up

* Updated lora inference instructions

* Update examples/dreambooth/README.md

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update README.md

* Update README.md

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

.github/ISSUE_TEMPLATE/config.yml

@@ -1,4 +1,4 @@
 contact_links:
-  - name: Forum
-    url: https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63
+  - name: Questions / Discussions
+    url: https://github.com/huggingface/diffusers/discussions
     about: General usage questions and community discussions

.github/PULL_REQUEST_TEMPLATE.md

@@ -38,7 +38,7 @@ members/contributors who may be interested in your PR.
 
 Core library:
 
-- Schedulers: @williamberman and @patrickvonplaten
+- Schedulers: @yiyixuxu and @patrickvonplaten
 - Pipelines:  @patrickvonplaten and @sayakpaul
 - Training examples: @sayakpaul and @patrickvonplaten
 - Docs: @stevhliu and @yiyixuxu

.github/workflows/benchmark.yml

@@ -0,0 +1,52 @@
+name: Benchmarking tests
+
+on:
+  schedule:
+    - cron: "30 1 1,15 * *" # every 2 weeks on the 1st and the 15th of every month at 1:30 AM
+
+env:
+  DIFFUSERS_IS_CI: yes
+  HF_HOME: /mnt/cache
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+
+jobs:
+  torch_pipelines_cuda_benchmark_tests:
+    name: Torch Core Pipelines CUDA Benchmarking Tests
+    strategy:
+      fail-fast: false
+      max-parallel: 1
+    runs-on: [single-gpu, nvidia-gpu, a10, ci]
+    container:
+      image: diffusers/diffusers-pytorch-cuda
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+      - name: Install dependencies
+        run: |
+          apt-get update && apt-get install libsndfile1-dev libgl1 -y
+          python -m pip install -e .[quality,test]
+          python -m pip install pandas
+      - name: Environment
+        run: |
+          python utils/print_env.py
+      - name: Diffusers Benchmarking
+        env:
+            HUGGING_FACE_HUB_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
+            BASE_PATH: benchmark_outputs
+        run: |
+          export TOTAL_GPU_MEMORY=$(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
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: benchmark_test_reports
+          path: benchmarks/benchmark_outputs

.github/workflows/delete_doc_comment.yml

@@ -1,14 +0,0 @@
-name: Delete doc comment
-
-on:
-  workflow_run:
-    workflows: ["Delete doc comment trigger"]
-    types:
-      - completed
-
-
-jobs:
-  delete:
-    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main
-    secrets:
-      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

.github/workflows/delete_doc_comment_trigger.yml

@@ -1,12 +0,0 @@
-name: Delete doc comment trigger
-
-on:
-  pull_request:
-    types: [ closed ]
-
-
-jobs:
-  delete:
-    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment_trigger.yml@main
-    with:
-      pr_number: ${{ github.event.number }}

.github/workflows/pr_test_peft_backend.yml

@@ -59,7 +59,7 @@ jobs:
 
     - name: Run fast PyTorch LoRA CPU tests with PEFT backend
       run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/lora/test_lora_layers_peft.py

.github/workflows/pr_tests.yml

@@ -34,11 +34,6 @@ jobs:
             runner: docker-cpu
             image: diffusers/diffusers-pytorch-cpu
             report: torch_cpu_models_schedulers
-          - name: LoRA
-            framework: lora
-            runner: docker-cpu
-            image: diffusers/diffusers-pytorch-cpu
-            report: torch_cpu_lora
           - name: Fast Flax CPU tests
             framework: flax
             runner: docker-cpu
@@ -94,14 +89,6 @@ jobs:
           --make-reports=tests_${{ matrix.config.report }} \
           tests/models tests/schedulers tests/others
 
-    - name: Run fast PyTorch LoRA CPU tests
-      if: ${{ matrix.config.framework == 'lora' }}
-      run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx and not Dependency" \
-          --make-reports=tests_${{ matrix.config.report }} \
-          tests/lora
-
     - name: Run fast Flax TPU tests
       if: ${{ matrix.config.framework == 'flax' }}
       run: |

.github/workflows/push_tests.yml

@@ -189,7 +189,7 @@ jobs:
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |
         python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
-          -s -v -k "not Flax and not Onnx" \
+          -s -v -k "not Flax and not Onnx and not PEFTLoRALoading" \
           --make-reports=tests_peft_cuda \
           tests/lora/
 

.github/workflows/push_tests_fast.yml

@@ -98,6 +98,7 @@ jobs:
     - name: Run example PyTorch CPU tests
       if: ${{ matrix.config.framework == 'pytorch_examples' }}
       run: |
+        python -m pip install peft
         python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
           --make-reports=tests_${{ matrix.config.report }} \
           examples

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

Makefile

@@ -3,7 +3,7 @@
 # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
 export PYTHONPATH = src
 
-check_dirs := examples scripts src tests utils
+check_dirs := examples scripts src tests utils benchmarks
 
 modified_only_fixup:
 	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))

