small nits.

adding lora to z-image controlnet pipelines

.github/workflows/benchmark.yml

@@ -62,20 +62,6 @@ jobs:
         with:
           name: benchmark_test_reports
           path: benchmarks/${{ env.BASE_PATH }}
-      
-      # TODO: enable this once the connection problem has been resolved.
-      - name: Update benchmarking results to DB
-        env:
-          PGDATABASE: metrics
-          PGHOST: ${{ secrets.DIFFUSERS_BENCHMARKS_PGHOST }}
-          PGUSER: transformers_benchmarks
-          PGPASSWORD: ${{ secrets.DIFFUSERS_BENCHMARKS_PGPASSWORD }}
-          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-        run: |
-          git config --global --add safe.directory /__w/diffusers/diffusers
-          commit_id=$GITHUB_SHA
-          commit_msg=$(git show -s --format=%s "$commit_id" | cut -c1-70)
-          cd benchmarks && python populate_into_db.py "$BRANCH_NAME" "$commit_id" "$commit_msg"
 
       - name: Report success status
         if: ${{ success() }}

.github/workflows/pypi_publish.yaml

@@ -54,7 +54,6 @@ jobs:
           python -m pip install --upgrade pip
           pip install -U setuptools wheel twine
           pip install -U torch --index-url https://download.pytorch.org/whl/cpu
-          pip install -U transformers
 
       - name: Build the dist files
         run: python setup.py bdist_wheel && python setup.py sdist
@@ -69,6 +68,8 @@ jobs:
         run: |
           pip install diffusers && pip uninstall diffusers -y
           pip install -i https://test.pypi.org/simple/ diffusers
+          pip install -U transformers
+          python utils/print_env.py
           python -c "from diffusers import __version__; print(__version__)"
           python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('fusing/unet-ldm-dummy-update'); pipe()"
           python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=None); pipe('ah suh du')"

benchmarks/populate_into_db.py

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

docs/source/en/_toctree.yml

@@ -194,6 +194,8 @@
   title: Model accelerators and hardware
 - isExpanded: false
   sections:
+  - local: using-diffusers/helios
+    title: Helios
   - local: using-diffusers/consisid
     title: ConsisID
   - local: using-diffusers/sdxl
@@ -350,6 +352,8 @@
         title: FluxTransformer2DModel
       - local: api/models/glm_image_transformer2d
         title: GlmImageTransformer2DModel
+      - local: api/models/helios_transformer3d
+        title: HeliosTransformer3DModel
       - local: api/models/hidream_image_transformer
         title: HiDreamImageTransformer2DModel
       - local: api/models/hunyuan_transformer2d
@@ -456,6 +460,8 @@
         title: AutoencoderKLQwenImage
       - local: api/models/autoencoder_kl_wan
         title: AutoencoderKLWan
+      - local: api/models/autoencoder_rae
+        title: AutoencoderRAE
       - local: api/models/consistency_decoder_vae
         title: ConsistencyDecoderVAE
       - local: api/models/autoencoder_oobleck
@@ -625,7 +631,6 @@
           title: Image-to-image
         - local: api/pipelines/stable_diffusion/inpaint
           title: Inpainting
-
         - local: api/pipelines/stable_diffusion/latent_upscale
           title: Latent upscaler
         - local: api/pipelines/stable_diffusion/ldm3d_diffusion
@@ -674,6 +679,8 @@
         title: ConsisID
       - local: api/pipelines/framepack
         title: Framepack
+      - local: api/pipelines/helios
+        title: Helios
       - local: api/pipelines/hunyuan_video
         title: HunyuanVideo
       - local: api/pipelines/hunyuan_video15
@@ -745,6 +752,10 @@
       title: FlowMatchEulerDiscreteScheduler
     - local: api/schedulers/flow_match_heun_discrete
       title: FlowMatchHeunDiscreteScheduler
+    - local: api/schedulers/helios_dmd
+      title: HeliosDMDScheduler
+    - local: api/schedulers/helios
+      title: HeliosScheduler
     - local: api/schedulers/heun
       title: HeunDiscreteScheduler
     - local: api/schedulers/ipndm

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

@@ -23,6 +23,7 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`AuraFlowLoraLoaderMixin`] provides similar functions for [AuraFlow](https://huggingface.co/fal/AuraFlow).
 - [`LTXVideoLoraLoaderMixin`] provides similar functions for [LTX-Video](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
 - [`SanaLoraLoaderMixin`] provides similar functions for [Sana](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana).
+- [`HeliosLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/helios).
 - [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
 - [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
@@ -86,6 +87,10 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 
 [[autodoc]] loaders.lora_pipeline.SanaLoraLoaderMixin
 
+## HeliosLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.HeliosLoraLoaderMixin
+
 ## HunyuanVideoLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.HunyuanVideoLoraLoaderMixin

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

@@ -0,0 +1,89 @@
+<!-- Copyright 2026 The NYU Vision-X and HuggingFace Teams. 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.
+-->
+
+# AutoencoderRAE
+
+The Representation Autoencoder (RAE) model introduced in [Diffusion Transformers with Representation Autoencoders](https://huggingface.co/papers/2510.11690) by Boyang Zheng, Nanye Ma, Shengbang Tong, Saining Xie from NYU VISIONx.
+
+RAE combines a frozen pretrained vision encoder (DINOv2, SigLIP2, or MAE) with a trainable ViT-MAE-style decoder. In the two-stage RAE training recipe, the autoencoder is trained in stage 1 (reconstruction), and then a diffusion model is trained on the resulting latent space in stage 2 (generation).
+
+The following RAE models are released and supported in Diffusers:
+
+| Model | Encoder | Latent shape (224px input) |
+|:------|:--------|:---------------------------|
+| [`nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08`](https://huggingface.co/nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08) | DINOv2-base | 768 x 16 x 16 |
+| [`nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08-i512`](https://huggingface.co/nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08-i512) | DINOv2-base (512px) | 768 x 32 x 32 |
+| [`nyu-visionx/RAE-dinov2-wReg-small-ViTXL-n08`](https://huggingface.co/nyu-visionx/RAE-dinov2-wReg-small-ViTXL-n08) | DINOv2-small | 384 x 16 x 16 |
+| [`nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08`](https://huggingface.co/nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08) | DINOv2-large | 1024 x 16 x 16 |
+| [`nyu-visionx/RAE-siglip2-base-p16-i256-ViTXL-n08`](https://huggingface.co/nyu-visionx/RAE-siglip2-base-p16-i256-ViTXL-n08) | SigLIP2-base | 768 x 16 x 16 |
+| [`nyu-visionx/RAE-mae-base-p16-ViTXL-n08`](https://huggingface.co/nyu-visionx/RAE-mae-base-p16-ViTXL-n08) | MAE-base | 768 x 16 x 16 |
+
+## Loading a pretrained model
+
+```python
+from diffusers import AutoencoderRAE
+
+model = AutoencoderRAE.from_pretrained(
+    "nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08"
+).to("cuda").eval()
+```
+
+## Encoding and decoding a real image
+
+```python
+import torch
+from diffusers import AutoencoderRAE
+from diffusers.utils import load_image
+from torchvision.transforms.functional import to_tensor, to_pil_image
+
+model = AutoencoderRAE.from_pretrained(
+    "nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08"
+).to("cuda").eval()
+
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
+image = image.convert("RGB").resize((224, 224))
+x = to_tensor(image).unsqueeze(0).to("cuda")  # (1, 3, 224, 224), values in [0, 1]
+
+with torch.no_grad():
+    latents = model.encode(x).latent        # (1, 768, 16, 16)
+    recon = model.decode(latents).sample     # (1, 3, 256, 256)
+
+recon_image = to_pil_image(recon[0].clamp(0, 1).cpu())
+recon_image.save("recon.png")
+```
+
+## Latent normalization
+
+Some pretrained checkpoints include per-channel `latents_mean` and `latents_std` statistics for normalizing the latent space. When present, `encode` and `decode` automatically apply the normalization and denormalization, respectively.
+
+```python
+model = AutoencoderRAE.from_pretrained(
+    "nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08"
+).to("cuda").eval()
+
+# Latent normalization is handled automatically inside encode/decode
+# when the checkpoint config includes latents_mean/latents_std.
+with torch.no_grad():
+    latents = model.encode(x).latent   # normalized latents
+    recon = model.decode(latents).sample
+```
+
+## AutoencoderRAE
+
+[[autodoc]] AutoencoderRAE
+  - encode
+  - decode
+  - all
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/helios_transformer3d.md

@@ -0,0 +1,35 @@
+<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# HeliosTransformer3DModel
+
+A 14B Real-Time Autogressive Diffusion Transformer model (support T2V, I2V and V2V) for 3D video-like data from [Helios](https://github.com/PKU-YuanGroup/Helios) was introduced in [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/) by Peking University & ByteDance & etc.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import HeliosTransformer3DModel
+
+# Best Quality
+transformer = HeliosTransformer3DModel.from_pretrained("BestWishYsh/Helios-Base", subfolder="transformer", torch_dtype=torch.bfloat16)
+# Intermediate Weight
+transformer = HeliosTransformer3DModel.from_pretrained("BestWishYsh/Helios-Mid", subfolder="transformer", torch_dtype=torch.bfloat16)
+# Best Efficiency
+transformer = HeliosTransformer3DModel.from_pretrained("BestWishYsh/Helios-Distilled", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## HeliosTransformer3DModel
+
+[[autodoc]] HeliosTransformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -0,0 +1,465 @@
+<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License. -->
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+    <a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
+      <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+    </a>
+  </div>
+</div>
+
+# Helios
+
+[Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/) from Peking University & ByteDance & etc, by Shenghai Yuan, Yuanyang Yin, Zongjian Li, Xinwei Huang, Xiao Yang, Li Yuan.
+
+*  <u>We introduce Helios, the first 14B video generation model that runs at 17 FPS on a single NVIDIA H100 GPU and supports minute-scale generation while matching a strong baseline in quality.</u> We make breakthroughs along three key dimensions: (1) robustness to long-video drifting without commonly used anti-drift heuristics such as self-forcing, error banks, or keyframe sampling; (2) real-time generation without standard acceleration techniques such as KV-cache, causal masking, or sparse attention; and (3) training without parallelism or sharding frameworks, enabling image-diffusion-scale batch sizes while fitting up to four 14B models within 80 GB of GPU memory. Specifically, Helios is a 14B autoregressive diffusion model with a unified input representation that natively supports T2V, I2V, and V2V tasks. To mitigate drifting in long-video generation, we characterize its typical failure modes and propose simple yet effective training strategies that explicitly simulate drifting during training, while eliminating repetitive motion at its source. For efficiency, we heavily compress the historical and noisy context and reduce the number of sampling steps, yielding computational costs comparable to—or lower than—those of 1.3B video generative models. Moreover, we introduce infrastructure-level optimizations that accelerate both inference and training while reducing memory consumption. Extensive experiments demonstrate that Helios consistently outperforms prior methods on both short- and long-video generation. All the code and models are available at [this https URL](https://pku-yuangroup.github.io/Helios-Page).
+
+The following Helios models are supported in Diffusers:
+
+- [Helios-Base](https://huggingface.co/BestWishYsh/Helios-Base): Best Quality, with v-prediction, standard CFG and custom HeliosScheduler.
+- [Helios-Mid](https://huggingface.co/BestWishYsh/Helios-Mid): Intermediate Weight, with v-prediction, CFG-Zero* and custom HeliosScheduler.
+- [Helios-Distilled](https://huggingface.co/BestWishYsh/Helios-Distilled): Best Efficiency, with x0-prediction and custom HeliosDMDScheduler.
+
+> [!TIP]
+> Click on the Helios models in the right sidebar for more examples of video generation.
+
+### Optimizing Memory and Inference Speed
+
+The example below demonstrates how to generate a video from text optimized for memory or inference speed.
+
+<hfoptions id="optimization">
+<hfoption id="memory">
+
+Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
+
+The Helios model below requires ~19GB of VRAM.
+
+```py
+import torch
+from diffusers import AutoModel, HeliosPipeline
+from diffusers.hooks.group_offloading import apply_group_offloading
+from diffusers.utils import export_to_video
+
+vae = AutoModel.from_pretrained("BestWishYsh/Helios-Base", subfolder="vae", torch_dtype=torch.float32)
+
+# group-offloading
+pipeline = HeliosPipeline.from_pretrained(
+    "BestWishYsh/Helios-Base",
+    vae=vae,
+    torch_dtype=torch.bfloat16
+)
+pipeline.enable_group_offload(
+    onload_device=torch.device("cuda"),
+    offload_device=torch.device("cpu"),
+    offload_type="block_level",
+    num_blocks_per_group=1,
+    use_stream=True,
+    record_stream=True,
+)
+
+prompt = """
+A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue 
+and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with 
+a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, 
+allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades 
+of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and 
+the vivid colors of its surroundings. A close-up shot with dynamic movement.
+"""
+negative_prompt = """
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
+"""
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_frames=99,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_base_t2v_output.mp4", fps=24)
+```
+
+</hfoption>
+<hfoption id="inference speed">
+
+[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster. [Attention Backends](../../optimization/attention_backends) such as FlashAttention and SageAttention can significantly increase speed by optimizing the computation of the attention mechanism. [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
+
+```py
+import torch
+from diffusers import AutoModel, HeliosPipeline
+from diffusers.utils import export_to_video
+
+vae = AutoModel.from_pretrained("BestWishYsh/Helios-Base", subfolder="vae", torch_dtype=torch.float32)
+
+pipeline = HeliosPipeline.from_pretrained(
+    "BestWishYsh/Helios-Base",
+    vae=vae,
+    torch_dtype=torch.bfloat16
+)
+pipeline.to("cuda")
+
+# attention backend
+# pipeline.transformer.set_attention_backend("flash")
+pipeline.transformer.set_attention_backend("_flash_3_hub") # For Hopper GPUs
+
+# torch.compile
+torch.backends.cudnn.benchmark = True
+pipeline.text_encoder.compile(mode="max-autotune-no-cudagraphs", dynamic=False)
+pipeline.vae.compile(mode="max-autotune-no-cudagraphs", dynamic=False)
+pipeline.transformer.compile(mode="max-autotune-no-cudagraphs", dynamic=False)
+
+prompt = """
+A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue 
+and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with 
+a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, 
+allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades 
+of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and 
+the vivid colors of its surroundings. A close-up shot with dynamic movement.
+"""
+negative_prompt = """
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
+"""
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_frames=99,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_base_t2v_output.mp4", fps=24)
+```
+
+</hfoption>
+</hfoptions>
+
+
+### Generation with Helios-Base
+
+The example below demonstrates how to use Helios-Base to generate video based on text, image or video.
+
+<hfoptions id="Helios-Base usage">
+<hfoption id="usage">
+
+```python
+import torch
+from diffusers import AutoModel, HeliosPipeline
+from diffusers.utils import export_to_video, load_video, load_image
+
+vae = AutoModel.from_pretrained("BestWishYsh/Helios-Base", subfolder="vae", torch_dtype=torch.float32)
+
+pipeline = HeliosPipeline.from_pretrained(
+    "BestWishYsh/Helios-Base",
+    vae=vae,
+    torch_dtype=torch.bfloat16
+)
+pipeline.to("cuda")
+
+negative_prompt = """
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
+"""
+
+# For Text-to-Video
+prompt = """
+A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue 
+and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with 
+a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, 
+allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades 
+of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and 
+the vivid colors of its surroundings. A close-up shot with dynamic movement.
+"""
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_frames=99,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_base_t2v_output.mp4", fps=24)
+
+# For Image-to-Video
+prompt = """
+A towering emerald wave surges forward, its crest curling with raw power and energy. Sunlight glints off the translucent water, 
+illuminating the intricate textures and deep green hues within the wave’s body. A thick spray erupts from the breaking crest, 
+casting a misty veil that dances above the churning surface. As the perspective widens, the immense scale of the wave becomes 
+apparent, revealing the restless expanse of the ocean stretching beyond. The scene captures the ocean’s untamed beauty and 
+relentless force, with every droplet and ripple shimmering in the light. The dynamic motion and vivid colors evoke both awe and 
+respect for nature’s might.
+"""
+image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/wave.jpg"
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    image=load_image(image_path).resize((640, 384)),
+    num_frames=99,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_base_i2v_output.mp4", fps=24)
+
+# For Video-to-Video
+prompt = """
+A bright yellow Lamborghini Huracn Tecnica speeds along a curving mountain road, surrounded by lush green trees 
+under a partly cloudy sky. The car's sleek design and vibrant color stand out against the natural backdrop, 
+emphasizing its dynamic movement. The road curves gently, with a guardrail visible on one side, adding depth to 
+the scene. The motion blur captures the sense of speed and energy, creating a thrilling and exhilarating atmosphere. 
+A front-facing shot from a slightly elevated angle, highlighting the car's aggressive stance and the surrounding greenery.
+"""
+video_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/car.mp4"
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    video=load_video(video_path),
+    num_frames=99,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_base_v2v_output.mp4", fps=24)
+```
+
+</hfoption>
+</hfoptions>
+
+
+### Generation with Helios-Mid
+
+The example below demonstrates how to use Helios-Mid to generate video based on text, image or video.
+
+<hfoptions id="Helios-Mid usage">
+<hfoption id="usage">
+
+```python
+import torch
+from diffusers import AutoModel, HeliosPyramidPipeline
+from diffusers.utils import export_to_video, load_video, load_image
+
+vae = AutoModel.from_pretrained("BestWishYsh/Helios-Mid", subfolder="vae", torch_dtype=torch.float32)
+
+pipeline = HeliosPyramidPipeline.from_pretrained(
+    "BestWishYsh/Helios-Mid",
+    vae=vae,
+    torch_dtype=torch.bfloat16
+)
+pipeline.to("cuda")
+
+negative_prompt = """
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
+"""
+
+# For Text-to-Video
+prompt = """
+A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue 
+and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with 
+a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, 
+allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades 
+of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and 
+the vivid colors of its surroundings. A close-up shot with dynamic movement.
+"""
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_frames=99,
+    pyramid_num_inference_steps_list=[20, 20, 20],
+    guidance_scale=5.0,
+    use_zero_init=True,
+    zero_steps=1,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_pyramid_t2v_output.mp4", fps=24)
+
+# For Image-to-Video
+prompt = """
+A towering emerald wave surges forward, its crest curling with raw power and energy. Sunlight glints off the translucent water, 
+illuminating the intricate textures and deep green hues within the wave’s body. A thick spray erupts from the breaking crest, 
+casting a misty veil that dances above the churning surface. As the perspective widens, the immense scale of the wave becomes 
+apparent, revealing the restless expanse of the ocean stretching beyond. The scene captures the ocean’s untamed beauty and 
+relentless force, with every droplet and ripple shimmering in the light. The dynamic motion and vivid colors evoke both awe and 
+respect for nature’s might.
+"""
+image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/wave.jpg"
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    image=load_image(image_path).resize((640, 384)),
+    num_frames=99,
+    pyramid_num_inference_steps_list=[20, 20, 20],
+    guidance_scale=5.0,
+    use_zero_init=True,
+    zero_steps=1,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_pyramid_i2v_output.mp4", fps=24)
+
+# For Video-to-Video
+prompt = """
+A bright yellow Lamborghini Huracn Tecnica speeds along a curving mountain road, surrounded by lush green trees 
+under a partly cloudy sky. The car's sleek design and vibrant color stand out against the natural backdrop, 
+emphasizing its dynamic movement. The road curves gently, with a guardrail visible on one side, adding depth to 
+the scene. The motion blur captures the sense of speed and energy, creating a thrilling and exhilarating atmosphere. 
+A front-facing shot from a slightly elevated angle, highlighting the car's aggressive stance and the surrounding greenery.
+"""
+video_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/car.mp4"
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    video=load_video(video_path),
+    num_frames=99,
+    pyramid_num_inference_steps_list=[20, 20, 20],
+    guidance_scale=5.0,
+    use_zero_init=True,
+    zero_steps=1,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_pyramid_v2v_output.mp4", fps=24)
+```
+
+</hfoption>
+</hfoptions>
+
+
+### Generation with Helios-Distilled
+
+The example below demonstrates how to use Helios-Distilled to generate video based on text, image or video.
+
+<hfoptions id="Helios-Distilled usage">
+<hfoption id="usage">
+
+```python
+import torch
+from diffusers import AutoModel, HeliosPyramidPipeline
+from diffusers.utils import export_to_video, load_video, load_image
+
+vae = AutoModel.from_pretrained("BestWishYsh/Helios-Distilled", subfolder="vae", torch_dtype=torch.float32)
+
+pipeline = HeliosPyramidPipeline.from_pretrained(
+    "BestWishYsh/Helios-Distilled",
+    vae=vae,
+    torch_dtype=torch.bfloat16
+)
+pipeline.to("cuda")
+
+negative_prompt = """
+Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality,
+low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured,
+misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards
+"""
+
+# For Text-to-Video
+prompt = """
+A vibrant tropical fish swimming gracefully among colorful coral reefs in a clear, turquoise ocean. The fish has bright blue 
+and yellow scales with a small, distinctive orange spot on its side, its fins moving fluidly. The coral reefs are alive with 
+a variety of marine life, including small schools of colorful fish and sea turtles gliding by. The water is crystal clear, 
+allowing for a view of the sandy ocean floor below. The reef itself is adorned with a mix of hard and soft corals in shades 
+of red, orange, and green. The photo captures the fish from a slightly elevated angle, emphasizing its lively movements and 
+the vivid colors of its surroundings. A close-up shot with dynamic movement.
+"""
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_frames=240,
+    pyramid_num_inference_steps_list=[2, 2, 2],
+    guidance_scale=1.0,
+    is_amplify_first_chunk=True,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_distilled_t2v_output.mp4", fps=24)
+
+# For Image-to-Video
+prompt = """
+A towering emerald wave surges forward, its crest curling with raw power and energy. Sunlight glints off the translucent water, 
+illuminating the intricate textures and deep green hues within the wave’s body. A thick spray erupts from the breaking crest, 
+casting a misty veil that dances above the churning surface. As the perspective widens, the immense scale of the wave becomes 
+apparent, revealing the restless expanse of the ocean stretching beyond. The scene captures the ocean’s untamed beauty and 
+relentless force, with every droplet and ripple shimmering in the light. The dynamic motion and vivid colors evoke both awe and 
+respect for nature’s might.
+"""
+image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/wave.jpg"
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    image=load_image(image_path).resize((640, 384)),
+    num_frames=240,
+    pyramid_num_inference_steps_list=[2, 2, 2],
+    guidance_scale=1.0,
+    is_amplify_first_chunk=True,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_distilled_i2v_output.mp4", fps=24)
+
+# For Video-to-Video
+prompt = """
+A bright yellow Lamborghini Huracn Tecnica speeds along a curving mountain road, surrounded by lush green trees 
+under a partly cloudy sky. The car's sleek design and vibrant color stand out against the natural backdrop, 
+emphasizing its dynamic movement. The road curves gently, with a guardrail visible on one side, adding depth to 
+the scene. The motion blur captures the sense of speed and energy, creating a thrilling and exhilarating atmosphere. 
+A front-facing shot from a slightly elevated angle, highlighting the car's aggressive stance and the surrounding greenery.
+"""
+video_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/car.mp4"
+
+output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    video=load_video(video_path),
+    num_frames=240,
+    pyramid_num_inference_steps_list=[2, 2, 2],
+    guidance_scale=1.0,
+    is_amplify_first_chunk=True,
+    generator=torch.Generator("cuda").manual_seed(42),
+).frames[0]
+export_to_video(output, "helios_distilled_v2v_output.mp4", fps=24)
+```
+
+</hfoption>
+</hfoptions>
+
+
+## HeliosPipeline
+
+[[autodoc]] HeliosPipeline
+
+  - all
+  - __call__
+
+## HeliosPyramidPipeline
+
+[[autodoc]] HeliosPyramidPipeline
+
+  - all
+  - __call__
+
+## HeliosPipelineOutput
+
+[[autodoc]] pipelines.helios.pipeline_output.HeliosPipelineOutput

docs/source/en/api/schedulers/helios.md

@@ -0,0 +1,20 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# HeliosScheduler
+
+`HeliosScheduler` is based on the pyramidal flow-matching sampling introduced in [Helios](https://huggingface.co/papers).
+
+## HeliosScheduler
+[[autodoc]] HeliosScheduler
+
+scheduling_helios

docs/source/en/api/schedulers/helios_dmd.md

@@ -0,0 +1,20 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# HeliosDMDScheduler
+
+`HeliosDMDScheduler` is based on the pyramidal flow-matching sampling introduced in [Helios](https://huggingface.co/papers).
+
+## HeliosDMDScheduler
+[[autodoc]] HeliosDMDScheduler
+
+scheduling_helios_dmd

docs/source/en/modular_diffusers/custom_blocks.md

@@ -332,4 +332,49 @@ Make your custom block work with Mellon's visual interface. See the [Mellon Cust
 Browse the [Modular Diffusers Custom Blocks](https://huggingface.co/collections/diffusers/modular-diffusers-custom-blocks) collection for inspiration and ready-to-use blocks.
 
