # via torch
-nvidia-curand-cu12==10.3.2.106
+nvidia-cufile-cu12==1.11.1.6
     # via torch
-nvidia-cusolver-cu12==11.4.5.107
+nvidia-curand-cu12==10.3.7.77
     # via torch
-nvidia-cusparse-cu12==12.1.0.106
+nvidia-cusolver-cu12==11.7.1.2
+    # via torch
+nvidia-cusparse-cu12==12.5.4.2
     # via
     #   nvidia-cusolver-cu12
     #   torch
-nvidia-nccl-cu12==2.20.5
+nvidia-cusparselt-cu12==0.6.3
     # via torch
-nvidia-nvjitlink-cu12==12.9.86
+nvidia-nccl-cu12==2.26.2
+    # via torch
+nvidia-nvjitlink-cu12==12.6.85
     # via
+    #   nvidia-cufft-cu12
     #   nvidia-cusolver-cu12
     #   nvidia-cusparse-cu12
-nvidia-nvtx-cu12==12.1.105
+    #   torch
+nvidia-nvtx-cu12==12.6.77
     # via torch
 packaging==24.1
     # via
@@ -105,7 +110,9 @@ prometheus-client==0.21.0
 prometheus-fastapi-instrumentator==7.0.0
     # via -r requirements.in
 propcache==0.2.0
-    # via yarl
+    # via
+    #   aiohttp
+    #   yarl
 py-consul==1.5.3
     # via -r requirements.in
 pydantic==2.9.2
@@ -137,7 +144,7 @@ sympy==1.13.3
     # via torch
 tokenizers==0.20.1
     # via transformers
-torch==2.4.1
+torch==2.7.0
     # via -r requirements.in
 tqdm==4.66.5
     # via
@@ -145,10 +152,11 @@ tqdm==4.66.5
     #   transformers
 transformers==4.46.1
     # via -r requirements.in
-triton==3.0.0
+triton==3.3.0
     # via torch
 typing-extensions==4.12.2
     # via
+    #   aiosignal
     #   anyio
     #   exceptiongroup
     #   fastapi
@@ -163,5 +171,5 @@ urllib3==2.5.0
     # via requests
 uvicorn==0.32.0
     # via -r requirements.in
-yarl==1.16.0
+yarl==1.18.3
     # via aiohttp

scripts/convert_skyreelsv2_to_diffusers.py

@@ -0,0 +1,637 @@
+import argparse
+import os
+import pathlib
+from typing import Any, Dict
+
+import torch
+from accelerate import init_empty_weights
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+from transformers import AutoProcessor, AutoTokenizer, CLIPVisionModelWithProjection, UMT5EncoderModel
+
+from diffusers import (
+    AutoencoderKLWan,
+    SkyReelsV2DiffusionForcingPipeline,
+    SkyReelsV2ImageToVideoPipeline,
+    SkyReelsV2Pipeline,
+    SkyReelsV2Transformer3DModel,
+    UniPCMultistepScheduler,
+)
+
+
+TRANSFORMER_KEYS_RENAME_DICT = {
+    "time_embedding.0": "condition_embedder.time_embedder.linear_1",
+    "time_embedding.2": "condition_embedder.time_embedder.linear_2",
+    "text_embedding.0": "condition_embedder.text_embedder.linear_1",
+    "text_embedding.2": "condition_embedder.text_embedder.linear_2",
+    "time_projection.1": "condition_embedder.time_proj",
+    "head.modulation": "scale_shift_table",
+    "head.head": "proj_out",
+    "modulation": "scale_shift_table",
+    "ffn.0": "ffn.net.0.proj",
+    "ffn.2": "ffn.net.2",
+    "fps_projection.0": "fps_projection.net.0.proj",
+    "fps_projection.2": "fps_projection.net.2",
+    # Hack to swap the layer names
+    # The original model calls the norms in following order: norm1, norm3, norm2
+    # We convert it to: norm1, norm2, norm3
+    "norm2": "norm__placeholder",
+    "norm3": "norm2",
+    "norm__placeholder": "norm3",
+    # For the I2V model
+    "img_emb.proj.0": "condition_embedder.image_embedder.norm1",
+    "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj",
+    "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2",
+    "img_emb.proj.4": "condition_embedder.image_embedder.norm2",
+    # for the FLF2V model
+    "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed",
+    # Add attention component mappings
+    "self_attn.q": "attn1.to_q",
+    "self_attn.k": "attn1.to_k",
+    "self_attn.v": "attn1.to_v",
+    "self_attn.o": "attn1.to_out.0",
+    "self_attn.norm_q": "attn1.norm_q",
+    "self_attn.norm_k": "attn1.norm_k",
+    "cross_attn.q": "attn2.to_q",
+    "cross_attn.k": "attn2.to_k",
+    "cross_attn.v": "attn2.to_v",
+    "cross_attn.o": "attn2.to_out.0",
+    "cross_attn.norm_q": "attn2.norm_q",
+    "cross_attn.norm_k": "attn2.norm_k",
+    "attn2.to_k_img": "attn2.add_k_proj",
+    "attn2.to_v_img": "attn2.add_v_proj",
+    "attn2.norm_k_img": "attn2.norm_added_k",
+}
+
+TRANSFORMER_SPECIAL_KEYS_REMAP = {}
+
+
+def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
+    state_dict[new_key] = state_dict.pop(old_key)
+
+
+def load_sharded_safetensors(dir: pathlib.Path):
+    if "720P" in str(dir):
+        file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors"))
+    else:
+        file_paths = list(dir.glob("model*.safetensors"))
+    state_dict = {}
+    for path in file_paths:
+        state_dict.update(load_file(path))
+    return state_dict
+
+
+def get_transformer_config(model_type: str) -> Dict[str, Any]:
+    if model_type == "SkyReels-V2-DF-1.3B-540P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-DF-1.3B-540P",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 12,
+                "inject_sample_info": True,
+                "num_layers": 30,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+            },
+        }
+    elif model_type == "SkyReels-V2-DF-14B-720P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-DF-14B-720P",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 40,
+                "inject_sample_info": False,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+            },
+        }
+    elif model_type == "SkyReels-V2-DF-14B-540P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-DF-14B-540P",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 40,
+                "inject_sample_info": False,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+            },
+        }
+    elif model_type == "SkyReels-V2-T2V-14B-720P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-T2V-14B-720P",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 40,
+                "inject_sample_info": False,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+            },
+        }
+    elif model_type == "SkyReels-V2-T2V-14B-540P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-T2V-14B-540P",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 40,
+                "inject_sample_info": False,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+            },
+        }
+    elif model_type == "SkyReels-V2-I2V-1.3B-540P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-I2V-1.3B-540P",
+            "diffusers_config": {
+                "added_kv_proj_dim": 1536,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "in_channels": 36,
+                "num_attention_heads": 12,
+                "inject_sample_info": False,
+                "num_layers": 30,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "image_dim": 1280,
+            },
+        }
+    elif model_type == "SkyReels-V2-I2V-14B-540P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-I2V-14B-540P",
+            "diffusers_config": {
+                "added_kv_proj_dim": 5120,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 36,
+                "num_attention_heads": 40,
+                "inject_sample_info": False,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "image_dim": 1280,
+            },
+        }
+    elif model_type == "SkyReels-V2-I2V-14B-720P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-I2V-14B-720P",
+            "diffusers_config": {
+                "added_kv_proj_dim": 5120,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 36,
+                "num_attention_heads": 40,
+                "inject_sample_info": False,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "image_dim": 1280,
+            },
+        }
+    elif model_type == "SkyReels-V2-FLF2V-1.3B-540P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-I2V-1.3B-540P",
+            "diffusers_config": {
+                "added_kv_proj_dim": 1536,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "in_channels": 36,
+                "num_attention_heads": 12,
+                "inject_sample_info": False,
+                "num_layers": 30,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "image_dim": 1280,
+                "pos_embed_seq_len": 514,
+            },
+        }
+    elif model_type == "SkyReels-V2-FLF2V-14B-540P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-I2V-14B-540P",
+            "diffusers_config": {
+                "added_kv_proj_dim": 5120,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 36,
+                "num_attention_heads": 40,
+                "inject_sample_info": False,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "image_dim": 1280,
+                "pos_embed_seq_len": 514,
+            },
+        }
+    elif model_type == "SkyReels-V2-FLF2V-14B-720P":
+        config = {
+            "model_id": "Skywork/SkyReels-V2-I2V-14B-720P",
+            "diffusers_config": {
+                "added_kv_proj_dim": 5120,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 36,
+                "num_attention_heads": 40,
+                "inject_sample_info": False,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "image_dim": 1280,
+                "pos_embed_seq_len": 514,
+            },
+        }
+    return config
+
+
+def convert_transformer(model_type: str):
+    config = get_transformer_config(model_type)
+    diffusers_config = config["diffusers_config"]
+    model_id = config["model_id"]
+
+    if "1.3B" in model_type:
+        original_state_dict = load_file(hf_hub_download(model_id, "model.safetensors"))
+    else:
+        os.makedirs(model_type, exist_ok=True)
+        model_dir = pathlib.Path(model_type)
+        if "720P" in model_type:
+            top_shard = 7 if "I2V" in model_type else 6
+            zeros = "0" * (4 if "I2V" or "T2V" in model_type else 3)
+            model_name = "diffusion_pytorch_model"
+        elif "540P" in model_type:
+            top_shard = 14 if "I2V" in model_type else 12
+            model_name = "model"
+
+        for i in range(1, top_shard + 1):
+            shard_path = f"{model_name}-{i:05d}-of-{zeros}{top_shard}.safetensors"
+            hf_hub_download(model_id, shard_path, local_dir=model_dir)
+        original_state_dict = load_sharded_safetensors(model_dir)
+
+    with init_empty_weights():
+        transformer = SkyReelsV2Transformer3DModel.from_config(diffusers_config)
+
+    for key in list(original_state_dict.keys()):
+        new_key = key[:]
+        for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
+            new_key = new_key.replace(replace_key, rename_key)
+        update_state_dict_(original_state_dict, key, new_key)
+
+    for key in list(original_state_dict.keys()):
+        for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
+            if special_key not in key:
+                continue
+            handler_fn_inplace(key, original_state_dict)
+
+    if "FLF2V" in model_type:
+        if (
+            hasattr(transformer.condition_embedder, "image_embedder")
+            and hasattr(transformer.condition_embedder.image_embedder, "pos_embed")
+            and transformer.condition_embedder.image_embedder.pos_embed is not None
+        ):
+            pos_embed_shape = transformer.condition_embedder.image_embedder.pos_embed.shape
+            original_state_dict["condition_embedder.image_embedder.pos_embed"] = torch.zeros(pos_embed_shape)
+
+    transformer.load_state_dict(original_state_dict, strict=True, assign=True)
+    return transformer
+
+
+def convert_vae():
+    vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.1-T2V-14B", "Wan2.1_VAE.pth")
+    old_state_dict = torch.load(vae_ckpt_path, weights_only=True)
+    new_state_dict = {}
+
+    # Create mappings for specific components
+    middle_key_mapping = {
+        # Encoder middle block
+        "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma",
+        "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias",
+        "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight",
+        "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma",
+        "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias",
+        "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight",
+        "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma",
+        "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias",
+        "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight",
+        "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma",
+        "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias",
+        "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight",
+        # Decoder middle block
+        "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma",
+        "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias",
+        "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight",
+        "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma",
+        "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias",
+        "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight",
+        "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma",
+        "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias",
+        "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight",
+        "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma",
+        "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias",
+        "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight",
+    }
+
+    # Create a mapping for attention blocks
+    attention_mapping = {
+        # Encoder middle attention
+        "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma",
+        "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight",
+        "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias",
+        "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight",
+        "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias",
+        # Decoder middle attention
+        "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma",
+        "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight",
+        "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias",
+        "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight",
+        "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias",
+    }
+
+    # Create a mapping for the head components
+    head_mapping = {
+        # Encoder head
+        "encoder.head.0.gamma": "encoder.norm_out.gamma",
+        "encoder.head.2.bias": "encoder.conv_out.bias",
+        "encoder.head.2.weight": "encoder.conv_out.weight",
+        # Decoder head
+        "decoder.head.0.gamma": "decoder.norm_out.gamma",
+        "decoder.head.2.bias": "decoder.conv_out.bias",
+        "decoder.head.2.weight": "decoder.conv_out.weight",
+    }
+
+    # Create a mapping for the quant components
+    quant_mapping = {
+        "conv1.weight": "quant_conv.weight",
+        "conv1.bias": "quant_conv.bias",
+        "conv2.weight": "post_quant_conv.weight",
+        "conv2.bias": "post_quant_conv.bias",
+    }
+
+    # Process each key in the state dict
+    for key, value in old_state_dict.items():
+        # Handle middle block keys using the mapping
+        if key in middle_key_mapping:
+            new_key = middle_key_mapping[key]
+            new_state_dict[new_key] = value
+        # Handle attention blocks using the mapping
+        elif key in attention_mapping:
+            new_key = attention_mapping[key]
+            new_state_dict[new_key] = value
+        # Handle head keys using the mapping
+        elif key in head_mapping:
+            new_key = head_mapping[key]
+            new_state_dict[new_key] = value
+        # Handle quant keys using the mapping
+        elif key in quant_mapping:
+            new_key = quant_mapping[key]
+            new_state_dict[new_key] = value
+        # Handle encoder conv1
+        elif key == "encoder.conv1.weight":
+            new_state_dict["encoder.conv_in.weight"] = value
+        elif key == "encoder.conv1.bias":
+            new_state_dict["encoder.conv_in.bias"] = value
+        # Handle decoder conv1
+        elif key == "decoder.conv1.weight":
+            new_state_dict["decoder.conv_in.weight"] = value
+        elif key == "decoder.conv1.bias":
+            new_state_dict["decoder.conv_in.bias"] = value
+        # Handle encoder downsamples
+        elif key.startswith("encoder.downsamples."):
+            # Convert to down_blocks
+            new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.")
+
+            # Convert residual block naming but keep the original structure
+            if ".residual.0.gamma" in new_key:
+                new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma")
+            elif ".residual.2.bias" in new_key:
+                new_key = new_key.replace(".residual.2.bias", ".conv1.bias")
+            elif ".residual.2.weight" in new_key:
+                new_key = new_key.replace(".residual.2.weight", ".conv1.weight")
+            elif ".residual.3.gamma" in new_key:
+                new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma")
+            elif ".residual.6.bias" in new_key:
+                new_key = new_key.replace(".residual.6.bias", ".conv2.bias")
+            elif ".residual.6.weight" in new_key:
+                new_key = new_key.replace(".residual.6.weight", ".conv2.weight")
+            elif ".shortcut.bias" in new_key:
+                new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias")
+            elif ".shortcut.weight" in new_key:
+                new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight")
+
+            new_state_dict[new_key] = value
+
+        # Handle decoder upsamples
+        elif key.startswith("decoder.upsamples."):
+            # Convert to up_blocks
+            parts = key.split(".")
+            block_idx = int(parts[2])
+
+            # Group residual blocks
+            if "residual" in key:
+                if block_idx in [0, 1, 2]:
+                    new_block_idx = 0
+                    resnet_idx = block_idx
+                elif block_idx in [4, 5, 6]:
+                    new_block_idx = 1
+                    resnet_idx = block_idx - 4
+                elif block_idx in [8, 9, 10]:
+                    new_block_idx = 2
+                    resnet_idx = block_idx - 8
+                elif block_idx in [12, 13, 14]:
+                    new_block_idx = 3
+                    resnet_idx = block_idx - 12
+                else:
+                    # Keep as is for other blocks
+                    new_state_dict[key] = value
+                    continue
+
+                # Convert residual block naming
+                if ".residual.0.gamma" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma"
+                elif ".residual.2.bias" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias"
+                elif ".residual.2.weight" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight"
+                elif ".residual.3.gamma" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma"
+                elif ".residual.6.bias" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias"
+                elif ".residual.6.weight" in key:
+                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight"
+                else:
+                    new_key = key
+
+                new_state_dict[new_key] = value
+
+            # Handle shortcut connections
+            elif ".shortcut." in key:
+                if block_idx == 4:
+                    new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.")
+                    new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1")
+                else:
+                    new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
+                    new_key = new_key.replace(".shortcut.", ".conv_shortcut.")
+
+                new_state_dict[new_key] = value
+
+            # Handle upsamplers
+            elif ".resample." in key or ".time_conv." in key:
+                if block_idx == 3:
+                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0")
+                elif block_idx == 7:
+                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0")
+                elif block_idx == 11:
+                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0")
+                else:
+                    new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
+
+                new_state_dict[new_key] = value
+            else:
+                new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
+                new_state_dict[new_key] = value
+        else:
+            # Keep other keys unchanged
+            new_state_dict[key] = value
+
+    with init_empty_weights():
+        vae = AutoencoderKLWan()
+    vae.load_state_dict(new_state_dict, strict=True, assign=True)
+    return vae
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_type", type=str, default=None)
+    parser.add_argument("--output_path", type=str, required=True)
+    parser.add_argument("--dtype", default="fp32")
+    return parser.parse_args()
+
+
+DTYPE_MAPPING = {
+    "fp32": torch.float32,
+    "fp16": torch.float16,
+    "bf16": torch.bfloat16,
+}
+
+
+if __name__ == "__main__":
+    args = get_args()
+
+    transformer = None
+    dtype = DTYPE_MAPPING[args.dtype]
+
+    transformer = convert_transformer(args.model_type).to(dtype=dtype)
+    vae = convert_vae()
+    text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl")
+    tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
+    scheduler = UniPCMultistepScheduler(
+        prediction_type="flow_prediction",
+        num_train_timesteps=1000,
+        use_flow_sigmas=True,
+    )
+
+    if "I2V" in args.model_type or "FLF2V" in args.model_type:
+        image_encoder = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+        image_processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+        pipe = SkyReelsV2ImageToVideoPipeline(
+            transformer=transformer,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            vae=vae,
+            scheduler=scheduler,
+            image_encoder=image_encoder,
+            image_processor=image_processor,
+        )
+    elif "T2V" in args.model_type:
+        pipe = SkyReelsV2Pipeline(
+            transformer=transformer,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            vae=vae,
+            scheduler=scheduler,
+        )
+    elif "DF" in args.model_type:
+        pipe = SkyReelsV2DiffusionForcingPipeline(
+            transformer=transformer,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            vae=vae,
+            scheduler=scheduler,
+        )
+
+    pipe.save_pretrained(
+        args.output_path,
+        safe_serialization=True,
+        max_shard_size="5GB",
+        # push_to_hub=True,
+        # repo_id=f"<place_holder>/{args.model_type}-Diffusers",
+    )