PHILOSOPHY.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

README.md

@@ -77,7 +77,7 @@ Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggi
 
 ## Quickstart
 
-Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 15000+ checkpoints):
+Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 19000+ checkpoints):
 
 ```python
 from diffusers import DiffusionPipeline
@@ -219,7 +219,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 - https://github.com/deep-floyd/IF
 - https://github.com/bentoml/BentoML
 - https://github.com/bmaltais/kohya_ss
-- +6000 other amazing GitHub repositories 💪
+- +8000 other amazing GitHub repositories 💪
 
 Thank you for using us ❤️.
 

benchmarks/base_classes.py

@@ -0,0 +1,316 @@
+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 = {
+    "runwayml/stable-diffusion-v1-5": (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.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 ControlNetBenchmark(TextToImageBenchmark):
+    pipeline_class = StableDiffusionControlNetPipeline
+    aux_network_class = ControlNetModel
+    root_ckpt = "runwayml/stable-diffusion-v1-5"
+
+    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 = "CompVis/stable-diffusion-v1-4"
+
+    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

@@ -0,0 +1,26 @@
+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_sd_img.py

@@ -0,0 +1,29 @@
+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="runwayml/stable-diffusion-v1-5",
+        choices=[
+            "runwayml/stable-diffusion-v1-5",
+            "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

@@ -0,0 +1,28 @@
+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="runwayml/stable-diffusion-v1-5",
+        choices=[
+            "runwayml/stable-diffusion-v1-5",
+            "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

@@ -0,0 +1,28 @@
+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

@@ -0,0 +1,23 @@
+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

@@ -0,0 +1,40 @@
+import argparse
+import sys
+
+
+sys.path.append(".")
+from base_classes import TextToImageBenchmark, TurboTextToImageBenchmark  # noqa: E402
+
+
+ALL_T2I_CKPTS = [
+    "runwayml/stable-diffusion-v1-5",
+    "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="runwayml/stable-diffusion-v1-5",
+        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/push_results.py

@@ -0,0 +1,72 @@
+import glob
+import sys
+
+import pandas as pd
+from huggingface_hub import hf_hub_download, upload_file
+from huggingface_hub.utils._errors import EntryNotFoundError
+
+
+sys.path.append(".")
+from utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv  # noqa: E402
+
+
+def has_previous_benchmark() -> str:
+    csv_path = None
+    try:
+        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILE)
+    except EntryNotFoundError:
+        csv_path = None
+    return csv_path
+
+
+def filter_float(value):
+    if isinstance(value, str):
+        return float(value.split()[0])
+    return value
+
+
+def push_to_hf_dataset():
+    all_csvs = sorted(glob.glob(f"{BASE_PATH}/*.csv"))
+    collate_csv(all_csvs, FINAL_CSV_FILE)
+
+    # 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)
+        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))
+
+            # 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
+
+            # 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}%)"
+            )
+            # 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)
+
+    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,
+        repo_type="dataset",
+        commit_message=commit_message,
+    )
+
+
+if __name__ == "__main__":
+    push_to_hf_dataset()

benchmarks/run_all.py

@@ -0,0 +1,97 @@
+import glob
+import subprocess
+import sys
+from typing import List
+
+
+sys.path.append(".")
+from benchmark_text_to_image import ALL_T2I_CKPTS  # noqa: E402
+
+
+PATTERN = "benchmark_*.py"
+
+
+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`
+    """
+    try:
+        output = subprocess.check_output(command, stderr=subprocess.STDOUT)
+        if return_stdout:
+            if hasattr(output, "decode"):
+                output = output.decode("utf-8")
+            return output
+    except subprocess.CalledProcessError as e:
+        raise SubprocessCallException(
+            f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
+        ) from e
+
+
+def main():
+    python_files = glob.glob(PATTERN)
+
+    for file in python_files:
+        print(f"****** Running file: {file} ******")
+
+        # Run with canonical settings.
+        if file != "benchmark_text_to_image.py":
+            command = f"python {file}"
+            run_command(command.split())
+
+            command += " --run_compile"
+            run_command(command.split())
+
+    # Run variants.
+    for file in python_files:
+        if file == "benchmark_text_to_image.py":
+            for ckpt in ALL_T2I_CKPTS:
+                command = f"python {file} --ckpt {ckpt}"
+
+                if "turbo" in ckpt:
+                    command += " --num_inference_steps 1"
+
+                run_command(command.split())
+
+                command += " --run_compile"
+                run_command(command.split())
+
+        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}"
+
+                if ckpt == "stabilityai/sdxl-turbo":
+                    command += " --num_inference_steps 2"
+
+                run_command(command.split())
+                command += " --run_compile"
+                run_command(command.split())
+
+        elif file == "benchmark_sd_inpainting.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())
+
+
+if __name__ == "__main__":
+    main()

benchmarks/utils.py

@@ -0,0 +1,98 @@
+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-onnxruntime-cpu/Dockerfile

@@ -24,9 +24,9 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3 -m pip install --no-cache-dir --upgrade pip && \
     python3 -m pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
+        torch==2.1.2 \
+        torchvision==0.16.2 \
+        torchaudio==2.1.2 \
         onnxruntime \
         --extra-index-url https://download.pytorch.org/whl/cpu && \
     python3 -m pip install --no-cache-dir \

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -24,9 +24,9 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3 -m pip install --no-cache-dir --upgrade pip && \
     python3 -m pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
+        torch==2.1.2 \
+        torchvision==0.16.2 \
+        torchaudio==2.1.2 \
         "onnxruntime-gpu>=1.13.1" \
         --extra-index-url https://download.pytorch.org/whl/cu117 && \
     python3 -m pip install --no-cache-dir \

docker/diffusers-pytorch-compile-cuda/Dockerfile

@@ -40,7 +40,6 @@ RUN python3.9 -m pip install --no-cache-dir --upgrade pip && \
     numpy \
     scipy \
     tensorboard \
-    transformers \
-    omegaconf
+    transformers
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-cuda/Dockerfile

@@ -40,7 +40,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
     scipy \
     tensorboard \
     transformers \
-    omegaconf \
     pytorch-lightning
 
 CMD ["/bin/bash"]

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -40,7 +40,6 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip && \
         scipy \
         tensorboard \
         transformers \
-        omegaconf \
         xformers
 
 CMD ["/bin/bash"]

docs/README.md

@@ -1,5 +1,5 @@
 <!---
-Copyright 2023- The HuggingFace Team. All rights reserved.
+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.

docs/TRANSLATING.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/_toctree.yml

@@ -19,6 +19,8 @@
     title: Train a diffusion model
   - local: tutorials/using_peft_for_inference
     title: Inference with PEFT
+  - local: tutorials/fast_diffusion
+    title: Accelerate inference of text-to-image diffusion models
   title: Tutorials
 - sections:
   - sections:
@@ -158,6 +160,8 @@
       title: xFormers
     - local: optimization/tome
       title: Token merging
+    - local: optimization/deepcache
+      title: DeepCache
     title: General optimizations
   - sections:
     - local: using-diffusers/stable_diffusion_jax_how_to
@@ -198,6 +202,8 @@
       title: Outputs
     title: Main Classes
   - sections:
+    - local: api/loaders/ip_adapter
+      title: IP-Adapter
     - local: api/loaders/lora
       title: LoRA
     - local: api/loaders/single_file
@@ -206,6 +212,8 @@
       title: Textual Inversion
     - local: api/loaders/unet
       title: UNet
+    - local: api/loaders/peft
+      title: PEFT
     title: Loaders
   - sections:
     - local: api/models/overview
@@ -220,6 +228,8 @@
       title: UNet3DConditionModel
     - local: api/models/unet-motion
       title: UNetMotionModel
+    - local: api/models/uvit2d
+      title: UViT2DModel
     - local: api/models/vq
       title: VQModel
     - local: api/models/autoencoderkl
@@ -242,14 +252,12 @@
   - sections:
     - local: api/pipelines/overview
       title: Overview
-    - local: api/pipelines/alt_diffusion
-      title: AltDiffusion
+    - local: api/pipelines/amused
+      title: aMUSEd
     - local: api/pipelines/animatediff
       title: AnimateDiff
     - local: api/pipelines/attend_and_excite
       title: Attend-and-Excite
-    - local: api/pipelines/audio_diffusion
-      title: Audio Diffusion
     - local: api/pipelines/audioldm
       title: AudioLDM
     - local: api/pipelines/audioldm2
@@ -264,12 +272,6 @@
       title: ControlNet
     - local: api/pipelines/controlnet_sdxl
       title: ControlNet with Stable Diffusion XL
-    - local: api/pipelines/controlnetxs
-      title: ControlNet-XS
-    - local: api/pipelines/controlnetxs_sdxl
-      title: ControlNet-XS with Stable Diffusion XL
-    - local: api/pipelines/cycle_diffusion
-      title: Cycle Diffusion
     - local: api/pipelines/dance_diffusion
       title: Dance Diffusion
     - local: api/pipelines/ddim
@@ -282,6 +284,8 @@
       title: DiffEdit
     - local: api/pipelines/dit
       title: DiT
+    - local: api/pipelines/i2vgenxl
+      title: I2VGen-XL
     - local: api/pipelines/pix2pix
       title: InstructPix2Pix
     - local: api/pipelines/kandinsky
@@ -300,26 +304,16 @@
       title: MusicLDM
     - local: api/pipelines/paint_by_example
       title: Paint by Example
-    - local: api/pipelines/paradigms
-      title: Parallel Sampling of Diffusion Models
-    - local: api/pipelines/pix2pix_zero
-      title: Pix2Pix Zero
+    - local: api/pipelines/pia
+      title: Personalized Image Animator (PIA)
     - local: api/pipelines/pixart
       title: PixArt-α
-    - local: api/pipelines/pndm
-      title: PNDM
-    - local: api/pipelines/repaint
-      title: RePaint
-    - local: api/pipelines/score_sde_ve
-      title: Score SDE VE
     - local: api/pipelines/self_attention_guidance
       title: Self-Attention Guidance
     - local: api/pipelines/semantic_stable_diffusion
       title: Semantic Guidance
     - local: api/pipelines/shap_e
       title: Shap-E
-    - local: api/pipelines/spectrogram_diffusion
-      title: Spectrogram Diffusion
     - sections:
       - local: api/pipelines/stable_diffusion/overview
         title: Overview
@@ -345,6 +339,8 @@
         title: Latent upscaler
       - local: api/pipelines/stable_diffusion/upscale
         title: Super-resolution
+      - local: api/pipelines/stable_diffusion/k_diffusion
+        title: K-Diffusion
       - local: api/pipelines/stable_diffusion/ldm3d_diffusion
         title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
       - local: api/pipelines/stable_diffusion/adapter
@@ -354,26 +350,16 @@
       title: Stable Diffusion
     - local: api/pipelines/stable_unclip
       title: Stable unCLIP
-    - local: api/pipelines/stochastic_karras_ve
-      title: Stochastic Karras VE
-    - local: api/pipelines/model_editing
-      title: Text-to-image model editing
     - local: api/pipelines/text_to_video
       title: Text-to-video
     - local: api/pipelines/text_to_video_zero
       title: Text2Video-Zero
     - local: api/pipelines/unclip
       title: unCLIP
-    - local: api/pipelines/latent_diffusion_uncond
-      title: Unconditional Latent Diffusion
     - local: api/pipelines/unidiffuser
       title: UniDiffuser
     - local: api/pipelines/value_guided_sampling
       title: Value-guided sampling
-    - local: api/pipelines/versatile_diffusion