 </hfoption>
-</hfoptions>
+</hfoptions>
+
+## Dependencies
+
+Declaring package dependencies in custom blocks prevents runtime import errors later on. Diffusers validates the dependencies and returns a warning if a package is missing or incompatible.
+
+Set a `_requirements` attribute in your block class, mapping package names to version specifiers.
+
+```py
+from diffusers.modular_pipelines import PipelineBlock
+
+class MyCustomBlock(PipelineBlock):
+    _requirements = {
+        "transformers": ">=4.44.0",
+        "sentencepiece": ">=0.2.0"
+    }
+```
+
+When there are blocks with different requirements, Diffusers merges their requirements.
+
+```py
+from diffusers.modular_pipelines import SequentialPipelineBlocks
+
+class BlockA(PipelineBlock):
+    _requirements = {"transformers": ">=4.44.0"}
+    # ...
+
+class BlockB(PipelineBlock):
+    _requirements = {"sentencepiece": ">=0.2.0"}
+    # ...
+
+pipe = SequentialPipelineBlocks.from_blocks_dict({
+    "block_a": BlockA,
+    "block_b": BlockB,
+})
+```
+
+When this block is saved with [`~ModularPipeline.save_pretrained`], the requirements are saved to the `modular_config.json` file. When this block is loaded, Diffusers checks each requirement against the current environment. If there is a mismatch or a package isn't found, Diffusers returns the following warning.
+
+```md
+# missing package
+xyz-package was specified in the requirements but wasn't found in the current environment.
+
+# version mismatch
+xyz requirement 'specific-version' is not satisfied by the installed version 'actual-version'. Things might work unexpected.
+```

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

@@ -97,5 +97,32 @@ If the custom model inherits from the [`ModelMixin`] class, it gets access to th
 > )
 > ```
 
+### Saving custom models
+
+Use [`~ConfigMixin.register_for_auto_class`] to add the `auto_map` entry to `config.json` automatically when saving. This avoids having to manually edit the config file.
+
+```py
+# my_model.py
+from diffusers import ModelMixin, ConfigMixin
+
+class MyCustomModel(ModelMixin, ConfigMixin):
+    ...
+
+MyCustomModel.register_for_auto_class("AutoModel")
+
+model = MyCustomModel(...)
+model.save_pretrained("./my_model")
+```
+
+The saved `config.json` will include the `auto_map` field.
+
+```json
+{
+  "auto_map": {
+    "AutoModel": "my_model.MyCustomModel"
+  }
+}
+```
+
 > [!NOTE]
 > Learn more about implementing custom models in the [Community components](../using-diffusers/custom_pipeline_overview#community-components) guide.

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

@@ -60,7 +60,7 @@ export_to_video(video.frames[0], "output.mp4", fps=8)
   <tr>
     <th style="text-align: center;">Face Image</th>
     <th style="text-align: center;">Video</th>
-    <th style="text-align: center;">Description</th
+    <th style="text-align: center;">Description</th>
   </tr>
   <tr>
     <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/consisid/consisid_image_0.png?download=true" style="height: auto; width: 600px;"></td>

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

@@ -0,0 +1,133 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+# Helios
+
+[Helios](https://github.com/PKU-YuanGroup/Helios) is the first 14B video generation model that runs at 19.5 FPS on a single NVIDIA H100 GPU and supports minute-scale generation while matching the quality of a strong baseline, natively integrating T2V, I2V, and V2V tasks within a unified architecture. The main features of Helios are:
+
+- Without commonly used anti-drifting strategies (eg, self-forcing, error-banks, keyframe sampling, or inverted sampling), Helios generates minute-scale videos with high quality and strong coherence.
+- Without standard acceleration techniques (eg, KV-cache, causal masking, sparse/linear attention, TinyVAE, progressive noise schedules, hidden-state caching, or quantization), Helios achieves 19.5 FPS in end-to-end inference for a 14B video generation model on a single H100 GPU.
+- Introducing optimizations that improve both training and inference throughput while reducing memory consumption. These changes enable training a 14B video generation model without parallelism or sharding infrastructure, with batch sizes comparable to image models.
+
+This guide will walk you through using Helios for use cases.
+
+## Load Model Checkpoints
+
+Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~DiffusionPipeline.from_pretrained`] method.
+
+```python
+import torch
+from diffusers import HeliosPipeline, HeliosPyramidPipeline
+from huggingface_hub import snapshot_download
+
+# For Best Quality
+snapshot_download(repo_id="BestWishYsh/Helios-Base", local_dir="BestWishYsh/Helios-Base")
+pipe = HeliosPipeline.from_pretrained("BestWishYsh/Helios-Base", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+# Intermediate Weight
+snapshot_download(repo_id="BestWishYsh/Helios-Mid", local_dir="BestWishYsh/Helios-Mid")
+pipe = HeliosPyramidPipeline.from_pretrained("BestWishYsh/Helios-Mid", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+# For Best Efficiency
+snapshot_download(repo_id="BestWishYsh/Helios-Distilled", local_dir="BestWishYsh/Helios-Distilled")
+pipe = HeliosPyramidPipeline.from_pretrained("BestWishYsh/Helios-Distilled", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+```
+
+## Text-to-Video Showcases
+
+<table>
+  <tr>
+    <th style="text-align: center;">Prompt</th>
+    <th style="text-align: center;">Generated Video</th>
+  </tr>
+  <tr>
+    <td><small>A Viking warrior driving a modern city bus filled with passengers. The Viking has long blonde hair tied back, a beard, and is adorned with a fur-lined helmet and armor. He wears a traditional tunic and trousers, but also sports a seatbelt as he focuses on navigating the busy streets. The interior of the bus is typical, with rows of seats occupied by diverse passengers going about their daily routines. The exterior shots show the bustling urban environment, including tall buildings and traffic. Medium shot focusing on the Viking at the wheel, with occasional close-ups of his determined expression.
+    </small></td>
+    <td>
+      <video width="4000" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/t2v_showcases1.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+  <tr>
+    <td><small>A documentary-style nature photography shot from a camera truck moving to the left, capturing a crab quickly scurrying into its burrow. The crab has a hard, greenish-brown shell and long claws, moving with determined speed across the sandy ground. Its body is slightly arched as it burrows into the sand, leaving a small trail behind. The background shows a shallow beach with scattered rocks and seashells, and the horizon features a gentle curve of the coastline. The photo has a natural and realistic texture, emphasizing the crab's natural movement and the texture of the sand. A close-up shot from a slightly elevated angle.
+    </small></td>
+    <td>
+      <video width="4000" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/t2v_showcases2.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+</table>
+
+## Image-to-Video Showcases
+
+<table>
+  <tr>
+    <th style="text-align: center;">Image</th>
+    <th style="text-align: center;">Prompt</th>
+    <th style="text-align: center;">Generated Video</th>
+  </tr>
+  <tr>
+    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/i2v_showcases1.jpg" style="height: auto; width: 300px;"></td>
+    <td><small>A sleek red Kia car speeds along a rural road under a cloudy sky, its modern design and dynamic movement emphasized by the blurred motion of the surrounding fields and trees stretching into the distance. The car's glossy exterior reflects the overcast sky, highlighting its aerodynamic shape and sporty stance. The license plate reads "KIA 626," and the vehicle's headlights are on, adding to the sense of motion and energy. The road curves gently, with the car positioned slightly off-center, creating a sense of forward momentum. A dynamic front three-quarter view captures the car's powerful presence against the serene backdrop of rolling hills and scattered trees.
+    </small></td>
+    <td>
+      <video width="2000" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/i2v_showcases1.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+  <tr>
+    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/i2v_showcases2.jpg" style="height: auto; width: 300px;"></td>
+    <td><small>A close-up captures a fluffy orange cat with striking green eyes and white whiskers, gazing intently towards the camera. The cat's fur is soft and well-groomed, with a mix of warm orange and cream tones. Its large, expressive eyes are a vivid green, reflecting curiosity and alertness. The cat's nose is small and pink, and its mouth is slightly open, revealing a hint of its pink tongue. The background is softly blurred, suggesting a cozy indoor setting with neutral tones. The photo has a shallow depth of field, focusing sharply on the cat's face while the background remains out of focus. A close-up shot from a slightly elevated perspective.
+    </small></td>
+    <td>
+      <video width="2000" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/i2v_showcases2.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+</table>
+
+## Interactive-Video Showcases
+
+<table>
+  <tr>
+    <th style="text-align: center;">Prompt</th>
+    <th style="text-align: center;">Generated Video</th>
+  </tr>
+  <tr>
+    <td><small>The prompt can be found <a href="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/interactive_showcases1.txt">here</a></small></td>
+    <td>
+      <video width="680" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/interactive_showcases1.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+  <tr>
+    <td><small>The prompt can be found <a href="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/interactive_showcases2.txt">here</a></small></td>
+    <td>
+      <video width="680" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/interactive_showcases2.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+</table>
+
+## Resources
+
+Learn more about Helios with the following resources.
+- Watch [video1](https://www.youtube.com/watch?v=vd_AgHtOUFQ) and [video2](https://www.youtube.com/watch?v=1GeIU2Dn7UY) for a demonstration of Helios's key features.
+- The research paper, [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/) for more details.

docs/source/zh/_toctree.yml

@@ -132,6 +132,8 @@
   sections:
   - local: using-diffusers/consisid
     title: ConsisID
+  - local: using-diffusers/helios
+    title: Helios
 
 - title: Resources
   isExpanded: false

docs/source/zh/community_projects.md

@@ -26,6 +26,14 @@ http://www.apache.org/licenses/LICENSE-2.0
         <th>项目名称</th>
         <th>描述</th>
     </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/PKU-YuanGroup/Helios"> helios </a></td>
+    <td>Helios：比1.3B更低开销、更快且更强的14B的实时长视频生成模型</td>
+  </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/PKU-YuanGroup/ConsisID"> consisid </a></td>
+    <td>ConsisID：零样本身份保持的文本到视频生成模型</td>
+  </tr>
   <tr style="border-top: 2px solid black">
     <td><a href="https://github.com/carson-katri/dream-textures"> dream-textures </a></td>
     <td>Stable Diffusion内置到Blender</td>

docs/source/zh/using-diffusers/helios.md

@@ -0,0 +1,134 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+# Helios
+
+[Helios](https://github.com/PKU-YuanGroup/Helios) 是首个能够在单张 NVIDIA H100 GPU 上以 19.5 FPS 运行的 14B 视频生成模型。它在支持分钟级视频生成的同时，拥有媲美强大基线模型的生成质量，并在统一架构下原生集成了文生视频（T2V）、图生视频（I2V）和视频生视频（V2V）任务。Helios 的主要特性包括：
+
+- 无需常用的防漂移策略（例如：自强制/self-forcing、误差库/error-banks、关键帧采样或逆采样），我们的模型即可生成高质量且高度连贯的分钟级视频。
+- 无需标准的加速技术（例如：KV 缓存、因果掩码、稀疏/线性注意力机制、TinyVAE、渐进式噪声调度、隐藏状态缓存或量化），作为一款 14B 规模的视频生成模型，我们在单张 H100 GPU 上的端到端推理速度便达到了 19.5 FPS。
+- 引入了多项优化方案，在降低显存消耗的同时，显著提升了训练与推理的吞吐量。这些改进使得我们无需借助并行或分片（sharding）等基础设施，即可使用与图像模型相当的批大小（batch sizes）来训练 14B 的视频生成模型。
+
+本指南将引导您完成 Helios 在不同场景下的使用。
+
+## Load Model Checkpoints
+
+模型权重可以存储在Hub上或本地的单独子文件夹中，在这种情况下，您应该使用 [`~DiffusionPipeline.from_pretrained`] 方法。
+
+```python
+import torch
+from diffusers import HeliosPipeline, HeliosPyramidPipeline
+from huggingface_hub import snapshot_download
+
+# For Best Quality
+snapshot_download(repo_id="BestWishYsh/Helios-Base", local_dir="BestWishYsh/Helios-Base")
+pipe = HeliosPipeline.from_pretrained("BestWishYsh/Helios-Base", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+# Intermediate Weight
+snapshot_download(repo_id="BestWishYsh/Helios-Mid", local_dir="BestWishYsh/Helios-Mid")
+pipe = HeliosPyramidPipeline.from_pretrained("BestWishYsh/Helios-Mid", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+# For Best Efficiency
+snapshot_download(repo_id="BestWishYsh/Helios-Distilled", local_dir="BestWishYsh/Helios-Distilled")
+pipe = HeliosPyramidPipeline.from_pretrained("BestWishYsh/Helios-Distilled", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+```
+
+## Text-to-Video Showcases
+
+<table>
+  <tr>
+    <th style="text-align: center;">Prompt</th>
+    <th style="text-align: center;">Generated Video</th>
+  </tr>
+  <tr>
+    <td><small>A Viking warrior driving a modern city bus filled with passengers. The Viking has long blonde hair tied back, a beard, and is adorned with a fur-lined helmet and armor. He wears a traditional tunic and trousers, but also sports a seatbelt as he focuses on navigating the busy streets. The interior of the bus is typical, with rows of seats occupied by diverse passengers going about their daily routines. The exterior shots show the bustling urban environment, including tall buildings and traffic. Medium shot focusing on the Viking at the wheel, with occasional close-ups of his determined expression.
+    </small></td>
+    <td>
+      <video width="4000" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/t2v_showcases1.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+  <tr>
+    <td><small>A documentary-style nature photography shot from a camera truck moving to the left, capturing a crab quickly scurrying into its burrow. The crab has a hard, greenish-brown shell and long claws, moving with determined speed across the sandy ground. Its body is slightly arched as it burrows into the sand, leaving a small trail behind. The background shows a shallow beach with scattered rocks and seashells, and the horizon features a gentle curve of the coastline. The photo has a natural and realistic texture, emphasizing the crab's natural movement and the texture of the sand. A close-up shot from a slightly elevated angle.
+    </small></td>
+    <td>
+      <video width="4000" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/t2v_showcases2.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+</table>
+
+## Image-to-Video Showcases
+
+<table>
+  <tr>
+    <th style="text-align: center;">Image</th>
+    <th style="text-align: center;">Prompt</th>
+    <th style="text-align: center;">Generated Video</th>
+  </tr>
+  <tr>
+    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/i2v_showcases1.jpg" style="height: auto; width: 300px;"></td>
+    <td><small>A sleek red Kia car speeds along a rural road under a cloudy sky, its modern design and dynamic movement emphasized by the blurred motion of the surrounding fields and trees stretching into the distance. The car's glossy exterior reflects the overcast sky, highlighting its aerodynamic shape and sporty stance. The license plate reads "KIA 626," and the vehicle's headlights are on, adding to the sense of motion and energy. The road curves gently, with the car positioned slightly off-center, creating a sense of forward momentum. A dynamic front three-quarter view captures the car's powerful presence against the serene backdrop of rolling hills and scattered trees.
+    </small></td>
+    <td>
+      <video width="2000" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/i2v_showcases1.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+  <tr>
+    <td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/i2v_showcases2.jpg" style="height: auto; width: 300px;"></td>
+    <td><small>A close-up captures a fluffy orange cat with striking green eyes and white whiskers, gazing intently towards the camera. The cat's fur is soft and well-groomed, with a mix of warm orange and cream tones. Its large, expressive eyes are a vivid green, reflecting curiosity and alertness. The cat's nose is small and pink, and its mouth is slightly open, revealing a hint of its pink tongue. The background is softly blurred, suggesting a cozy indoor setting with neutral tones. The photo has a shallow depth of field, focusing sharply on the cat's face while the background remains out of focus. A close-up shot from a slightly elevated perspective.
+    </small></td>
+    <td>
+      <video width="2000" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/i2v_showcases2.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+</table>
+
+## Interactive-Video Showcases
+
+<table>
+  <tr>
+    <th style="text-align: center;">Prompt</th>
+    <th style="text-align: center;">Generated Video</th>
+  </tr>
+  <tr>
+    <td><small>The prompt can be found <a href="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/interactive_showcases1.txt">here</a></small></td>
+    <td>
+      <video width="680" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/interactive_showcases1.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+  <tr>
+    <td><small>The prompt can be found <a href="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/interactive_showcases2.txt">here</a></small></td>
+    <td>
+      <video width="680" controls>
+        <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/helios/interactive_showcases2.mp4" type="video/mp4">
+      </video>
+    </td>
+  </tr>
+</table>
+
+## Resources
+
+通过以下资源了解有关 Helios 的更多信息：
+
+- [视频1](https://www.youtube.com/watch?v=vd_AgHtOUFQ)和[视频2](https://www.youtube.com/watch?v=1GeIU2Dn7UY)演示了 Helios 的主要功能;
+- 有关更多详细信息，请参阅研究论文 [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/)。

examples/cogvideo/train_cogvideox_lora.py

@@ -1232,22 +1232,49 @@ def main(args):
         id_token=args.id_token,
     )
 
-    def encode_video(video, bar):
-        bar.update(1)
+    def encode_video(video):
         video = video.to(accelerator.device, dtype=vae.dtype).unsqueeze(0)
         video = video.permute(0, 2, 1, 3, 4)  # [B, C, F, H, W]
         latent_dist = vae.encode(video).latent_dist
         return latent_dist
 
+    # Distribute video encoding across processes: each process only encodes its own shard
+    num_videos = len(train_dataset.instance_videos)
+    num_procs = accelerator.num_processes
+    local_rank = accelerator.process_index
+    local_count = len(range(local_rank, num_videos, num_procs))
+
     progress_encode_bar = tqdm(
-        range(0, len(train_dataset.instance_videos)),
-        desc="Loading Encode videos",
+        range(local_count),
+        desc="Encoding videos",
+        disable=not accelerator.is_local_main_process,
     )
-    train_dataset.instance_videos = [
-        encode_video(video, progress_encode_bar) for video in train_dataset.instance_videos
-    ]
+
+    encoded_videos = [None] * num_videos
+    for i, video in enumerate(train_dataset.instance_videos):
+        if i % num_procs == local_rank:
+            encoded_videos[i] = encode_video(video)
+            progress_encode_bar.update(1)
     progress_encode_bar.close()
 
+    # Broadcast encoded latent distributions so every process has the full set
+    if num_procs > 1:
+        import torch.distributed as dist
+
+        from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
+
+        ref_params = next(v for v in encoded_videos if v is not None).parameters
+        for i in range(num_videos):
+            src = i % num_procs
+            if encoded_videos[i] is not None:
+                params = encoded_videos[i].parameters.contiguous()
+            else:
+                params = torch.empty_like(ref_params)
+            dist.broadcast(params, src=src)
+            encoded_videos[i] = DiagonalGaussianDistribution(params)
+
+    train_dataset.instance_videos = encoded_videos
+
     def collate_fn(examples):
         videos = [example["instance_video"].sample() * vae.config.scaling_factor for example in examples]
         prompts = [example["instance_prompt"] for example in examples]

examples/research_projects/autoencoder_rae/README.md

@@ -0,0 +1,66 @@
+# Training AutoencoderRAE
+
+This example trains the decoder of `AutoencoderRAE` (stage-1 style), while keeping the representation encoder frozen.
+
+It follows the same high-level training recipe as the official RAE stage-1 setup:
+- frozen encoder
+- train decoder
+- pixel reconstruction loss
+- optional encoder feature consistency loss
+
+## Quickstart
+
+### Resume or finetune from pretrained weights
+
+```bash
+accelerate launch examples/research_projects/autoencoder_rae/train_autoencoder_rae.py \
+  --pretrained_model_name_or_path nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08 \
+  --train_data_dir /path/to/imagenet_like_folder \
+  --output_dir /tmp/autoencoder-rae \
+  --resolution 256 \
+  --train_batch_size 8 \
+  --learning_rate 1e-4 \
+  --num_train_epochs 10 \
+  --report_to wandb \
+  --reconstruction_loss_type l1 \
+  --use_encoder_loss \
+  --encoder_loss_weight 0.1
+```
+
+### Train from scratch with a pretrained encoder
+The following command launches RAE training with "facebook/dinov2-with-registers-base" as the base.
+
+```bash
+accelerate launch examples/research_projects/autoencoder_rae/train_autoencoder_rae.py \
+  --train_data_dir /path/to/imagenet_like_folder \
+  --output_dir /tmp/autoencoder-rae \
+  --resolution 256 \
+  --encoder_type dinov2 \
+  --encoder_name_or_path facebook/dinov2-with-registers-base \
+  --encoder_input_size 224 \
+  --patch_size 16 \
+  --image_size 256 \
+  --decoder_hidden_size 1152 \
+  --decoder_num_hidden_layers 28 \
+  --decoder_num_attention_heads 16 \
+  --decoder_intermediate_size 4096 \
+  --train_batch_size 8 \
+  --learning_rate 1e-4 \
+  --num_train_epochs 10 \
+  --report_to wandb \
+  --reconstruction_loss_type l1 \
+  --use_encoder_loss \
+  --encoder_loss_weight 0.1
+```
+
+Note: stage-1 reconstruction loss assumes matching target/output spatial size, so `--resolution` must equal `--image_size`.
+
+Dataset format is expected to be `ImageFolder`-compatible:
+
+```text
+train_data_dir/
+  class_a/
+    img_0001.jpg
+  class_b/
+    img_0002.jpg
+```