setup.py

@@ -110,7 +110,7 @@ _deps = [
     "jax>=0.4.1",
     "jaxlib>=0.4.1",
     "Jinja2",
-    "k-diffusion>=0.0.12",
+    "k-diffusion==0.0.12",
     "torchsde",
     "note_seq",
     "librosa",

src/diffusers/__init__.py

@@ -34,10 +34,13 @@ from .utils import (
 
 _import_structure = {
     "configuration_utils": ["ConfigMixin"],
+    "guiders": [],
     "hooks": [],
     "loaders": ["FromOriginalModelMixin"],
     "models": [],
+    "modular_pipelines": [],
     "pipelines": [],
+    "quantizers.pipe_quant_config": ["PipelineQuantizationConfig"],
     "quantizers.quantization_config": [],
     "schedulers": [],
     "utils": [
@@ -130,12 +133,29 @@ except OptionalDependencyNotAvailable:
     _import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")]
 
 else:
+    _import_structure["guiders"].extend(
+        [
+            "AdaptiveProjectedGuidance",
+            "AutoGuidance",
+            "ClassifierFreeGuidance",
+            "ClassifierFreeZeroStarGuidance",
+            "PerturbedAttentionGuidance",
+            "SkipLayerGuidance",
+            "SmoothedEnergyGuidance",
+            "TangentialClassifierFreeGuidance",
+        ]
+    )
     _import_structure["hooks"].extend(
         [
             "FasterCacheConfig",
+            "FirstBlockCacheConfig",
             "HookRegistry",
+            "LayerSkipConfig",
             "PyramidAttentionBroadcastConfig",
+            "SmoothedEnergyGuidanceConfig",
             "apply_faster_cache",
+            "apply_first_block_cache",
+            "apply_layer_skip",
             "apply_pyramid_attention_broadcast",
         ]
     )
@@ -143,6 +163,7 @@ else:
         [
             "AllegroTransformer3DModel",
             "AsymmetricAutoencoderKL",
+            "AttentionBackendName",
             "AuraFlowTransformer2DModel",
             "AutoencoderDC",
             "AutoencoderKL",
@@ -199,6 +220,7 @@ else:
             "SD3ControlNetModel",
             "SD3MultiControlNetModel",
             "SD3Transformer2DModel",
+            "SkyReelsV2Transformer3DModel",
             "SparseControlNetModel",
             "StableAudioDiTModel",
             "StableCascadeUNet",
@@ -217,6 +239,15 @@ else:
             "VQModel",
             "WanTransformer3DModel",
             "WanVACETransformer3DModel",
+            "attention_backend",
+        ]
+    )
+    _import_structure["modular_pipelines"].extend(
+        [
+            "ComponentsManager",
+            "ComponentSpec",
+            "ModularPipeline",
+            "ModularPipelineBlocks",
         ]
     )
     _import_structure["optimization"] = [
@@ -331,6 +362,12 @@ except OptionalDependencyNotAvailable:
     ]
 
 else:
+    _import_structure["modular_pipelines"].extend(
+        [
+            "StableDiffusionXLAutoBlocks",
+            "StableDiffusionXLModularPipeline",
+        ]
+    )
     _import_structure["pipelines"].extend(
         [
             "AllegroPipeline",
@@ -381,6 +418,7 @@ else:
             "FluxFillPipeline",
             "FluxImg2ImgPipeline",
             "FluxInpaintPipeline",
+            "FluxKontextInpaintPipeline",
             "FluxKontextPipeline",
             "FluxPipeline",
             "FluxPriorReduxPipeline",
@@ -453,6 +491,11 @@ else:
             "SemanticStableDiffusionPipeline",
             "ShapEImg2ImgPipeline",
             "ShapEPipeline",
+            "SkyReelsV2DiffusionForcingImageToVideoPipeline",
+            "SkyReelsV2DiffusionForcingPipeline",
+            "SkyReelsV2DiffusionForcingVideoToVideoPipeline",
+            "SkyReelsV2ImageToVideoPipeline",
+            "SkyReelsV2Pipeline",
             "StableAudioPipeline",
             "StableAudioProjectionModel",
             "StableCascadeCombinedPipeline",
@@ -542,6 +585,7 @@ else:
         ]
     )
 