-      title: Versatile Diffusion
-    - local: api/pipelines/vq_diffusion
-      title: VQ Diffusion
     - local: api/pipelines/wuerstchen
       title: Wuerstchen
     title: Pipelines

docs/source/en/api/activations.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/attnprocessor.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/configuration.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/image_processor.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/internal_classes_overview.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -0,0 +1,25 @@
+<!--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.
+-->
+
+# IP-Adapter
+
+[IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder. Files generated from IP-Adapter are only ~100MBs.
+
+<Tip>
+
+Learn how to load an IP-Adapter checkpoint and image in the [IP-Adapter](../../using-diffusers/loading_adapters#ip-adapter) loading guide.
+
+</Tip>
+
+## IPAdapterMixin
+
+[[autodoc]] loaders.ip_adapter.IPAdapterMixin

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -0,0 +1,25 @@
+<!--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.
+-->
+
+# PEFT
+
+Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`] to load an adapter.
+
+<Tip>
+
+Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.
+
+</Tip>
+
+## PeftAdapterMixin
+
+[[autodoc]] loaders.peft.PeftAdapterMixin

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -30,8 +30,8 @@ To learn more about how to load single file weights, see the [Load different Sta
 
 ## FromOriginalVAEMixin
 
-[[autodoc]] loaders.single_file.FromOriginalVAEMixin
+[[autodoc]] loaders.autoencoder.FromOriginalVAEMixin
 
 ## FromOriginalControlnetMixin
 
-[[autodoc]] loaders.single_file.FromOriginalControlnetMixin
+[[autodoc]] loaders.controlnet.FromOriginalControlNetMixin

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/logging.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -49,12 +49,12 @@ make_image_grid([original_image, mask_image, image], rows=1, cols=3)
 
 ## AsymmetricAutoencoderKL
 
-[[autodoc]] models.autoencoder_asym_kl.AsymmetricAutoencoderKL
+[[autodoc]] models.autoencoders.autoencoder_asym_kl.AsymmetricAutoencoderKL
 
 ## AutoencoderKLOutput
 
-[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
 
 ## DecoderOutput
 
-[[autodoc]] models.vae.DecoderOutput
+[[autodoc]] models.autoencoders.vae.DecoderOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -54,4 +54,4 @@ image
 
 ## AutoencoderTinyOutput
 
-[[autodoc]] models.autoencoder_tiny.AutoencoderTinyOutput
+[[autodoc]] models.autoencoders.autoencoder_tiny.AutoencoderTinyOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -33,14 +33,17 @@ model = AutoencoderKL.from_single_file(url)
 ## AutoencoderKL
 
 [[autodoc]] AutoencoderKL
+    - decode
+    - encode
+    - all
 
 ## AutoencoderKLOutput
 
-[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
 
 ## DecoderOutput
 
-[[autodoc]] models.vae.DecoderOutput
+[[autodoc]] models.autoencoders.vae.DecoderOutput
 
 ## FlaxAutoencoderKL
 

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -24,4 +24,4 @@ The abstract from the paper is:
 
 ## PriorTransformerOutput
 
-[[autodoc]] models.prior_transformer.PriorTransformerOutput
+[[autodoc]] models.transformers.prior_transformer.PriorTransformerOutput

docs/source/en/api/models/transformer2d.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -38,4 +38,4 @@ It is assumed one of the input classes is the masked latent pixel. The predicted
 
 ## Transformer2DModelOutput
 
-[[autodoc]] models.transformer_2d.Transformer2DModelOutput
+[[autodoc]] models.transformers.transformer_2d.Transformer2DModelOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -16,8 +16,8 @@ A Transformer model for video-like data.
 
 ## TransformerTemporalModel
 
-[[autodoc]] models.transformer_temporal.TransformerTemporalModel
+[[autodoc]] models.transformers.transformer_temporal.TransformerTemporalModel
 
 ## TransformerTemporalModelOutput
 
-[[autodoc]] models.transformer_temporal.TransformerTemporalModelOutput
+[[autodoc]] models.transformers.transformer_temporal.TransformerTemporalModelOutput

docs/source/en/api/models/unet-motion.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -22,4 +22,4 @@ The abstract from the paper is:
 [[autodoc]] UNetMotionModel
 
 ## UNet3DConditionOutput
-[[autodoc]] models.unet_3d_condition.UNet3DConditionOutput
+[[autodoc]] models.unets.unet_3d_condition.UNet3DConditionOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -22,4 +22,4 @@ The abstract from the paper is:
 [[autodoc]] UNet1DModel
 
 ## UNet1DOutput
-[[autodoc]] models.unet_1d.UNet1DOutput
+[[autodoc]] models.unets.unet_1d.UNet1DOutput

docs/source/en/api/models/unet2d-cond.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -22,10 +22,10 @@ The abstract from the paper is:
 [[autodoc]] UNet2DConditionModel
 
 ## UNet2DConditionOutput
-[[autodoc]] models.unet_2d_condition.UNet2DConditionOutput
+[[autodoc]] models.unets.unet_2d_condition.UNet2DConditionOutput
 
 ## FlaxUNet2DConditionModel
-[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionModel
+[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionModel
 
 ## FlaxUNet2DConditionOutput
-[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionOutput
+[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput

docs/source/en/api/models/unet2d.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -22,4 +22,4 @@ The abstract from the paper is:
 [[autodoc]] UNet2DModel
 
 ## UNet2DOutput
-[[autodoc]] models.unet_2d.UNet2DOutput
+[[autodoc]] models.unets.unet_2d.UNet2DOutput

docs/source/en/api/models/unet3d-cond.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -22,4 +22,4 @@ The abstract from the paper is:
 [[autodoc]] UNet3DConditionModel
 
 ## UNet3DConditionOutput
-[[autodoc]] models.unet_3d_condition.UNet3DConditionOutput
+[[autodoc]] models.unets.unet_3d_condition.UNet3DConditionOutput

docs/source/en/api/models/uvit2d.md

@@ -0,0 +1,39 @@
+<!--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.
+-->
+
+# UVit2DModel
+
+The [U-ViT](https://hf.co/papers/2301.11093) model is a vision transformer (ViT) based UNet. This model incorporates elements from ViT (considers all inputs such as time, conditions and noisy image patches as tokens) and a UNet (long skip connections between the shallow and deep layers). The skip connection is important for predicting pixel-level features. An additional 3x3 convolutional block is applied prior to the final output to improve image quality.
+
+The abstract from the paper is:
+
+*Currently, applying diffusion models in pixel space of high resolution images is difficult. Instead, existing approaches focus on diffusion in lower dimensional spaces (latent diffusion), or have multiple super-resolution levels of generation referred to as cascades. The downside is that these approaches add additional complexity to the diffusion framework. This paper aims to improve denoising diffusion for high resolution images while keeping the model as simple as possible. The paper is centered around the research question: How can one train a standard denoising diffusion models on high resolution images, and still obtain performance comparable to these alternate approaches? The four main findings are: 1) the noise schedule should be adjusted for high resolution images, 2) It is sufficient to scale only a particular part of the architecture, 3) dropout should be added at specific locations in the architecture, and 4) downsampling is an effective strategy to avoid high resolution feature maps. Combining these simple yet effective techniques, we achieve state-of-the-art on image generation among diffusion models without sampling modifiers on ImageNet.*
+
+## UVit2DModel
+
+[[autodoc]] UVit2DModel
+
+## UVit2DConvEmbed
+
+[[autodoc]] models.unets.uvit_2d.UVit2DConvEmbed
+
+## UVitBlock
+
+[[autodoc]] models.unets.uvit_2d.UVitBlock
+
+## ConvNextBlock
+
+[[autodoc]] models.unets.uvit_2d.ConvNextBlock
+
+## ConvMlmLayer
+
+[[autodoc]] models.unets.uvit_2d.ConvMlmLayer

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/normalization.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/outputs.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,47 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# AltDiffusion
-
-AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://huggingface.co/papers/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu.
-
-The abstract from the paper is:
-
-*In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model. Starting from the pre-trained multimodal representation model CLIP released by OpenAI, we altered its text encoder with a pre-trained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k-CN, COCO-CN and XTD. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding. Our models and code are available at [this https URL](https://github.com/FlagAI-Open/FlagAI).*
-
-## Tips
-
-`AltDiffusion` is conceptually the same as [Stable Diffusion](./stable_diffusion/overview).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## AltDiffusionPipeline
-
-[[autodoc]] AltDiffusionPipeline
-	- all
-	- __call__
-
-## AltDiffusionImg2ImgPipeline
-
-[[autodoc]] AltDiffusionImg2ImgPipeline
-	- all
-	- __call__
-
-## AltDiffusionPipelineOutput
-
-[[autodoc]] pipelines.alt_diffusion.AltDiffusionPipelineOutput
-	- all
-	- __call__

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

@@ -0,0 +1,48 @@
+<!--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.
+-->
+
+# 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.
+
+Amused is a lightweight text to image model based off of the [MUSE](https://arxiv.org/abs/2301.00704) architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.
+
+Amused is a vqvae token based transformer that can generate an image in fewer forward passes than many diffusion models. In contrast with muse, it uses the smaller text encoder CLIP-L/14 instead of t5-xxl. Due to its small parameter count and few forward pass generation process, amused can generate many images quickly. This benefit is seen particularly at larger batch sizes. 
+
+The abstract from the paper is:
+
+*We present aMUSEd, an open-source, lightweight masked image model (MIM) for text-to-image generation based on MUSE. With 10 percent of MUSE's parameters, aMUSEd is focused on fast image generation. We believe MIM is under-explored compared to latent diffusion, the prevailing approach for text-to-image generation. Compared to latent diffusion, MIM requires fewer inference steps and is more interpretable. Additionally, MIM can be fine-tuned to learn additional styles with only a single image. We hope to encourage further exploration of MIM by demonstrating its effectiveness on large-scale text-to-image generation and releasing reproducible training code. We also release checkpoints for two models which directly produce images at 256x256 and 512x512 resolutions.*
+
+| Model | Params |
+|-------|--------|
+| [amused-256](https://huggingface.co/amused/amused-256) | 603M |
+| [amused-512](https://huggingface.co/amused/amused-512) | 608M |
+
+## AmusedPipeline
+
+[[autodoc]] AmusedPipeline
+	- __call__
+	- all
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention
+
+[[autodoc]] AmusedImg2ImgPipeline
+	- __call__
+	- all
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention
+
+[[autodoc]] AmusedInpaintPipeline
+	- __call__
+	- all
+	- enable_xformers_memory_efficient_attention
+	- disable_xformers_memory_efficient_attention

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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
@@ -25,6 +25,7 @@ The abstract of the paper is the following:
 | Pipeline | Tasks | Demo
 |---|---|:---:|
 | [AnimateDiffPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff.py) | *Text-to-Video Generation with AnimateDiff* |
+| [AnimateDiffVideoToVideoPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py) | *Video-to-Video Generation with AnimateDiff* |
 
 ## Available checkpoints
 
@@ -32,22 +33,29 @@ Motion Adapter checkpoints can be found under [guoyww](https://huggingface.co/gu
 
 ## Usage example
 
+### AnimateDiffPipeline
+
 AnimateDiff works with a MotionAdapter checkpoint and a Stable Diffusion model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in Stable Diffusion UNet.
 
 The following example demonstrates how to use a *MotionAdapter* checkpoint with Diffusers for inference based on StableDiffusion-1.4/1.5.
 