examples/research_projects/autoencoder_rae/train_autoencoder_rae.py

@@ -0,0 +1,405 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import logging
+import math
+import os
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from torchvision.datasets import ImageFolder
+from tqdm.auto import tqdm
+
+from diffusers import AutoencoderRAE
+from diffusers.optimization import get_scheduler
+
+
+logger = get_logger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Train a stage-1 Representation Autoencoder (RAE) decoder.")
+    parser.add_argument(
+        "--train_data_dir",
+        type=str,
+        required=True,
+        help="Path to an ImageFolder-style dataset root.",
+    )
+    parser.add_argument(
+        "--output_dir", type=str, default="autoencoder-rae", help="Directory to save checkpoints/model."
+    )
+    parser.add_argument("--logging_dir", type=str, default="logs", help="Accelerate logging directory.")
+    parser.add_argument("--seed", type=int, default=42)
+
+    parser.add_argument("--resolution", type=int, default=256)
+    parser.add_argument("--center_crop", action="store_true")
+    parser.add_argument("--random_flip", action="store_true")
+
+    parser.add_argument("--train_batch_size", type=int, default=8)
+    parser.add_argument("--dataloader_num_workers", type=int, default=4)
+    parser.add_argument("--num_train_epochs", type=int, default=10)
+    parser.add_argument("--max_train_steps", type=int, default=None)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
+    parser.add_argument("--max_grad_norm", type=float, default=1.0)
+
+    parser.add_argument("--learning_rate", type=float, default=1e-4)
+    parser.add_argument("--adam_beta1", type=float, default=0.9)
+    parser.add_argument("--adam_beta2", type=float, default=0.999)
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2)
+    parser.add_argument("--adam_epsilon", type=float, default=1e-8)
+    parser.add_argument("--lr_scheduler", type=str, default="cosine")
+    parser.add_argument("--lr_warmup_steps", type=int, default=500)
+
+    parser.add_argument("--checkpointing_steps", type=int, default=1000)
+    parser.add_argument("--validation_steps", type=int, default=500)
+
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to a pretrained AutoencoderRAE model (or HF Hub id) to resume training from.",
+    )
+    parser.add_argument(
+        "--encoder_name_or_path",
+        type=str,
+        default=None,
+        help=(
+            "HF Hub id or local path of the pretrained encoder (e.g. 'facebook/dinov2-with-registers-base'). "
+            "When --pretrained_model_name_or_path is not set, the encoder weights are loaded from this path "
+            "into a freshly constructed AutoencoderRAE. Ignored when --pretrained_model_name_or_path is set."
+        ),
+    )
+
+    parser.add_argument("--encoder_type", type=str, choices=["dinov2", "siglip2", "mae"], default="dinov2")
+    parser.add_argument("--encoder_hidden_size", type=int, default=768)
+    parser.add_argument("--encoder_patch_size", type=int, default=14)
+    parser.add_argument("--encoder_num_hidden_layers", type=int, default=12)
+    parser.add_argument("--encoder_input_size", type=int, default=224)
+    parser.add_argument("--patch_size", type=int, default=16)
+    parser.add_argument("--image_size", type=int, default=256)
+    parser.add_argument("--num_channels", type=int, default=3)
+
+    parser.add_argument("--decoder_hidden_size", type=int, default=1152)
+    parser.add_argument("--decoder_num_hidden_layers", type=int, default=28)
+    parser.add_argument("--decoder_num_attention_heads", type=int, default=16)
+    parser.add_argument("--decoder_intermediate_size", type=int, default=4096)
+
+    parser.add_argument("--noise_tau", type=float, default=0.0)
+    parser.add_argument("--scaling_factor", type=float, default=1.0)
+    parser.add_argument("--reshape_to_2d", action=argparse.BooleanOptionalAction, default=True)
+
+    parser.add_argument(
+        "--reconstruction_loss_type",
+        type=str,
+        choices=["l1", "mse"],
+        default="l1",
+        help="Pixel reconstruction loss.",
+    )
+    parser.add_argument(
+        "--encoder_loss_weight",
+        type=float,
+        default=0.0,
+        help="Weight for encoder feature consistency loss in the training loop.",
+    )
+    parser.add_argument(
+        "--use_encoder_loss",
+        action="store_true",
+        help="Enable encoder feature consistency loss term in the training loop.",
+    )
+    parser.add_argument("--report_to", type=str, default="tensorboard")
+
+    return parser.parse_args()
+
+
+def build_transforms(args):
+    image_transforms = [
+        transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BICUBIC),
+        transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
+    ]
+    if args.random_flip:
+        image_transforms.append(transforms.RandomHorizontalFlip())
+    image_transforms.append(transforms.ToTensor())
+    return transforms.Compose(image_transforms)
+
+
+def compute_losses(
+    model, pixel_values, reconstruction_loss_type: str, use_encoder_loss: bool, encoder_loss_weight: float
+):
+    decoded = model(pixel_values).sample
+
+    if decoded.shape[-2:] != pixel_values.shape[-2:]:
+        raise ValueError(
+            "Training requires matching reconstruction and target sizes, got "
+            f"decoded={tuple(decoded.shape[-2:])}, target={tuple(pixel_values.shape[-2:])}."
+        )
+
+    if reconstruction_loss_type == "l1":
+        reconstruction_loss = F.l1_loss(decoded.float(), pixel_values.float())
+    else:
+        reconstruction_loss = F.mse_loss(decoded.float(), pixel_values.float())
+
+    encoder_loss = torch.zeros_like(reconstruction_loss)
+    if use_encoder_loss and encoder_loss_weight > 0:
+        base_model = model.module if hasattr(model, "module") else model
+        target_encoder_input = base_model._resize_and_normalize(pixel_values)
+        reconstructed_encoder_input = base_model._resize_and_normalize(decoded)
+
+        encoder_forward_kwargs = {"model": base_model.encoder}
+        if base_model.config.encoder_type == "mae":
+            encoder_forward_kwargs["patch_size"] = base_model.config.encoder_patch_size
+        with torch.no_grad():
+            target_tokens = base_model._encoder_forward_fn(images=target_encoder_input, **encoder_forward_kwargs)
+        reconstructed_tokens = base_model._encoder_forward_fn(
+            images=reconstructed_encoder_input, **encoder_forward_kwargs
+        )
+        encoder_loss = F.mse_loss(reconstructed_tokens.float(), target_tokens.float())
+
+    loss = reconstruction_loss + float(encoder_loss_weight) * encoder_loss
+    return decoded, loss, reconstruction_loss, encoder_loss
+
+
+def _strip_final_layernorm_affine(state_dict, prefix=""):
+    """Remove final layernorm weight/bias so the model keeps its default init (identity)."""
+    keys_to_strip = {f"{prefix}weight", f"{prefix}bias"}
+    return {k: v for k, v in state_dict.items() if k not in keys_to_strip}
+
+
+def _load_pretrained_encoder_weights(model, encoder_type, encoder_name_or_path):
+    """Load pretrained HF transformers encoder weights into the model's encoder."""
+    if encoder_type == "dinov2":
+        from transformers import Dinov2WithRegistersModel
+
+        hf_encoder = Dinov2WithRegistersModel.from_pretrained(encoder_name_or_path)
+        state_dict = hf_encoder.state_dict()
+        state_dict = _strip_final_layernorm_affine(state_dict, prefix="layernorm.")
+    elif encoder_type == "siglip2":
+        from transformers import SiglipModel
+
+        hf_encoder = SiglipModel.from_pretrained(encoder_name_or_path).vision_model
+        state_dict = {f"vision_model.{k}": v for k, v in hf_encoder.state_dict().items()}
+        state_dict = _strip_final_layernorm_affine(state_dict, prefix="vision_model.post_layernorm.")
+    elif encoder_type == "mae":
+        from transformers import ViTMAEForPreTraining
+
+        hf_encoder = ViTMAEForPreTraining.from_pretrained(encoder_name_or_path).vit
+        state_dict = hf_encoder.state_dict()
+        state_dict = _strip_final_layernorm_affine(state_dict, prefix="layernorm.")
+    else:
+        raise ValueError(f"Unknown encoder_type: {encoder_type}")
+
+    model.encoder.load_state_dict(state_dict, strict=False)
+
+
+def main():
+    args = parse_args()
+    if args.resolution != args.image_size:
+        raise ValueError(
+            f"`--resolution` ({args.resolution}) must match `--image_size` ({args.image_size}) "
+            "for stage-1 reconstruction loss."
+        )
+
+    logging_dir = Path(args.output_dir, args.logging_dir)
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        project_config=accelerator_project_config,
+        log_with=args.report_to,
+    )
+
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    if accelerator.is_main_process:
+        os.makedirs(args.output_dir, exist_ok=True)
+    accelerator.wait_for_everyone()
+
+    dataset = ImageFolder(args.train_data_dir, transform=build_transforms(args))
+
+    def collate_fn(examples):
+        pixel_values = torch.stack([example[0] for example in examples]).float()
+        return {"pixel_values": pixel_values}
+
+    train_dataloader = DataLoader(
+        dataset,
+        shuffle=True,
+        collate_fn=collate_fn,
+        batch_size=args.train_batch_size,
+        num_workers=args.dataloader_num_workers,
+        pin_memory=True,
+        drop_last=True,
+    )
+
+    if args.pretrained_model_name_or_path is not None:
+        model = AutoencoderRAE.from_pretrained(args.pretrained_model_name_or_path)
+        logger.info(f"Loaded pretrained AutoencoderRAE from {args.pretrained_model_name_or_path}")
+    else:
+        model = AutoencoderRAE(
+            encoder_type=args.encoder_type,
+            encoder_hidden_size=args.encoder_hidden_size,
+            encoder_patch_size=args.encoder_patch_size,
+            encoder_num_hidden_layers=args.encoder_num_hidden_layers,
+            decoder_hidden_size=args.decoder_hidden_size,
+            decoder_num_hidden_layers=args.decoder_num_hidden_layers,
+            decoder_num_attention_heads=args.decoder_num_attention_heads,
+            decoder_intermediate_size=args.decoder_intermediate_size,
+            patch_size=args.patch_size,
+            encoder_input_size=args.encoder_input_size,
+            image_size=args.image_size,
+            num_channels=args.num_channels,
+            noise_tau=args.noise_tau,
+            reshape_to_2d=args.reshape_to_2d,
+            use_encoder_loss=args.use_encoder_loss,
+            scaling_factor=args.scaling_factor,
+        )
+        if args.encoder_name_or_path is not None:
+            _load_pretrained_encoder_weights(model, args.encoder_type, args.encoder_name_or_path)
+            logger.info(f"Loaded pretrained encoder weights from {args.encoder_name_or_path}")
+    model.encoder.requires_grad_(False)
+    model.decoder.requires_grad_(True)
+    model.train()
+
+    optimizer = torch.optim.AdamW(
+        (p for p in model.parameters() if p.requires_grad),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
+    )
+
+    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        model, optimizer, train_dataloader, lr_scheduler
+    )
+
+    if overrode_max_train_steps:
+        num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    if accelerator.is_main_process:
+        accelerator.init_trackers("train_autoencoder_rae", config=vars(args))
+
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+
+    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
+    progress_bar.set_description("Steps")
+    global_step = 0
+
+    for epoch in range(args.num_train_epochs):
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(model):
+                pixel_values = batch["pixel_values"]
+
+                _, loss, reconstruction_loss, encoder_loss = compute_losses(
+                    model,
+                    pixel_values,
+                    reconstruction_loss_type=args.reconstruction_loss_type,
+                    use_encoder_loss=args.use_encoder_loss,
+                    encoder_loss_weight=args.encoder_loss_weight,
+                )
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                logs = {
+                    "loss": loss.detach().item(),
+                    "reconstruction_loss": reconstruction_loss.detach().item(),
+                    "encoder_loss": encoder_loss.detach().item(),
+                    "lr": lr_scheduler.get_last_lr()[0],
+                }
+                progress_bar.set_postfix(**logs)
+                accelerator.log(logs, step=global_step)
+
+                if global_step % args.validation_steps == 0:
+                    with torch.no_grad():
+                        _, val_loss, val_reconstruction_loss, val_encoder_loss = compute_losses(
+                            model,
+                            pixel_values,
+                            reconstruction_loss_type=args.reconstruction_loss_type,
+                            use_encoder_loss=args.use_encoder_loss,
+                            encoder_loss_weight=args.encoder_loss_weight,
+                        )
+                    accelerator.log(
+                        {
+                            "val/loss": val_loss.detach().item(),
+                            "val/reconstruction_loss": val_reconstruction_loss.detach().item(),
+                            "val/encoder_loss": val_encoder_loss.detach().item(),
+                        },
+                        step=global_step,
+                    )
+
+                if global_step % args.checkpointing_steps == 0:
+                    if accelerator.is_main_process:
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        unwrapped_model = accelerator.unwrap_model(model)
+                        unwrapped_model.save_pretrained(save_path)
+                        logger.info(f"Saved checkpoint to {save_path}")
+
+            if global_step >= args.max_train_steps:
+                break
+
+        if global_step >= args.max_train_steps:
+            break
+
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        unwrapped_model = accelerator.unwrap_model(model)
+        unwrapped_model.save_pretrained(args.output_dir)
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    main()

scripts/convert_rae_to_diffusers.py

@@ -0,0 +1,403 @@
+import argparse
+from pathlib import Path
+from typing import Any
+
+import torch
+from huggingface_hub import HfApi, hf_hub_download
+
+from diffusers import AutoencoderRAE
+
+
+DECODER_CONFIGS = {
+    "ViTB": {
+        "decoder_hidden_size": 768,
+        "decoder_intermediate_size": 3072,
+        "decoder_num_attention_heads": 12,
+        "decoder_num_hidden_layers": 12,
+    },
+    "ViTL": {
+        "decoder_hidden_size": 1024,
+        "decoder_intermediate_size": 4096,
+        "decoder_num_attention_heads": 16,
+        "decoder_num_hidden_layers": 24,
+    },
+    "ViTXL": {
+        "decoder_hidden_size": 1152,
+        "decoder_intermediate_size": 4096,
+        "decoder_num_attention_heads": 16,
+        "decoder_num_hidden_layers": 28,
+    },
+}
+
+ENCODER_DEFAULT_NAME_OR_PATH = {
+    "dinov2": "facebook/dinov2-with-registers-base",
+    "siglip2": "google/siglip2-base-patch16-256",
+    "mae": "facebook/vit-mae-base",
+}
+
+ENCODER_HIDDEN_SIZE = {
+    "dinov2": 768,
+    "siglip2": 768,
+    "mae": 768,
+}
+
+ENCODER_PATCH_SIZE = {
+    "dinov2": 14,
+    "siglip2": 16,
+    "mae": 16,
+}
+
+DEFAULT_DECODER_SUBDIR = {
+    "dinov2": "decoders/dinov2/wReg_base",
+    "mae": "decoders/mae/base_p16",
+    "siglip2": "decoders/siglip2/base_p16_i256",
+}
+
+DEFAULT_STATS_SUBDIR = {
+    "dinov2": "stats/dinov2/wReg_base",
+    "mae": "stats/mae/base_p16",
+    "siglip2": "stats/siglip2/base_p16_i256",
+}
+
+DECODER_FILE_CANDIDATES = ("dinov2_decoder.pt", "model.pt")
+STATS_FILE_CANDIDATES = ("stat.pt",)
+
+
+def dataset_case_candidates(name: str) -> tuple[str, ...]:
+    return (name, name.lower(), name.upper(), name.title(), "imagenet1k", "ImageNet1k")
+
+
+class RepoAccessor:
+    def __init__(self, repo_or_path: str, cache_dir: str | None = None):
+        self.repo_or_path = repo_or_path
+        self.cache_dir = cache_dir
+        self.local_root: Path | None = None
+        self.repo_id: str | None = None
+        self.repo_files: set[str] | None = None
+
+        root = Path(repo_or_path)
+        if root.exists() and root.is_dir():
+            self.local_root = root
+        else:
+            self.repo_id = repo_or_path
+            self.repo_files = set(HfApi().list_repo_files(repo_or_path))
+
+    def exists(self, relative_path: str) -> bool:
+        relative_path = relative_path.replace("\\", "/")
+        if self.local_root is not None:
+            return (self.local_root / relative_path).is_file()
+        return relative_path in self.repo_files
+
+    def fetch(self, relative_path: str) -> Path:
+        relative_path = relative_path.replace("\\", "/")
+        if self.local_root is not None:
+            return self.local_root / relative_path
+        downloaded = hf_hub_download(repo_id=self.repo_id, filename=relative_path, cache_dir=self.cache_dir)
+        return Path(downloaded)
+
+
+def unwrap_state_dict(maybe_wrapped: dict[str, Any]) -> dict[str, Any]:
+    state_dict = maybe_wrapped
+    for k in ("model", "module", "state_dict"):
+        if isinstance(state_dict, dict) and k in state_dict and isinstance(state_dict[k], dict):
+            state_dict = state_dict[k]
+
+    out = dict(state_dict)
+    if len(out) > 0 and all(key.startswith("module.") for key in out):
+        out = {key[len("module.") :]: value for key, value in out.items()}
+    if len(out) > 0 and all(key.startswith("decoder.") for key in out):
+        out = {key[len("decoder.") :]: value for key, value in out.items()}
+    return out
+
+
+def remap_decoder_attention_keys_for_diffusers(state_dict: dict[str, Any]) -> dict[str, Any]:
+    """
+    Map official RAE decoder attention key layout to diffusers Attention layout used by AutoencoderRAE decoder.
+
+    Example mappings:
+    - `...attention.attention.query.*` -> `...attention.to_q.*`
+    - `...attention.attention.key.*`   -> `...attention.to_k.*`
+    - `...attention.attention.value.*` -> `...attention.to_v.*`
+    - `...attention.output.dense.*`    -> `...attention.to_out.0.*`
+    """
+    remapped: dict[str, Any] = {}
+    for key, value in state_dict.items():
+        new_key = key
+        new_key = new_key.replace(".attention.attention.query.", ".attention.to_q.")
+        new_key = new_key.replace(".attention.attention.key.", ".attention.to_k.")
+        new_key = new_key.replace(".attention.attention.value.", ".attention.to_v.")
+        new_key = new_key.replace(".attention.output.dense.", ".attention.to_out.0.")
+        remapped[new_key] = value
+    return remapped
+
+
+def resolve_decoder_file(
+    accessor: RepoAccessor, encoder_type: str, variant: str, decoder_checkpoint: str | None
+) -> str:
+    if decoder_checkpoint is not None:
+        if accessor.exists(decoder_checkpoint):
+            return decoder_checkpoint
+        raise FileNotFoundError(f"Decoder checkpoint not found: {decoder_checkpoint}")
+
+    base = f"{DEFAULT_DECODER_SUBDIR[encoder_type]}/{variant}"
+    for name in DECODER_FILE_CANDIDATES:
+        candidate = f"{base}/{name}"
+        if accessor.exists(candidate):
+            return candidate
+
+    raise FileNotFoundError(
+        f"Could not find decoder checkpoint under `{base}`. Tried: {list(DECODER_FILE_CANDIDATES)}"
+    )
+
+
+def resolve_stats_file(
+    accessor: RepoAccessor,
+    encoder_type: str,
+    dataset_name: str,
+    stats_checkpoint: str | None,
+) -> str | None:
+    if stats_checkpoint is not None:
+        if accessor.exists(stats_checkpoint):
+            return stats_checkpoint
+        raise FileNotFoundError(f"Stats checkpoint not found: {stats_checkpoint}")
+
+    base = DEFAULT_STATS_SUBDIR[encoder_type]
+    for dataset in dataset_case_candidates(dataset_name):
+        for name in STATS_FILE_CANDIDATES:
+            candidate = f"{base}/{dataset}/{name}"
+            if accessor.exists(candidate):
+                return candidate
+
+    return None
+
+
+def extract_latent_stats(stats_obj: Any) -> tuple[Any | None, Any | None]:
+    if not isinstance(stats_obj, dict):
+        return None, None
+
+    if "latents_mean" in stats_obj or "latents_std" in stats_obj:
+        return stats_obj.get("latents_mean", None), stats_obj.get("latents_std", None)
+
+    mean = stats_obj.get("mean", None)
+    var = stats_obj.get("var", None)
+    if mean is None and var is None:
+        return None, None
+
+    latents_std = None
+    if var is not None:
+        if isinstance(var, torch.Tensor):
+            latents_std = torch.sqrt(var + 1e-5)
+        else:
+            latents_std = torch.sqrt(torch.tensor(var) + 1e-5)
+    return mean, latents_std
+
+
+def _strip_final_layernorm_affine(state_dict: dict[str, Any], prefix: str = "") -> dict[str, Any]:
+    """Remove final layernorm weight/bias from encoder state dict.
+
+    RAE uses non-affine layernorm (weight=1, bias=0 is the default identity).
+    Stripping these keys means the model keeps its default init values, which
+    is functionally equivalent to setting elementwise_affine=False.
+    """
+    keys_to_strip = {f"{prefix}weight", f"{prefix}bias"}
+    return {k: v for k, v in state_dict.items() if k not in keys_to_strip}
+
+
+def _load_hf_encoder_state_dict(encoder_type: str, encoder_name_or_path: str) -> dict[str, Any]:
+    """Download the HF encoder and extract the state dict for the inner model."""
+    if encoder_type == "dinov2":
+        from transformers import Dinov2WithRegistersModel
+
+        hf_model = Dinov2WithRegistersModel.from_pretrained(encoder_name_or_path)
+        sd = hf_model.state_dict()
+        return _strip_final_layernorm_affine(sd, prefix="layernorm.")
+    elif encoder_type == "siglip2":
+        from transformers import SiglipModel
+
+        # SiglipModel.vision_model is a SiglipVisionTransformer.
+        # Our Siglip2Encoder wraps it inside SiglipVisionModel which nests it
+        # under .vision_model, so we add the prefix to match the diffusers key layout.
+        hf_model = SiglipModel.from_pretrained(encoder_name_or_path).vision_model
+        sd = {f"vision_model.{k}": v for k, v in hf_model.state_dict().items()}
+        return _strip_final_layernorm_affine(sd, prefix="vision_model.post_layernorm.")
+    elif encoder_type == "mae":
+        from transformers import ViTMAEForPreTraining
+
+        hf_model = ViTMAEForPreTraining.from_pretrained(encoder_name_or_path).vit
+        sd = hf_model.state_dict()
+        return _strip_final_layernorm_affine(sd, prefix="layernorm.")
+    else:
+        raise ValueError(f"Unknown encoder_type: {encoder_type}")
+
+
+def convert(args: argparse.Namespace) -> None:
+    accessor = RepoAccessor(args.repo_or_path, cache_dir=args.cache_dir)
+    encoder_name_or_path = args.encoder_name_or_path or ENCODER_DEFAULT_NAME_OR_PATH[args.encoder_type]
+
+    decoder_relpath = resolve_decoder_file(accessor, args.encoder_type, args.variant, args.decoder_checkpoint)
+    stats_relpath = resolve_stats_file(accessor, args.encoder_type, args.dataset_name, args.stats_checkpoint)
+
+    print(f"Using decoder checkpoint: {decoder_relpath}")
+    if stats_relpath is not None:
+        print(f"Using stats checkpoint:   {stats_relpath}")
+    else:
+        print("No stats checkpoint found; conversion will proceed without latent stats.")
+
+    if args.dry_run:
+        return
+
+    decoder_path = accessor.fetch(decoder_relpath)
+    decoder_obj = torch.load(decoder_path, map_location="cpu")
+    decoder_state_dict = unwrap_state_dict(decoder_obj)
+    decoder_state_dict = remap_decoder_attention_keys_for_diffusers(decoder_state_dict)
+
+    latents_mean, latents_std = None, None
+    if stats_relpath is not None:
+        stats_path = accessor.fetch(stats_relpath)
+        stats_obj = torch.load(stats_path, map_location="cpu")
+        latents_mean, latents_std = extract_latent_stats(stats_obj)
+
+    decoder_cfg = DECODER_CONFIGS[args.decoder_config_name]
+
+    # Read encoder normalization stats from the HF image processor (only place that downloads encoder info)
+    from transformers import AutoConfig, AutoImageProcessor
+
+    proc = AutoImageProcessor.from_pretrained(encoder_name_or_path)
+    encoder_norm_mean = list(proc.image_mean)
+    encoder_norm_std = list(proc.image_std)
+
+    # Read encoder hidden size and patch size from HF config
+    encoder_hidden_size = ENCODER_HIDDEN_SIZE[args.encoder_type]
+    encoder_patch_size = ENCODER_PATCH_SIZE[args.encoder_type]
+    try:
+        hf_config = AutoConfig.from_pretrained(encoder_name_or_path)
+        # For models like SigLIP that nest vision config
+        if hasattr(hf_config, "vision_config"):
+            hf_config = hf_config.vision_config
+        encoder_hidden_size = hf_config.hidden_size
+        encoder_patch_size = hf_config.patch_size
+    except Exception:
+        pass
+
+    # Load the actual encoder weights from HF to include in the saved model
+    encoder_state_dict = _load_hf_encoder_state_dict(args.encoder_type, encoder_name_or_path)
+
+    # Build model on meta device to avoid double init overhead
+    with torch.device("meta"):
+        model = AutoencoderRAE(
+            encoder_type=args.encoder_type,
+            encoder_hidden_size=encoder_hidden_size,
+            encoder_patch_size=encoder_patch_size,
+            encoder_input_size=args.encoder_input_size,
+            patch_size=args.patch_size,
+            image_size=args.image_size,
+            num_channels=args.num_channels,
+            encoder_norm_mean=encoder_norm_mean,
+            encoder_norm_std=encoder_norm_std,
+            decoder_hidden_size=decoder_cfg["decoder_hidden_size"],
+            decoder_num_hidden_layers=decoder_cfg["decoder_num_hidden_layers"],
+            decoder_num_attention_heads=decoder_cfg["decoder_num_attention_heads"],
+            decoder_intermediate_size=decoder_cfg["decoder_intermediate_size"],
+            latents_mean=latents_mean,
+            latents_std=latents_std,
+            scaling_factor=args.scaling_factor,
+        )
+
+    # Assemble full state dict and load with assign=True
+    full_state_dict = {}
+
+    # Encoder weights (prefixed with "encoder.")
+    for k, v in encoder_state_dict.items():
+        full_state_dict[f"encoder.{k}"] = v
+
+    # Decoder weights (prefixed with "decoder.")
+    for k, v in decoder_state_dict.items():
+        full_state_dict[f"decoder.{k}"] = v
+
+    # Buffers from config
+    full_state_dict["encoder_mean"] = torch.tensor(encoder_norm_mean, dtype=torch.float32).view(1, 3, 1, 1)
+    full_state_dict["encoder_std"] = torch.tensor(encoder_norm_std, dtype=torch.float32).view(1, 3, 1, 1)
+    if latents_mean is not None:
+        latents_mean_t = latents_mean if isinstance(latents_mean, torch.Tensor) else torch.tensor(latents_mean)
+        full_state_dict["_latents_mean"] = latents_mean_t
+    else:
+        full_state_dict["_latents_mean"] = torch.zeros(1)
+    if latents_std is not None:
+        latents_std_t = latents_std if isinstance(latents_std, torch.Tensor) else torch.tensor(latents_std)
+        full_state_dict["_latents_std"] = latents_std_t
+    else:
+        full_state_dict["_latents_std"] = torch.ones(1)
+
+    model.load_state_dict(full_state_dict, strict=False, assign=True)
+
+    # Verify no critical keys are missing
+    model_keys = {name for name, _ in model.named_parameters()}
+    model_keys |= {name for name, _ in model.named_buffers()}
+    loaded_keys = set(full_state_dict.keys())
+    missing = model_keys - loaded_keys
+    # trainable_cls_token and decoder_pos_embed are initialized, not loaded from original checkpoint
+    allowed_missing = {"decoder.trainable_cls_token", "decoder.decoder_pos_embed"}
+    if missing - allowed_missing:
+        print(f"Warning: missing keys after conversion: {sorted(missing - allowed_missing)}")
+
+    output_path = Path(args.output_path)
+    output_path.mkdir(parents=True, exist_ok=True)
+    model.save_pretrained(output_path)
+
+    if args.verify_load:
+        print("Verifying converted checkpoint with AutoencoderRAE.from_pretrained(low_cpu_mem_usage=False)...")
+        loaded_model = AutoencoderRAE.from_pretrained(output_path, low_cpu_mem_usage=False)
+        if not isinstance(loaded_model, AutoencoderRAE):
+            raise RuntimeError("Verification failed: loaded object is not AutoencoderRAE.")
+        print("Verification passed.")
+
+    print(f"Saved converted AutoencoderRAE to: {output_path}")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert RAE decoder checkpoints to diffusers AutoencoderRAE format")
+    parser.add_argument(
+        "--repo_or_path", type=str, required=True, help="Hub repo id (e.g. nyu-visionx/RAE-collections) or local path"
+    )
+    parser.add_argument("--output_path", type=str, required=True, help="Directory to save converted model")
+
+    parser.add_argument("--encoder_type", type=str, choices=["dinov2", "mae", "siglip2"], required=True)
+    parser.add_argument(
+        "--encoder_name_or_path", type=str, default=None, help="Optional encoder HF model id or local path override"
+    )
+
+    parser.add_argument("--variant", type=str, default="ViTXL_n08", help="Decoder variant folder name")
+    parser.add_argument("--dataset_name", type=str, default="imagenet1k", help="Stats dataset folder name")
+
+    parser.add_argument(
+        "--decoder_checkpoint", type=str, default=None, help="Relative path to decoder checkpoint inside repo/path"
+    )
+    parser.add_argument(
+        "--stats_checkpoint", type=str, default=None, help="Relative path to stats checkpoint inside repo/path"
+    )
+
+    parser.add_argument("--decoder_config_name", type=str, choices=list(DECODER_CONFIGS.keys()), default="ViTXL")
+    parser.add_argument("--encoder_input_size", type=int, default=224)
+    parser.add_argument("--patch_size", type=int, default=16)
+    parser.add_argument("--image_size", type=int, default=None)
+    parser.add_argument("--num_channels", type=int, default=3)
+    parser.add_argument("--scaling_factor", type=float, default=1.0)
+
+    parser.add_argument("--cache_dir", type=str, default=None)
+    parser.add_argument("--dry_run", action="store_true", help="Only resolve and print selected files")
+    parser.add_argument(
+        "--verify_load",
+        action="store_true",
+        help="After conversion, load back with AutoencoderRAE.from_pretrained(low_cpu_mem_usage=False).",
+    )
+
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    convert(args)
+
+
+if __name__ == "__main__":
+    main()

src/diffusers/__init__.py

@@ -202,6 +202,7 @@ else:
             "AutoencoderKLTemporalDecoder",
             "AutoencoderKLWan",
             "AutoencoderOobleck",
+            "AutoencoderRAE",
             "AutoencoderTiny",
             "AutoModel",
             "BriaFiboTransformer2DModel",
@@ -227,6 +228,7 @@ else:
             "FluxMultiControlNetModel",
             "FluxTransformer2DModel",
             "GlmImageTransformer2DModel",
+            "HeliosTransformer3DModel",
             "HiDreamImageTransformer2DModel",
             "HunyuanDiT2DControlNetModel",
             "HunyuanDiT2DModel",
@@ -359,6 +361,8 @@ else:
             "FlowMatchEulerDiscreteScheduler",
             "FlowMatchHeunDiscreteScheduler",
             "FlowMatchLCMScheduler",
+            "HeliosDMDScheduler",
+            "HeliosScheduler",
             "HeunDiscreteScheduler",
             "IPNDMScheduler",
             "KarrasVeScheduler",
@@ -515,6 +519,8 @@ else:
             "FluxPipeline",
             "FluxPriorReduxPipeline",
             "GlmImagePipeline",
+            "HeliosPipeline",
+            "HeliosPyramidPipeline",
             "HiDreamImagePipeline",
             "HunyuanDiTControlNetPipeline",
             "HunyuanDiTPAGPipeline",
@@ -969,6 +975,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKLTemporalDecoder,
             AutoencoderKLWan,
             AutoencoderOobleck,
+            AutoencoderRAE,
             AutoencoderTiny,
             AutoModel,
             BriaFiboTransformer2DModel,
@@ -994,6 +1001,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxMultiControlNetModel,
             FluxTransformer2DModel,
             GlmImageTransformer2DModel,
+            HeliosTransformer3DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DControlNetModel,
             HunyuanDiT2DModel,
@@ -1122,6 +1130,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FlowMatchEulerDiscreteScheduler,
             FlowMatchHeunDiscreteScheduler,
             FlowMatchLCMScheduler,
+            HeliosDMDScheduler,
+            HeliosScheduler,
             HeunDiscreteScheduler,
             IPNDMScheduler,
             KarrasVeScheduler,
@@ -1257,6 +1267,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             FluxPipeline,
             FluxPriorReduxPipeline,
             GlmImagePipeline,
+            HeliosPipeline,
+            HeliosPyramidPipeline,
             HiDreamImagePipeline,
             HunyuanDiTControlNetPipeline,
             HunyuanDiTPAGPipeline,

src/diffusers/commands/custom_blocks.py

@@ -89,8 +89,6 @@ class CustomBlocksCommand(BaseDiffusersCLICommand):
         # automap = self._create_automap(parent_class=parent_class, child_class=child_class)
         # with open(CONFIG, "w") as f:
         #     json.dump(automap, f)
-        with open("requirements.txt", "w") as f:
-            f.write("")
 
     def _choose_block(self, candidates, chosen=None):
         for cls, base in candidates:

src/diffusers/configuration_utils.py

@@ -107,6 +107,38 @@ class ConfigMixin:
     has_compatibles = False
 
     _deprecated_kwargs = []
+    _auto_class = None
+
+    @classmethod
+    def register_for_auto_class(cls, auto_class="AutoModel"):
+        """
+        Register this class with the given auto class so that it can be loaded with `AutoModel.from_pretrained(...,
+        trust_remote_code=True)`.
+
+        When the config is saved, the resulting `config.json` will include an `auto_map` entry mapping the auto class
+        to this class's module and class name.
+
+        Args:
+            auto_class (`str` or type, *optional*, defaults to `"AutoModel"`):
+                The auto class to register this class with. Can be a string (e.g. `"AutoModel"`) or the class itself.
+                Currently only `"AutoModel"` is supported.
+
+        Example:
+
+        ```python
+        from diffusers import ModelMixin, ConfigMixin
+
+
+        class MyCustomModel(ModelMixin, ConfigMixin): ...
+
+
+        MyCustomModel.register_for_auto_class("AutoModel")
+        ```
+        """
+        if auto_class != "AutoModel":
+            raise ValueError(f"Only 'AutoModel' is supported, got '{auto_class}'.")
+
+        cls._auto_class = auto_class
 
     def register_to_config(self, **kwargs):
         if self.config_name is None:
@@ -621,6 +653,12 @@ class ConfigMixin:
         # pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
         _ = config_dict.pop("_pre_quantization_dtype", None)
 