+
 try:
     if not (is_torch_available() and is_transformers_available() and is_opencv_available()):
         raise OptionalDependencyNotAvailable()
@@ -748,16 +792,32 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     except OptionalDependencyNotAvailable:
         from .utils.dummy_pt_objects import *  # noqa F403
     else:
+        from .guiders import (
+            AdaptiveProjectedGuidance,
+            AutoGuidance,
+            ClassifierFreeGuidance,
+            ClassifierFreeZeroStarGuidance,
+            PerturbedAttentionGuidance,
+            SkipLayerGuidance,
+            SmoothedEnergyGuidance,
+            TangentialClassifierFreeGuidance,
+        )
         from .hooks import (
             FasterCacheConfig,
+            FirstBlockCacheConfig,
             HookRegistry,
+            LayerSkipConfig,
             PyramidAttentionBroadcastConfig,
+            SmoothedEnergyGuidanceConfig,
             apply_faster_cache,
+            apply_first_block_cache,
+            apply_layer_skip,
             apply_pyramid_attention_broadcast,
         )
         from .models import (
             AllegroTransformer3DModel,
             AsymmetricAutoencoderKL,
+            AttentionBackendName,
             AuraFlowTransformer2DModel,
             AutoencoderDC,
             AutoencoderKL,
@@ -814,6 +874,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             SD3ControlNetModel,
             SD3MultiControlNetModel,
             SD3Transformer2DModel,
+            SkyReelsV2Transformer3DModel,
             SparseControlNetModel,
             StableAudioDiTModel,
             T2IAdapter,
@@ -831,6 +892,13 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             VQModel,
             WanTransformer3DModel,
             WanVACETransformer3DModel,
+            attention_backend,
+        )
+        from .modular_pipelines import (
+            ComponentsManager,
+            ComponentSpec,
+            ModularPipeline,
+            ModularPipelineBlocks,
         )
         from .optimization import (
             get_constant_schedule,
@@ -928,6 +996,10 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     except OptionalDependencyNotAvailable:
         from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
     else:
+        from .modular_pipelines import (
+            StableDiffusionXLAutoBlocks,
+            StableDiffusionXLModularPipeline,
+        )
         from .pipelines import (
             AllegroPipeline,
             AltDiffusionImg2ImgPipeline,
@@ -975,6 +1047,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxFillPipeline,
             FluxImg2ImgPipeline,
             FluxInpaintPipeline,
+            FluxKontextInpaintPipeline,
             FluxKontextPipeline,
             FluxPipeline,
             FluxPriorReduxPipeline,
@@ -1047,6 +1120,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             SemanticStableDiffusionPipeline,
             ShapEImg2ImgPipeline,
             ShapEPipeline,
+            SkyReelsV2DiffusionForcingImageToVideoPipeline,
+            SkyReelsV2DiffusionForcingPipeline,
+            SkyReelsV2DiffusionForcingVideoToVideoPipeline,
+            SkyReelsV2ImageToVideoPipeline,
+            SkyReelsV2Pipeline,
             StableAudioPipeline,
             StableAudioProjectionModel,
             StableCascadeCombinedPipeline,

src/diffusers/callbacks.py

@@ -207,3 +207,38 @@ class IPAdapterScaleCutoffCallback(PipelineCallback):
         if step_index == cutoff_step:
             pipeline.set_ip_adapter_scale(0.0)
         return callback_kwargs
+
+
+class SD3CFGCutoffCallback(PipelineCallback):
+    """
+    Callback function for Stable Diffusion 3 Pipelines. After certain number of steps (set by `cutoff_step_ratio` or
+    `cutoff_step_index`), this callback will disable the CFG.
+
+    Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.
+    """
+
+    tensor_inputs = ["prompt_embeds", "pooled_prompt_embeds"]
+
+    def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:
+        cutoff_step_ratio = self.config.cutoff_step_ratio
+        cutoff_step_index = self.config.cutoff_step_index
+
+        # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio
+        cutoff_step = (
+            cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)
+        )
+
+        if step_index == cutoff_step:
+            prompt_embeds = callback_kwargs[self.tensor_inputs[0]]
+            prompt_embeds = prompt_embeds[-1:]  # "-1" denotes the embeddings for conditional text tokens.
+
+            pooled_prompt_embeds = callback_kwargs[self.tensor_inputs[1]]
+            pooled_prompt_embeds = pooled_prompt_embeds[
+                -1:
+            ]  # "-1" denotes the embeddings for conditional pooled text tokens.
+
+            pipeline._guidance_scale = 0.0
+
+            callback_kwargs[self.tensor_inputs[0]] = prompt_embeds
+            callback_kwargs[self.tensor_inputs[1]] = pooled_prompt_embeds
+        return callback_kwargs

src/diffusers/commands/custom_blocks.py

@@ -0,0 +1,134 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Usage example:
+    TODO
+"""
+
+import ast
+import importlib.util
+import os
+from argparse import ArgumentParser, Namespace
+from pathlib import Path
+
+from ..utils import logging
+from . import BaseDiffusersCLICommand
+
+
+EXPECTED_PARENT_CLASSES = ["ModularPipelineBlocks"]
+CONFIG = "config.json"
+
+
+def conversion_command_factory(args: Namespace):
+    return CustomBlocksCommand(args.block_module_name, args.block_class_name)
+
+
+class CustomBlocksCommand(BaseDiffusersCLICommand):
+    @staticmethod
+    def register_subcommand(parser: ArgumentParser):
+        conversion_parser = parser.add_parser("custom_blocks")
+        conversion_parser.add_argument(
+            "--block_module_name",
+            type=str,
+            default="block.py",
+            help="Module filename in which the custom block will be implemented.",
+        )
+        conversion_parser.add_argument(
+            "--block_class_name",
+            type=str,
+            default=None,
+            help="Name of the custom block. If provided None, we will try to infer it.",
+        )
+        conversion_parser.set_defaults(func=conversion_command_factory)
+
+    def __init__(self, block_module_name: str = "block.py", block_class_name: str = None):
+        self.logger = logging.get_logger("diffusers-cli/custom_blocks")
+        self.block_module_name = Path(block_module_name)
+        self.block_class_name = block_class_name
+
+    def run(self):
+        # determine the block to be saved.
+        out = self._get_class_names(self.block_module_name)
+        classes_found = list({cls for cls, _ in out})
+
+        if self.block_class_name is not None:
+            child_class, parent_class = self._choose_block(out, self.block_class_name)
+            if child_class is None and parent_class is None:
+                raise ValueError(
+                    "`block_class_name` could not be retrieved. Available classes from "
+                    f"{self.block_module_name}:\n{classes_found}"
+                )
+        else:
+            self.logger.info(
+                f"Found classes: {classes_found} will be using {classes_found[0]}. "
+                "If this needs to be changed, re-run the command specifying `block_class_name`."
+            )
+            child_class, parent_class = out[0][0], out[0][1]
+
+        # dynamically get the custom block and initialize it to call `save_pretrained` in the current directory.
+        # the user is responsible for running it, so I guess that is safe?
+        module_name = f"__dynamic__{self.block_module_name.stem}"
+        spec = importlib.util.spec_from_file_location(module_name, str(self.block_module_name))
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        getattr(module, child_class)().save_pretrained(os.getcwd())
+
+        # or, we could create it manually.
+        # automap = self._create_automap(parent_class=parent_class, child_class=child_class)
+        # with open(CONFIG, "w") as f:
+        #     json.dump(automap, f)
+        with open("requirements.txt", "w") as f:
+            f.write("")
+
+    def _choose_block(self, candidates, chosen=None):
+        for cls, base in candidates:
+            if cls == chosen:
+                return cls, base
+        return None, None
+
+    def _get_class_names(self, file_path):
+        source = file_path.read_text(encoding="utf-8")
+        try:
+            tree = ast.parse(source, filename=file_path)
+        except SyntaxError as e:
+            raise ValueError(f"Could not parse {file_path!r}: {e}") from e
+
+        results: list[tuple[str, str]] = []
+        for node in tree.body:
+            if not isinstance(node, ast.ClassDef):
+                continue
+
+            # extract all base names for this class
+            base_names = [bname for b in node.bases if (bname := self._get_base_name(b)) is not None]
+
+            # for each allowed base that appears in the class's bases, emit a tuple
+            for allowed in EXPECTED_PARENT_CLASSES:
+                if allowed in base_names:
+                    results.append((node.name, allowed))
+
+        return results
+
+    def _get_base_name(self, node: ast.expr):
+        if isinstance(node, ast.Name):
+            return node.id
+        elif isinstance(node, ast.Attribute):
+            val = self._get_base_name(node.value)
+            return f"{val}.{node.attr}" if val else node.attr
+        return None
+
+    def _create_automap(self, parent_class, child_class):
+        module = str(self.block_module_name).replace(".py", "").rsplit(".", 1)[-1]
+        auto_map = {f"{parent_class}": f"{module}.{child_class}"}
+        return {"auto_map": auto_map}

src/diffusers/commands/diffusers_cli.py

@@ -15,6 +15,7 @@
 
 from argparse import ArgumentParser
 
+from .custom_blocks import CustomBlocksCommand
 from .env import EnvironmentCommand
 from .fp16_safetensors import FP16SafetensorsCommand
 
@@ -26,6 +27,7 @@ def main():
     # Register commands
     EnvironmentCommand.register_subcommand(commands_parser)
     FP16SafetensorsCommand.register_subcommand(commands_parser)
+    CustomBlocksCommand.register_subcommand(commands_parser)
 
     # Let's go
     args = parser.parse_args()

src/diffusers/configuration_utils.py

@@ -176,6 +176,7 @@ class ConfigMixin:
             token = kwargs.pop("token", None)
             repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
             repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
+            subfolder = kwargs.pop("subfolder", None)
 
             self._upload_folder(
                 save_directory,
@@ -183,6 +184,7 @@ class ConfigMixin:
                 token=token,
                 commit_message=commit_message,
                 create_pr=create_pr,
+                subfolder=subfolder,
             )
 
     @classmethod
@@ -601,6 +603,10 @@ class ConfigMixin:
                 value = value.tolist()
             elif isinstance(value, Path):
                 value = value.as_posix()
+            elif hasattr(value, "to_dict") and callable(value.to_dict):
+                value = value.to_dict()
+            elif isinstance(value, list):
+                value = [to_json_saveable(v) for v in value]
             return value
 
         if "quantization_config" in config_dict:
@@ -757,4 +763,7 @@ class LegacyConfigMixin(ConfigMixin):
         # resolve remapping
         remapped_class = _fetch_remapped_cls_from_config(config, cls)
 