 ```python
 import torch
-from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
+from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
 from diffusers.utils import export_to_gif
 
 # Load the motion adapter
-adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
 # load SD 1.5 based finetuned model
 model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
-pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter)
+pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
 scheduler = DDIMScheduler.from_pretrained(
-    model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", steps_offset=1
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    beta_schedule="linear",
+    steps_offset=1,
 )
 pipe.scheduler = scheduler
 
@@ -70,6 +78,7 @@ output = pipe(
 )
 frames = output.frames[0]
 export_to_gif(frames, "animation.gif")
+
 ```
 
 Here are some sample outputs:
@@ -88,28 +97,143 @@ Here are some sample outputs:
 
 <Tip>
 
-AnimateDiff tends to work better with finetuned Stable Diffusion models. If you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples.
+AnimateDiff tends to work better with finetuned Stable Diffusion models. If you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples. Additionally, the AnimateDiff checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to `linear`.
 
 </Tip>
 
+### AnimateDiffVideoToVideoPipeline
+
+AnimateDiff can also be used to generate visually similar videos or enable style/character/background or other edits starting from an initial video, allowing you to seamlessly explore creative possibilities.
+
+```python
+import imageio
+import requests
+import torch
+from diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter
+from diffusers.utils import export_to_gif
+from io import BytesIO
+from PIL import Image
+
+# Load the motion adapter
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
+# load SD 1.5 based finetuned model
+model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
+pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to("cuda")
+scheduler = DDIMScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    beta_schedule="linear",
+    steps_offset=1,
+)
+pipe.scheduler = scheduler
+
+# enable memory savings
+pipe.enable_vae_slicing()
+pipe.enable_model_cpu_offload()
+
+# helper function to load videos
+def load_video(file_path: str):
+    images = []
+
+    if file_path.startswith(('http://', 'https://')):
+        # If the file_path is a URL
+        response = requests.get(file_path)
+        response.raise_for_status()
+        content = BytesIO(response.content)
+        vid = imageio.get_reader(content)
+    else:
+        # Assuming it's a local file path
+        vid = imageio.get_reader(file_path)
+
+    for frame in vid:
+        pil_image = Image.fromarray(frame)
+        images.append(pil_image)
+
+    return images
+
+video = load_video("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif")
+
+output = pipe(
+    video = video,
+    prompt="panda playing a guitar, on a boat, in the ocean, high quality",
+    negative_prompt="bad quality, worse quality",
+    guidance_scale=7.5,
+    num_inference_steps=25,
+    strength=0.5,
+    generator=torch.Generator("cpu").manual_seed(42),
+)
+frames = output.frames[0]
+export_to_gif(frames, "animation.gif")
+```
+
+Here are some sample outputs:
+
+<table>
+    <tr>
+      <th align=center>Source Video</th>
+      <th align=center>Output Video</th>
+    </tr>
+    <tr>
+        <td align=center>
+          raccoon playing a guitar
+          <br />
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif"
+              alt="racoon playing a guitar"
+              style="width: 300px;" />
+        </td>
+        <td align=center>
+          panda playing a guitar
+          <br/>
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-output-1.gif"
+              alt="panda playing a guitar"
+              style="width: 300px;" />
+        </td>
+    </tr>
+    <tr>
+        <td align=center>
+          closeup of margot robbie, fireworks in the background, high quality
+          <br />
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-2.gif"
+              alt="closeup of margot robbie, fireworks in the background, high quality"
+              style="width: 300px;" />
+        </td>
+        <td align=center>
+          closeup of tony stark, robert downey jr, fireworks
+          <br/>
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-output-2.gif"
+              alt="closeup of tony stark, robert downey jr, fireworks"
+              style="width: 300px;" />
+        </td>
+    </tr>
+</table>
+
 ## Using Motion LoRAs
 
 Motion LoRAs are a collection of LoRAs that work with the `guoyww/animatediff-motion-adapter-v1-5-2` checkpoint. These LoRAs are responsible for adding specific types of motion to the animations.
 