+        if getattr(self, "_auto_class", None) is not None:
+            module = self.__class__.__module__.split(".")[-1]
+            auto_map = config_dict.get("auto_map", {})
+            auto_map[self._auto_class] = f"{module}.{self.__class__.__name__}"
+            config_dict["auto_map"] = auto_map
+
         return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
 
     def to_json_file(self, json_file_path: str | os.PathLike):
@@ -648,28 +686,6 @@ class ConfigMixin:
             )
         return config_file
 
-    @classmethod
-    def _get_dataclass_from_config(cls, config_dict: dict[str, Any]):
-        sig = inspect.signature(cls.__init__)
-        fields = []
-        for name, param in sig.parameters.items():
-            if name == "self" or name == "kwargs" or name in cls.ignore_for_config:
-                continue
-            annotation = param.annotation if param.annotation is not inspect.Parameter.empty else Any
-            if param.default is not inspect.Parameter.empty:
-                fields.append((name, annotation, dataclasses.field(default=param.default)))
-            else:
-                fields.append((name, annotation))
-
-        dc_cls = dataclasses.make_dataclass(
-            f"{cls.__name__}Config",
-            fields,
-            frozen=True,
-        )
-        valid_fields = {f.name for f in dataclasses.fields(dc_cls)}
-        init_kwargs = {k: v for k, v in config_dict.items() if k in valid_fields}
-        return dc_cls(**init_kwargs)
-
 
 def register_to_config(init):
     r"""

src/diffusers/hooks/group_offloading.py

@@ -307,6 +307,17 @@ class GroupOffloadingHook(ModelHook):
         if self.group.onload_leader == module:
             if self.group.onload_self:
                 self.group.onload_()
+            else:
+                # onload_self=False means this group relies on prefetching from a previous group.
+                # However, for conditionally-executed modules (e.g. patch_short/patch_mid/patch_long in Helios),
+                # the prefetch chain may not cover them if they were absent during the first forward pass
+                # when the execution order was traced. In that case, their weights remain on offload_device,
+                # so we fall back to a synchronous onload here.
+                params = [p for m in self.group.modules for p in m.parameters()] + list(self.group.parameters)
+                if params and params[0].device == self.group.offload_device:
+                    self.group.onload_()
+                    if self.group.stream is not None:
+                        self.group.stream.synchronize()
 
             should_onload_next_group = self.next_group is not None and not self.next_group.onload_self
             if should_onload_next_group:

src/diffusers/loaders/__init__.py

@@ -78,6 +78,7 @@ if is_torch_available():
             "SanaLoraLoaderMixin",
             "Lumina2LoraLoaderMixin",
             "WanLoraLoaderMixin",
+            "HeliosLoraLoaderMixin",
             "KandinskyLoraLoaderMixin",
             "HiDreamImageLoraLoaderMixin",
             "SkyReelsV2LoraLoaderMixin",
@@ -118,6 +119,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
                 CogView4LoraLoaderMixin,
                 Flux2LoraLoaderMixin,
                 FluxLoraLoaderMixin,
+                HeliosLoraLoaderMixin,
                 HiDreamImageLoraLoaderMixin,
                 HunyuanVideoLoraLoaderMixin,
                 KandinskyLoraLoaderMixin,

src/diffusers/loaders/lora_conversion_utils.py

@@ -2519,6 +2519,13 @@ def _convert_non_diffusers_z_image_lora_to_diffusers(state_dict):
     if has_default:
         state_dict = {k.replace("default.", ""): v for k, v in state_dict.items()}
 
+    # Normalize ZImage-specific dot-separated module names to underscore form so they
+    # match the diffusers model parameter names (context_refiner, noise_refiner).
+    state_dict = {
+        k.replace("context.refiner.", "context_refiner.").replace("noise.refiner.", "noise_refiner."): v
+        for k, v in state_dict.items()
+    }
+
     converted_state_dict = {}
     all_keys = list(state_dict.keys())
     down_key = ".lora_down.weight"
@@ -2529,19 +2536,18 @@ def _convert_non_diffusers_z_image_lora_to_diffusers(state_dict):
     has_non_diffusers_lora_id = any(down_key in k or up_key in k for k in all_keys)
     has_diffusers_lora_id = any(a_key in k or b_key in k for k in all_keys)
 
+    def get_alpha_scales(down_weight, alpha_key):
+        rank = down_weight.shape[0]
+        alpha = state_dict.pop(alpha_key).item()
+        scale = alpha / rank  # LoRA is scaled by 'alpha / rank' in forward pass, so we need to scale it back here
+        scale_down = scale
+        scale_up = 1.0
+        while scale_down * 2 < scale_up:
+            scale_down *= 2
+            scale_up /= 2
+        return scale_down, scale_up
+
     if has_non_diffusers_lora_id:
-
-        def get_alpha_scales(down_weight, alpha_key):
-            rank = down_weight.shape[0]
-            alpha = state_dict.pop(alpha_key).item()
-            scale = alpha / rank  # LoRA is scaled by 'alpha / rank' in forward pass, so we need to scale it back here
-            scale_down = scale
-            scale_up = 1.0
-            while scale_down * 2 < scale_up:
-                scale_down *= 2
-                scale_up /= 2
-            return scale_down, scale_up
-
         for k in all_keys:
             if k.endswith(down_key):
                 diffusers_down_key = k.replace(down_key, ".lora_A.weight")
@@ -2554,13 +2560,69 @@ def _convert_non_diffusers_z_image_lora_to_diffusers(state_dict):
                 converted_state_dict[diffusers_down_key] = down_weight * scale_down
                 converted_state_dict[diffusers_up_key] = up_weight * scale_up
 
-    # Already in diffusers format (lora_A/lora_B), just pop
+    # Already in diffusers format (lora_A/lora_B), apply alpha scaling and pop.
     elif has_diffusers_lora_id:
         for k in all_keys:
-            if a_key in k or b_key in k:
-                converted_state_dict[k] = state_dict.pop(k)
-            elif ".alpha" in k:
+            if k.endswith(a_key):
+                diffusers_up_key = k.replace(a_key, b_key)
+                alpha_key = k.replace(a_key, ".alpha")
+
+                down_weight = state_dict.pop(k)
+                up_weight = state_dict.pop(diffusers_up_key)
+                scale_down, scale_up = get_alpha_scales(down_weight, alpha_key)
+                converted_state_dict[k] = down_weight * scale_down
+                converted_state_dict[diffusers_up_key] = up_weight * scale_up
+
+    # Handle dot-format LoRA keys: ".lora.down.weight" / ".lora.up.weight".
+    # Some external ZImage trainers (e.g. Anime-Z) use dots instead of underscores in
+    # lora weight names and also include redundant keys:
+    #   - "qkv.lora.*"    duplicates individual "to.q/k/v.lora.*" keys → skip qkv
+    #   - "out.lora.*"    duplicates "to_out.0.lora.*" keys → skip bare out
+    #   - "to.q/k/v.lora.*" → normalise to "to_q/k/v.lora_A/B.weight"
+    lora_dot_down_key = ".lora.down.weight"
+    lora_dot_up_key = ".lora.up.weight"
+    has_lora_dot_format = any(lora_dot_down_key in k for k in state_dict)
+
+    if has_lora_dot_format:
+        dot_keys = list(state_dict.keys())
+        for k in dot_keys:
+            if lora_dot_down_key not in k:
+                continue
+            if k not in state_dict:
+                continue  # already popped by a prior iteration
+
+            base = k[: -len(lora_dot_down_key)]
+
+            # Skip combined "qkv" projection — individual to.q/k/v keys are also present.
+            if base.endswith(".qkv"):
                 state_dict.pop(k)
+                state_dict.pop(k.replace(lora_dot_down_key, lora_dot_up_key), None)
+                state_dict.pop(base + ".alpha", None)
+                continue
+
+            # Skip bare "out.lora.*" — "to_out.0.lora.*" covers the same projection.
+            if re.search(r"\.out$", base) and ".to_out" not in base:
+                state_dict.pop(k)
+                state_dict.pop(k.replace(lora_dot_down_key, lora_dot_up_key), None)
+                continue
+
+            # Normalise "to.q/k/v" → "to_q/k/v" for the diffusers output key.
+            norm_k = re.sub(
+                r"\.to\.([qkv])" + re.escape(lora_dot_down_key) + r"$",
+                r".to_\1" + lora_dot_down_key,
+                k,
+            )
+            norm_base = norm_k[: -len(lora_dot_down_key)]
+            alpha_key = norm_base + ".alpha"
+
+            diffusers_down = norm_k.replace(lora_dot_down_key, ".lora_A.weight")
+            diffusers_up = norm_k.replace(lora_dot_down_key, ".lora_B.weight")
+
+            down_weight = state_dict.pop(k)
+            up_weight = state_dict.pop(k.replace(lora_dot_down_key, lora_dot_up_key))
+            scale_down, scale_up = get_alpha_scales(down_weight, alpha_key)
+            converted_state_dict[diffusers_down] = down_weight * scale_down
+            converted_state_dict[diffusers_up] = up_weight * scale_up
 
     if len(state_dict) > 0:
         raise ValueError(f"`state_dict` should be empty at this point but has {state_dict.keys()=}")

src/diffusers/loaders/lora_pipeline.py

@@ -3440,6 +3440,207 @@ class SanaLoraLoaderMixin(LoraBaseMixin):
         super().unfuse_lora(components=components, **kwargs)
 
 
+class HeliosLoraLoaderMixin(LoraBaseMixin):
+    r"""
+    Load LoRA layers into [`HeliosTransformer3DModel`]. Specific to [`HeliosPipeline`] and [`HeliosPyramidPipeline`].
+    """
+
+    _lora_loadable_modules = ["transformer"]
+    transformer_name = TRANSFORMER_NAME
+
+    @classmethod
+    @validate_hf_hub_args
+    def lora_state_dict(
+        cls,
+        pretrained_model_name_or_path_or_dict: str | dict[str, torch.Tensor],
+        **kwargs,
+    ):
+        r"""
+        See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details.
+        """
+        # Load the main state dict first which has the LoRA layers for either of
+        # transformer and text encoder or both.
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+        subfolder = kwargs.pop("subfolder", None)
+        weight_name = kwargs.pop("weight_name", None)
+        use_safetensors = kwargs.pop("use_safetensors", None)
+        return_lora_metadata = kwargs.pop("return_lora_metadata", False)
+
+        allow_pickle = False
+        if use_safetensors is None:
+            use_safetensors = True
+            allow_pickle = True
+
+        user_agent = {"file_type": "attn_procs_weights", "framework": "pytorch"}
+
+        state_dict, metadata = _fetch_state_dict(
+            pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
+            weight_name=weight_name,
+            use_safetensors=use_safetensors,
+            local_files_only=local_files_only,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            token=token,
+            revision=revision,
+            subfolder=subfolder,
+            user_agent=user_agent,
+            allow_pickle=allow_pickle,
+        )
+        if any(k.startswith("diffusion_model.") for k in state_dict):
+            state_dict = _convert_non_diffusers_wan_lora_to_diffusers(state_dict)
+        elif any(k.startswith("lora_unet_") for k in state_dict):
+            state_dict = _convert_musubi_wan_lora_to_diffusers(state_dict)
+
+        is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+        if is_dora_scale_present:
+            warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+            logger.warning(warn_msg)
+            state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
+
+        out = (state_dict, metadata) if return_lora_metadata else state_dict
+        return out
+
+    def load_lora_weights(
+        self,
+        pretrained_model_name_or_path_or_dict: str | dict[str, torch.Tensor],
+        adapter_name: str | None = None,
+        hotswap: bool = False,
+        **kwargs,
+    ):
+        """
+        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for more details.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT_LORA)
+        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+            )
+
+        # if a dict is passed, copy it instead of modifying it inplace
+        if isinstance(pretrained_model_name_or_path_or_dict, dict):
+            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
+
+        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
+        kwargs["return_lora_metadata"] = True
+        state_dict, metadata = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
+        is_correct_format = all("lora" in key for key in state_dict.keys())
+        if not is_correct_format:
+            raise ValueError("Invalid LoRA checkpoint. Make sure all LoRA param names contain `'lora'` substring.")
+
+        self.load_lora_into_transformer(
+            state_dict,
+            transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
+            adapter_name=adapter_name,
+            metadata=metadata,
+            _pipeline=self,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+            hotswap=hotswap,
+        )
+
+    @classmethod
+    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->WanTransformer3DModel
+    def load_lora_into_transformer(
+        cls,
+        state_dict,
+        transformer,
+        adapter_name=None,
+        _pipeline=None,
+        low_cpu_mem_usage=False,
+        hotswap: bool = False,
+        metadata=None,
+    ):
+        """
+        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet`] for more details.
+        """
+        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+            )
+
+        # Load the layers corresponding to transformer.
+        logger.info(f"Loading {cls.transformer_name}.")
+        transformer.load_lora_adapter(
+            state_dict,
+            network_alphas=None,
+            adapter_name=adapter_name,
+            metadata=metadata,
+            _pipeline=_pipeline,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+            hotswap=hotswap,
+        )
+
+    @classmethod
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.save_lora_weights
+    def save_lora_weights(
+        cls,
+        save_directory: str | os.PathLike,
+        transformer_lora_layers: dict[str, torch.nn.Module | torch.Tensor] = None,
+        is_main_process: bool = True,
+        weight_name: str = None,
+        save_function: Callable = None,
+        safe_serialization: bool = True,
+        transformer_lora_adapter_metadata: dict | None = None,
+    ):
+        r"""
+        See [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for more information.
+        """
+        lora_layers = {}
+        lora_metadata = {}
+
+        if transformer_lora_layers:
+            lora_layers[cls.transformer_name] = transformer_lora_layers
+            lora_metadata[cls.transformer_name] = transformer_lora_adapter_metadata
+
+        if not lora_layers:
+            raise ValueError("You must pass at least one of `transformer_lora_layers` or `text_encoder_lora_layers`.")
+
+        cls._save_lora_weights(
+            save_directory=save_directory,
+            lora_layers=lora_layers,
+            lora_metadata=lora_metadata,
+            is_main_process=is_main_process,
+            weight_name=weight_name,
+            save_function=save_function,
+            safe_serialization=safe_serialization,
+        )
+
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.fuse_lora
+    def fuse_lora(
+        self,
+        components: list[str] = ["transformer"],
+        lora_scale: float = 1.0,
+        safe_fusing: bool = False,
+        adapter_names: list[str] | None = None,
+        **kwargs,
+    ):
+        r"""
+        See [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] for more details.
+        """
+        super().fuse_lora(
+            components=components,
+            lora_scale=lora_scale,
+            safe_fusing=safe_fusing,
+            adapter_names=adapter_names,
+            **kwargs,
+        )
+
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.unfuse_lora
+    def unfuse_lora(self, components: list[str] = ["transformer"], **kwargs):
+        r"""
+        See [`~loaders.StableDiffusionLoraLoaderMixin.unfuse_lora`] for more details.
+        """
+        super().unfuse_lora(components=components, **kwargs)
+
+
 class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
     r"""
     Load LoRA layers into [`HunyuanVideoTransformer3DModel`]. Specific to [`HunyuanVideoPipeline`].

src/diffusers/loaders/peft.py

@@ -51,6 +51,7 @@ _SET_ADAPTER_SCALE_FN_MAPPING = {
     "FluxTransformer2DModel": lambda model_cls, weights: weights,
     "CogVideoXTransformer3DModel": lambda model_cls, weights: weights,
     "ConsisIDTransformer3DModel": lambda model_cls, weights: weights,
+    "HeliosTransformer3DModel": lambda model_cls, weights: weights,
     "MochiTransformer3DModel": lambda model_cls, weights: weights,
     "HunyuanVideoTransformer3DModel": lambda model_cls, weights: weights,
     "LTXVideoTransformer3DModel": lambda model_cls, weights: weights,

src/diffusers/models/__init__.py

@@ -49,6 +49,7 @@ if is_torch_available():
     _import_structure["autoencoders.autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
     _import_structure["autoencoders.autoencoder_kl_wan"] = ["AutoencoderKLWan"]
     _import_structure["autoencoders.autoencoder_oobleck"] = ["AutoencoderOobleck"]
+    _import_structure["autoencoders.autoencoder_rae"] = ["AutoencoderRAE"]
     _import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
     _import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
     _import_structure["autoencoders.vq_model"] = ["VQModel"]
@@ -100,6 +101,7 @@ if is_torch_available():
     _import_structure["transformers.transformer_flux"] = ["FluxTransformer2DModel"]
     _import_structure["transformers.transformer_flux2"] = ["Flux2Transformer2DModel"]
     _import_structure["transformers.transformer_glm_image"] = ["GlmImageTransformer2DModel"]
+    _import_structure["transformers.transformer_helios"] = ["HeliosTransformer3DModel"]
     _import_structure["transformers.transformer_hidream_image"] = ["HiDreamImageTransformer2DModel"]
     _import_structure["transformers.transformer_hunyuan_video"] = ["HunyuanVideoTransformer3DModel"]
     _import_structure["transformers.transformer_hunyuan_video15"] = ["HunyuanVideo15Transformer3DModel"]
@@ -167,6 +169,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AutoencoderKLTemporalDecoder,
             AutoencoderKLWan,
             AutoencoderOobleck,
+            AutoencoderRAE,
             AutoencoderTiny,
             ConsistencyDecoderVAE,
             VQModel,
@@ -212,6 +215,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             Flux2Transformer2DModel,
             FluxTransformer2DModel,
             GlmImageTransformer2DModel,
+            HeliosTransformer3DModel,
             HiDreamImageTransformer2DModel,
             HunyuanDiT2DModel,
             HunyuanImageTransformer2DModel,

src/diffusers/models/autoencoders/__init__.py

@@ -18,6 +18,7 @@ from .autoencoder_kl_qwenimage import AutoencoderKLQwenImage
 from .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder
 from .autoencoder_kl_wan import AutoencoderKLWan
 from .autoencoder_oobleck import AutoencoderOobleck
+from .autoencoder_rae import AutoencoderRAE
 from .autoencoder_tiny import AutoencoderTiny
 from .consistency_decoder_vae import ConsistencyDecoderVAE
 from .vq_model import VQModel