-        return remapped_class.from_config(config, return_unused_kwargs, **kwargs)
+        if remapped_class is cls:
+            return super(LegacyConfigMixin, remapped_class).from_config(config, return_unused_kwargs, **kwargs)
+        else:
+            return remapped_class.from_config(config, return_unused_kwargs, **kwargs)

src/diffusers/dependency_versions_table.py

@@ -17,7 +17,7 @@ deps = {
     "jax": "jax>=0.4.1",
     "jaxlib": "jaxlib>=0.4.1",
     "Jinja2": "Jinja2",
-    "k-diffusion": "k-diffusion>=0.0.12",
+    "k-diffusion": "k-diffusion==0.0.12",
     "torchsde": "torchsde",
     "note_seq": "note_seq",
     "librosa": "librosa",

src/diffusers/guiders/__init__.py

@@ -0,0 +1,39 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Union
+
+from ..utils import is_torch_available
+
+
+if is_torch_available():
+    from .adaptive_projected_guidance import AdaptiveProjectedGuidance
+    from .auto_guidance import AutoGuidance
+    from .classifier_free_guidance import ClassifierFreeGuidance
+    from .classifier_free_zero_star_guidance import ClassifierFreeZeroStarGuidance
+    from .perturbed_attention_guidance import PerturbedAttentionGuidance
+    from .skip_layer_guidance import SkipLayerGuidance
+    from .smoothed_energy_guidance import SmoothedEnergyGuidance
+    from .tangential_classifier_free_guidance import TangentialClassifierFreeGuidance
+
+    GuiderType = Union[
+        AdaptiveProjectedGuidance,
+        AutoGuidance,
+        ClassifierFreeGuidance,
+        ClassifierFreeZeroStarGuidance,
+        PerturbedAttentionGuidance,
+        SkipLayerGuidance,
+        SmoothedEnergyGuidance,
+        TangentialClassifierFreeGuidance,
+    ]

src/diffusers/guiders/adaptive_projected_guidance.py

@@ -0,0 +1,188 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from .guider_utils import BaseGuidance, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+class AdaptiveProjectedGuidance(BaseGuidance):
+    """
+    Adaptive Projected Guidance (APG): https://huggingface.co/papers/2410.02416
+
+    Args:
+        guidance_scale (`float`, defaults to `7.5`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        adaptive_projected_guidance_momentum (`float`, defaults to `None`):
+            The momentum parameter for the adaptive projected guidance. Disabled if set to `None`.
+        adaptive_projected_guidance_rescale (`float`, defaults to `15.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.0`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `1.0`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    _input_predictions = ["pred_cond", "pred_uncond"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        adaptive_projected_guidance_momentum: Optional[float] = None,
+        adaptive_projected_guidance_rescale: float = 15.0,
+        eta: float = 1.0,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__(start, stop)
+
+        self.guidance_scale = guidance_scale
+        self.adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum
+        self.adaptive_projected_guidance_rescale = adaptive_projected_guidance_rescale
+        self.eta = eta
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+        self.momentum_buffer = None
+
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
+        if self._step == 0:
+            if self.adaptive_projected_guidance_momentum is not None:
+                self.momentum_buffer = MomentumBuffer(self.adaptive_projected_guidance_momentum)
+        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
+        data_batches = []
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
+            data_batches.append(data_batch)
+        return data_batches
+
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+        pred = None
+
+        if not self._is_apg_enabled():
+            pred = pred_cond
+        else:
+            pred = normalized_guidance(
+                pred_cond,
+                pred_uncond,
+                self.guidance_scale,
+                self.momentum_buffer,
+                self.eta,
+                self.adaptive_projected_guidance_rescale,
+                self.use_original_formulation,
+            )
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return pred, {}
+
+    @property
+    def is_conditional(self) -> bool:
+        return self._count_prepared == 1
+
+    @property
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_apg_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    def _is_apg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close
+
+
+class MomentumBuffer:
+    def __init__(self, momentum: float):
+        self.momentum = momentum
+        self.running_average = 0
+
+    def update(self, update_value: torch.Tensor):
+        new_average = self.momentum * self.running_average
+        self.running_average = update_value + new_average
+
+
+def normalized_guidance(
+    pred_cond: torch.Tensor,
+    pred_uncond: torch.Tensor,
+    guidance_scale: float,
+    momentum_buffer: Optional[MomentumBuffer] = None,
+    eta: float = 1.0,
+    norm_threshold: float = 0.0,
+    use_original_formulation: bool = False,
+):
+    diff = pred_cond - pred_uncond
+    dim = [-i for i in range(1, len(diff.shape))]
+
+    if momentum_buffer is not None:
+        momentum_buffer.update(diff)
+        diff = momentum_buffer.running_average
+
+    if norm_threshold > 0:
+        ones = torch.ones_like(diff)
+        diff_norm = diff.norm(p=2, dim=dim, keepdim=True)
+        scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
+        diff = diff * scale_factor
+
+    v0, v1 = diff.double(), pred_cond.double()
+    v1 = torch.nn.functional.normalize(v1, dim=dim)
+    v0_parallel = (v0 * v1).sum(dim=dim, keepdim=True) * v1
+    v0_orthogonal = v0 - v0_parallel
+    diff_parallel, diff_orthogonal = v0_parallel.type_as(diff), v0_orthogonal.type_as(diff)
+    normalized_update = diff_orthogonal + eta * diff_parallel
+
+    pred = pred_cond if use_original_formulation else pred_uncond
+    pred = pred + guidance_scale * normalized_update
+
+    return pred

src/diffusers/guiders/auto_guidance.py

@@ -0,0 +1,190 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from ..hooks import HookRegistry, LayerSkipConfig
+from ..hooks.layer_skip import _apply_layer_skip_hook
+from .guider_utils import BaseGuidance, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+class AutoGuidance(BaseGuidance):
+    """
+    AutoGuidance: https://huggingface.co/papers/2406.02507
+
+    Args:
+        guidance_scale (`float`, defaults to `7.5`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        auto_guidance_layers (`int` or `List[int]`, *optional*):
+            The layer indices to apply skip layer guidance to. Can be a single integer or a list of integers. If not
+            provided, `skip_layer_config` must be provided.
+        auto_guidance_config (`LayerSkipConfig` or `List[LayerSkipConfig]`, *optional*):
+            The configuration for the skip layer guidance. Can be a single `LayerSkipConfig` or a list of
+            `LayerSkipConfig`. If not provided, `skip_layer_guidance_layers` must be provided.
+        dropout (`float`, *optional*):
+            The dropout probability for autoguidance on the enabled skip layers (either with `auto_guidance_layers` or
+            `auto_guidance_config`). If not provided, the dropout probability will be set to 1.0.
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.0`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `1.0`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    _input_predictions = ["pred_cond", "pred_uncond"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        auto_guidance_layers: Optional[Union[int, List[int]]] = None,
+        auto_guidance_config: Union[LayerSkipConfig, List[LayerSkipConfig], Dict[str, Any]] = None,
+        dropout: Optional[float] = None,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__(start, stop)
+
+        self.guidance_scale = guidance_scale
+        self.auto_guidance_layers = auto_guidance_layers
+        self.auto_guidance_config = auto_guidance_config
+        self.dropout = dropout
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+
+        if auto_guidance_layers is None and auto_guidance_config is None:
+            raise ValueError(
+                "Either `auto_guidance_layers` or `auto_guidance_config` must be provided to enable Skip Layer Guidance."
+            )
+        if auto_guidance_layers is not None and auto_guidance_config is not None:
+            raise ValueError("Only one of `auto_guidance_layers` or `auto_guidance_config` can be provided.")
+        if (dropout is None and auto_guidance_layers is not None) or (
+            dropout is not None and auto_guidance_layers is None
+        ):
+            raise ValueError("`dropout` must be provided if `auto_guidance_layers` is provided.")
+
+        if auto_guidance_layers is not None:
+            if isinstance(auto_guidance_layers, int):
+                auto_guidance_layers = [auto_guidance_layers]
+            if not isinstance(auto_guidance_layers, list):
+                raise ValueError(
+                    f"Expected `auto_guidance_layers` to be an int or a list of ints, but got {type(auto_guidance_layers)}."
+                )
+            auto_guidance_config = [
+                LayerSkipConfig(layer, fqn="auto", dropout=dropout) for layer in auto_guidance_layers
+            ]
+
+        if isinstance(auto_guidance_config, dict):
+            auto_guidance_config = LayerSkipConfig.from_dict(auto_guidance_config)
+
+        if isinstance(auto_guidance_config, LayerSkipConfig):
+            auto_guidance_config = [auto_guidance_config]
+
+        if not isinstance(auto_guidance_config, list):
+            raise ValueError(
+                f"Expected `auto_guidance_config` to be a LayerSkipConfig or a list of LayerSkipConfig, but got {type(auto_guidance_config)}."
+            )
+        elif isinstance(next(iter(auto_guidance_config), None), dict):
+            auto_guidance_config = [LayerSkipConfig.from_dict(config) for config in auto_guidance_config]
+
+        self.auto_guidance_config = auto_guidance_config
+        self._auto_guidance_hook_names = [f"AutoGuidance_{i}" for i in range(len(self.auto_guidance_config))]
+
+    def prepare_models(self, denoiser: torch.nn.Module) -> None:
+        self._count_prepared += 1
+        if self._is_ag_enabled() and self.is_unconditional:
+            for name, config in zip(self._auto_guidance_hook_names, self.auto_guidance_config):
+                _apply_layer_skip_hook(denoiser, config, name=name)
+
+    def cleanup_models(self, denoiser: torch.nn.Module) -> None:
+        if self._is_ag_enabled() and self.is_unconditional:
+            for name in self._auto_guidance_hook_names:
+                registry = HookRegistry.check_if_exists_or_initialize(denoiser)
+                registry.remove_hook(name, recurse=True)
+
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
+        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
+        data_batches = []
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
+            data_batches.append(data_batch)
+        return data_batches
+
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+        pred = None
+
+        if not self._is_ag_enabled():
+            pred = pred_cond
+        else:
+            shift = pred_cond - pred_uncond
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return pred, {}
+
+    @property
+    def is_conditional(self) -> bool:
+        return self._count_prepared == 1
+
+    @property
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_ag_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    def _is_ag_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close