 ```python
 import torch
-from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
+from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
 from diffusers.utils import export_to_gif
 
 # Load the motion adapter
-adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
 # load SD 1.5 based finetuned model
 model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
-pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter)
-pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out")
+pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
+pipe.load_lora_weights(
+    "guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out"
+)
 
 scheduler = DDIMScheduler.from_pretrained(
-    model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", steps_offset=1
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    beta_schedule="linear",
+    timestep_spacing="linspace",
+    steps_offset=1,
 )
 pipe.scheduler = scheduler
 
@@ -132,6 +256,7 @@ output = pipe(
 )
 frames = output.frames[0]
 export_to_gif(frames, "animation.gif")
+
 ```
 
 <table>
@@ -160,21 +285,30 @@ Then you can use the following code to combine Motion LoRAs.
 
 ```python
 import torch
-from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
+from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
 from diffusers.utils import export_to_gif
 
 # Load the motion adapter
-adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
 # load SD 1.5 based finetuned model
 model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
-pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter)
+pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
 
-pipe.load_lora_weights("diffusers/animatediff-motion-lora-zoom-out", adapter_name="zoom-out")
-pipe.load_lora_weights("diffusers/animatediff-motion-lora-pan-left", adapter_name="pan-left")
+pipe.load_lora_weights(
+    "diffusers/animatediff-motion-lora-zoom-out", adapter_name="zoom-out",
+)
+pipe.load_lora_weights(
+    "diffusers/animatediff-motion-lora-pan-left", adapter_name="pan-left",
+)
 pipe.set_adapters(["zoom-out", "pan-left"], adapter_weights=[1.0, 1.0])
 
 scheduler = DDIMScheduler.from_pretrained(
-    model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", steps_offset=1
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    beta_schedule="linear",
+    steps_offset=1,
 )
 pipe.scheduler = scheduler
 