src/diffusers/models/autoencoders/autoencoder_rae.py

@@ -0,0 +1,692 @@
+# Copyright 2026 The NYU Vision-X and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from math import sqrt
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...utils import BaseOutput, logging
+from ...utils.accelerate_utils import apply_forward_hook
+from ...utils.import_utils import is_transformers_available
+from ...utils.torch_utils import randn_tensor
+
+
+if is_transformers_available():
+    from transformers import (
+        Dinov2WithRegistersConfig,
+        Dinov2WithRegistersModel,
+        SiglipVisionConfig,
+        SiglipVisionModel,
+        ViTMAEConfig,
+        ViTMAEModel,
+    )
+
+from ..activations import get_activation
+from ..attention import AttentionMixin
+from ..attention_processor import Attention
+from ..embeddings import get_2d_sincos_pos_embed
+from ..modeling_utils import ModelMixin
+from .vae import AutoencoderMixin, DecoderOutput, EncoderOutput
+
+
+logger = logging.get_logger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Per-encoder forward functions
+# ---------------------------------------------------------------------------
+# Each function takes the raw transformers model + images and returns patch
+# tokens of shape (B, N, C), stripping CLS / register tokens as needed.
+
+
+def _dinov2_encoder_forward(model: nn.Module, images: torch.Tensor) -> torch.Tensor:
+    outputs = model(images, output_hidden_states=True)
+    unused_token_num = 5  # 1 CLS + 4 register tokens
+    return outputs.last_hidden_state[:, unused_token_num:]
+
+
+def _siglip2_encoder_forward(model: nn.Module, images: torch.Tensor) -> torch.Tensor:
+    outputs = model(images, output_hidden_states=True, interpolate_pos_encoding=True)
+    return outputs.last_hidden_state
+
+
+def _mae_encoder_forward(model: nn.Module, images: torch.Tensor, patch_size: int) -> torch.Tensor:
+    h, w = images.shape[2], images.shape[3]
+    patch_num = int(h * w // patch_size**2)
+    if patch_num * patch_size**2 != h * w:
+        raise ValueError("Image size should be divisible by patch size.")
+    noise = torch.arange(patch_num).unsqueeze(0).expand(images.shape[0], -1).to(images.device).to(images.dtype)
+    outputs = model(images, noise, interpolate_pos_encoding=True)
+    return outputs.last_hidden_state[:, 1:]  # remove cls token
+
+
+# ---------------------------------------------------------------------------
+# Encoder construction helpers
+# ---------------------------------------------------------------------------
+
+
+def _build_encoder(
+    encoder_type: str, hidden_size: int, patch_size: int, num_hidden_layers: int, head_dim: int = 64
+) -> nn.Module:
+    """Build a frozen encoder from config (no pretrained download)."""
+    num_attention_heads = hidden_size // head_dim  # all supported encoders use head_dim=64
+
+    if encoder_type == "dinov2":
+        config = Dinov2WithRegistersConfig(
+            hidden_size=hidden_size,
+            patch_size=patch_size,
+            image_size=518,
+            num_attention_heads=num_attention_heads,
+            num_hidden_layers=num_hidden_layers,
+        )
+        model = Dinov2WithRegistersModel(config)
+        # RAE strips the final layernorm affine params (identity LN). Remove them from
+        # the architecture so `from_pretrained` doesn't leave them on the meta device.
+        model.layernorm.weight = None
+        model.layernorm.bias = None
+    elif encoder_type == "siglip2":
+        config = SiglipVisionConfig(
+            hidden_size=hidden_size,
+            patch_size=patch_size,
+            image_size=256,
+            num_attention_heads=num_attention_heads,
+            num_hidden_layers=num_hidden_layers,
+        )
+        model = SiglipVisionModel(config)
+        # See dinov2 comment above.
+        model.vision_model.post_layernorm.weight = None
+        model.vision_model.post_layernorm.bias = None
+    elif encoder_type == "mae":
+        config = ViTMAEConfig(
+            hidden_size=hidden_size,
+            patch_size=patch_size,
+            image_size=224,
+            num_attention_heads=num_attention_heads,
+            num_hidden_layers=num_hidden_layers,
+            mask_ratio=0.0,
+        )
+        model = ViTMAEModel(config)
+        # See dinov2 comment above.
+        model.layernorm.weight = None
+        model.layernorm.bias = None
+    else:
+        raise ValueError(f"Unknown encoder_type='{encoder_type}'. Available: dinov2, siglip2, mae")
+
+    model.requires_grad_(False)
+    return model
+
+
+_ENCODER_FORWARD_FNS = {
+    "dinov2": _dinov2_encoder_forward,
+    "siglip2": _siglip2_encoder_forward,
+    "mae": _mae_encoder_forward,
+}
+
+
+@dataclass
+class RAEDecoderOutput(BaseOutput):
+    """
+    Output of `RAEDecoder`.
+
+    Args:
+        logits (`torch.Tensor`):
+            Patch reconstruction logits of shape `(batch_size, num_patches, patch_size**2 * num_channels)`.
+    """
+
+    logits: torch.Tensor
+
+
+class ViTMAEIntermediate(nn.Module):
+    def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str = "gelu"):
+        super().__init__()
+        self.dense = nn.Linear(hidden_size, intermediate_size)
+        self.intermediate_act_fn = get_activation(hidden_act)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class ViTMAEOutput(nn.Module):
+    def __init__(self, hidden_size: int, intermediate_size: int, hidden_dropout_prob: float = 0.0):
+        super().__init__()
+        self.dense = nn.Linear(intermediate_size, hidden_size)
+        self.dropout = nn.Dropout(hidden_dropout_prob)
+
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = hidden_states + input_tensor
+        return hidden_states
+
+
+class ViTMAELayer(nn.Module):
+    """
+    This matches the naming/parameter structure used in RAE-main (ViTMAE decoder block).
+    """
+
+    def __init__(
+        self,
+        *,
+        hidden_size: int,
+        num_attention_heads: int,
+        intermediate_size: int,
+        qkv_bias: bool = True,
+        layer_norm_eps: float = 1e-12,
+        hidden_dropout_prob: float = 0.0,
+        attention_probs_dropout_prob: float = 0.0,
+        hidden_act: str = "gelu",
+    ):
+        super().__init__()
+        if hidden_size % num_attention_heads != 0:
+            raise ValueError(
+                f"hidden_size={hidden_size} must be divisible by num_attention_heads={num_attention_heads}"
+            )
+        self.attention = Attention(
+            query_dim=hidden_size,
+            heads=num_attention_heads,
+            dim_head=hidden_size // num_attention_heads,
+            dropout=attention_probs_dropout_prob,
+            bias=qkv_bias,
+        )
+        self.intermediate = ViTMAEIntermediate(
+            hidden_size=hidden_size, intermediate_size=intermediate_size, hidden_act=hidden_act
+        )
+        self.output = ViTMAEOutput(
+            hidden_size=hidden_size, intermediate_size=intermediate_size, hidden_dropout_prob=hidden_dropout_prob
+        )
+        self.layernorm_before = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+        self.layernorm_after = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        attention_output = self.attention(self.layernorm_before(hidden_states))
+        hidden_states = attention_output + hidden_states
+
+        layer_output = self.layernorm_after(hidden_states)
+        layer_output = self.intermediate(layer_output)
+        layer_output = self.output(layer_output, hidden_states)
+        return layer_output
+
+
+class RAEDecoder(nn.Module):
+    """Lightweight RAE decoder."""
+
+    def __init__(
+        self,
+        hidden_size: int = 768,
+        decoder_hidden_size: int = 512,
+        decoder_num_hidden_layers: int = 8,
+        decoder_num_attention_heads: int = 16,
+        decoder_intermediate_size: int = 2048,
+        num_patches: int = 256,
+        patch_size: int = 16,
+        num_channels: int = 3,
+        image_size: int = 256,
+        qkv_bias: bool = True,
+        layer_norm_eps: float = 1e-12,
+        hidden_dropout_prob: float = 0.0,
+        attention_probs_dropout_prob: float = 0.0,
+        hidden_act: str = "gelu",
+    ):
+        super().__init__()
+        self.decoder_hidden_size = decoder_hidden_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.num_patches = num_patches
+
+        self.decoder_embed = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
+        self.register_buffer("decoder_pos_embed", torch.zeros(1, num_patches + 1, decoder_hidden_size))
+
+        self.decoder_layers = nn.ModuleList(
+            [
+                ViTMAELayer(
+                    hidden_size=decoder_hidden_size,
+                    num_attention_heads=decoder_num_attention_heads,
+                    intermediate_size=decoder_intermediate_size,
+                    qkv_bias=qkv_bias,
+                    layer_norm_eps=layer_norm_eps,
+                    hidden_dropout_prob=hidden_dropout_prob,
+                    attention_probs_dropout_prob=attention_probs_dropout_prob,
+                    hidden_act=hidden_act,
+                )
+                for _ in range(decoder_num_hidden_layers)
+            ]
+        )
+
+        self.decoder_norm = nn.LayerNorm(decoder_hidden_size, eps=layer_norm_eps)
+        self.decoder_pred = nn.Linear(decoder_hidden_size, patch_size**2 * num_channels, bias=True)
+        self.gradient_checkpointing = False
+
+        self._initialize_weights(num_patches)
+        self.trainable_cls_token = nn.Parameter(torch.zeros(1, 1, decoder_hidden_size))
+
+    def _initialize_weights(self, num_patches: int):
+        # Skip initialization when parameters are on meta device (e.g. during
+        # accelerate.init_empty_weights() used by low_cpu_mem_usage loading).
+        # The weights are initialized.
+        if self.decoder_pos_embed.device.type == "meta":
+            return
+
+        grid_size = int(num_patches**0.5)
+        pos_embed = get_2d_sincos_pos_embed(
+            self.decoder_pos_embed.shape[-1],
+            grid_size,
+            cls_token=True,
+            extra_tokens=1,
+            output_type="pt",
+            device=self.decoder_pos_embed.device,
+        )
+        self.decoder_pos_embed.data.copy_(pos_embed.unsqueeze(0).to(dtype=self.decoder_pos_embed.dtype))
+
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor) -> torch.Tensor:
+        embeddings_positions = embeddings.shape[1] - 1
+        num_positions = self.decoder_pos_embed.shape[1] - 1
+
+        class_pos_embed = self.decoder_pos_embed[:, 0, :]
+        patch_pos_embed = self.decoder_pos_embed[:, 1:, :]
+        dim = self.decoder_pos_embed.shape[-1]
+
+        patch_pos_embed = patch_pos_embed.reshape(1, 1, -1, dim).permute(0, 3, 1, 2)
+        patch_pos_embed = F.interpolate(
+            patch_pos_embed,
+            scale_factor=(1, embeddings_positions / num_positions),
+            mode="bicubic",
+            align_corners=False,
+        )
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
+
+    def interpolate_latent(self, x: torch.Tensor) -> torch.Tensor:
+        b, l, c = x.shape
+        if l == self.num_patches:
+            return x
+        h = w = int(l**0.5)
+        x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
+        target_size = (int(self.num_patches**0.5), int(self.num_patches**0.5))
+        x = F.interpolate(x, size=target_size, mode="bilinear", align_corners=False)
+        x = x.permute(0, 2, 3, 1).contiguous().view(b, self.num_patches, c)
+        return x
+
+    def unpatchify(self, patchified_pixel_values: torch.Tensor, original_image_size: tuple[int, int] | None = None):
+        patch_size, num_channels = self.patch_size, self.num_channels
+        original_image_size = (
+            original_image_size if original_image_size is not None else (self.image_size, self.image_size)
+        )
+        original_height, original_width = original_image_size
+        num_patches_h = original_height // patch_size
+        num_patches_w = original_width // patch_size
+        if num_patches_h * num_patches_w != patchified_pixel_values.shape[1]:
+            raise ValueError(
+                f"The number of patches in the patchified pixel values {patchified_pixel_values.shape[1]}, does not match the number of patches on original image {num_patches_h}*{num_patches_w}"
+            )
+
+        batch_size = patchified_pixel_values.shape[0]
+        patchified_pixel_values = patchified_pixel_values.reshape(
+            batch_size,
+            num_patches_h,
+            num_patches_w,
+            patch_size,
+            patch_size,
+            num_channels,
+        )
+        patchified_pixel_values = torch.einsum("nhwpqc->nchpwq", patchified_pixel_values)
+        pixel_values = patchified_pixel_values.reshape(
+            batch_size,
+            num_channels,
+            num_patches_h * patch_size,
+            num_patches_w * patch_size,
+        )
+        return pixel_values
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        *,
+        interpolate_pos_encoding: bool = False,
+        drop_cls_token: bool = False,
+        return_dict: bool = True,
+    ) -> RAEDecoderOutput | tuple[torch.Tensor]:
+        x = self.decoder_embed(hidden_states)
+        if drop_cls_token:
+            x_ = x[:, 1:, :]
+            x_ = self.interpolate_latent(x_)
+        else:
+            x_ = self.interpolate_latent(x)
+
+        cls_token = self.trainable_cls_token.expand(x_.shape[0], -1, -1)
+        x = torch.cat([cls_token, x_], dim=1)
+
+        if interpolate_pos_encoding:
+            if not drop_cls_token:
+                raise ValueError("interpolate_pos_encoding only supports drop_cls_token=True")
+            decoder_pos_embed = self.interpolate_pos_encoding(x)
+        else:
+            decoder_pos_embed = self.decoder_pos_embed
+
+        hidden_states = x + decoder_pos_embed.to(device=x.device, dtype=x.dtype)
+
+        for layer_module in self.decoder_layers:
+            hidden_states = layer_module(hidden_states)
+
+        hidden_states = self.decoder_norm(hidden_states)
+        logits = self.decoder_pred(hidden_states)
+        logits = logits[:, 1:, :]
+
+        if not return_dict:
+            return (logits,)
+        return RAEDecoderOutput(logits=logits)
+
+
+class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
+    r"""
+    Representation Autoencoder (RAE) model for encoding images to latents and decoding latents to images.
+
+    This model uses a frozen pretrained encoder (DINOv2, SigLIP2, or MAE) with a trainable ViT decoder to reconstruct
+    images from learned representations.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for its generic methods implemented for
+    all models (such as downloading or saving).
+
+    Args:
+        encoder_type (`str`, *optional*, defaults to `"dinov2"`):
+            Type of frozen encoder to use. One of `"dinov2"`, `"siglip2"`, or `"mae"`.
+        encoder_hidden_size (`int`, *optional*, defaults to `768`):
+            Hidden size of the encoder model.
+        encoder_patch_size (`int`, *optional*, defaults to `14`):
+            Patch size of the encoder model.
+        encoder_num_hidden_layers (`int`, *optional*, defaults to `12`):
+            Number of hidden layers in the encoder model.
+        patch_size (`int`, *optional*, defaults to `16`):
+            Decoder patch size (used for unpatchify and decoder head).
+        encoder_input_size (`int`, *optional*, defaults to `224`):
+            Input size expected by the encoder.
+        image_size (`int`, *optional*):
+            Decoder output image size. If `None`, it is derived from encoder token count and `patch_size` like
+            RAE-main: `image_size = patch_size * sqrt(num_patches)`, where `num_patches = (encoder_input_size //
+            encoder_patch_size) ** 2`.
+        num_channels (`int`, *optional*, defaults to `3`):
+            Number of input/output channels.
+        encoder_norm_mean (`list`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
+            Channel-wise mean for encoder input normalization (ImageNet defaults).
+        encoder_norm_std (`list`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
+            Channel-wise std for encoder input normalization (ImageNet defaults).
+        latents_mean (`list` or `tuple`, *optional*):
+            Optional mean for latent normalization. Tensor inputs are accepted and converted to config-serializable
+            lists.
+        latents_std (`list` or `tuple`, *optional*):
+            Optional standard deviation for latent normalization. Tensor inputs are accepted and converted to
+            config-serializable lists.
+        noise_tau (`float`, *optional*, defaults to `0.0`):
+            Noise level for training (adds noise to latents during training).
+        reshape_to_2d (`bool`, *optional*, defaults to `True`):
+            Whether to reshape latents to 2D (B, C, H, W) format.
+        use_encoder_loss (`bool`, *optional*, defaults to `False`):
+            Whether to use encoder hidden states in the loss (for advanced training).
+    """
+
+    # NOTE: gradient checkpointing is not wired up for this model yet.
+    _supports_gradient_checkpointing = False
+    _no_split_modules = ["ViTMAELayer"]
+
+    @register_to_config
+    def __init__(
+        self,
+        encoder_type: str = "dinov2",
+        encoder_hidden_size: int = 768,
+        encoder_patch_size: int = 14,
+        encoder_num_hidden_layers: int = 12,
+        decoder_hidden_size: int = 512,
+        decoder_num_hidden_layers: int = 8,
+        decoder_num_attention_heads: int = 16,
+        decoder_intermediate_size: int = 2048,
+        patch_size: int = 16,
+        encoder_input_size: int = 224,
+        image_size: int | None = None,
+        num_channels: int = 3,
+        encoder_norm_mean: list | None = None,
+        encoder_norm_std: list | None = None,
+        latents_mean: list | tuple | torch.Tensor | None = None,
+        latents_std: list | tuple | torch.Tensor | None = None,
+        noise_tau: float = 0.0,
+        reshape_to_2d: bool = True,
+        use_encoder_loss: bool = False,
+        scaling_factor: float = 1.0,
+    ):
+        super().__init__()
+
+        if encoder_type not in _ENCODER_FORWARD_FNS:
+            raise ValueError(
+                f"Unknown encoder_type='{encoder_type}'. Available: {sorted(_ENCODER_FORWARD_FNS.keys())}"
+            )
+
+        if encoder_input_size % encoder_patch_size != 0:
+            raise ValueError(
+                f"encoder_input_size={encoder_input_size} must be divisible by encoder_patch_size={encoder_patch_size}."
+            )
+
+        decoder_patch_size = patch_size
+        if decoder_patch_size <= 0:
+            raise ValueError("patch_size must be a positive integer (this is decoder_patch_size).")
+
+        num_patches = (encoder_input_size // encoder_patch_size) ** 2
+        grid = int(sqrt(num_patches))
+        if grid * grid != num_patches:
+            raise ValueError(f"Computed num_patches={num_patches} must be a perfect square.")
+
+        derived_image_size = decoder_patch_size * grid
+        if image_size is None:
+            image_size = derived_image_size
+        else:
+            image_size = int(image_size)
+            if image_size != derived_image_size:
+                raise ValueError(
+                    f"image_size={image_size} must equal decoder_patch_size*sqrt(num_patches)={derived_image_size} "
+                    f"for patch_size={decoder_patch_size} and computed num_patches={num_patches}."
+                )
+
+        def _to_config_compatible(value: Any) -> Any:
+            if isinstance(value, torch.Tensor):
+                return value.detach().cpu().tolist()
+            if isinstance(value, tuple):
+                return [_to_config_compatible(v) for v in value]
+            if isinstance(value, list):
+                return [_to_config_compatible(v) for v in value]
+            return value
+
+        def _as_optional_tensor(value: torch.Tensor | list | tuple | None) -> torch.Tensor | None:
+            if value is None:
+                return None
+            if isinstance(value, torch.Tensor):
+                return value.detach().clone()
+            return torch.tensor(value, dtype=torch.float32)
+
+        latents_std_tensor = _as_optional_tensor(latents_std)
+
+        # Ensure config values are JSON-serializable (list/None), even if caller passes torch.Tensors.
+        self.register_to_config(
+            latents_mean=_to_config_compatible(latents_mean),
+            latents_std=_to_config_compatible(latents_std),
+        )
+
+        # Frozen representation encoder (built from config, no downloads)
+        self.encoder: nn.Module = _build_encoder(
+            encoder_type=encoder_type,
+            hidden_size=encoder_hidden_size,
+            patch_size=encoder_patch_size,
+            num_hidden_layers=encoder_num_hidden_layers,
+        )
+        self._encoder_forward_fn = _ENCODER_FORWARD_FNS[encoder_type]
+        num_patches = (encoder_input_size // encoder_patch_size) ** 2
+
+        # Encoder input normalization stats (ImageNet defaults)
+        if encoder_norm_mean is None:
+            encoder_norm_mean = [0.485, 0.456, 0.406]
+        if encoder_norm_std is None:
+            encoder_norm_std = [0.229, 0.224, 0.225]
+        encoder_mean_tensor = torch.tensor(encoder_norm_mean, dtype=torch.float32).view(1, 3, 1, 1)
+        encoder_std_tensor = torch.tensor(encoder_norm_std, dtype=torch.float32).view(1, 3, 1, 1)
+
+        self.register_buffer("encoder_mean", encoder_mean_tensor, persistent=True)
+        self.register_buffer("encoder_std", encoder_std_tensor, persistent=True)
+
+        # Latent normalization buffers (defaults are no-ops; actual values come from checkpoint)
+        latents_mean_tensor = _as_optional_tensor(latents_mean)
+        if latents_mean_tensor is None:
+            latents_mean_tensor = torch.zeros(1)
+        self.register_buffer("_latents_mean", latents_mean_tensor, persistent=True)
+
+        if latents_std_tensor is None:
+            latents_std_tensor = torch.ones(1)
+        self.register_buffer("_latents_std", latents_std_tensor, persistent=True)
+
+        # ViT-MAE style decoder
+        self.decoder = RAEDecoder(
+            hidden_size=int(encoder_hidden_size),
+            decoder_hidden_size=int(decoder_hidden_size),
+            decoder_num_hidden_layers=int(decoder_num_hidden_layers),
+            decoder_num_attention_heads=int(decoder_num_attention_heads),
+            decoder_intermediate_size=int(decoder_intermediate_size),
+            num_patches=int(num_patches),
+            patch_size=int(decoder_patch_size),
+            num_channels=int(num_channels),
+            image_size=int(image_size),
+        )
+
+        self.num_patches = int(num_patches)
+        self.decoder_patch_size = int(decoder_patch_size)
+        self.decoder_image_size = int(image_size)
+
+        # Slicing support (batch dimension) similar to other diffusers autoencoders
+        self.use_slicing = False
+
+    def _noising(self, x: torch.Tensor, generator: torch.Generator | None = None) -> torch.Tensor:
+        # Per-sample random sigma in [0, noise_tau]
+        noise_sigma = self.config.noise_tau * torch.rand(
+            (x.size(0),) + (1,) * (x.ndim - 1), device=x.device, dtype=x.dtype, generator=generator
+        )
+        return x + noise_sigma * randn_tensor(x.shape, generator=generator, device=x.device, dtype=x.dtype)
+
+    def _resize_and_normalize(self, x: torch.Tensor) -> torch.Tensor:
+        _, _, h, w = x.shape
+        if h != self.config.encoder_input_size or w != self.config.encoder_input_size:
+            x = F.interpolate(
+                x,
+                size=(self.config.encoder_input_size, self.config.encoder_input_size),
+                mode="bicubic",
+                align_corners=False,
+            )
+        mean = self.encoder_mean.to(device=x.device, dtype=x.dtype)
+        std = self.encoder_std.to(device=x.device, dtype=x.dtype)
+        return (x - mean) / std
+
+    def _denormalize_image(self, x: torch.Tensor) -> torch.Tensor:
+        mean = self.encoder_mean.to(device=x.device, dtype=x.dtype)
+        std = self.encoder_std.to(device=x.device, dtype=x.dtype)
+        return x * std + mean
+
+    def _normalize_latents(self, z: torch.Tensor) -> torch.Tensor:
+        latents_mean = self._latents_mean.to(device=z.device, dtype=z.dtype)
+        latents_std = self._latents_std.to(device=z.device, dtype=z.dtype)
+        return (z - latents_mean) / (latents_std + 1e-5)
+
+    def _denormalize_latents(self, z: torch.Tensor) -> torch.Tensor:
+        latents_mean = self._latents_mean.to(device=z.device, dtype=z.dtype)
+        latents_std = self._latents_std.to(device=z.device, dtype=z.dtype)
+        return z * (latents_std + 1e-5) + latents_mean
+
+    def _encode(self, x: torch.Tensor, generator: torch.Generator | None = None) -> torch.Tensor:
+        x = self._resize_and_normalize(x)
+
+        if self.config.encoder_type == "mae":
+            tokens = self._encoder_forward_fn(self.encoder, x, self.config.encoder_patch_size)
+        else:
+            tokens = self._encoder_forward_fn(self.encoder, x)  # (B, N, C)
+
+        if self.training and self.config.noise_tau > 0:
+            tokens = self._noising(tokens, generator=generator)
+
+        if self.config.reshape_to_2d:
+            b, n, c = tokens.shape
+            side = int(sqrt(n))
+            if side * side != n:
+                raise ValueError(f"Token length n={n} is not a perfect square; cannot reshape to 2D.")
+            z = tokens.transpose(1, 2).contiguous().view(b, c, side, side)  # (B, C, h, w)
+        else:
+            z = tokens
+
+        z = self._normalize_latents(z)
+
+        # Follow diffusers convention: optionally scale latents for diffusion
+        if self.config.scaling_factor != 1.0:
+            z = z * self.config.scaling_factor
+
+        return z
+
+    @apply_forward_hook
+    def encode(
+        self, x: torch.Tensor, return_dict: bool = True, generator: torch.Generator | None = None
+    ) -> EncoderOutput | tuple[torch.Tensor]:
+        if self.use_slicing and x.shape[0] > 1:
+            latents = torch.cat([self._encode(x_slice, generator=generator) for x_slice in x.split(1)], dim=0)
+        else:
+            latents = self._encode(x, generator=generator)
+
+        if not return_dict:
+            return (latents,)
+        return EncoderOutput(latent=latents)
+
+    def _decode(self, z: torch.Tensor) -> torch.Tensor:
+        # Undo scaling factor if applied at encode time
+        if self.config.scaling_factor != 1.0:
+            z = z / self.config.scaling_factor
+
+        z = self._denormalize_latents(z)
+
+        if self.config.reshape_to_2d:
+            b, c, h, w = z.shape
+            tokens = z.view(b, c, h * w).transpose(1, 2).contiguous()  # (B, N, C)
+        else:
+            tokens = z
+
+        logits = self.decoder(tokens, return_dict=True).logits
+        x_rec = self.decoder.unpatchify(logits)
+        x_rec = self._denormalize_image(x_rec)
+        return x_rec
+
+    @apply_forward_hook
+    def decode(self, z: torch.Tensor, return_dict: bool = True) -> DecoderOutput | tuple[torch.Tensor]:
+        if self.use_slicing and z.shape[0] > 1:
+            decoded = torch.cat([self._decode(z_slice) for z_slice in z.split(1)], dim=0)
+        else:
+            decoded = self._decode(z)
+
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)
+
+    def forward(
+        self, sample: torch.Tensor, return_dict: bool = True, generator: torch.Generator | None = None
+    ) -> DecoderOutput | tuple[torch.Tensor]:
+        latents = self.encode(sample, return_dict=False, generator=generator)[0]
+        decoded = self.decode(latents, return_dict=False)[0]
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)

src/diffusers/models/transformers/__init__.py

@@ -28,6 +28,7 @@ if is_torch_available():
     from .transformer_flux import FluxTransformer2DModel
     from .transformer_flux2 import Flux2Transformer2DModel
     from .transformer_glm_image import GlmImageTransformer2DModel
+    from .transformer_helios import HeliosTransformer3DModel
     from .transformer_hidream_image import HiDreamImageTransformer2DModel
     from .transformer_hunyuan_video import HunyuanVideoTransformer3DModel
     from .transformer_hunyuan_video15 import HunyuanVideo15Transformer3DModel