src/diffusers/guiders/classifier_free_guidance.py

@@ -0,0 +1,141 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from .guider_utils import BaseGuidance, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+class ClassifierFreeGuidance(BaseGuidance):
+    """
+    Classifier-free guidance (CFG): https://huggingface.co/papers/2207.12598
+
+    CFG is a technique used to improve generation quality and condition-following in diffusion models. It works by
+    jointly training a model on both conditional and unconditional data, and using a weighted sum of the two during
+    inference. This allows the model to tradeoff between generation quality and sample diversity. The original paper
+    proposes scaling and shifting the conditional distribution based on the difference between conditional and
+    unconditional predictions. [x_pred = x_cond + scale * (x_cond - x_uncond)]
+
+    Diffusers implemented the scaling and shifting on the unconditional prediction instead based on the [Imagen
+    paper](https://huggingface.co/papers/2205.11487), which is equivalent to what the original paper proposed in
+    theory. [x_pred = x_uncond + scale * (x_cond - x_uncond)]
+
+    The intution behind the original formulation can be thought of as moving the conditional distribution estimates
+    further away from the unconditional distribution estimates, while the diffusers-native implementation can be
+    thought of as moving the unconditional distribution towards the conditional distribution estimates to get rid of
+    the unconditional predictions (usually negative features like "bad quality, bad anotomy, watermarks", etc.)
+
+    The `use_original_formulation` argument can be set to `True` to use the original CFG formulation mentioned in the
+    paper. By default, we use the diffusers-native implementation that has been in the codebase for a long time.
+
+    Args:
+        guidance_scale (`float`, defaults to `7.5`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.0`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `1.0`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    _input_predictions = ["pred_cond", "pred_uncond"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__(start, stop)
+
+        self.guidance_scale = guidance_scale
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
+        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
+        data_batches = []
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
+            data_batches.append(data_batch)
+        return data_batches
+
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+        pred = None
+
+        if not self._is_cfg_enabled():
+            pred = pred_cond
+        else:
+            shift = pred_cond - pred_uncond
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return pred, {}
+
+    @property
+    def is_conditional(self) -> bool:
+        return self._count_prepared == 1
+
+    @property
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_cfg_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    def _is_cfg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close

src/diffusers/guiders/classifier_free_zero_star_guidance.py

@@ -0,0 +1,152 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from .guider_utils import BaseGuidance, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+class ClassifierFreeZeroStarGuidance(BaseGuidance):
+    """
+    Classifier-free Zero* (CFG-Zero*): https://huggingface.co/papers/2503.18886
+
+    This is an implementation of the Classifier-Free Zero* guidance technique, which is a variant of classifier-free
+    guidance. It proposes zero initialization of the noise predictions for the first few steps of the diffusion
+    process, and also introduces an optimal rescaling factor for the noise predictions, which can help in improving the
+    quality of generated images.
+
+    The authors of the paper suggest setting zero initialization in the first 4% of the inference steps.
+
+    Args:
+        guidance_scale (`float`, defaults to `7.5`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        zero_init_steps (`int`, defaults to `1`):
+            The number of inference steps for which the noise predictions are zeroed out (see Section 4.2).
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.01`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `0.2`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    _input_predictions = ["pred_cond", "pred_uncond"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        zero_init_steps: int = 1,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__(start, stop)
+
+        self.guidance_scale = guidance_scale
+        self.zero_init_steps = zero_init_steps
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
+        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
+        data_batches = []
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
+            data_batches.append(data_batch)
+        return data_batches
+
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+        pred = None
+
+        if self._step < self.zero_init_steps:
+            pred = torch.zeros_like(pred_cond)
+        elif not self._is_cfg_enabled():
+            pred = pred_cond
+        else:
+            pred_cond_flat = pred_cond.flatten(1)
+            pred_uncond_flat = pred_uncond.flatten(1)
+            alpha = cfg_zero_star_scale(pred_cond_flat, pred_uncond_flat)
+            alpha = alpha.view(-1, *(1,) * (len(pred_cond.shape) - 1))
+            pred_uncond = pred_uncond * alpha
+            shift = pred_cond - pred_uncond
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return pred, {}
+
+    @property
+    def is_conditional(self) -> bool:
+        return self._count_prepared == 1
+
+    @property
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_cfg_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    def _is_cfg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close
+
+
+def cfg_zero_star_scale(cond: torch.Tensor, uncond: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
+    cond_dtype = cond.dtype
+    cond = cond.float()
+    uncond = uncond.float()
+    dot_product = torch.sum(cond * uncond, dim=1, keepdim=True)
+    squared_norm = torch.sum(uncond**2, dim=1, keepdim=True) + eps
+    # st_star = v_cond^T * v_uncond / ||v_uncond||^2
+    scale = dot_product / squared_norm
+    return scale.to(dtype=cond_dtype)

src/diffusers/guiders/guider_utils.py

@@ -0,0 +1,309 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from huggingface_hub.utils import validate_hf_hub_args
+from typing_extensions import Self
+
+from ..configuration_utils import ConfigMixin
+from ..utils import PushToHubMixin, get_logger
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+GUIDER_CONFIG_NAME = "guider_config.json"
+
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class BaseGuidance(ConfigMixin, PushToHubMixin):
+    r"""Base class providing the skeleton for implementing guidance techniques."""
+
+    config_name = GUIDER_CONFIG_NAME
+    _input_predictions = None
+    _identifier_key = "__guidance_identifier__"
+
+    def __init__(self, start: float = 0.0, stop: float = 1.0):
+        self._start = start
+        self._stop = stop
+        self._step: int = None
+        self._num_inference_steps: int = None
+        self._timestep: torch.LongTensor = None
+        self._count_prepared = 0
+        self._input_fields: Dict[str, Union[str, Tuple[str, str]]] = None
+        self._enabled = True
+
+        if not (0.0 <= start < 1.0):
+            raise ValueError(f"Expected `start` to be between 0.0 and 1.0, but got {start}.")
+        if not (start <= stop <= 1.0):
+            raise ValueError(f"Expected `stop` to be between {start} and 1.0, but got {stop}.")
+
+        if self._input_predictions is None or not isinstance(self._input_predictions, list):
+            raise ValueError(
+                "`_input_predictions` must be a list of required prediction names for the guidance technique."
+            )
+
+    def disable(self):
+        self._enabled = False
+
+    def enable(self):
+        self._enabled = True
+
+    def set_state(self, step: int, num_inference_steps: int, timestep: torch.LongTensor) -> None:
+        self._step = step
+        self._num_inference_steps = num_inference_steps
+        self._timestep = timestep
+        self._count_prepared = 0
+
+    def set_input_fields(self, **kwargs: Dict[str, Union[str, Tuple[str, str]]]) -> None:
+        """
+        Set the input fields for the guidance technique. The input fields are used to specify the names of the returned
+        attributes containing the prepared data after `prepare_inputs` is called. The prepared data is obtained from
+        the values of the provided keyword arguments to this method.
+
+        Args:
+            **kwargs (`Dict[str, Union[str, Tuple[str, str]]]`):
+                A dictionary where the keys are the names of the fields that will be used to store the data once it is
+                prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used
+                to look up the required data provided for preparation.
+
+                If a string is provided, it will be used as the conditional data (or unconditional if used with a
+                guidance method that requires it). If a tuple of length 2 is provided, the first element must be the
+                conditional data identifier and the second element must be the unconditional data identifier or None.
+
+                Example:
+                ```
+                data = {"prompt_embeds": <some tensor>, "negative_prompt_embeds": <some tensor>, "latents": <some tensor>}
+
+                BaseGuidance.set_input_fields(
+                    latents="latents",
+                    prompt_embeds=("prompt_embeds", "negative_prompt_embeds"),
+                )
+                ```
+        """
+        for key, value in kwargs.items():
+            is_string = isinstance(value, str)
+            is_tuple_of_str_with_len_2 = (
+                isinstance(value, tuple) and len(value) == 2 and all(isinstance(v, str) for v in value)
+            )
+            if not (is_string or is_tuple_of_str_with_len_2):
+                raise ValueError(
+                    f"Expected `set_input_fields` to be called with a string or a tuple of string with length 2, but got {type(value)} for key {key}."
+                )
+        self._input_fields = kwargs
+
+    def prepare_models(self, denoiser: torch.nn.Module) -> None:
+        """
+        Prepares the models for the guidance technique on a given batch of data. This method should be overridden in
+        subclasses to implement specific model preparation logic.
+        """
+        self._count_prepared += 1
+
+    def cleanup_models(self, denoiser: torch.nn.Module) -> None:
+        """
+        Cleans up the models for the guidance technique after a given batch of data. This method should be overridden
+        in subclasses to implement specific model cleanup logic. It is useful for removing any hooks or other stateful
+        modifications made during `prepare_models`.
+        """
+        pass
+
+    def prepare_inputs(self, data: "BlockState") -> List["BlockState"]:
+        raise NotImplementedError("BaseGuidance::prepare_inputs must be implemented in subclasses.")
+
+    def __call__(self, data: List["BlockState"]) -> Any:
+        if not all(hasattr(d, "noise_pred") for d in data):
+            raise ValueError("Expected all data to have `noise_pred` attribute.")
+        if len(data) != self.num_conditions:
+            raise ValueError(
+                f"Expected {self.num_conditions} data items, but got {len(data)}. Please check the input data."
+            )
+        forward_inputs = {getattr(d, self._identifier_key): d.noise_pred for d in data}
+        return self.forward(**forward_inputs)
+
+    def forward(self, *args, **kwargs) -> Any:
+        raise NotImplementedError("BaseGuidance::forward must be implemented in subclasses.")
+
+    @property
+    def is_conditional(self) -> bool:
+        raise NotImplementedError("BaseGuidance::is_conditional must be implemented in subclasses.")
+
+    @property
+    def is_unconditional(self) -> bool:
+        return not self.is_conditional
+
+    @property
+    def num_conditions(self) -> int:
+        raise NotImplementedError("BaseGuidance::num_conditions must be implemented in subclasses.")
+
+    @classmethod
+    def _prepare_batch(
+        cls,
+        input_fields: Dict[str, Union[str, Tuple[str, str]]],
+        data: "BlockState",
+        tuple_index: int,
+        identifier: str,
+    ) -> "BlockState":
+        """
+        Prepares a batch of data for the guidance technique. This method is used in the `prepare_inputs` method of the
+        `BaseGuidance` class. It prepares the batch based on the provided tuple index.
+
+        Args:
+            input_fields (`Dict[str, Union[str, Tuple[str, str]]]`):
+                A dictionary where the keys are the names of the fields that will be used to store the data once it is
+                prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used
+                to look up the required data provided for preparation. If a string is provided, it will be used as the
+                conditional data (or unconditional if used with a guidance method that requires it). If a tuple of
+                length 2 is provided, the first element must be the conditional data identifier and the second element
+                must be the unconditional data identifier or None.
+            data (`BlockState`):
+                The input data to be prepared.
+            tuple_index (`int`):
+                The index to use when accessing input fields that are tuples.
+
+        Returns:
+            `BlockState`: The prepared batch of data.
+        """
+        from ..modular_pipelines.modular_pipeline import BlockState
+
+        if input_fields is None:
+            raise ValueError(
+                "Input fields cannot be None. Please pass `input_fields` to `prepare_inputs` or call `set_input_fields` before preparing inputs."
+            )
+        data_batch = {}
+        for key, value in input_fields.items():
+            try:
+                if isinstance(value, str):
+                    data_batch[key] = getattr(data, value)
+                elif isinstance(value, tuple):
+                    data_batch[key] = getattr(data, value[tuple_index])
+                else:
+                    # We've already checked that value is a string or a tuple of strings with length 2
+                    pass
+            except AttributeError:
+                logger.debug(f"`data` does not have attribute(s) {value}, skipping.")
+        data_batch[cls._identifier_key] = identifier
+        return BlockState(**data_batch)
+
+    @classmethod
+    @validate_hf_hub_args
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,
+        subfolder: Optional[str] = None,
+        return_unused_kwargs=False,
+        **kwargs,
+    ) -> Self:
+        r"""
+        Instantiate a guider from a pre-defined JSON configuration file in a local directory or Hub repository.
+
+        Parameters:
+            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
+                Can be either:
+
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the guider configuration
+                      saved with [`~BaseGuidance.save_pretrained`].
+            subfolder (`str`, *optional*):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
+                Whether kwargs that are not consumed by the Python class should be returned or not.
+            cache_dir (`Union[str, os.PathLike]`, *optional*):
+                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
+                is not used.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+                cached versions if they exist.
+
+            proxies (`Dict[str, str]`, *optional*):
+                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+            output_loading_info(`bool`, *optional*, defaults to `False`):
+                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
+            local_files_only(`bool`, *optional*, defaults to `False`):
+                Whether to only load local model weights and configuration files or not. If set to `True`, the model
+                won't be downloaded from the Hub.
+            token (`str` or *bool*, *optional*):
+                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
+                `diffusers-cli login` (stored in `~/.huggingface`) is used.
+            revision (`str`, *optional*, defaults to `"main"`):
+                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
+                allowed by Git.
+
+        <Tip>
+
+        To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
+        `huggingface-cli login`. You can also activate the special
+        ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a
+        firewalled environment.
+
+        </Tip>
+
+        """
+        config, kwargs, commit_hash = cls.load_config(
+            pretrained_model_name_or_path=pretrained_model_name_or_path,
+            subfolder=subfolder,
+            return_unused_kwargs=True,
+            return_commit_hash=True,
+            **kwargs,
+        )
+        return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs)
+
+    def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
+        """
+        Save a guider configuration object to a directory so that it can be reloaded using the
+        [`~BaseGuidance.from_pretrained`] class method.
+
+        Args:
+            save_directory (`str` or `os.PathLike`):
+                Directory where the configuration JSON file will be saved (will be created if it does not exist).
+            push_to_hub (`bool`, *optional*, defaults to `False`):
+                Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
+                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
+                namespace).
+            kwargs (`Dict[str, Any]`, *optional*):
+                Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
+        """
+        self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
+
+
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    r"""
+    Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
+    Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+    Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+
+    Args:
+        noise_cfg (`torch.Tensor`):
+            The predicted noise tensor for the guided diffusion process.
+        noise_pred_text (`torch.Tensor`):
+            The predicted noise tensor for the text-guided diffusion process.
+        guidance_rescale (`float`, *optional*, defaults to 0.0):
+            A rescale factor applied to the noise predictions.
+    Returns:
+        noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg

src/diffusers/guiders/perturbed_attention_guidance.py

@@ -0,0 +1,271 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from ..hooks import HookRegistry, LayerSkipConfig
+from ..hooks.layer_skip import _apply_layer_skip_hook
+from ..utils import get_logger
+from .guider_utils import BaseGuidance, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class PerturbedAttentionGuidance(BaseGuidance):
+    """
+    Perturbed Attention Guidance (PAG): https://huggingface.co/papers/2403.17377
+
+    The intution behind PAG can be thought of as moving the CFG predicted distribution estimates further away from
+    worse versions of the conditional distribution estimates. PAG was one of the first techniques to introduce the idea
+    of using a worse version of the trained model for better guiding itself in the denoising process. It perturbs the
+    attention scores of the latent stream by replacing the score matrix with an identity matrix for selectively chosen
+    layers.
+
+    Additional reading:
+    - [Guiding a Diffusion Model with a Bad Version of Itself](https://huggingface.co/papers/2406.02507)
+
+    PAG is implemented with similar implementation to SkipLayerGuidance due to overlap in the configuration parameters
+    and implementation details.
+
+    Args:
+        guidance_scale (`float`, defaults to `7.5`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        perturbed_guidance_scale (`float`, defaults to `2.8`):
+            The scale parameter for perturbed attention guidance.
+        perturbed_guidance_start (`float`, defaults to `0.01`):
+            The fraction of the total number of denoising steps after which perturbed attention guidance starts.
+        perturbed_guidance_stop (`float`, defaults to `0.2`):
+            The fraction of the total number of denoising steps after which perturbed attention guidance stops.
+        perturbed_guidance_layers (`int` or `List[int]`, *optional*):
+            The layer indices to apply perturbed attention guidance to. Can be a single integer or a list of integers.
+            If not provided, `perturbed_guidance_config` must be provided.
+        perturbed_guidance_config (`LayerSkipConfig` or `List[LayerSkipConfig]`, *optional*):
+            The configuration for the perturbed attention guidance. Can be a single `LayerSkipConfig` or a list of
+            `LayerSkipConfig`. If not provided, `perturbed_guidance_layers` must be provided.
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.01`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `0.2`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    # NOTE: The current implementation does not account for joint latent conditioning (text + image/video tokens in
+    # the same latent stream). It assumes the entire latent is a single stream of visual tokens. It would be very
+    # complex to support joint latent conditioning in a model-agnostic manner without specializing the implementation
+    # for each model architecture.
+
+    _input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        perturbed_guidance_scale: float = 2.8,
+        perturbed_guidance_start: float = 0.01,
+        perturbed_guidance_stop: float = 0.2,
+        perturbed_guidance_layers: Optional[Union[int, List[int]]] = None,
+        perturbed_guidance_config: Union[LayerSkipConfig, List[LayerSkipConfig], Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__(start, stop)
+
+        self.guidance_scale = guidance_scale
+        self.skip_layer_guidance_scale = perturbed_guidance_scale
+        self.skip_layer_guidance_start = perturbed_guidance_start
+        self.skip_layer_guidance_stop = perturbed_guidance_stop
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+
+        if perturbed_guidance_config is None:
+            if perturbed_guidance_layers is None:
+                raise ValueError(
+                    "`perturbed_guidance_layers` must be provided if `perturbed_guidance_config` is not specified."
+                )
+            perturbed_guidance_config = LayerSkipConfig(
+                indices=perturbed_guidance_layers,
+                fqn="auto",
+                skip_attention=False,
+                skip_attention_scores=True,
+                skip_ff=False,
+            )
+        else:
+            if perturbed_guidance_layers is not None:
+                raise ValueError(
+                    "`perturbed_guidance_layers` should not be provided if `perturbed_guidance_config` is specified."
+                )
+
+        if isinstance(perturbed_guidance_config, dict):
+            perturbed_guidance_config = LayerSkipConfig.from_dict(perturbed_guidance_config)
+
+        if isinstance(perturbed_guidance_config, LayerSkipConfig):
+            perturbed_guidance_config = [perturbed_guidance_config]
+
+        if not isinstance(perturbed_guidance_config, list):
+            raise ValueError(
+                "`perturbed_guidance_config` must be a `LayerSkipConfig`, a list of `LayerSkipConfig`, or a dict that can be converted to a `LayerSkipConfig`."
+            )
+        elif isinstance(next(iter(perturbed_guidance_config), None), dict):
+            perturbed_guidance_config = [LayerSkipConfig.from_dict(config) for config in perturbed_guidance_config]
+
+        for config in perturbed_guidance_config:
+            if config.skip_attention or not config.skip_attention_scores or config.skip_ff:
+                logger.warning(
+                    "Perturbed Attention Guidance is designed to perturb attention scores, so `skip_attention` should be False, `skip_attention_scores` should be True, and `skip_ff` should be False. "
+                    "Please check your configuration. Modifying the config to match the expected values."
+                )
+            config.skip_attention = False
+            config.skip_attention_scores = True
+            config.skip_ff = False
+
+        self.skip_layer_config = perturbed_guidance_config
+        self._skip_layer_hook_names = [f"SkipLayerGuidance_{i}" for i in range(len(self.skip_layer_config))]
+
+    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.prepare_models
+    def prepare_models(self, denoiser: torch.nn.Module) -> None:
+        self._count_prepared += 1
+        if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
+            for name, config in zip(self._skip_layer_hook_names, self.skip_layer_config):
+                _apply_layer_skip_hook(denoiser, config, name=name)
+
+    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.cleanup_models
+    def cleanup_models(self, denoiser: torch.nn.Module) -> None:
+        if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
+            registry = HookRegistry.check_if_exists_or_initialize(denoiser)
+            # Remove the hooks after inference
+            for hook_name in self._skip_layer_hook_names:
+                registry.remove_hook(hook_name, recurse=True)
+
+    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.prepare_inputs
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
+        if self.num_conditions == 1:
+            tuple_indices = [0]
+            input_predictions = ["pred_cond"]
+        elif self.num_conditions == 2:
+            tuple_indices = [0, 1]
+            input_predictions = (
+                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_skip"]
+            )
+        else:
+            tuple_indices = [0, 1, 0]
+            input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]
+        data_batches = []
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
+            data_batches.append(data_batch)
+        return data_batches
+
+    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.forward
+    def forward(
+        self,
+        pred_cond: torch.Tensor,
+        pred_uncond: Optional[torch.Tensor] = None,
+        pred_cond_skip: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        pred = None
+
+        if not self._is_cfg_enabled() and not self._is_slg_enabled():
+            pred = pred_cond
+        elif not self._is_cfg_enabled():
+            shift = pred_cond - pred_cond_skip
+            pred = pred_cond if self.use_original_formulation else pred_cond_skip
+            pred = pred + self.skip_layer_guidance_scale * shift
+        elif not self._is_slg_enabled():
+            shift = pred_cond - pred_uncond
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift
+        else:
+            shift = pred_cond - pred_uncond
+            shift_skip = pred_cond - pred_cond_skip
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift + self.skip_layer_guidance_scale * shift_skip
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return pred, {}
+
+    @property
+    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.is_conditional
+    def is_conditional(self) -> bool:
+        return self._count_prepared == 1 or self._count_prepared == 3
+
+    @property
+    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance.num_conditions
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_cfg_enabled():
+            num_conditions += 1
+        if self._is_slg_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance._is_cfg_enabled
+    def _is_cfg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close
+
+    # Copied from diffusers.guiders.skip_layer_guidance.SkipLayerGuidance._is_slg_enabled
+    def _is_slg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self.skip_layer_guidance_start * self._num_inference_steps)
+            skip_stop_step = int(self.skip_layer_guidance_stop * self._num_inference_steps)
+            is_within_range = skip_start_step < self._step < skip_stop_step
+
+        is_zero = math.isclose(self.skip_layer_guidance_scale, 0.0)
+
+        return is_within_range and not is_zero