@@ -197,6 +331,7 @@ output = pipe(
 )
 frames = output.frames[0]
 export_to_gif(frames, "animation.gif")
+
 ```
 
 <table>
@@ -211,6 +346,62 @@ export_to_gif(frames, "animation.gif")
     </tr>
 </table>
 
+## Using FreeInit
+
+[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://arxiv.org/abs/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.
+
+FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper.
+
+The following example demonstrates the usage of FreeInit.
+
+```python
+import torch
+from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
+from diffusers.utils import export_to_gif
+
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
+model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
+pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to("cuda")
+pipe.scheduler = DDIMScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    beta_schedule="linear",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    steps_offset=1
+)
+
+# enable memory savings
+pipe.enable_vae_slicing()
+pipe.enable_vae_tiling()
+
+# enable FreeInit
+# Refer to the enable_free_init documentation for a full list of configurable parameters
+pipe.enable_free_init(method="butterworth", use_fast_sampling=True)
+
+# run inference
+output = pipe(
+    prompt="a panda playing a guitar, on a boat, in the ocean, high quality",
+    negative_prompt="bad quality, worse quality",
+    num_frames=16,
+    guidance_scale=7.5,
+    num_inference_steps=20,
+    generator=torch.Generator("cpu").manual_seed(666),
+)
+
+# disable FreeInit
+pipe.disable_free_init()
+
+frames = output.frames[0]
+export_to_gif(frames, "animation.gif")
+```
+
+<Tip warning={true}>
+
+FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the `num_iters` parameter that is set when enabling it. Setting the `use_fast_sampling` parameter to `True` can improve the overall performance (at the cost of lower quality compared to when `use_fast_sampling=False` but still better results than vanilla video generation models).
+
+</Tip>
+
 <Tip>
 
 Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
@@ -220,14 +411,14 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 ## AnimateDiffPipeline
 
 [[autodoc]] AnimateDiffPipeline
-	- all
-	- __call__
-    - enable_freeu
-    - disable_freeu
-    - enable_vae_slicing
-    - disable_vae_slicing
-    - enable_vae_tiling
-    - disable_vae_tiling
+  - all
+  - __call__
+
+## AnimateDiffVideoToVideoPipeline
+
+[[autodoc]] AnimateDiffVideoToVideoPipeline
+  - all
+  - __call__
 
 ## AnimateDiffPipelineOutput
 

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,35 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Audio Diffusion
-
-[Audio Diffusion](https://github.com/teticio/audio-diffusion) is by Robert Dargavel Smith, and it leverages the recent advances in image generation from diffusion models by converting audio samples to and from Mel spectrogram images.
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## AudioDiffusionPipeline
-[[autodoc]] AudioDiffusionPipeline
-	- all
-	- __call__
-
-## AudioPipelineOutput
-[[autodoc]] pipelines.AudioPipelineOutput
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput
-
-## Mel
-[[autodoc]] Mel

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/pipelines/audioldm2.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,33 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Cycle Diffusion
-
-Cycle Diffusion is a text guided image-to-image generation model proposed in [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://huggingface.co/papers/2210.05559) by Chen Henry Wu, Fernando De la Torre.
-
-The abstract from the paper is:
-
-*Diffusion models have achieved unprecedented performance in generative modeling. The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e.g., Gaussian) latent space of GANs, VAEs, and normalizing flows. This paper provides an alternative, Gaussian formulation of the latent space of various diffusion models, as well as an invertible DPM-Encoder that maps images into the latent space. While our formulation is purely based on the definition of diffusion models, we demonstrate several intriguing consequences. (1) Empirically, we observe that a common latent space emerges from two diffusion models trained independently on related domains. In light of this finding, we propose CycleDiffusion, which uses DPM-Encoder for unpaired image-to-image translation. Furthermore, applying CycleDiffusion to text-to-image diffusion models, we show that large-scale text-to-image diffusion models can be used as zero-shot image-to-image editors. (2) One can guide pre-trained diffusion models and GANs by controlling the latent codes in a unified, plug-and-play formulation based on energy-based models. Using the CLIP model and a face recognition model as guidance, we demonstrate that diffusion models have better coverage of low-density sub-populations and individuals than GANs. The code is publicly available at [this https URL](https://github.com/ChenWu98/cycle-diffusion).*
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## CycleDiffusionPipeline
-[[autodoc]] CycleDiffusionPipeline
-	- all
-	- __call__
-
-## StableDiffusionPiplineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -0,0 +1,57 @@
+<!--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.
+-->
+
+# 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.
+
+The abstract from the paper is:
+
+*Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity and spatio-temporal continuity. They primarily arise from the scarcity of well-aligned text-video data and the complex inherent structure of videos, making it difficult for the model to simultaneously ensure semantic and qualitative excellence. In this report, we propose a cascaded I2VGen-XL approach that enhances model performance by decoupling these two factors and ensures the alignment of the input data by utilizing static images as a form of crucial guidance. I2VGen-XL consists of two stages: i) the base stage guarantees coherent semantics and preserves content from input images by using two hierarchical encoders, and ii) the refinement stage enhances the video's details by incorporating an additional brief text and improves the resolution to 1280×720. To improve the diversity, we collect around 35 million single-shot text-video pairs and 6 billion text-image pairs to optimize the model. By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos. Through extensive experiments, we have investigated the underlying principles of I2VGen-XL and compared it with current top methods, which can demonstrate its effectiveness on diverse data. The source code and models will be publicly available at [this https URL](https://i2vgen-xl.github.io/).*
+
+The original codebase can be found [here](https://github.com/ali-vilab/i2vgen-xl/). The model checkpoints can be found [here](https://huggingface.co/ali-vilab/).
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section [here](../../using-diffusers/svd#reduce-memory-usage).
+
+</Tip>
+
+Sample output with I2VGenXL:
+
+<table>
+    <tr>
+        <td><center>
+        library.
+        <br>
+        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/i2vgen-xl-example.gif"
+            alt="library"
+            style="width: 300px;" />
+        </center></td>
+    </tr>
+</table>
+
+## Notes
+
+* I2VGenXL always uses a `clip_skip` value of 1. This means it leverages the penultimate layer representations from the text encoder of CLIP.
+* It can generate videos of quality that is often on par with [Stable Video Diffusion](../../using-diffusers/svd) (SVD).
+* Unlike SVD, it additionally accepts text prompts as inputs.
+* It can generate higher resolution videos.
+* When using the [`DDIMScheduler`] (which is default for this pipeline), less than 50 steps for inference leads to bad results.
+
+## I2VGenXLPipeline
+[[autodoc]] I2VGenXLPipeline
+	- all
+	- __call__
+
+## I2VGenXLPipelineOutput
+[[autodoc]] pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/pipelines/kandinsky3.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/pipelines/kandinsky_v22.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,35 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Unconditional Latent Diffusion
-
-Unconditional Latent Diffusion was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.
-
-The abstract from the paper is:
-
-*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.*
-
-The original codebase can be found at [CompVis/latent-diffusion](https://github.com/CompVis/latent-diffusion).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## LDMPipeline
-[[autodoc]] LDMPipeline
-	- all
-	- __call__
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput

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

@@ -1,35 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Text-to-image model editing
-
-[Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://huggingface.co/papers/2303.08084) is by Hadas Orgad, Bahjat Kawar, and Yonatan Belinkov. This pipeline enables editing diffusion model weights, such that its assumptions of a given concept are changed. The resulting change is expected to take effect in all prompt generations related to the edited concept.
-
-The abstract from the paper is:
-
-*Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training data. Thus, there is a need to control these assumptions without requiring explicit user input or costly re-training. In this work, we aim to edit a given implicit assumption in a pre-trained diffusion model. Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a "source" under-specified prompt for which the model makes an implicit assumption (e.g., "a pack of roses"), and a "destination" prompt that describes the same setting, but with a specified desired attribute (e.g., "a pack of blue roses"). TIME then updates the model's cross-attention layers, as these layers assign visual meaning to textual tokens. We edit the projection matrices in these layers such that the source prompt is projected close to the destination prompt. Our method is highly efficient, as it modifies a mere 2.2% of the model's parameters in under one second. To evaluate model editing approaches, we introduce TIMED (TIME Dataset), containing 147 source and destination prompt pairs from various domains. Our experiments (using Stable Diffusion) show that TIME is successful in model editing, generalizes well for related prompts unseen during editing, and imposes minimal effect on unrelated generations.*
-
-You can find additional information about model editing on the [project page](https://time-diffusion.github.io/), [original codebase](https://github.com/bahjat-kawar/time-diffusion), and try it out in a [demo](https://huggingface.co/spaces/bahjat-kawar/time-diffusion).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## StableDiffusionModelEditingPipeline
-[[autodoc]] StableDiffusionModelEditingPipeline
-	- __call__
-	- all
-
-## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,51 +0,0 @@
-<!--Copyright 2023 ParaDiGMS authors and 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.
--->
-
-# Parallel Sampling of Diffusion Models
-
-[Parallel Sampling of Diffusion Models](https://huggingface.co/papers/2305.16317) is by Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari.
-
-The abstract from the paper is:
-