src/diffusers/models/transformers/transformer_helios.py

@@ -0,0 +1,814 @@
+# Copyright 2025 The Helios Team 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.
+
+import math
+from functools import lru_cache
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
+from ...utils import apply_lora_scale, logging
+from ...utils.torch_utils import maybe_allow_in_graph
+from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
+from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
+from ..attention_dispatch import dispatch_attention_fn
+from ..cache_utils import CacheMixin
+from ..embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps
+from ..modeling_outputs import Transformer2DModelOutput
+from ..modeling_utils import ModelMixin
+from ..normalization import FP32LayerNorm
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def pad_for_3d_conv(x, kernel_size):
+    b, c, t, h, w = x.shape
+    pt, ph, pw = kernel_size
+    pad_t = (pt - (t % pt)) % pt
+    pad_h = (ph - (h % ph)) % ph
+    pad_w = (pw - (w % pw)) % pw
+    return torch.nn.functional.pad(x, (0, pad_w, 0, pad_h, 0, pad_t), mode="replicate")
+
+
+def center_down_sample_3d(x, kernel_size):
+    return torch.nn.functional.avg_pool3d(x, kernel_size, stride=kernel_size)
+
+
+def apply_rotary_emb_transposed(
+    hidden_states: torch.Tensor,
+    freqs_cis: torch.Tensor,
+):
+    x_1, x_2 = hidden_states.unflatten(-1, (-1, 2)).unbind(-1)
+    cos, sin = freqs_cis.unsqueeze(-2).chunk(2, dim=-1)
+    out = torch.empty_like(hidden_states)
+    out[..., 0::2] = x_1 * cos[..., 0::2] - x_2 * sin[..., 1::2]
+    out[..., 1::2] = x_1 * sin[..., 1::2] + x_2 * cos[..., 0::2]
+    return out.type_as(hidden_states)
+
+
+def _get_qkv_projections(attn: "HeliosAttention", hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor):
+    # encoder_hidden_states is only passed for cross-attention
+    if encoder_hidden_states is None:
+        encoder_hidden_states = hidden_states
+
+    if attn.fused_projections:
+        if not attn.is_cross_attention:
+            # In self-attention layers, we can fuse the entire QKV projection into a single linear
+            query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
+        else:
+            # In cross-attention layers, we can only fuse the KV projections into a single linear
+            query = attn.to_q(hidden_states)
+            key, value = attn.to_kv(encoder_hidden_states).chunk(2, dim=-1)
+    else:
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+    return query, key, value
+
+
+class HeliosOutputNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine: bool = False):
+        super().__init__()
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
+        self.norm = FP32LayerNorm(dim, eps, elementwise_affine=False)
+
+    def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor, original_context_length: int):
+        temb = temb[:, -original_context_length:, :]
+        shift, scale = (self.scale_shift_table.unsqueeze(0).to(temb.device) + temb.unsqueeze(2)).chunk(2, dim=2)
+        shift, scale = shift.squeeze(2).to(hidden_states.device), scale.squeeze(2).to(hidden_states.device)
+        hidden_states = hidden_states[:, -original_context_length:, :]
+        hidden_states = (self.norm(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
+        return hidden_states
+
+
+class HeliosAttnProcessor:
+    _attention_backend = None
+    _parallel_config = None
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "HeliosAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to version 2.0 or higher."
+            )
+
+    def __call__(
+        self,
+        attn: "HeliosAttention",
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+        original_context_length: int = None,
+    ) -> torch.Tensor:
+        query, key, value = _get_qkv_projections(attn, hidden_states, encoder_hidden_states)
+
+        query = attn.norm_q(query)
+        key = attn.norm_k(key)
+
+        query = query.unflatten(2, (attn.heads, -1))
+        key = key.unflatten(2, (attn.heads, -1))
+        value = value.unflatten(2, (attn.heads, -1))
+
+        if rotary_emb is not None:
+            query = apply_rotary_emb_transposed(query, rotary_emb)
+            key = apply_rotary_emb_transposed(key, rotary_emb)
+
+        if not attn.is_cross_attention and attn.is_amplify_history:
+            history_seq_len = hidden_states.shape[1] - original_context_length
+
+            if history_seq_len > 0:
+                scale_key = 1.0 + torch.sigmoid(attn.history_key_scale) * (attn.max_scale - 1.0)
+                if attn.history_scale_mode == "per_head":
+                    scale_key = scale_key.view(1, 1, -1, 1)
+                key = torch.cat([key[:, :history_seq_len] * scale_key, key[:, history_seq_len:]], dim=1)
+
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+            backend=self._attention_backend,
+            # Reference: https://github.com/huggingface/diffusers/pull/12909
+            parallel_config=(self._parallel_config if encoder_hidden_states is None else None),
+        )
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.type_as(query)
+
+        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[1](hidden_states)
+        return hidden_states
+
+
+class HeliosAttention(torch.nn.Module, AttentionModuleMixin):
+    _default_processor_cls = HeliosAttnProcessor
+    _available_processors = [HeliosAttnProcessor]
+
+    def __init__(
+        self,
+        dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        eps: float = 1e-5,
+        dropout: float = 0.0,
+        added_kv_proj_dim: int | None = None,
+        cross_attention_dim_head: int | None = None,
+        processor=None,
+        is_cross_attention=None,
+        is_amplify_history=False,
+        history_scale_mode="per_head",  # [scalar, per_head]
+    ):
+        super().__init__()
+
+        self.inner_dim = dim_head * heads
+        self.heads = heads
+        self.added_kv_proj_dim = added_kv_proj_dim
+        self.cross_attention_dim_head = cross_attention_dim_head
+        self.kv_inner_dim = self.inner_dim if cross_attention_dim_head is None else cross_attention_dim_head * heads
+
+        self.to_q = torch.nn.Linear(dim, self.inner_dim, bias=True)
+        self.to_k = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
+        self.to_v = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
+        self.to_out = torch.nn.ModuleList(
+            [
+                torch.nn.Linear(self.inner_dim, dim, bias=True),
+                torch.nn.Dropout(dropout),
+            ]
+        )
+        self.norm_q = torch.nn.RMSNorm(dim_head * heads, eps=eps, elementwise_affine=True)
+        self.norm_k = torch.nn.RMSNorm(dim_head * heads, eps=eps, elementwise_affine=True)
+
+        self.add_k_proj = self.add_v_proj = None
+        if added_kv_proj_dim is not None:
+            self.add_k_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
+            self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
+            self.norm_added_k = torch.nn.RMSNorm(dim_head * heads, eps=eps)
+
+        if is_cross_attention is not None:
+            self.is_cross_attention = is_cross_attention
+        else:
+            self.is_cross_attention = cross_attention_dim_head is not None
+
+        self.set_processor(processor)
+
+        self.is_amplify_history = is_amplify_history
+        if is_amplify_history:
+            if history_scale_mode == "scalar":
+                self.history_key_scale = nn.Parameter(torch.ones(1))
+            elif history_scale_mode == "per_head":
+                self.history_key_scale = nn.Parameter(torch.ones(heads))
+            else:
+                raise ValueError(f"Unknown history_scale_mode: {history_scale_mode}")
+            self.history_scale_mode = history_scale_mode
+            self.max_scale = 10.0
+
+    def fuse_projections(self):
+        if getattr(self, "fused_projections", False):
+            return
+
+        if not self.is_cross_attention:
+            concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
+            concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_qkv = nn.Linear(in_features, out_features, bias=True)
+            self.to_qkv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+        else:
+            concatenated_weights = torch.cat([self.to_k.weight.data, self.to_v.weight.data])
+            concatenated_bias = torch.cat([self.to_k.bias.data, self.to_v.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_kv = nn.Linear(in_features, out_features, bias=True)
+            self.to_kv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+
+        if self.added_kv_proj_dim is not None:
+            concatenated_weights = torch.cat([self.add_k_proj.weight.data, self.add_v_proj.weight.data])
+            concatenated_bias = torch.cat([self.add_k_proj.bias.data, self.add_v_proj.bias.data])
+            out_features, in_features = concatenated_weights.shape
+            with torch.device("meta"):
+                self.to_added_kv = nn.Linear(in_features, out_features, bias=True)
+            self.to_added_kv.load_state_dict(
+                {"weight": concatenated_weights, "bias": concatenated_bias}, strict=True, assign=True
+            )
+
+        self.fused_projections = True
+
+    @torch.no_grad()
+    def unfuse_projections(self):
+        if not getattr(self, "fused_projections", False):
+            return
+
+        if hasattr(self, "to_qkv"):
+            delattr(self, "to_qkv")
+        if hasattr(self, "to_kv"):
+            delattr(self, "to_kv")
+        if hasattr(self, "to_added_kv"):
+            delattr(self, "to_added_kv")
+
+        self.fused_projections = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+        original_context_length: int = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        return self.processor(
+            self,
+            hidden_states,
+            encoder_hidden_states,
+            attention_mask,
+            rotary_emb,
+            original_context_length,
+            **kwargs,
+        )
+
+
+class HeliosTimeTextEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        time_freq_dim: int,
+        time_proj_dim: int,
+        text_embed_dim: int,
+    ):
+        super().__init__()
+
+        self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.time_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim)
+        self.act_fn = nn.SiLU()
+        self.time_proj = nn.Linear(dim, time_proj_dim)
+        self.text_embedder = PixArtAlphaTextProjection(text_embed_dim, dim, act_fn="gelu_tanh")
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | None = None,
+        is_return_encoder_hidden_states: bool = True,
+    ):
+        timestep = self.timesteps_proj(timestep)
+
+        time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype
+        if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8:
+            timestep = timestep.to(time_embedder_dtype)
+        temb = self.time_embedder(timestep).type_as(encoder_hidden_states)
+        timestep_proj = self.time_proj(self.act_fn(temb))
+
+        if encoder_hidden_states is not None and is_return_encoder_hidden_states:
+            encoder_hidden_states = self.text_embedder(encoder_hidden_states)
+
+        return temb, timestep_proj, encoder_hidden_states
+
+
+class HeliosRotaryPosEmbed(nn.Module):
+    def __init__(self, rope_dim, theta):
+        super().__init__()
+        self.DT, self.DY, self.DX = rope_dim
+        self.theta = theta
+        self.register_buffer("freqs_base_t", self._get_freqs_base(self.DT), persistent=False)
+        self.register_buffer("freqs_base_y", self._get_freqs_base(self.DY), persistent=False)
+        self.register_buffer("freqs_base_x", self._get_freqs_base(self.DX), persistent=False)
+
+    def _get_freqs_base(self, dim):
+        return 1.0 / (self.theta ** (torch.arange(0, dim, 2, dtype=torch.float32)[: (dim // 2)] / dim))
+
+    @torch.no_grad()
+    def get_frequency_batched(self, freqs_base, pos):
+        freqs = torch.einsum("d,bthw->dbthw", freqs_base, pos)
+        freqs = freqs.repeat_interleave(2, dim=0)
+        return freqs.cos(), freqs.sin()
+
+    @torch.no_grad()
+    @lru_cache(maxsize=32)
+    def _get_spatial_meshgrid(self, height, width, device_str):
+        device = torch.device(device_str)
+        grid_y_coords = torch.arange(height, device=device, dtype=torch.float32)
+        grid_x_coords = torch.arange(width, device=device, dtype=torch.float32)
+        grid_y, grid_x = torch.meshgrid(grid_y_coords, grid_x_coords, indexing="ij")
+        return grid_y, grid_x
+
+    @torch.no_grad()
+    def forward(self, frame_indices, height, width, device):
+        batch_size = frame_indices.shape[0]
+        num_frames = frame_indices.shape[1]
+
+        frame_indices = frame_indices.to(device=device, dtype=torch.float32)
+        grid_y, grid_x = self._get_spatial_meshgrid(height, width, str(device))
+
+        grid_t = frame_indices[:, :, None, None].expand(batch_size, num_frames, height, width)
+        grid_y_batch = grid_y[None, None, :, :].expand(batch_size, num_frames, -1, -1)
+        grid_x_batch = grid_x[None, None, :, :].expand(batch_size, num_frames, -1, -1)
+
+        freqs_cos_t, freqs_sin_t = self.get_frequency_batched(self.freqs_base_t, grid_t)
+        freqs_cos_y, freqs_sin_y = self.get_frequency_batched(self.freqs_base_y, grid_y_batch)
+        freqs_cos_x, freqs_sin_x = self.get_frequency_batched(self.freqs_base_x, grid_x_batch)
+
+        result = torch.cat([freqs_cos_t, freqs_cos_y, freqs_cos_x, freqs_sin_t, freqs_sin_y, freqs_sin_x], dim=0)
+
+        return result.permute(1, 0, 2, 3, 4)
+
+
+@maybe_allow_in_graph
+class HeliosTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        ffn_dim: int,
+        num_heads: int,
+        qk_norm: str = "rms_norm_across_heads",
+        cross_attn_norm: bool = False,
+        eps: float = 1e-6,
+        added_kv_proj_dim: int | None = None,
+        guidance_cross_attn: bool = False,
+        is_amplify_history: bool = False,
+        history_scale_mode: str = "per_head",  # [scalar, per_head]
+    ):
+        super().__init__()
+
+        # 1. Self-attention
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.attn1 = HeliosAttention(
+            dim=dim,
+            heads=num_heads,
+            dim_head=dim // num_heads,
+            eps=eps,
+            cross_attention_dim_head=None,
+            processor=HeliosAttnProcessor(),
+            is_amplify_history=is_amplify_history,
+            history_scale_mode=history_scale_mode,
+        )
+
+        # 2. Cross-attention
+        self.attn2 = HeliosAttention(
+            dim=dim,
+            heads=num_heads,
+            dim_head=dim // num_heads,
+            eps=eps,
+            added_kv_proj_dim=added_kv_proj_dim,
+            cross_attention_dim_head=dim // num_heads,
+            processor=HeliosAttnProcessor(),
+        )
+        self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
+
+        # 3. Feed-forward
+        self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")
+        self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+
+        # 4. Guidance cross-attention
+        self.guidance_cross_attn = guidance_cross_attn
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        rotary_emb: torch.Tensor,
+        original_context_length: int = None,
+    ) -> torch.Tensor:
+        if temb.ndim == 4:
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+                self.scale_shift_table.unsqueeze(0) + temb.float()
+            ).chunk(6, dim=2)
+            # batch_size, seq_len, 1, inner_dim
+            shift_msa = shift_msa.squeeze(2)
+            scale_msa = scale_msa.squeeze(2)
+            gate_msa = gate_msa.squeeze(2)
+            c_shift_msa = c_shift_msa.squeeze(2)
+            c_scale_msa = c_scale_msa.squeeze(2)
+            c_gate_msa = c_gate_msa.squeeze(2)
+        else:
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+                self.scale_shift_table + temb.float()
+            ).chunk(6, dim=1)
+
+        # 1. Self-attention
+        norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
+        attn_output = self.attn1(
+            norm_hidden_states,
+            None,
+            None,
+            rotary_emb,
+            original_context_length,
+        )
+        hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states)
+
+        # 2. Cross-attention
+        if self.guidance_cross_attn:
+            history_seq_len = hidden_states.shape[1] - original_context_length
+
+            history_hidden_states, hidden_states = torch.split(
+                hidden_states, [history_seq_len, original_context_length], dim=1
+            )
+            norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states)
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states,
+                None,
+                None,
+                original_context_length,
+            )
+            hidden_states = hidden_states + attn_output
+            hidden_states = torch.cat([history_hidden_states, hidden_states], dim=1)
+        else:
+            norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states)
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states,
+                None,
+                None,
+                original_context_length,
+            )
+            hidden_states = hidden_states + attn_output
+
+        # 3. Feed-forward
+        norm_hidden_states = (self.norm3(hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as(
+            hidden_states
+        )
+        ff_output = self.ffn(norm_hidden_states)
+        hidden_states = (hidden_states.float() + ff_output.float() * c_gate_msa).type_as(hidden_states)
+
+        return hidden_states
+
+
+class HeliosTransformer3DModel(
+    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin, AttentionMixin
+):
+    r"""
+    A Transformer model for video-like data used in the Helios model.
+
+    Args:
+        patch_size (`tuple[int]`, defaults to `(1, 2, 2)`):
+            3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
+        num_attention_heads (`int`, defaults to `40`):
+            Fixed length for text embeddings.
+        attention_head_dim (`int`, defaults to `128`):
+            The number of channels in each head.
+        in_channels (`int`, defaults to `16`):
+            The number of channels in the input.
+        out_channels (`int`, defaults to `16`):
+            The number of channels in the output.
+        text_dim (`int`, defaults to `512`):
+            Input dimension for text embeddings.
+        freq_dim (`int`, defaults to `256`):
+            Dimension for sinusoidal time embeddings.
+        ffn_dim (`int`, defaults to `13824`):
+            Intermediate dimension in feed-forward network.
+        num_layers (`int`, defaults to `40`):
+            The number of layers of transformer blocks to use.
+        window_size (`tuple[int]`, defaults to `(-1, -1)`):
+            Window size for local attention (-1 indicates global attention).
+        cross_attn_norm (`bool`, defaults to `True`):
+            Enable cross-attention normalization.
+        qk_norm (`bool`, defaults to `True`):
+            Enable query/key normalization.
+        eps (`float`, defaults to `1e-6`):
+            Epsilon value for normalization layers.
+        add_img_emb (`bool`, defaults to `False`):
+            Whether to use img_emb.
+        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
+            The number of channels to use for the added key and value projections. If `None`, no projection is used.
+    """
+
+    _supports_gradient_checkpointing = True
+    _skip_layerwise_casting_patterns = [
+        "patch_embedding",
+        "patch_short",
+        "patch_mid",
+        "patch_long",
+        "condition_embedder",
+        "norm",
+    ]
+    _no_split_modules = ["HeliosTransformerBlock", "HeliosOutputNorm"]
+    _keep_in_fp32_modules = [
+        "time_embedder",
+        "scale_shift_table",
+        "norm1",
+        "norm2",
+        "norm3",
+        "history_key_scale",
+    ]
+    _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
+    _repeated_blocks = ["HeliosTransformerBlock"]
+    _cp_plan = {
+        "blocks.0": {
+            "hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
+        },
+        "blocks.*": {
+            "temb": ContextParallelInput(split_dim=1, expected_dims=4, split_output=False),
+            "rotary_emb": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
+        },
+        "blocks.39": ContextParallelOutput(gather_dim=1, expected_dims=3),
+    }
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: tuple[int, ...] = (1, 2, 2),
+        num_attention_heads: int = 40,
+        attention_head_dim: int = 128,
+        in_channels: int = 16,
+        out_channels: int = 16,
+        text_dim: int = 4096,
+        freq_dim: int = 256,
+        ffn_dim: int = 13824,
+        num_layers: int = 40,
+        cross_attn_norm: bool = True,
+        qk_norm: str | None = "rms_norm_across_heads",
+        eps: float = 1e-6,
+        added_kv_proj_dim: int | None = None,
+        rope_dim: tuple[int, ...] = (44, 42, 42),
+        rope_theta: float = 10000.0,
+        guidance_cross_attn: bool = True,
+        zero_history_timestep: bool = True,
+        has_multi_term_memory_patch: bool = True,
+        is_amplify_history: bool = False,
+        history_scale_mode: str = "per_head",  # [scalar, per_head]
+    ) -> None:
+        super().__init__()
+
+        inner_dim = num_attention_heads * attention_head_dim
+        out_channels = out_channels or in_channels
+
+        # 1. Patch & position embedding
+        self.rope = HeliosRotaryPosEmbed(rope_dim=rope_dim, theta=rope_theta)
+        self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)
+
+        # 2. Initial Multi Term Memory Patch
+        self.zero_history_timestep = zero_history_timestep
+        if has_multi_term_memory_patch:
+            self.patch_short = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)
+            self.patch_mid = nn.Conv3d(
+                in_channels,
+                inner_dim,
+                kernel_size=tuple(2 * p for p in patch_size),
+                stride=tuple(2 * p for p in patch_size),
+            )
+            self.patch_long = nn.Conv3d(
+                in_channels,
+                inner_dim,
+                kernel_size=tuple(4 * p for p in patch_size),
+                stride=tuple(4 * p for p in patch_size),
+            )
+
+        # 3. Condition embeddings
+        self.condition_embedder = HeliosTimeTextEmbedding(
+            dim=inner_dim,
+            time_freq_dim=freq_dim,
+            time_proj_dim=inner_dim * 6,
+            text_embed_dim=text_dim,
+        )
+
+        # 4. Transformer blocks
+        self.blocks = nn.ModuleList(
+            [
+                HeliosTransformerBlock(
+                    inner_dim,
+                    ffn_dim,
+                    num_attention_heads,
+                    qk_norm,
+                    cross_attn_norm,
+                    eps,
+                    added_kv_proj_dim,
+                    guidance_cross_attn=guidance_cross_attn,
+                    is_amplify_history=is_amplify_history,
+                    history_scale_mode=history_scale_mode,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 5. Output norm & projection
+        self.norm_out = HeliosOutputNorm(inner_dim, eps, elementwise_affine=False)
+        self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))
+
+        self.gradient_checkpointing = False
+
+    @apply_lora_scale("attention_kwargs")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        encoder_hidden_states: torch.Tensor,
+        # ------------ Stage 1 ------------
+        indices_hidden_states=None,
+        indices_latents_history_short=None,
+        indices_latents_history_mid=None,
+        indices_latents_history_long=None,
+        latents_history_short=None,
+        latents_history_mid=None,
+        latents_history_long=None,
+        return_dict: bool = True,
+        attention_kwargs: dict[str, Any] | None = None,
+    ) -> torch.Tensor | dict[str, torch.Tensor]:
+        # 1. Input
+        batch_size = hidden_states.shape[0]
+        p_t, p_h, p_w = self.config.patch_size
+
+        # 2. Process noisy latents
+        hidden_states = self.patch_embedding(hidden_states)
+        _, _, post_patch_num_frames, post_patch_height, post_patch_width = hidden_states.shape
+
+        if indices_hidden_states is None:
+            indices_hidden_states = torch.arange(0, post_patch_num_frames).unsqueeze(0).expand(batch_size, -1)
+
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+        rotary_emb = self.rope(
+            frame_indices=indices_hidden_states,
+            height=post_patch_height,
+            width=post_patch_width,
+            device=hidden_states.device,
+        )
+        rotary_emb = rotary_emb.flatten(2).transpose(1, 2)
+        original_context_length = hidden_states.shape[1]
+
+        # 3. Process short history latents
+        if latents_history_short is not None and indices_latents_history_short is not None:
+            latents_history_short = self.patch_short(latents_history_short)
+            _, _, _, H1, W1 = latents_history_short.shape
+            latents_history_short = latents_history_short.flatten(2).transpose(1, 2)
+
+            rotary_emb_history_short = self.rope(
+                frame_indices=indices_latents_history_short,
+                height=H1,
+                width=W1,
+                device=latents_history_short.device,
+            )
+            rotary_emb_history_short = rotary_emb_history_short.flatten(2).transpose(1, 2)
+
+            hidden_states = torch.cat([latents_history_short, hidden_states], dim=1)
+            rotary_emb = torch.cat([rotary_emb_history_short, rotary_emb], dim=1)
+
+        # 4. Process mid history latents
+        if latents_history_mid is not None and indices_latents_history_mid is not None:
+            latents_history_mid = pad_for_3d_conv(latents_history_mid, (2, 4, 4))
+            latents_history_mid = self.patch_mid(latents_history_mid)
+            latents_history_mid = latents_history_mid.flatten(2).transpose(1, 2)
+
+            rotary_emb_history_mid = self.rope(
+                frame_indices=indices_latents_history_mid,
+                height=H1,
+                width=W1,
+                device=latents_history_mid.device,
+            )
+            rotary_emb_history_mid = pad_for_3d_conv(rotary_emb_history_mid, (2, 2, 2))
+            rotary_emb_history_mid = center_down_sample_3d(rotary_emb_history_mid, (2, 2, 2))
+            rotary_emb_history_mid = rotary_emb_history_mid.flatten(2).transpose(1, 2)
+
+            hidden_states = torch.cat([latents_history_mid, hidden_states], dim=1)
+            rotary_emb = torch.cat([rotary_emb_history_mid, rotary_emb], dim=1)
+
+        # 5. Process long history latents
+        if latents_history_long is not None and indices_latents_history_long is not None:
+            latents_history_long = pad_for_3d_conv(latents_history_long, (4, 8, 8))
+            latents_history_long = self.patch_long(latents_history_long)
+            latents_history_long = latents_history_long.flatten(2).transpose(1, 2)
+
+            rotary_emb_history_long = self.rope(
+                frame_indices=indices_latents_history_long,
+                height=H1,
+                width=W1,
+                device=latents_history_long.device,
+            )
+            rotary_emb_history_long = pad_for_3d_conv(rotary_emb_history_long, (4, 4, 4))
+            rotary_emb_history_long = center_down_sample_3d(rotary_emb_history_long, (4, 4, 4))
+            rotary_emb_history_long = rotary_emb_history_long.flatten(2).transpose(1, 2)
+
+            hidden_states = torch.cat([latents_history_long, hidden_states], dim=1)
+            rotary_emb = torch.cat([rotary_emb_history_long, rotary_emb], dim=1)
+
+        history_context_length = hidden_states.shape[1] - original_context_length
+
+        if indices_hidden_states is not None and self.zero_history_timestep:
+            timestep_t0 = torch.zeros((1), dtype=timestep.dtype, device=timestep.device)
+            temb_t0, timestep_proj_t0, _ = self.condition_embedder(
+                timestep_t0, encoder_hidden_states, is_return_encoder_hidden_states=False
+            )
+            temb_t0 = temb_t0.unsqueeze(1).expand(batch_size, history_context_length, -1)
+            timestep_proj_t0 = (
+                timestep_proj_t0.unflatten(-1, (6, -1))
+                .view(1, 6, 1, -1)
+                .expand(batch_size, -1, history_context_length, -1)
+            )
+
+        temb, timestep_proj, encoder_hidden_states = self.condition_embedder(timestep, encoder_hidden_states)
+        timestep_proj = timestep_proj.unflatten(-1, (6, -1))
+
+        if indices_hidden_states is not None and not self.zero_history_timestep:
+            main_repeat_size = hidden_states.shape[1]
+        else:
+            main_repeat_size = original_context_length
+        temb = temb.view(batch_size, 1, -1).expand(batch_size, main_repeat_size, -1)
+        timestep_proj = timestep_proj.view(batch_size, 6, 1, -1).expand(batch_size, 6, main_repeat_size, -1)
+
+        if indices_hidden_states is not None and self.zero_history_timestep:
+            temb = torch.cat([temb_t0, temb], dim=1)
+            timestep_proj = torch.cat([timestep_proj_t0, timestep_proj], dim=2)
+
+        if timestep_proj.ndim == 4:
+            timestep_proj = timestep_proj.permute(0, 2, 1, 3)
+
+        # 6. Transformer blocks
+        hidden_states = hidden_states.contiguous()
+        encoder_hidden_states = encoder_hidden_states.contiguous()
+        rotary_emb = rotary_emb.contiguous()
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for block in self.blocks:
+                hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    timestep_proj,
+                    rotary_emb,
+                    original_context_length,
+                )
+        else:
+            for block in self.blocks:
+                hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states,
+                    timestep_proj,
+                    rotary_emb,
+                    original_context_length,
+                )
+
+        # 7. Normalization
+        hidden_states = self.norm_out(hidden_states, temb, original_context_length)
+        hidden_states = self.proj_out(hidden_states)
+
+        # 8. Unpatchify
+        hidden_states = hidden_states.reshape(
+            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1
+        )
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -14,6 +14,7 @@
 import importlib
 import inspect
 import os
+import sys
 import traceback
 import warnings
 from collections import OrderedDict
@@ -28,10 +29,16 @@ from tqdm.auto import tqdm
 from typing_extensions import Self
 
 from ..configuration_utils import ConfigMixin, FrozenDict
-from ..pipelines.pipeline_loading_utils import _fetch_class_library_tuple, simple_get_class_obj
+from ..pipelines.pipeline_loading_utils import (
+    LOADABLE_CLASSES,
+    _fetch_class_library_tuple,
+    _unwrap_model,
+    simple_get_class_obj,
+)
 from ..utils import PushToHubMixin, is_accelerate_available, logging
 from ..utils.dynamic_modules_utils import get_class_from_dynamic_module, resolve_trust_remote_code
 from ..utils.hub_utils import load_or_create_model_card, populate_model_card
+from ..utils.torch_utils import is_compiled_module
 from .components_manager import ComponentsManager
 from .modular_pipeline_utils import (
     MODULAR_MODEL_CARD_TEMPLATE,
@@ -40,6 +47,7 @@ from .modular_pipeline_utils import (
     InputParam,
     InsertableDict,
     OutputParam,
+    _validate_requirements,
     combine_inputs,
     combine_outputs,
     format_components,
@@ -290,6 +298,7 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
 
     config_name = "modular_config.json"
     model_name = None
+    _requirements: dict[str, str] | None = None
     _workflow_map = None
 
     @classmethod
@@ -404,6 +413,9 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
                 "Selected model repository does not happear to have any custom code or does not have a valid `config.json` file."
             )
 
+        if "requirements" in config and config["requirements"] is not None:
+            _ = _validate_requirements(config["requirements"])
+
         class_ref = config["auto_map"][cls.__name__]
         module_file, class_name = class_ref.split(".")
         module_file = module_file + ".py"
@@ -428,8 +440,13 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         module = full_mod.rsplit(".", 1)[-1].replace("__dynamic__", "")
         parent_module = self.save_pretrained.__func__.__qualname__.split(".", 1)[0]
         auto_map = {f"{parent_module}": f"{module}.{cls_name}"}
-
         self.register_to_config(auto_map=auto_map)
+
+        # resolve requirements
+        requirements = _validate_requirements(getattr(self, "_requirements", None))
+        if requirements:
+            self.register_to_config(requirements=requirements)
+
         self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
         config = dict(self.config)
         self._internal_dict = FrozenDict(config)
@@ -651,6 +668,15 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
         combined_outputs = combine_outputs(*named_outputs)
         return combined_outputs
 
+    @property
+    # Copied from diffusers.modular_pipelines.modular_pipeline.SequentialPipelineBlocks._requirements
+    def _requirements(self) -> dict[str, str]:
+        requirements = {}
+        for block_name, block in self.sub_blocks.items():
+            if getattr(block, "_requirements", None):
+                requirements[block_name] = block._requirements
+        return requirements
+
     # used for `__repr__`
     def _get_trigger_inputs(self) -> set:
         """
@@ -1240,6 +1266,14 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
             expected_configs=self.expected_configs,
         )
 
+    @property
+    def _requirements(self) -> dict[str, str]:
+        requirements = {}
+        for block_name, block in self.sub_blocks.items():
+            if getattr(block, "_requirements", None):
+                requirements[block_name] = block._requirements
+        return requirements
+
 
 class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
     """
@@ -1378,6 +1412,15 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
     def outputs(self) -> list[str]:
         return next(reversed(self.sub_blocks.values())).intermediate_outputs
 
+    @property
+    # Copied from diffusers.modular_pipelines.modular_pipeline.SequentialPipelineBlocks._requirements
+    def _requirements(self) -> dict[str, str]:
+        requirements = {}
+        for block_name, block in self.sub_blocks.items():
+            if getattr(block, "_requirements", None):
+                requirements[block_name] = block._requirements
+        return requirements
+
     def __init__(self):
         sub_blocks = InsertableDict()
         for block_name, block in zip(self.block_names, self.block_classes):
@@ -1700,6 +1743,8 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             _blocks_class_name=self._blocks.__class__.__name__ if self._blocks is not None else None
         )
 
+        self._pretrained_model_name_or_path = pretrained_model_name_or_path
+
     @property
     def default_call_parameters(self) -> dict[str, Any]:
         """
@@ -1826,44 +1871,136 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         )
         return pipeline
 
-    def save_pretrained(self, save_directory: str | os.PathLike, push_to_hub: bool = False, **kwargs):
+    def save_pretrained(
+        self,
+        save_directory: str | os.PathLike,
+        safe_serialization: bool = True,
+        variant: str | None = None,
+        max_shard_size: int | str | None = None,
+        push_to_hub: bool = False,
+        **kwargs,
+    ):
         """
-        Save the pipeline to a directory. It does not save components, you need to save them separately.
+        Save the pipeline and all its components to a directory, so that it can be re-loaded using the
+        [`~ModularPipeline.from_pretrained`] class method.
 