src/diffusers/guiders/skip_layer_guidance.py

@@ -0,0 +1,262 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from ..hooks import HookRegistry, LayerSkipConfig
+from ..hooks.layer_skip import _apply_layer_skip_hook
+from .guider_utils import BaseGuidance, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+class SkipLayerGuidance(BaseGuidance):
+    """
+    Skip Layer Guidance (SLG): https://github.com/Stability-AI/sd3.5
+
+    Spatio-Temporal Guidance (STG): https://huggingface.co/papers/2411.18664
+
+    SLG was introduced by StabilityAI for improving structure and anotomy coherence in generated images. It works by
+    skipping the forward pass of specified transformer blocks during the denoising process on an additional conditional
+    batch of data, apart from the conditional and unconditional batches already used in CFG
+    ([~guiders.classifier_free_guidance.ClassifierFreeGuidance]), and then scaling and shifting the CFG predictions
+    based on the difference between conditional without skipping and conditional with skipping predictions.
+
+    The intution behind SLG can be thought of as moving the CFG predicted distribution estimates further away from
+    worse versions of the conditional distribution estimates (because skipping layers is equivalent to using a worse
+    version of the model for the conditional prediction).
+
+    STG is an improvement and follow-up work combining ideas from SLG, PAG and similar techniques for improving
+    generation quality in video diffusion models.
+
+    Additional reading:
+    - [Guiding a Diffusion Model with a Bad Version of Itself](https://huggingface.co/papers/2406.02507)
+
+    The values for `skip_layer_guidance_scale`, `skip_layer_guidance_start`, and `skip_layer_guidance_stop` are
+    defaulted to the recommendations by StabilityAI for Stable Diffusion 3.5 Medium.
+
+    Args:
+        guidance_scale (`float`, defaults to `7.5`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        skip_layer_guidance_scale (`float`, defaults to `2.8`):
+            The scale parameter for skip layer guidance. Anatomy and structure coherence may improve with higher
+            values, but it may also lead to overexposure and saturation.
+        skip_layer_guidance_start (`float`, defaults to `0.01`):
+            The fraction of the total number of denoising steps after which skip layer guidance starts.
+        skip_layer_guidance_stop (`float`, defaults to `0.2`):
+            The fraction of the total number of denoising steps after which skip layer guidance stops.
+        skip_layer_guidance_layers (`int` or `List[int]`, *optional*):
+            The layer indices to apply skip layer guidance to. Can be a single integer or a list of integers. If not
+            provided, `skip_layer_config` must be provided. The recommended values are `[7, 8, 9]` for Stable Diffusion
+            3.5 Medium.
+        skip_layer_config (`LayerSkipConfig` or `List[LayerSkipConfig]`, *optional*):
+            The configuration for the skip layer guidance. Can be a single `LayerSkipConfig` or a list of
+            `LayerSkipConfig`. If not provided, `skip_layer_guidance_layers` must be provided.
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.01`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `0.2`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    _input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        skip_layer_guidance_scale: float = 2.8,
+        skip_layer_guidance_start: float = 0.01,
+        skip_layer_guidance_stop: float = 0.2,
+        skip_layer_guidance_layers: Optional[Union[int, List[int]]] = None,
+        skip_layer_config: Union[LayerSkipConfig, List[LayerSkipConfig], Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__(start, stop)
+
+        self.guidance_scale = guidance_scale
+        self.skip_layer_guidance_scale = skip_layer_guidance_scale
+        self.skip_layer_guidance_start = skip_layer_guidance_start
+        self.skip_layer_guidance_stop = skip_layer_guidance_stop
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+
+        if not (0.0 <= skip_layer_guidance_start < 1.0):
+            raise ValueError(
+                f"Expected `skip_layer_guidance_start` to be between 0.0 and 1.0, but got {skip_layer_guidance_start}."
+            )
+        if not (skip_layer_guidance_start <= skip_layer_guidance_stop <= 1.0):
+            raise ValueError(
+                f"Expected `skip_layer_guidance_stop` to be between 0.0 and 1.0, but got {skip_layer_guidance_stop}."
+            )
+
+        if skip_layer_guidance_layers is None and skip_layer_config is None:
+            raise ValueError(
+                "Either `skip_layer_guidance_layers` or `skip_layer_config` must be provided to enable Skip Layer Guidance."
+            )
+        if skip_layer_guidance_layers is not None and skip_layer_config is not None:
+            raise ValueError("Only one of `skip_layer_guidance_layers` or `skip_layer_config` can be provided.")
+
+        if skip_layer_guidance_layers is not None:
+            if isinstance(skip_layer_guidance_layers, int):
+                skip_layer_guidance_layers = [skip_layer_guidance_layers]
+            if not isinstance(skip_layer_guidance_layers, list):
+                raise ValueError(
+                    f"Expected `skip_layer_guidance_layers` to be an int or a list of ints, but got {type(skip_layer_guidance_layers)}."
+                )
+            skip_layer_config = [LayerSkipConfig(layer, fqn="auto") for layer in skip_layer_guidance_layers]
+
+        if isinstance(skip_layer_config, dict):
+            skip_layer_config = LayerSkipConfig.from_dict(skip_layer_config)
+
+        if isinstance(skip_layer_config, LayerSkipConfig):
+            skip_layer_config = [skip_layer_config]
+
+        if not isinstance(skip_layer_config, list):
+            raise ValueError(
+                f"Expected `skip_layer_config` to be a LayerSkipConfig or a list of LayerSkipConfig, but got {type(skip_layer_config)}."
+            )
+        elif isinstance(next(iter(skip_layer_config), None), dict):
+            skip_layer_config = [LayerSkipConfig.from_dict(config) for config in skip_layer_config]
+
+        self.skip_layer_config = skip_layer_config
+        self._skip_layer_hook_names = [f"SkipLayerGuidance_{i}" for i in range(len(self.skip_layer_config))]
+
+    def prepare_models(self, denoiser: torch.nn.Module) -> None:
+        self._count_prepared += 1
+        if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
+            for name, config in zip(self._skip_layer_hook_names, self.skip_layer_config):
+                _apply_layer_skip_hook(denoiser, config, name=name)
+
+    def cleanup_models(self, denoiser: torch.nn.Module) -> None:
+        if self._is_slg_enabled() and self.is_conditional and self._count_prepared > 1:
+            registry = HookRegistry.check_if_exists_or_initialize(denoiser)
+            # Remove the hooks after inference
+            for hook_name in self._skip_layer_hook_names:
+                registry.remove_hook(hook_name, recurse=True)
+
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
+        if self.num_conditions == 1:
+            tuple_indices = [0]
+            input_predictions = ["pred_cond"]
+        elif self.num_conditions == 2:
+            tuple_indices = [0, 1]
+            input_predictions = (
+                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_skip"]
+            )
+        else:
+            tuple_indices = [0, 1, 0]
+            input_predictions = ["pred_cond", "pred_uncond", "pred_cond_skip"]
+        data_batches = []
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
+            data_batches.append(data_batch)
+        return data_batches
+
+    def forward(
+        self,
+        pred_cond: torch.Tensor,
+        pred_uncond: Optional[torch.Tensor] = None,
+        pred_cond_skip: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        pred = None
+
+        if not self._is_cfg_enabled() and not self._is_slg_enabled():
+            pred = pred_cond
+        elif not self._is_cfg_enabled():
+            shift = pred_cond - pred_cond_skip
+            pred = pred_cond if self.use_original_formulation else pred_cond_skip
+            pred = pred + self.skip_layer_guidance_scale * shift
+        elif not self._is_slg_enabled():
+            shift = pred_cond - pred_uncond
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift
+        else:
+            shift = pred_cond - pred_uncond
+            shift_skip = pred_cond - pred_cond_skip
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift + self.skip_layer_guidance_scale * shift_skip
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return pred, {}
+
+    @property
+    def is_conditional(self) -> bool:
+        return self._count_prepared == 1 or self._count_prepared == 3
+
+    @property
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_cfg_enabled():
+            num_conditions += 1
+        if self._is_slg_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    def _is_cfg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close
+
+    def _is_slg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self.skip_layer_guidance_start * self._num_inference_steps)
+            skip_stop_step = int(self.skip_layer_guidance_stop * self._num_inference_steps)
+            is_within_range = skip_start_step < self._step < skip_stop_step
+
+        is_zero = math.isclose(self.skip_layer_guidance_scale, 0.0)
+
+        return is_within_range and not is_zero