-*Diffusion models are powerful generative models but suffer from slow sampling, often taking 1000 sequential denoising steps for one sample. As a result, considerable efforts have been directed toward reducing the number of denoising steps, but these methods hurt sample quality. Instead of reducing the number of denoising steps (trading quality for speed), in this paper we explore an orthogonal approach: can we run the denoising steps in parallel (trading compute for speed)? In spite of the sequential nature of the denoising steps, we show that surprisingly it is possible to parallelize sampling via Picard iterations, by guessing the solution of future denoising steps and iteratively refining until convergence. With this insight, we present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel. ParaDiGMS is the first diffusion sampling method that enables trading compute for speed and is even compatible with existing fast sampling techniques such as DDIM and DPMSolver. Using ParaDiGMS, we improve sampling speed by 2-4x across a range of robotics and image generation models, giving state-of-the-art sampling speeds of 0.2s on 100-step DiffusionPolicy and 14.6s on 1000-step StableDiffusion-v2 with no measurable degradation of task reward, FID score, or CLIP score.*
-
-The original codebase can be found at [AndyShih12/paradigms](https://github.com/AndyShih12/paradigms), and the pipeline was contributed by [AndyShih12](https://github.com/AndyShih12). ❤️
-
-## Tips
-
-This pipeline improves sampling speed by running denoising steps in parallel, at the cost of increased total FLOPs.
-Therefore, it is better to call this pipeline when running on multiple GPUs. Otherwise, without enough GPU bandwidth
-sampling may be even slower than sequential sampling.
-
-The two parameters to play with are `parallel` (batch size) and `tolerance`.
-- If it fits in memory, for a 1000-step DDPM you can aim for a batch size of around 100 (for example, 8 GPUs and `batch_per_device=12` to get `parallel=96`). A higher batch size may not fit in memory, and lower batch size gives less parallelism.
-- For tolerance, using a higher tolerance may get better speedups but can risk sample quality degradation. If there is quality degradation with the default tolerance, then use a lower tolerance like `0.001`.
-
-For a 1000-step DDPM on 8 A100 GPUs, you can expect around a 3x speedup from [`StableDiffusionParadigmsPipeline`] compared to the [`StableDiffusionPipeline`]
-by setting `parallel=80` and `tolerance=0.1`.
-
-🤗 Diffusers offers [distributed inference support](../../training/distributed_inference) for generating multiple prompts
-in parallel on multiple GPUs. But [`StableDiffusionParadigmsPipeline`] is designed for speeding up sampling of a single prompt by using multiple GPUs.
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## StableDiffusionParadigmsPipeline
-[[autodoc]] StableDiffusionParadigmsPipeline
-	- __call__
-	- all
-
-## StableDiffusionPipelineOutput
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput

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

@@ -0,0 +1,167 @@
+<!--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.
+-->
+
+# Image-to-Video Generation with PIA (Personalized Image Animator)
+
+## Overview
+
+[PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://arxiv.org/abs/2312.13964) by Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, Kai Chen
+
+Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to generate stunning images with unique styles. While promising, adding realistic motions into these personalized images by text poses significant challenges in preserving distinct styles, high-fidelity details, and achieving motion controllability by text. In this paper, we present PIA, a Personalized Image Animator that excels in aligning with condition images, achieving motion controllability by text, and the compatibility with various personalized T2I models without specific tuning. To achieve these goals, PIA builds upon a base T2I model with well-trained temporal alignment layers, allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information guided by the affinity hint for individual frame synthesis in the latent space. This design mitigates the challenges of appearance-related image alignment within and allows for a stronger focus on aligning with motion-related guidance.
+
+[Project page](https://pi-animator.github.io/)
+
+## Available Pipelines
+
+| Pipeline | Tasks | Demo
+|---|---|:---:|
+| [PIAPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pia/pipeline_pia.py) | *Image-to-Video Generation with PIA* |
+
+## Available checkpoints
+
+Motion Adapter checkpoints for PIA can be found under the [OpenMMLab org](https://huggingface.co/openmmlab/PIA-condition-adapter). These checkpoints are meant to work with any model based on Stable Diffusion 1.5
+
+## Usage example
+
+PIA works with a MotionAdapter checkpoint and a Stable Diffusion 1.5 model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in the Stable Diffusion UNet. In addition to the motion modules, PIA also replaces the input convolution layer of the SD 1.5 UNet model with a 9 channel input convolution layer.
+
+The following example demonstrates how to use PIA to generate a video from a single image.
+
+```python
+import torch
+from diffusers import (
+    EulerDiscreteScheduler,
+    MotionAdapter,
+    PIAPipeline,
+)
+from diffusers.utils import export_to_gif, load_image
+
+adapter = MotionAdapter.from_pretrained("openmmlab/PIA-condition-adapter")
+pipe = PIAPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter, torch_dtype=torch.float16)
+
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.enable_model_cpu_offload()
+pipe.enable_vae_slicing()
+
+image = load_image(
+    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true"
+)
+image = image.resize((512, 512))
+prompt = "cat in a field"
+negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality"
+
+generator = torch.Generator("cpu").manual_seed(0)
+output = pipe(image=image, prompt=prompt, generator=generator)
+frames = output.frames[0]
+export_to_gif(frames, "pia-animation.gif")
+```
+
+Here are some sample outputs:
+
+<table>
+    <tr>
+        <td><center>
+        cat in a field.
+        <br>
+        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pia-default-output.gif"
+            alt="cat in a field"
+            style="width: 300px;" />
+        </center></td>
+    </tr>
+</table>
+
+
+<Tip>
+
+If you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples. Additionally, the PIA checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to `linear`.
+
+</Tip>
+
+## Using FreeInit
+
+[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://arxiv.org/abs/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.
+
+FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to PIA, AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper.
+
+The following example demonstrates the usage of FreeInit.
+
+```python
+import torch
+from diffusers import (
+    DDIMScheduler,
+    MotionAdapter,
+    PIAPipeline,
+)
+from diffusers.utils import export_to_gif, load_image
+
+adapter = MotionAdapter.from_pretrained("openmmlab/PIA-condition-adapter")
+pipe = PIAPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter)
+
+# enable FreeInit
+# Refer to the enable_free_init documentation for a full list of configurable parameters
+pipe.enable_free_init(method="butterworth", use_fast_sampling=True)
+
+# Memory saving options
+pipe.enable_model_cpu_offload()
+pipe.enable_vae_slicing()
+
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+image = load_image(
+    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true"
+)
+image = image.resize((512, 512))
+prompt = "cat in a field"
+negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality"
+
+generator = torch.Generator("cpu").manual_seed(0)
+
+output = pipe(image=image, prompt=prompt, generator=generator)
+frames = output.frames[0]
+export_to_gif(frames, "pia-freeinit-animation.gif")
+```
+
+<table>
+    <tr>
+        <td><center>
+        cat in a field.
+        <br>
+        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pia-freeinit-output-cat.gif"
+            alt="cat in a field"
+            style="width: 300px;" />
+        </center></td>
+    </tr>
+</table>
+
+
+<Tip warning={true}>
+
+FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the `num_iters` parameter that is set when enabling it. Setting the `use_fast_sampling` parameter to `True` can improve the overall performance (at the cost of lower quality compared to when `use_fast_sampling=False` but still better results than vanilla video generation models).
+
+</Tip>
+
+## PIAPipeline
+
+[[autodoc]] PIAPipeline
+	- all
+	- __call__
+    - enable_freeu
+    - disable_freeu
+    - enable_free_init
+    - disable_free_init
+    - enable_vae_slicing
+    - disable_vae_slicing
+    - enable_vae_tiling
+    - disable_vae_tiling
+
+## PIAPipelineOutput
+
+[[autodoc]] pipelines.pia.PIAPipelineOutput

docs/source/en/api/pipelines/pix2pix.md

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

docs/source/en/api/pipelines/pix2pix_zero.md

@@ -1,289 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Pix2Pix Zero
-
-[Zero-shot Image-to-Image Translation](https://huggingface.co/papers/2302.03027) is by Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu.
-
-The abstract from the paper is:
-
-*Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is still challenging to directly apply these models for editing real images for two reasons. First, it is hard for users to come up with a perfect text prompt that accurately describes every visual detail in the input image. Second, while existing models can introduce desirable changes in certain regions, they often dramatically alter the input content and introduce unexpected changes in unwanted regions. In this work, we propose pix2pix-zero, an image-to-image translation method that can preserve the content of the original image without manual prompting. We first automatically discover editing directions that reflect desired edits in the text embedding space. To preserve the general content structure after editing, we further propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process. In addition, our method does not need additional training for these edits and can directly use the existing pre-trained text-to-image diffusion model. We conduct extensive experiments and show that our method outperforms existing and concurrent works for both real and synthetic image editing.*
-
-You can find additional information about Pix2Pix Zero on the [project page](https://pix2pixzero.github.io/),  [original codebase](https://github.com/pix2pixzero/pix2pix-zero), and try it out in a [demo](https://huggingface.co/spaces/pix2pix-zero-library/pix2pix-zero-demo).
-
-## Tips
-
-* The pipeline can be conditioned on real input images. Check out the code examples below to know more.
-* The pipeline exposes two arguments namely `source_embeds` and `target_embeds`
-that let you control the direction of the semantic edits in the final image to be generated. Let's say,
-you wanted to translate from "cat" to "dog". In this case, the edit direction will be "cat -> dog". To reflect
-this in the pipeline, you simply have to set the embeddings related to the phrases including "cat" to
-`source_embeds` and "dog" to `target_embeds`. Refer to the code example below for more details.
-* When you're using this pipeline from a prompt, specify the _source_ concept in the prompt. Taking
-the above example, a valid input prompt would be: "a high resolution painting of a **cat** in the style of van gogh".
-* If you wanted to reverse the direction in the example above, i.e., "dog -> cat", then it's recommended to:
-    * Swap the `source_embeds` and `target_embeds`.
-    * Change the input prompt to include "dog".
-* To learn more about how the source and target embeddings are generated, refer to the [original paper](https://arxiv.org/abs/2302.03027). Below, we also provide some directions on how to generate the embeddings.
-* Note that the quality of the outputs generated with this pipeline is dependent on how good the `source_embeds` and `target_embeds` are. Please, refer to [this discussion](#generating-source-and-target-embeddings) for some suggestions on the topic.
-
-## Available Pipelines:
-
-| Pipeline | Tasks | Demo
-|---|---|:---:|
-| [StableDiffusionPix2PixZeroPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py) | *Text-Based Image Editing* | [🤗 Space](https://huggingface.co/spaces/pix2pix-zero-library/pix2pix-zero-demo) |
-
-<!-- TODO: add Colab -->
-
-## Usage example
-
-### Based on an image generated with the input prompt
-
-```python
-import requests
-import torch
-
-from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline
-
-
-def download(embedding_url, local_filepath):
-    r = requests.get(embedding_url)
-    with open(local_filepath, "wb") as f:
-        f.write(r.content)
-
-
-model_ckpt = "CompVis/stable-diffusion-v1-4"
-pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
-    model_ckpt, conditions_input_image=False, torch_dtype=torch.float16
-)
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-pipeline.to("cuda")
-
-prompt = "a high resolution painting of a cat in the style of van gogh"
-src_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/cat.pt"
-target_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/dog.pt"
-
-for url in [src_embs_url, target_embs_url]:
-    download(url, url.split("/")[-1])
-
-src_embeds = torch.load(src_embs_url.split("/")[-1])
-target_embeds = torch.load(target_embs_url.split("/")[-1])
-
-image = pipeline(
-    prompt,
-    source_embeds=src_embeds,
-    target_embeds=target_embeds,
-    num_inference_steps=50,
-    cross_attention_guidance_amount=0.15,
-).images[0]
-image
-```
-
-### Based on an input image
-
-When the pipeline is conditioned on an input image, we first obtain an inverted
-noise from it using a `DDIMInverseScheduler` with the help of a generated caption. Then the inverted noise is used to start the generation process.
-
-First, let's load our pipeline:
-
-```py
-import torch
-from transformers import BlipForConditionalGeneration, BlipProcessor
-from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline
-
-captioner_id = "Salesforce/blip-image-captioning-base"
-processor = BlipProcessor.from_pretrained(captioner_id)
-model = BlipForConditionalGeneration.from_pretrained(captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True)
-
-sd_model_ckpt = "CompVis/stable-diffusion-v1-4"
-pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
-    sd_model_ckpt,
-    caption_generator=model,
-    caption_processor=processor,
-    torch_dtype=torch.float16,
-    safety_checker=None,
-)
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
-pipeline.enable_model_cpu_offload()
-```
-
-Then, we load an input image for conditioning and obtain a suitable caption for it:
-
-```py
-from diffusers.utils import load_image
-
-img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"
-raw_image = load_image(url).resize((512, 512))
-caption = pipeline.generate_caption(raw_image)
-caption
-```
-
-Then we employ the generated caption and the input image to get the inverted noise:
-
-```py
-generator = torch.manual_seed(0)
-inv_latents = pipeline.invert(caption, image=raw_image, generator=generator).latents
-```
-
-Now, generate the image with edit directions:
-
-```py
-# See the "Generating source and target embeddings" section below to
-# automate the generation of these captions with a pre-trained model like Flan-T5 as explained below.
-source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]
-target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"]
-
-source_embeds = pipeline.get_embeds(source_prompts, batch_size=2)
-target_embeds = pipeline.get_embeds(target_prompts, batch_size=2)
-
-
-image = pipeline(
-    caption,
-    source_embeds=source_embeds,
-    target_embeds=target_embeds,
-    num_inference_steps=50,
-    cross_attention_guidance_amount=0.15,
-    generator=generator,
-    latents=inv_latents,
-    negative_prompt=caption,
-).images[0]
-image
-```
-
-## Generating source and target embeddings
-
-The authors originally used the [GPT-3 API](https://openai.com/api/) to generate the source and target captions for discovering
-edit directions. However, we can also leverage open source and public models for the same purpose.
-Below, we provide an end-to-end example with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model
-for generating captions and [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for
-computing embeddings on the generated captions.
-
-**1. Load the generation model**:
-
-```py
-import torch
-from transformers import AutoTokenizer, T5ForConditionalGeneration
-
-tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)
-```
-
-**2. Construct a starting prompt**:
-
-```py
-source_concept = "cat"
-target_concept = "dog"
-
-source_text = f"Provide a caption for images containing a {source_concept}. "
-"The captions should be in English and should be no longer than 150 characters."
-
-target_text = f"Provide a caption for images containing a {target_concept}. "
-"The captions should be in English and should be no longer than 150 characters."
-```
-
-Here, we're interested in the "cat -> dog" direction.
-
-**3. Generate captions**:
-
-We can use a utility like so for this purpose.
-
-```py
-def generate_captions(input_prompt):
-    input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda")
-
-    outputs = model.generate(
-        input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
-    )
-    return tokenizer.batch_decode(outputs, skip_special_tokens=True)
-```
-
-And then we just call it to generate our captions:
-
-```py
-source_captions = generate_captions(source_text)
-target_captions = generate_captions(target_concept)
-print(source_captions, target_captions, sep='\n')
-```
-
-We encourage you to play around with the different parameters supported by the
-`generate()` method ([documentation](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin.generate)) for the generation quality you are looking for.
-
-**4. Load the embedding model**:
-
-Here, we need to use the same text encoder model used by the subsequent Stable Diffusion model.
-
-```py
-from diffusers import StableDiffusionPix2PixZeroPipeline
-
-pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
-    "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
-)
-pipeline = pipeline.to("cuda")
-tokenizer = pipeline.tokenizer
-text_encoder = pipeline.text_encoder
-```
-
-**5. Compute embeddings**:
-
-```py
-import torch
-
-def embed_captions(sentences, tokenizer, text_encoder, device="cuda"):
-    with torch.no_grad():
-        embeddings = []
-        for sent in sentences:
-            text_inputs = tokenizer(
-                sent,
-                padding="max_length",
-                max_length=tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
-            embeddings.append(prompt_embeds)
-    return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)
-
-source_embeddings = embed_captions(source_captions, tokenizer, text_encoder)
-target_embeddings = embed_captions(target_captions, tokenizer, text_encoder)
-```
-
-And you're done! [Here](https://colab.research.google.com/drive/1tz2C1EdfZYAPlzXXbTnf-5PRBiR8_R1F?usp=sharing) is a Colab Notebook that you can use to interact with the entire process.
-
-Now, you can use these embeddings directly while calling the pipeline:
-
-```py
-from diffusers import DDIMScheduler
-
-pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-
-image = pipeline(
-    prompt,
-    source_embeds=source_embeddings,
-    target_embeds=target_embeddings,
-    num_inference_steps=50,
-    cross_attention_guidance_amount=0.15,
-).images[0]
-image
-```
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## StableDiffusionPix2PixZeroPipeline
-[[autodoc]] StableDiffusionPix2PixZeroPipeline
-	- __call__
-	- all

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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

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

@@ -1,35 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# PNDM
-
-[Pseudo Numerical Methods for Diffusion Models on Manifolds](https://huggingface.co/papers/2202.09778) (PNDM) is by Luping Liu, Yi Ren, Zhijie Lin and Zhou Zhao.
-
-The abstract from the paper is:
-
-*Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at a high speedup rate, which limit their practicability. To accelerate the inference process while keeping the sample quality, we provide a fresh perspective that DDPMs should be treated as solving differential equations on manifolds. Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs). Specifically, we figure out how to solve differential equations on manifolds and show that DDIMs are simple cases of pseudo numerical methods. We change several classical numerical methods to corresponding pseudo numerical methods and find that the pseudo linear multi-step method is the best in most situations. According to our experiments, by directly using pre-trained models on Cifar10, CelebA and LSUN, PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup), significantly outperform DDIMs with 250 steps (by around 0.4 in FID) and have good generalization on different variance schedules.*
-
-The original codebase can be found at [luping-liu/PNDM](https://github.com/luping-liu/PNDM).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## PNDMPipeline
-[[autodoc]] PNDMPipeline
-	- all
-	- __call__
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput

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

@@ -1,37 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# RePaint
-
-[RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2201.09865) is by Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool.
-
-The abstract from the paper is:
-
-*Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks.
-RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions.*
-
-The original codebase can be found at [andreas128/RePaint](https://github.com/andreas128/RePaint).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-
-## RePaintPipeline
-[[autodoc]] RePaintPipeline
-	- all
-	- __call__
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput

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

@@ -1,35 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Score SDE VE
-
-[Score-Based Generative Modeling through Stochastic Differential Equations](https://huggingface.co/papers/2011.13456) (Score SDE) is by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon and Ben Poole. This pipeline implements the variance expanding (VE) variant of the stochastic differential equation method.
-
-The abstract from the paper is:
-
-*Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.*
-
-The original codebase can be found at [yang-song/score_sde_pytorch](https://github.com/yang-song/score_sde_pytorch).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## ScoreSdeVePipeline
-[[autodoc]] ScoreSdeVePipeline
-	- all
-	- __call__
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+<!--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