         Args:
             save_directory (`str` or `os.PathLike`):
-                Path to the directory where the pipeline will be saved.
-            push_to_hub (`bool`, optional):
-                Whether to push the pipeline to the huggingface hub.
-            **kwargs: Additional arguments passed to `save_config()` method
+                Directory to save the pipeline to. Will be created if it doesn't exist.
+            safe_serialization (`bool`, *optional*, defaults to `True`):
+                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
+            variant (`str`, *optional*):
+                If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
+            max_shard_size (`int` or `str`, defaults to `None`):
+                The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
+                lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5GB"`).
+                If expressed as an integer, the unit is bytes.
+            push_to_hub (`bool`, *optional*, defaults to `False`):
+                Whether to push the pipeline to the Hugging Face model hub after saving it.
+            **kwargs: Additional keyword arguments:
+                - `overwrite_modular_index` (`bool`, *optional*, defaults to `False`):
+                    When saving a Modular Pipeline, its components in `modular_model_index.json` may reference repos
+                    different from the destination repo. Setting this to `True` updates all component references in
+                    `modular_model_index.json` so they point to the repo specified by `repo_id`.
+                - `repo_id` (`str`, *optional*):
+                    The repository ID to push the pipeline to. Defaults to the last component of `save_directory`.
+                - `commit_message` (`str`, *optional*):
+                    Commit message for the push to hub operation.
+                - `private` (`bool`, *optional*):
+                    Whether the repository should be private.
+                - `create_pr` (`bool`, *optional*, defaults to `False`):
+                    Whether to create a pull request instead of pushing directly.
+                - `token` (`str`, *optional*):
+                    The Hugging Face token to use for authentication.
         """
+        overwrite_modular_index = kwargs.pop("overwrite_modular_index", False)
+        repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
+
         if push_to_hub:
             commit_message = kwargs.pop("commit_message", None)
             private = kwargs.pop("private", None)
             create_pr = kwargs.pop("create_pr", False)
             token = kwargs.pop("token", None)
-            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
+            update_model_card = kwargs.pop("update_model_card", False)
             repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
 
-            # Generate modular pipeline card content
-            card_content = generate_modular_model_card_content(self.blocks)
+        for component_name, component_spec in self._component_specs.items():
+            if component_spec.default_creation_method != "from_pretrained":
+                continue
 
-            # Create a new empty model card and eventually tag it
+            component = getattr(self, component_name, None)
+            if component is None:
+                continue
+
+            model_cls = component.__class__
+            if is_compiled_module(component):
+                component = _unwrap_model(component)
+                model_cls = component.__class__
+
+            save_method_name = None
+            for library_name, library_classes in LOADABLE_CLASSES.items():
+                if library_name in sys.modules:
+                    library = importlib.import_module(library_name)
+                else:
+                    logger.info(
+                        f"{library_name} is not installed. Cannot save {component_name} as {library_classes} from {library_name}"
+                    )
+                    continue
+
+                for base_class, save_load_methods in library_classes.items():
+                    class_candidate = getattr(library, base_class, None)
+                    if class_candidate is not None and issubclass(model_cls, class_candidate):
+                        save_method_name = save_load_methods[0]
+                        break
+                if save_method_name is not None:
+                    break
+
+            if save_method_name is None:
+                logger.warning(f"self.{component_name}={component} of type {type(component)} cannot be saved.")
+                continue
+
+            save_method = getattr(component, save_method_name)
+            save_method_signature = inspect.signature(save_method)
+            save_method_accept_safe = "safe_serialization" in save_method_signature.parameters
+            save_method_accept_variant = "variant" in save_method_signature.parameters
+            save_method_accept_max_shard_size = "max_shard_size" in save_method_signature.parameters
+
+            save_kwargs = {}
+            if save_method_accept_safe:
+                save_kwargs["safe_serialization"] = safe_serialization
+            if save_method_accept_variant:
+                save_kwargs["variant"] = variant
+            if save_method_accept_max_shard_size and max_shard_size is not None:
+                save_kwargs["max_shard_size"] = max_shard_size
+
+            component_save_path = os.path.join(save_directory, component_name)
+            save_method(component_save_path, **save_kwargs)
+
+            if component_name not in self.config:
+                continue
+
+            has_no_load_id = not hasattr(component, "_diffusers_load_id") or component._diffusers_load_id == "null"
+            if overwrite_modular_index or has_no_load_id:
+                library, class_name, component_spec_dict = self.config[component_name]
+                component_spec_dict["pretrained_model_name_or_path"] = repo_id if push_to_hub else save_directory
+                component_spec_dict["subfolder"] = component_name
+                self.register_to_config(**{component_name: (library, class_name, component_spec_dict)})
+
+        self.save_config(save_directory=save_directory)
+
+        if push_to_hub:
+            card_content = generate_modular_model_card_content(self.blocks)
             model_card = load_or_create_model_card(
                 repo_id,
                 token=token,
                 is_pipeline=True,
                 model_description=MODULAR_MODEL_CARD_TEMPLATE.format(**card_content),
                 is_modular=True,
+                update_model_card=update_model_card,
             )
             model_card = populate_model_card(model_card, tags=card_content["tags"])
-
             model_card.save(os.path.join(save_directory, "README.md"))
 
-        # YiYi TODO: maybe order the json file to make it more readable: configs first, then components
-        self.save_config(save_directory=save_directory)
-
-        if push_to_hub:
             self._upload_folder(
                 save_directory,
                 repo_id,
@@ -2131,8 +2268,9 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             ```
 
         Notes:
-            - Components with trained weights should be loaded with `AutoModel.from_pretrained()` or
-            `ComponentSpec.load()` so that loading specs are preserved for serialization.
+            - Components loaded with `AutoModel.from_pretrained()` or `ComponentSpec.load()` will have
+            loading specs preserved for serialization. Custom or locally loaded components without Hub references will
+            have their `modular_model_index.json` entries updated automatically during `save_pretrained()`.
             - ConfigMixin objects without weights (e.g., schedulers, guiders) can be passed directly.
         """
 
@@ -2154,13 +2292,10 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                 new_component_spec = current_component_spec
                 if hasattr(self, name) and getattr(self, name) is not None:
                     logger.warning(f"ModularPipeline.update_components: setting {name} to None (spec unchanged)")
-            elif current_component_spec.default_creation_method == "from_pretrained" and not (
-                hasattr(component, "_diffusers_load_id") and component._diffusers_load_id is not None
+            elif (
+                current_component_spec.default_creation_method == "from_pretrained"
+                and getattr(component, "_diffusers_load_id", None) is None
             ):
-                logger.warning(
-                    f"ModularPipeline.update_components: {name} has no valid _diffusers_load_id. "
-                    f"This will result in empty loading spec, use ComponentSpec.load() for proper specs"
-                )
                 new_component_spec = ComponentSpec(name=name, type_hint=type(component))
             else:
                 new_component_spec = ComponentSpec.from_component(name, component)
@@ -2233,17 +2368,49 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                     elif "default" in value:
                         # check if the default is specified
                         component_load_kwargs[key] = value["default"]
+            # Only pass trust_remote_code to components from the same repo as the pipeline.
+            # When a user passes trust_remote_code=True, they intend to trust code from the
+            # pipeline's repo, not from external repos referenced in modular_model_index.json.
+            trust_remote_code_stripped = False
+            if (
+                "trust_remote_code" in component_load_kwargs
+                and self._pretrained_model_name_or_path is not None
+                and spec.pretrained_model_name_or_path != self._pretrained_model_name_or_path
+            ):
+                component_load_kwargs.pop("trust_remote_code")
+                trust_remote_code_stripped = True
+
+            if not spec.pretrained_model_name_or_path:
+                logger.info(f"Skipping component `{name}`: no pretrained model path specified.")
+                continue
+
             try:
                 components_to_register[name] = spec.load(**component_load_kwargs)
             except Exception:
-                logger.warning(
-                    f"\nFailed to create component {name}:\n"
-                    f"- Component spec: {spec}\n"
-                    f"- load() called with kwargs: {component_load_kwargs}\n"
-                    "If this component is not required for your workflow you can safely ignore this message.\n\n"
-                    "Traceback:\n"
-                    f"{traceback.format_exc()}"
-                )
+                tb = traceback.format_exc()
+                if trust_remote_code_stripped and "trust_remote_code" in tb:
+                    warning_msg = (
+                        f"Failed to load component `{name}` from external repository "
+                        f"`{spec.pretrained_model_name_or_path}`.\n\n"
+                        f"`trust_remote_code=True` was not forwarded to `{name}` because it comes from "
+                        f"a different repository than the pipeline (`{self._pretrained_model_name_or_path}`). "
+                        f"For safety, `trust_remote_code` is only forwarded to components from the same "
+                        f"repository as the pipeline.\n\n"
+                        f"You need to load this component manually with `trust_remote_code=True` and pass it "
+                        f"to the pipeline via `pipe.update_components()`. For example, if it is a custom model:\n\n"
+                        f'  {name} = AutoModel.from_pretrained("{spec.pretrained_model_name_or_path}", trust_remote_code=True)\n'
+                        f"  pipe.update_components({name}={name})\n"
+                    )
+                else:
+                    warning_msg = (
+                        f"Failed to create component {name}:\n"
+                        f"- Component spec: {spec}\n"
+                        f"- load() called with kwargs: {component_load_kwargs}\n"
+                        "If this component is not required for your workflow you can safely ignore this message.\n\n"
+                        "Traceback:\n"
+                        f"{tb}"
+                    )
+                logger.warning(warning_msg)
 
         # Register all components at once
         self.register_components(**components_to_register)

src/diffusers/modular_pipelines/modular_pipeline_utils.py

@@ -22,10 +22,12 @@ from typing import Any, Literal, Type, Union, get_args, get_origin
 
 import PIL.Image
 import torch
+from packaging.specifiers import InvalidSpecifier, SpecifierSet
 
 from ..configuration_utils import ConfigMixin, FrozenDict
 from ..loaders.single_file_utils import _is_single_file_path_or_url
 from ..utils import DIFFUSERS_LOAD_ID_FIELDS, is_torch_available, logging
+from ..utils.import_utils import _is_package_available
 
 
 if is_torch_available():
@@ -50,11 +52,7 @@ This modular pipeline is composed of the following blocks:
 
 {components_description} {configs_section}
 
-## Input/Output Specification
-
-### Inputs {inputs_description}
-
-### Outputs {outputs_description}
+{io_specification_section}
 """
 
 
@@ -311,6 +309,12 @@ class ComponentSpec:
                 f"`type_hint` is required when loading a single file model but is missing for component: {self.name}"
             )
 
+        # `torch_dtype` is not an accepted parameter for tokenizers and processors.
+        # As a result, it gets stored in `init_kwargs`, which are written to the config
+        # during save. This causes JSON serialization to fail when saving the component.
+        if self.type_hint is not None and not issubclass(self.type_hint, torch.nn.Module):
+            kwargs.pop("torch_dtype", None)
+
         if self.type_hint is None:
             try:
                 from diffusers import AutoModel
@@ -328,6 +332,12 @@ class ComponentSpec:
                 else getattr(self.type_hint, "from_pretrained")
             )
 
+            # `torch_dtype` is not an accepted parameter for tokenizers and processors.
+            # As a result, it gets stored in `init_kwargs`, which are written to the config
+            # during save. This causes JSON serialization to fail when saving the component.
+            if not issubclass(self.type_hint, torch.nn.Module):
+                kwargs.pop("torch_dtype", None)
+
             try:
                 component = load_method(pretrained_model_name_or_path, **load_kwargs, **kwargs)
             except Exception as e:
@@ -799,6 +809,46 @@ def format_output_params(output_params, indent_level=4, max_line_length=115):
     return format_params(output_params, "Outputs", indent_level, max_line_length)
 
 
+def format_params_markdown(params, header="Inputs"):
+    """Format a list of InputParam or OutputParam objects as a markdown bullet-point list.
+
+    Suitable for model cards rendered on Hugging Face Hub.
+
+    Args:
+        params: list of InputParam or OutputParam objects to format
+        header: Header text (e.g. "Inputs" or "Outputs")
+
+    Returns:
+        A formatted markdown string, or empty string if params is empty.
+    """
+    if not params:
+        return ""
+
+    def get_type_str(type_hint):
+        if isinstance(type_hint, UnionType) or get_origin(type_hint) is Union:
+            type_strs = [t.__name__ if hasattr(t, "__name__") else str(t) for t in get_args(type_hint)]
+            return " | ".join(type_strs)
+        return type_hint.__name__ if hasattr(type_hint, "__name__") else str(type_hint)
+
+    lines = [f"**{header}:**\n"] if header else []
+    for param in params:
+        type_str = get_type_str(param.type_hint) if param.type_hint != Any else ""
+        name = f"**{param.kwargs_type}" if param.name is None and param.kwargs_type is not None else param.name
+        param_str = f"- `{name}` (`{type_str}`"
+
+        if hasattr(param, "required") and not param.required:
+            param_str += ", *optional*"
+            if param.default is not None:
+                param_str += f", defaults to `{param.default}`"
+        param_str += ")"
+
+        desc = param.description if param.description else "No description provided"
+        param_str += f": {desc}"
+        lines.append(param_str)
+
+    return "\n".join(lines)
+
+
 def format_components(components, indent_level=4, max_line_length=115, add_empty_lines=True):
     """Format a list of ComponentSpec objects into a readable string representation.
 
@@ -972,6 +1022,89 @@ def make_doc_string(
     return output
 
 
+def _validate_requirements(reqs):
+    if reqs is None:
+        normalized_reqs = {}
+    else:
+        if not isinstance(reqs, dict):
+            raise ValueError(
+                "Requirements must be provided as a dictionary mapping package names to version specifiers."
+            )
+        normalized_reqs = _normalize_requirements(reqs)
+
+    if not normalized_reqs:
+        return {}
+
+    final: dict[str, str] = {}
+    for req, specified_ver in normalized_reqs.items():
+        req_available, req_actual_ver = _is_package_available(req)
+        if not req_available:
+            logger.warning(f"{req} was specified in the requirements but wasn't found in the current environment.")
+
+        if specified_ver:
+            try:
+                specifier = SpecifierSet(specified_ver)
+            except InvalidSpecifier as err:
+                raise ValueError(f"Requirement specifier '{specified_ver}' for {req} is invalid.") from err
+
+            if req_actual_ver == "N/A":
+                logger.warning(
+                    f"Version of {req} could not be determined to validate requirement '{specified_ver}'. Things might work unexpected."
+                )
+            elif not specifier.contains(req_actual_ver, prereleases=True):
+                logger.warning(
+                    f"{req} requirement '{specified_ver}' is not satisfied by the installed version {req_actual_ver}. Things might work unexpected."
+                )
+
+        final[req] = specified_ver
+
+    return final
+
+
+def _normalize_requirements(reqs):
+    if not reqs:
+        return {}
+
+    normalized: "OrderedDict[str, str]" = OrderedDict()
+
+    def _accumulate(mapping: dict[str, Any]):
+        for pkg, spec in mapping.items():
+            if isinstance(spec, dict):
+                # This is recursive because blocks are composable. This way, we can merge requirements
+                # from multiple blocks.
+                _accumulate(spec)
+                continue
+
+            pkg_name = str(pkg).strip()
+            if not pkg_name:
+                raise ValueError("Requirement package name cannot be empty.")
+
+            spec_str = "" if spec is None else str(spec).strip()
+            if spec_str and not spec_str.startswith(("<", ">", "=", "!", "~")):
+                spec_str = f"=={spec_str}"
+
+            existing_spec = normalized.get(pkg_name)
+            if existing_spec is not None:
+                if not existing_spec and spec_str:
+                    normalized[pkg_name] = spec_str
+                elif existing_spec and spec_str and existing_spec != spec_str:
+                    try:
+                        combined_spec = SpecifierSet(",".join(filter(None, [existing_spec, spec_str])))
+                    except InvalidSpecifier:
+                        logger.warning(
+                            f"Conflicting requirements for '{pkg_name}' detected: '{existing_spec}' vs '{spec_str}'. Keeping '{existing_spec}'."
+                        )
+                    else:
+                        normalized[pkg_name] = str(combined_spec)
+                continue
+
+            normalized[pkg_name] = spec_str
+
+    _accumulate(reqs)
+
+    return normalized
+
+
 def combine_inputs(*named_input_lists: list[tuple[str, list[InputParam]]]) -> list[InputParam]:
     """
     Combines multiple lists of InputParam objects from different blocks. For duplicate inputs, updates only if current
@@ -1055,8 +1188,7 @@ def generate_modular_model_card_content(blocks) -> dict[str, Any]:
             - blocks_description: Detailed architecture of blocks
             - components_description: List of required components
             - configs_section: Configuration parameters section
-            - inputs_description: Input parameters specification
-            - outputs_description: Output parameters specification
+            - io_specification_section: Input/Output specification (per-workflow or unified)
             - trigger_inputs_section: Conditional execution information
             - tags: List of relevant tags for the model card
     """
@@ -1075,15 +1207,6 @@ def generate_modular_model_card_content(blocks) -> dict[str, Any]:
             if block_desc:
                 blocks_desc_parts.append(f"   - {block_desc}")
 
-            # add sub-blocks if any
-            if hasattr(block, "sub_blocks") and block.sub_blocks:
-                for sub_name, sub_block in block.sub_blocks.items():
-                    sub_class = sub_block.__class__.__name__
-                    sub_desc = sub_block.description.split("\n")[0] if getattr(sub_block, "description", "") else ""
-                    blocks_desc_parts.append(f"   - *{sub_name}*: `{sub_class}`")
-                    if sub_desc:
-                        blocks_desc_parts.append(f"     - {sub_desc}")
-
     blocks_description = "\n".join(blocks_desc_parts) if blocks_desc_parts else "No blocks defined."
 
     components = getattr(blocks, "expected_components", [])
@@ -1109,63 +1232,76 @@ def generate_modular_model_card_content(blocks) -> dict[str, Any]:
         if configs_description:
             configs_section = f"\n\n## Configuration Parameters\n\n{configs_description}"
 
-    inputs = blocks.inputs
-    outputs = blocks.outputs
+    # Branch on whether workflows are defined
+    has_workflows = getattr(blocks, "_workflow_map", None) is not None
 
-    # format inputs as markdown list
-    inputs_parts = []
-    required_inputs = [inp for inp in inputs if inp.required]
-    optional_inputs = [inp for inp in inputs if not inp.required]
+    if has_workflows:
+        workflow_map = blocks._workflow_map
+        parts = []
 
-    if required_inputs:
-        inputs_parts.append("**Required:**\n")
-        for inp in required_inputs:
-            if hasattr(inp.type_hint, "__name__"):
-                type_str = inp.type_hint.__name__
-            elif inp.type_hint is not None:
-                type_str = str(inp.type_hint).replace("typing.", "")
-            else:
-                type_str = "Any"
-            desc = inp.description or "No description provided"
-            inputs_parts.append(f"- `{inp.name}` (`{type_str}`): {desc}")
+        # If blocks overrides outputs (e.g. to return just "images" instead of all intermediates),
+        # use that as the shared output for all workflows
+        blocks_outputs = blocks.outputs
+        blocks_intermediate = getattr(blocks, "intermediate_outputs", None)
+        shared_outputs = (
+            blocks_outputs if blocks_intermediate is not None and blocks_outputs != blocks_intermediate else None
+        )
 
-    if optional_inputs:
-        if required_inputs:
-            inputs_parts.append("")
-        inputs_parts.append("**Optional:**\n")
-        for inp in optional_inputs:
-            if hasattr(inp.type_hint, "__name__"):
-                type_str = inp.type_hint.__name__
-            elif inp.type_hint is not None:
-                type_str = str(inp.type_hint).replace("typing.", "")
-            else:
-                type_str = "Any"
-            desc = inp.description or "No description provided"
-            default_str = f", default: `{inp.default}`" if inp.default is not None else ""
-            inputs_parts.append(f"- `{inp.name}` (`{type_str}`){default_str}: {desc}")
+        parts.append("## Workflow Input Specification\n")
 
-    inputs_description = "\n".join(inputs_parts) if inputs_parts else "No specific inputs defined."
+        # Per-workflow details: show trigger inputs with full param descriptions
+        for wf_name, trigger_inputs in workflow_map.items():
+            trigger_input_names = set(trigger_inputs.keys())
+            try:
+                workflow_blocks = blocks.get_workflow(wf_name)
+            except Exception:
+                parts.append(f"<details>\n<summary><strong>{wf_name}</strong></summary>\n")
+                parts.append("*Could not resolve workflow blocks.*\n")
+                parts.append("</details>\n")
+                continue
 
-    # format outputs as markdown list
-    outputs_parts = []
-    for out in outputs:
-        if hasattr(out.type_hint, "__name__"):
-            type_str = out.type_hint.__name__
-        elif out.type_hint is not None:
-            type_str = str(out.type_hint).replace("typing.", "")
-        else:
-            type_str = "Any"
-        desc = out.description or "No description provided"
-        outputs_parts.append(f"- `{out.name}` (`{type_str}`): {desc}")
+            wf_inputs = workflow_blocks.inputs
+            # Show only trigger inputs with full parameter descriptions
+            trigger_params = [p for p in wf_inputs if p.name in trigger_input_names]
 
-    outputs_description = "\n".join(outputs_parts) if outputs_parts else "Standard pipeline outputs."
+            parts.append(f"<details>\n<summary><strong>{wf_name}</strong></summary>\n")
 