src/diffusers/guiders/smoothed_energy_guidance.py

@@ -0,0 +1,251 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from ..hooks import HookRegistry
+from ..hooks.smoothed_energy_guidance_utils import SmoothedEnergyGuidanceConfig, _apply_smoothed_energy_guidance_hook
+from .guider_utils import BaseGuidance, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+class SmoothedEnergyGuidance(BaseGuidance):
+    """
+    Smoothed Energy Guidance (SEG): https://huggingface.co/papers/2408.00760
+
+    SEG is only supported as an experimental prototype feature for now, so the implementation may be modified in the
+    future without warning or guarantee of reproducibility. This implementation assumes:
+    - Generated images are square (height == width)
+    - The model does not combine different modalities together (e.g., text and image latent streams are not combined
+      together such as Flux)
+
+    Args:
+        guidance_scale (`float`, defaults to `7.5`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        seg_guidance_scale (`float`, defaults to `3.0`):
+            The scale parameter for smoothed energy guidance. Anatomy and structure coherence may improve with higher
+            values, but it may also lead to overexposure and saturation.
+        seg_blur_sigma (`float`, defaults to `9999999.0`):
+            The amount by which we blur the attention weights. Setting this value greater than 9999.0 results in
+            infinite blur, which means uniform queries. Controlling it exponentially is empirically effective.
+        seg_blur_threshold_inf (`float`, defaults to `9999.0`):
+            The threshold above which the blur is considered infinite.
+        seg_guidance_start (`float`, defaults to `0.0`):
+            The fraction of the total number of denoising steps after which smoothed energy guidance starts.
+        seg_guidance_stop (`float`, defaults to `1.0`):
+            The fraction of the total number of denoising steps after which smoothed energy guidance stops.
+        seg_guidance_layers (`int` or `List[int]`, *optional*):
+            The layer indices to apply smoothed energy guidance to. Can be a single integer or a list of integers. If
+            not provided, `seg_guidance_config` must be provided. The recommended values are `[7, 8, 9]` for Stable
+            Diffusion 3.5 Medium.
+        seg_guidance_config (`SmoothedEnergyGuidanceConfig` or `List[SmoothedEnergyGuidanceConfig]`, *optional*):
+            The configuration for the smoothed energy layer guidance. Can be a single `SmoothedEnergyGuidanceConfig` or
+            a list of `SmoothedEnergyGuidanceConfig`. If not provided, `seg_guidance_layers` must be provided.
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.01`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `0.2`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    _input_predictions = ["pred_cond", "pred_uncond", "pred_cond_seg"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        seg_guidance_scale: float = 2.8,
+        seg_blur_sigma: float = 9999999.0,
+        seg_blur_threshold_inf: float = 9999.0,
+        seg_guidance_start: float = 0.0,
+        seg_guidance_stop: float = 1.0,
+        seg_guidance_layers: Optional[Union[int, List[int]]] = None,
+        seg_guidance_config: Union[SmoothedEnergyGuidanceConfig, List[SmoothedEnergyGuidanceConfig]] = None,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__(start, stop)
+
+        self.guidance_scale = guidance_scale
+        self.seg_guidance_scale = seg_guidance_scale
+        self.seg_blur_sigma = seg_blur_sigma
+        self.seg_blur_threshold_inf = seg_blur_threshold_inf
+        self.seg_guidance_start = seg_guidance_start
+        self.seg_guidance_stop = seg_guidance_stop
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+
+        if not (0.0 <= seg_guidance_start < 1.0):
+            raise ValueError(f"Expected `seg_guidance_start` to be between 0.0 and 1.0, but got {seg_guidance_start}.")
+        if not (seg_guidance_start <= seg_guidance_stop <= 1.0):
+            raise ValueError(f"Expected `seg_guidance_stop` to be between 0.0 and 1.0, but got {seg_guidance_stop}.")
+
+        if seg_guidance_layers is None and seg_guidance_config is None:
+            raise ValueError(
+                "Either `seg_guidance_layers` or `seg_guidance_config` must be provided to enable Smoothed Energy Guidance."
+            )
+        if seg_guidance_layers is not None and seg_guidance_config is not None:
+            raise ValueError("Only one of `seg_guidance_layers` or `seg_guidance_config` can be provided.")
+
+        if seg_guidance_layers is not None:
+            if isinstance(seg_guidance_layers, int):
+                seg_guidance_layers = [seg_guidance_layers]
+            if not isinstance(seg_guidance_layers, list):
+                raise ValueError(
+                    f"Expected `seg_guidance_layers` to be an int or a list of ints, but got {type(seg_guidance_layers)}."
+                )
+            seg_guidance_config = [SmoothedEnergyGuidanceConfig(layer, fqn="auto") for layer in seg_guidance_layers]
+
+        if isinstance(seg_guidance_config, dict):
+            seg_guidance_config = SmoothedEnergyGuidanceConfig.from_dict(seg_guidance_config)
+
+        if isinstance(seg_guidance_config, SmoothedEnergyGuidanceConfig):
+            seg_guidance_config = [seg_guidance_config]
+
+        if not isinstance(seg_guidance_config, list):
+            raise ValueError(
+                f"Expected `seg_guidance_config` to be a SmoothedEnergyGuidanceConfig or a list of SmoothedEnergyGuidanceConfig, but got {type(seg_guidance_config)}."
+            )
+        elif isinstance(next(iter(seg_guidance_config), None), dict):
+            seg_guidance_config = [SmoothedEnergyGuidanceConfig.from_dict(config) for config in seg_guidance_config]
+
+        self.seg_guidance_config = seg_guidance_config
+        self._seg_layer_hook_names = [f"SmoothedEnergyGuidance_{i}" for i in range(len(self.seg_guidance_config))]
+
+    def prepare_models(self, denoiser: torch.nn.Module) -> None:
+        if self._is_seg_enabled() and self.is_conditional and self._count_prepared > 1:
+            for name, config in zip(self._seg_layer_hook_names, self.seg_guidance_config):
+                _apply_smoothed_energy_guidance_hook(denoiser, config, self.seg_blur_sigma, name=name)
+
+    def cleanup_models(self, denoiser: torch.nn.Module):
+        if self._is_seg_enabled() and self.is_conditional and self._count_prepared > 1:
+            registry = HookRegistry.check_if_exists_or_initialize(denoiser)
+            # Remove the hooks after inference
+            for hook_name in self._seg_layer_hook_names:
+                registry.remove_hook(hook_name, recurse=True)
+
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
+        if self.num_conditions == 1:
+            tuple_indices = [0]
+            input_predictions = ["pred_cond"]
+        elif self.num_conditions == 2:
+            tuple_indices = [0, 1]
+            input_predictions = (
+                ["pred_cond", "pred_uncond"] if self._is_cfg_enabled() else ["pred_cond", "pred_cond_seg"]
+            )
+        else:
+            tuple_indices = [0, 1, 0]
+            input_predictions = ["pred_cond", "pred_uncond", "pred_cond_seg"]
+        data_batches = []
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], input_predictions[i])
+            data_batches.append(data_batch)
+        return data_batches
+
+    def forward(
+        self,
+        pred_cond: torch.Tensor,
+        pred_uncond: Optional[torch.Tensor] = None,
+        pred_cond_seg: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        pred = None
+
+        if not self._is_cfg_enabled() and not self._is_seg_enabled():
+            pred = pred_cond
+        elif not self._is_cfg_enabled():
+            shift = pred_cond - pred_cond_seg
+            pred = pred_cond if self.use_original_formulation else pred_cond_seg
+            pred = pred + self.seg_guidance_scale * shift
+        elif not self._is_seg_enabled():
+            shift = pred_cond - pred_uncond
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift
+        else:
+            shift = pred_cond - pred_uncond
+            shift_seg = pred_cond - pred_cond_seg
+            pred = pred_cond if self.use_original_formulation else pred_uncond
+            pred = pred + self.guidance_scale * shift + self.seg_guidance_scale * shift_seg
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return pred, {}
+
+    @property
+    def is_conditional(self) -> bool:
+        return self._count_prepared == 1 or self._count_prepared == 3
+
+    @property
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_cfg_enabled():
+            num_conditions += 1
+        if self._is_seg_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    def _is_cfg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close
+
+    def _is_seg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self.seg_guidance_start * self._num_inference_steps)
+            skip_stop_step = int(self.seg_guidance_stop * self._num_inference_steps)
+            is_within_range = skip_start_step < self._step < skip_stop_step
+
+        is_zero = math.isclose(self.seg_guidance_scale, 0.0)
+
+        return is_within_range and not is_zero

src/diffusers/guiders/tangential_classifier_free_guidance.py

@@ -0,0 +1,143 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import torch
+
+from ..configuration_utils import register_to_config
+from .guider_utils import BaseGuidance, rescale_noise_cfg
+
+
+if TYPE_CHECKING:
+    from ..modular_pipelines.modular_pipeline import BlockState
+
+
+class TangentialClassifierFreeGuidance(BaseGuidance):
+    """
+    Tangential Classifier Free Guidance (TCFG): https://huggingface.co/papers/2503.18137
+
+    Args:
+        guidance_scale (`float`, defaults to `7.5`):
+            The scale parameter for classifier-free guidance. Higher values result in stronger conditioning on the text
+            prompt, while lower values allow for more freedom in generation. Higher values may lead to saturation and
+            deterioration of image quality.
+        guidance_rescale (`float`, defaults to `0.0`):
+            The rescale factor applied to the noise predictions. This is used to improve image quality and fix
+            overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+            Flawed](https://huggingface.co/papers/2305.08891).
+        use_original_formulation (`bool`, defaults to `False`):
+            Whether to use the original formulation of classifier-free guidance as proposed in the paper. By default,
+            we use the diffusers-native implementation that has been in the codebase for a long time. See
+            [~guiders.classifier_free_guidance.ClassifierFreeGuidance] for more details.
+        start (`float`, defaults to `0.0`):
+            The fraction of the total number of denoising steps after which guidance starts.
+        stop (`float`, defaults to `1.0`):
+            The fraction of the total number of denoising steps after which guidance stops.
+    """
+
+    _input_predictions = ["pred_cond", "pred_uncond"]
+
+    @register_to_config
+    def __init__(
+        self,
+        guidance_scale: float = 7.5,
+        guidance_rescale: float = 0.0,
+        use_original_formulation: bool = False,
+        start: float = 0.0,
+        stop: float = 1.0,
+    ):
+        super().__init__(start, stop)
+
+        self.guidance_scale = guidance_scale
+        self.guidance_rescale = guidance_rescale
+        self.use_original_formulation = use_original_formulation
+
+    def prepare_inputs(
+        self, data: "BlockState", input_fields: Optional[Dict[str, Union[str, Tuple[str, str]]]] = None
+    ) -> List["BlockState"]:
+        if input_fields is None:
+            input_fields = self._input_fields
+
+        tuple_indices = [0] if self.num_conditions == 1 else [0, 1]
+        data_batches = []
+        for i in range(self.num_conditions):
+            data_batch = self._prepare_batch(input_fields, data, tuple_indices[i], self._input_predictions[i])
+            data_batches.append(data_batch)
+        return data_batches
+
+    def forward(self, pred_cond: torch.Tensor, pred_uncond: Optional[torch.Tensor] = None) -> torch.Tensor:
+        pred = None
+
+        if not self._is_tcfg_enabled():
+            pred = pred_cond
+        else:
+            pred = normalized_guidance(pred_cond, pred_uncond, self.guidance_scale, self.use_original_formulation)
+
+        if self.guidance_rescale > 0.0:
+            pred = rescale_noise_cfg(pred, pred_cond, self.guidance_rescale)
+
+        return pred, {}
+
+    @property
+    def is_conditional(self) -> bool:
+        return self._num_outputs_prepared == 1
+
+    @property
+    def num_conditions(self) -> int:
+        num_conditions = 1
+        if self._is_tcfg_enabled():
+            num_conditions += 1
+        return num_conditions
+
+    def _is_tcfg_enabled(self) -> bool:
+        if not self._enabled:
+            return False
+
+        is_within_range = True
+        if self._num_inference_steps is not None:
+            skip_start_step = int(self._start * self._num_inference_steps)
+            skip_stop_step = int(self._stop * self._num_inference_steps)
+            is_within_range = skip_start_step <= self._step < skip_stop_step
+
+        is_close = False
+        if self.use_original_formulation:
+            is_close = math.isclose(self.guidance_scale, 0.0)
+        else:
+            is_close = math.isclose(self.guidance_scale, 1.0)
+
+        return is_within_range and not is_close
+
+
+def normalized_guidance(
+    pred_cond: torch.Tensor, pred_uncond: torch.Tensor, guidance_scale: float, use_original_formulation: bool = False
+) -> torch.Tensor:
+    cond_dtype = pred_cond.dtype
+    preds = torch.stack([pred_cond, pred_uncond], dim=1).float()
+    preds = preds.flatten(2)
+    U, S, Vh = torch.linalg.svd(preds, full_matrices=False)
+    Vh_modified = Vh.clone()
+    Vh_modified[:, 1] = 0
+
+    uncond_flat = pred_uncond.reshape(pred_uncond.size(0), 1, -1).float()
+    x_Vh = torch.matmul(uncond_flat, Vh.transpose(-2, -1))
+    x_Vh_V = torch.matmul(x_Vh, Vh_modified)
+    pred_uncond = x_Vh_V.reshape(pred_uncond.shape).to(cond_dtype)
+
+    pred = pred_cond if use_original_formulation else pred_uncond
+    shift = pred_cond - pred_uncond
+    pred = pred + guidance_scale * shift
+
+    return pred

src/diffusers/hooks/__init__.py

@@ -1,9 +1,26 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from ..utils import is_torch_available
 
 
 if is_torch_available():
     from .faster_cache import FasterCacheConfig, apply_faster_cache
+    from .first_block_cache import FirstBlockCacheConfig, apply_first_block_cache
     from .group_offloading import apply_group_offloading
     from .hooks import HookRegistry, ModelHook
+    from .layer_skip import LayerSkipConfig, apply_layer_skip
     from .layerwise_casting import apply_layerwise_casting, apply_layerwise_casting_hook
     from .pyramid_attention_broadcast import PyramidAttentionBroadcastConfig, apply_pyramid_attention_broadcast
+    from .smoothed_energy_guidance_utils import SmoothedEnergyGuidanceConfig