-    trigger_inputs_section = ""
-    if hasattr(blocks, "trigger_inputs") and blocks.trigger_inputs:
-        trigger_inputs_list = sorted([t for t in blocks.trigger_inputs if t is not None])
-        if trigger_inputs_list:
-            trigger_inputs_str = ", ".join(f"`{t}`" for t in trigger_inputs_list)
-            trigger_inputs_section = f"""
+            inputs_str = format_params_markdown(trigger_params, header=None)
+            parts.append(inputs_str if inputs_str else "No additional inputs required.")
+            parts.append("")
+
+            parts.append("</details>\n")
+
+        # Common Inputs & Outputs section (like non-workflow pipelines)
+        all_inputs = blocks.inputs
+        all_outputs = shared_outputs if shared_outputs is not None else blocks.outputs
+
+        inputs_str = format_params_markdown(all_inputs, "Inputs")
+        outputs_str = format_params_markdown(all_outputs, "Outputs")
+        inputs_description = inputs_str if inputs_str else "No specific inputs defined."
+        outputs_description = outputs_str if outputs_str else "Standard pipeline outputs."
+
+        parts.append(f"\n## Input/Output Specification\n\n{inputs_description}\n\n{outputs_description}")
+
+        io_specification_section = "\n".join(parts)
+        # Suppress trigger_inputs_section when workflows are shown (it's redundant)
+        trigger_inputs_section = ""
+    else:
+        # Unified I/O section (original behavior)
+        inputs = blocks.inputs
+        outputs = blocks.outputs
+        inputs_str = format_params_markdown(inputs, "Inputs")
+        outputs_str = format_params_markdown(outputs, "Outputs")
+        inputs_description = inputs_str if inputs_str else "No specific inputs defined."
+        outputs_description = outputs_str if outputs_str else "Standard pipeline outputs."
+        io_specification_section = f"## Input/Output Specification\n\n{inputs_description}\n\n{outputs_description}"
+
+        trigger_inputs_section = ""
+        if hasattr(blocks, "trigger_inputs") and blocks.trigger_inputs:
+            trigger_inputs_list = sorted([t for t in blocks.trigger_inputs if t is not None])
+            if trigger_inputs_list:
+                trigger_inputs_str = ", ".join(f"`{t}`" for t in trigger_inputs_list)
+                trigger_inputs_section = f"""
 ### Conditional Execution
 
 This pipeline contains blocks that are selected at runtime based on inputs:
@@ -1178,7 +1314,18 @@ This pipeline contains blocks that are selected at runtime based on inputs:
     if hasattr(blocks, "model_name") and blocks.model_name:
         tags.append(blocks.model_name)
 
-    if hasattr(blocks, "trigger_inputs") and blocks.trigger_inputs:
+    if has_workflows:
+        # Derive tags from workflow names
+        workflow_names = set(blocks._workflow_map.keys())
+        if any("inpainting" in wf for wf in workflow_names):
+            tags.append("inpainting")
+        if any("image2image" in wf for wf in workflow_names):
+            tags.append("image-to-image")
+        if any("controlnet" in wf for wf in workflow_names):
+            tags.append("controlnet")
+        if any("text2image" in wf for wf in workflow_names):
+            tags.append("text-to-image")
+    elif hasattr(blocks, "trigger_inputs") and blocks.trigger_inputs:
         triggers = blocks.trigger_inputs
         if any(t in triggers for t in ["mask", "mask_image"]):
             tags.append("inpainting")
@@ -1206,8 +1353,7 @@ This pipeline uses a {block_count}-block architecture that can be customized and
         "blocks_description": blocks_description,
         "components_description": components_description,
         "configs_section": configs_section,
-        "inputs_description": inputs_description,
-        "outputs_description": outputs_description,
+        "io_specification_section": io_specification_section,
         "trigger_inputs_section": trigger_inputs_section,
         "tags": tags,
     }

src/diffusers/pipelines/__init__.py

@@ -237,6 +237,7 @@ else:
         "EasyAnimateInpaintPipeline",
         "EasyAnimateControlPipeline",
     ]
+    _import_structure["helios"] = ["HeliosPipeline", "HeliosPyramidPipeline"]
     _import_structure["hidream_image"] = ["HiDreamImagePipeline"]
     _import_structure["hunyuandit"] = ["HunyuanDiTPipeline"]
     _import_structure["hunyuan_video"] = [
@@ -667,6 +668,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         )
         from .flux2 import Flux2KleinPipeline, Flux2Pipeline
         from .glm_image import GlmImagePipeline
+        from .helios import HeliosPipeline, HeliosPyramidPipeline
         from .hidream_image import HiDreamImagePipeline
         from .hunyuan_image import HunyuanImagePipeline, HunyuanImageRefinerPipeline
         from .hunyuan_video import (

src/diffusers/pipelines/auto_pipeline.py

@@ -54,6 +54,7 @@ from .flux import (
 )
 from .flux2 import Flux2KleinPipeline, Flux2Pipeline
 from .glm_image import GlmImagePipeline
+from .helios import HeliosPipeline, HeliosPyramidPipeline
 from .hunyuandit import HunyuanDiTPipeline
 from .kandinsky import (
     KandinskyCombinedPipeline,
@@ -174,6 +175,8 @@ AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
         ("cogview3", CogView3PlusPipeline),
         ("cogview4", CogView4Pipeline),
         ("glm_image", GlmImagePipeline),
+        ("helios", HeliosPipeline),
+        ("helios-pyramid", HeliosPyramidPipeline),
         ("cogview4-control", CogView4ControlPipeline),
         ("qwenimage", QwenImagePipeline),
         ("qwenimage-controlnet", QwenImageControlNetPipeline),

src/diffusers/pipelines/helios/__init__.py

@@ -0,0 +1,48 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_helios"] = ["HeliosPipeline"]
+    _import_structure["pipeline_helios_pyramid"] = ["HeliosPyramidPipeline"]
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_helios import HeliosPipeline
+        from .pipeline_helios_pyramid import HeliosPyramidPipeline
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/helios/pipeline_helios.py

@@ -0,0 +1,916 @@
+# Copyright 2025 The Helios Team 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.
+
+import html
+from typing import Any, Callable
+
+import numpy as np
+import regex as re
+import torch
+from transformers import AutoTokenizer, UMT5EncoderModel
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...image_processor import PipelineImageInput
+from ...loaders import HeliosLoraLoaderMixin
+from ...models import AutoencoderKLWan, HeliosTransformer3DModel
+from ...schedulers import HeliosScheduler
+from ...utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from ...video_processor import VideoProcessor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import HeliosPipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+if is_ftfy_available():
+    import ftfy
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers.utils import export_to_video
+        >>> from diffusers import AutoencoderKLWan, HeliosPipeline
+
+        >>> # Available models: BestWishYsh/Helios-Base, BestWishYsh/Helios-Mid, BestWishYsh/Helios-Distilled
+        >>> model_id = "BestWishYsh/Helios-Base"
+        >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+        >>> pipe = HeliosPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+
+        >>> prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
+        >>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+
+        >>> output = pipe(
+        ...     prompt=prompt,
+        ...     negative_prompt=negative_prompt,
+        ...     height=384,
+        ...     width=640,
+        ...     num_frames=132,
+        ...     guidance_scale=5.0,
+        ... ).frames[0]
+        >>> export_to_video(output, "output.mp4", fps=24)
+        ```
+"""
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+def prompt_clean(text):
+    text = whitespace_clean(basic_clean(text))
+    return text
+
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+class HeliosPipeline(DiffusionPipeline, HeliosLoraLoaderMixin):
+    r"""
+    Pipeline for text-to-video / image-to-video / video-to-video generation using Helios.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    Args:
+        tokenizer ([`T5Tokenizer`]):
+            Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer),
+            specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        text_encoder ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        transformer ([`HeliosTransformer3DModel`]):
+            Conditional Transformer to denoise the input latents.
+        scheduler ([`HeliosScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKLWan`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["transformer"]
+
+    def __init__(
+        self,
+        tokenizer: AutoTokenizer,
+        text_encoder: UMT5EncoderModel,
+        vae: AutoencoderKLWan,
+        scheduler: HeliosScheduler,
+        transformer: HeliosTransformer3DModel,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: str | list[str] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt = [prompt_clean(u) for u in prompt]
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+        text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
+        seq_lens = mask.gt(0).sum(dim=1).long()
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0
+        )
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds, text_inputs.attention_mask.bool()
+
+    def encode_prompt(
+        self,
+        prompt: str | list[str],
+        negative_prompt: str | list[str] | None = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: torch.Tensor | None = None,
+        negative_prompt_embeds: torch.Tensor | None = None,
+        max_sequence_length: int = 226,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds, _ = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds, _ = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def check_inputs(
+        self,
+        prompt,
+        negative_prompt,
+        height,
+        width,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        image=None,
+        video=None,
+    ):
+        if height % 16 != 0 or width % 16 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif negative_prompt is not None and (
+            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
+        ):
+            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
+
+        if image is not None and video is not None:
+            raise ValueError("image and video cannot be provided simultaneously")
+
+    def prepare_latents(
+        self,
+        batch_size: int,
+        num_channels_latents: int = 16,
+        height: int = 384,
+        width: int = 640,
+        num_frames: int = 33,
+        dtype: torch.dtype | None = None,
+        device: torch.device | None = None,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_latent_frames,
+            int(height) // self.vae_scale_factor_spatial,
+            int(width) // self.vae_scale_factor_spatial,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        return latents
+
+    def prepare_image_latents(
+        self,
+        image: torch.Tensor,
+        latents_mean: torch.Tensor,
+        latents_std: torch.Tensor,
+        num_latent_frames_per_chunk: int,
+        dtype: torch.dtype | None = None,
+        device: torch.device | None = None,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+        fake_latents: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        device = device or self._execution_device
+        if latents is None:
+            image = image.unsqueeze(2).to(device=device, dtype=self.vae.dtype)
+            latents = self.vae.encode(image).latent_dist.sample(generator=generator)
+            latents = (latents - latents_mean) * latents_std
+        if fake_latents is None:
+            min_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
+            fake_video = image.repeat(1, 1, min_frames, 1, 1).to(device=device, dtype=self.vae.dtype)
+            fake_latents_full = self.vae.encode(fake_video).latent_dist.sample(generator=generator)
+            fake_latents_full = (fake_latents_full - latents_mean) * latents_std
+            fake_latents = fake_latents_full[:, :, -1:, :, :]
+        return latents.to(device=device, dtype=dtype), fake_latents.to(device=device, dtype=dtype)
+
+    def prepare_video_latents(
+        self,
+        video: torch.Tensor,
+        latents_mean: torch.Tensor,
+        latents_std: torch.Tensor,
+        num_latent_frames_per_chunk: int,
+        dtype: torch.dtype | None = None,
+        device: torch.device | None = None,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        device = device or self._execution_device
+        video = video.to(device=device, dtype=self.vae.dtype)
+        if latents is None:
+            num_frames = video.shape[2]
+            min_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
+            num_chunks = num_frames // min_frames
+            if num_chunks == 0:
+                raise ValueError(
+                    f"Video must have at least {min_frames} frames "
+                    f"(got {num_frames} frames). "
+                    f"Required: (num_latent_frames_per_chunk - 1) * {self.vae_scale_factor_temporal} + 1 = ({num_latent_frames_per_chunk} - 1) * {self.vae_scale_factor_temporal} + 1 = {min_frames}"
+                )
+            total_valid_frames = num_chunks * min_frames
+            start_frame = num_frames - total_valid_frames
+
+            first_frame = video[:, :, 0:1, :, :]
+            first_frame_latent = self.vae.encode(first_frame).latent_dist.sample(generator=generator)
+            first_frame_latent = (first_frame_latent - latents_mean) * latents_std
+
+            latents_chunks = []
+            for i in range(num_chunks):
+                chunk_start = start_frame + i * min_frames
+                chunk_end = chunk_start + min_frames
+                video_chunk = video[:, :, chunk_start:chunk_end, :, :]
+                chunk_latents = self.vae.encode(video_chunk).latent_dist.sample(generator=generator)
+                chunk_latents = (chunk_latents - latents_mean) * latents_std
+                latents_chunks.append(chunk_latents)
+            latents = torch.cat(latents_chunks, dim=2)
+        return first_frame_latent.to(device=device, dtype=dtype), latents.to(device=device, dtype=dtype)
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1.0
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: str | list[str] = None,
+        negative_prompt: str | list[str] = None,
+        height: int = 384,
+        width: int = 640,
+        num_frames: int = 132,
+        num_inference_steps: int = 50,
+        sigmas: list[float] = None,
+        guidance_scale: float = 5.0,
+        num_videos_per_prompt: int | None = 1,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+        prompt_embeds: torch.Tensor | None = None,
+        negative_prompt_embeds: torch.Tensor | None = None,
+        output_type: str | None = "np",
+        return_dict: bool = True,
+        attention_kwargs: dict[str, Any] | None = None,
+        callback_on_step_end: Callable[[int, int], None] | PipelineCallback | MultiPipelineCallbacks | None = None,
+        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
+        max_sequence_length: int = 512,
+        # ------------ I2V ------------
+        image: PipelineImageInput | None = None,
+        image_latents: torch.Tensor | None = None,
+        fake_image_latents: torch.Tensor | None = None,
+        add_noise_to_image_latents: bool = True,
+        image_noise_sigma_min: float = 0.111,
+        image_noise_sigma_max: float = 0.135,
+        # ------------ V2V ------------
+        video: PipelineImageInput | None = None,
+        video_latents: torch.Tensor | None = None,
+        add_noise_to_video_latents: bool = True,
+        video_noise_sigma_min: float = 0.111,
+        video_noise_sigma_max: float = 0.135,
+        # ------------ Stage 1 ------------
+        history_sizes: list = [16, 2, 1],
+        num_latent_frames_per_chunk: int = 9,
+        keep_first_frame: bool = True,
+        is_skip_first_chunk: bool = False,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, pass `prompt_embeds` instead.
+            negative_prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts to avoid during image generation. If not defined, pass `negative_prompt_embeds`
+                instead. Ignored when not using guidance (`guidance_scale` < `1`).
+            height (`int`, defaults to `384`):
+                The height in pixels of the generated image.
+            width (`int`, defaults to `640`):
+                The width in pixels of the generated image.
+            num_frames (`int`, defaults to `132`):
+                The number of frames in the generated video.
+            num_inference_steps (`int`, defaults to `50`):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, defaults to `5.0`):
+                Guidance scale as defined in [Classifier-Free Diffusion
+                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+                of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
+                `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
+                the text `prompt`, usually at the expense of lower image quality.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `list[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"np"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`HeliosPipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`list`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, defaults to `512`):
+                The maximum sequence length of the text encoder. If the prompt is longer than this, it will be
+                truncated. If the prompt is shorter, it will be padded to this length.
+
+        Examples:
+
+        Returns:
+            [`~HeliosPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`HeliosPipelineOutput`] is returned, otherwise a `tuple` is returned where
+                the first element is a list with the generated images and the second element is a list of `bool`s
+                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
+        """
+
+        history_sizes = sorted(history_sizes, reverse=True)  # From big to small
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            negative_prompt,
+            height,
+            width,
+            prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+            image,
+            video,
+        )
+
+        num_frames = max(num_frames, 1)
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        device = self._execution_device
+        vae_dtype = self.vae.dtype
+
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, self.vae.config.z_dim, 1, 1, 1)
+            .to(device, self.vae.dtype)
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+            device, self.vae.dtype
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+
+        transformer_dtype = self.transformer.dtype
+        prompt_embeds = prompt_embeds.to(transformer_dtype)
+        if negative_prompt_embeds is not None:
+            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
+
+        # 4. Prepare image or video
+        if image is not None:
+            image = self.video_processor.preprocess(image, height=height, width=width)
+            image_latents, fake_image_latents = self.prepare_image_latents(
+                image,
+                latents_mean=latents_mean,
+                latents_std=latents_std,
+                num_latent_frames_per_chunk=num_latent_frames_per_chunk,
+                dtype=torch.float32,
+                device=device,
+                generator=generator,
+                latents=image_latents,
+                fake_latents=fake_image_latents,
+            )
+
+        if image_latents is not None and add_noise_to_image_latents:
+            image_noise_sigma = (
+                torch.rand(1, device=device, generator=generator) * (image_noise_sigma_max - image_noise_sigma_min)
+                + image_noise_sigma_min
+            )
+            image_latents = (
+                image_noise_sigma * randn_tensor(image_latents.shape, generator=generator, device=device)
+                + (1 - image_noise_sigma) * image_latents
+            )
+            fake_image_noise_sigma = (
+                torch.rand(1, device=device, generator=generator) * (video_noise_sigma_max - video_noise_sigma_min)
+                + video_noise_sigma_min
+            )
+            fake_image_latents = (
+                fake_image_noise_sigma * randn_tensor(fake_image_latents.shape, generator=generator, device=device)
+                + (1 - fake_image_noise_sigma) * fake_image_latents
+            )
+
+        if video is not None:
+            video = self.video_processor.preprocess_video(video, height=height, width=width)
+            image_latents, video_latents = self.prepare_video_latents(
+                video,
+                latents_mean=latents_mean,
+                latents_std=latents_std,
+                num_latent_frames_per_chunk=num_latent_frames_per_chunk,
+                dtype=torch.float32,
+                device=device,
+                generator=generator,
+                latents=video_latents,
+            )
+
+        if video_latents is not None and add_noise_to_video_latents:
+            image_noise_sigma = (
+                torch.rand(1, device=device, generator=generator) * (image_noise_sigma_max - image_noise_sigma_min)
+                + image_noise_sigma_min
+            )
+            image_latents = (
+                image_noise_sigma * randn_tensor(image_latents.shape, generator=generator, device=device)
+                + (1 - image_noise_sigma) * image_latents
+            )
+
+            noisy_latents_chunks = []
+            num_latent_chunks = video_latents.shape[2] // num_latent_frames_per_chunk
+            for i in range(num_latent_chunks):
+                chunk_start = i * num_latent_frames_per_chunk
+                chunk_end = chunk_start + num_latent_frames_per_chunk
+                latent_chunk = video_latents[:, :, chunk_start:chunk_end, :, :]
+
+                chunk_frames = latent_chunk.shape[2]
+                frame_sigmas = (
+                    torch.rand(chunk_frames, device=device, generator=generator)
+                    * (video_noise_sigma_max - video_noise_sigma_min)
+                    + video_noise_sigma_min
+                )
+                frame_sigmas = frame_sigmas.view(1, 1, chunk_frames, 1, 1)
+
+                noisy_chunk = (
+                    frame_sigmas * randn_tensor(latent_chunk.shape, generator=generator, device=device)
+                    + (1 - frame_sigmas) * latent_chunk
+                )
+                noisy_latents_chunks.append(noisy_chunk)
+            video_latents = torch.cat(noisy_latents_chunks, dim=2)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        window_num_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1
+        num_latent_chunk = max(1, (num_frames + window_num_frames - 1) // window_num_frames)
+        num_history_latent_frames = sum(history_sizes)
+        history_video = None
+        total_generated_latent_frames = 0
+
+        if not keep_first_frame:
+            history_sizes[-1] = history_sizes[-1] + 1
+        history_latents = torch.zeros(
+            batch_size,
+            num_channels_latents,
+            num_history_latent_frames,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+            device=device,
+            dtype=torch.float32,
+        )
+        if fake_image_latents is not None:
+            history_latents = torch.cat([history_latents[:, :, :-1, :, :], fake_image_latents], dim=2)
+            total_generated_latent_frames += 1
+        if video_latents is not None:
+            history_frames = history_latents.shape[2]
+            video_frames = video_latents.shape[2]
+            if video_frames < history_frames:
+                keep_frames = history_frames - video_frames
+                history_latents = torch.cat([history_latents[:, :, :keep_frames, :, :], video_latents], dim=2)
+            else:
+                history_latents = video_latents
+            total_generated_latent_frames += video_latents.shape[2]
+
+        if keep_first_frame:
+            indices = torch.arange(0, sum([1, *history_sizes, num_latent_frames_per_chunk]))
+            (
+                indices_prefix,
+                indices_latents_history_long,
+                indices_latents_history_mid,
+                indices_latents_history_1x,
+                indices_hidden_states,
+            ) = indices.split([1, *history_sizes, num_latent_frames_per_chunk], dim=0)
+            indices_latents_history_short = torch.cat([indices_prefix, indices_latents_history_1x], dim=0)
+        else:
+            indices = torch.arange(0, sum([*history_sizes, num_latent_frames_per_chunk]))
+            (
+                indices_latents_history_long,
+                indices_latents_history_mid,
+                indices_latents_history_short,
+                indices_hidden_states,
+            ) = indices.split([*history_sizes, num_latent_frames_per_chunk], dim=0)
+        indices_hidden_states = indices_hidden_states.unsqueeze(0)
+        indices_latents_history_short = indices_latents_history_short.unsqueeze(0)
+        indices_latents_history_mid = indices_latents_history_mid.unsqueeze(0)
+        indices_latents_history_long = indices_latents_history_long.unsqueeze(0)
+
+        # 6. Denoising loop
+        patch_size = self.transformer.config.patch_size
+        image_seq_len = (
+            num_latent_frames_per_chunk
+            * (height // self.vae_scale_factor_spatial)
+            * (width // self.vae_scale_factor_spatial)
+            // (patch_size[0] * patch_size[1] * patch_size[2])
+        )
+        sigmas = np.linspace(0.999, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+
+        for k in range(num_latent_chunk):
+            is_first_chunk = k == 0
+            is_second_chunk = k == 1
+            if keep_first_frame:
+                latents_history_long, latents_history_mid, latents_history_1x = history_latents[
+                    :, :, -num_history_latent_frames:
+                ].split(history_sizes, dim=2)
+                if image_latents is None and is_first_chunk:
+                    latents_prefix = torch.zeros(
+                        (
+                            batch_size,
+                            num_channels_latents,
+                            1,
+                            latents_history_1x.shape[-2],
+                            latents_history_1x.shape[-1],
+                        ),
+                        device=device,
+                        dtype=latents_history_1x.dtype,
+                    )
+                else:
+                    latents_prefix = image_latents
+                latents_history_short = torch.cat([latents_prefix, latents_history_1x], dim=2)
+            else:
+                latents_history_long, latents_history_mid, latents_history_short = history_latents[
+                    :, :, -num_history_latent_frames:
+                ].split(history_sizes, dim=2)
+
+            latents = self.prepare_latents(
+                batch_size,
+                num_channels_latents,
+                height,
+                width,
+                window_num_frames,
+                dtype=torch.float32,
+                device=device,
+                generator=generator,
+                latents=None,
+            )
+
+            self.scheduler.set_timesteps(num_inference_steps, device=device, sigmas=sigmas, mu=mu)
+            timesteps = self.scheduler.timesteps
+            num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+            self._num_timesteps = len(timesteps)
+
+            with self.progress_bar(total=num_inference_steps) as progress_bar:
+                for i, t in enumerate(timesteps):
+                    if self.interrupt:
+                        continue
+
+                    self._current_timestep = t
+                    timestep = t.expand(latents.shape[0])
+
+                    latent_model_input = latents.to(transformer_dtype)
+                    latents_history_short = latents_history_short.to(transformer_dtype)
+                    latents_history_mid = latents_history_mid.to(transformer_dtype)
+                    latents_history_long = latents_history_long.to(transformer_dtype)
+                    with self.transformer.cache_context("cond"):
+                        noise_pred = self.transformer(
+                            hidden_states=latent_model_input,
+                            timestep=timestep,
+                            encoder_hidden_states=prompt_embeds,
+                            indices_hidden_states=indices_hidden_states,
+                            indices_latents_history_short=indices_latents_history_short,
+                            indices_latents_history_mid=indices_latents_history_mid,
+                            indices_latents_history_long=indices_latents_history_long,
+                            latents_history_short=latents_history_short,
+                            latents_history_mid=latents_history_mid,
+                            latents_history_long=latents_history_long,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                        )[0]
+
+                    if self.do_classifier_free_guidance:
+                        with self.transformer.cache_context("uncond"):
+                            noise_uncond = self.transformer(
+                                hidden_states=latent_model_input,
+                                timestep=timestep,
+                                encoder_hidden_states=negative_prompt_embeds,
+                                indices_hidden_states=indices_hidden_states,
+                                indices_latents_history_short=indices_latents_history_short,
+                                indices_latents_history_mid=indices_latents_history_mid,
+                                indices_latents_history_long=indices_latents_history_long,
+                                latents_history_short=latents_history_short,
+                                latents_history_mid=latents_history_mid,
+                                latents_history_long=latents_history_long,
+                                attention_kwargs=attention_kwargs,
+                                return_dict=False,
+                            )[0]
+                        noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
+
+                    latents = self.scheduler.step(
+                        noise_pred,
+                        t,
+                        latents,
+                        generator=generator,
+                        return_dict=False,
+                    )[0]
+
+                    if callback_on_step_end is not None:
+                        callback_kwargs = {}
+                        for k in callback_on_step_end_tensor_inputs:
+                            callback_kwargs[k] = locals()[k]
+                        callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                        latents = callback_outputs.pop("latents", latents)
+                        prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                        negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                        progress_bar.update()
+
+                    if XLA_AVAILABLE:
+                        xm.mark_step()
+
+                if keep_first_frame and (
+                    (is_first_chunk and image_latents is None) or (is_skip_first_chunk and is_second_chunk)
+                ):
+                    image_latents = latents[:, :, 0:1, :, :]
+
+                total_generated_latent_frames += latents.shape[2]
+                history_latents = torch.cat([history_latents, latents], dim=2)
+                real_history_latents = history_latents[:, :, -total_generated_latent_frames:]
+                current_latents = (
+                    real_history_latents[:, :, -num_latent_frames_per_chunk:].to(vae_dtype) / latents_std
+                    + latents_mean
+                )
+                current_video = self.vae.decode(current_latents, return_dict=False)[0]
+
+                if history_video is None:
+                    history_video = current_video
+                else:
+                    history_video = torch.cat([history_video, current_video], dim=2)
+
+        self._current_timestep = None
+
+        if output_type != "latent":
+            generated_frames = history_video.size(2)
+            generated_frames = (
+                generated_frames - 1
+            ) // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
+            history_video = history_video[:, :, :generated_frames]
+            video = self.video_processor.postprocess_video(history_video, output_type=output_type)
+        else:
+            video = real_history_latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return HeliosPipelineOutput(frames=video)

src/diffusers/pipelines/helios/pipeline_output.py

@@ -0,0 +1,20 @@
+from dataclasses import dataclass
+
+import torch
+
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class HeliosPipelineOutput(BaseOutput):
+    r"""
+    Output class for Helios pipelines.
+
+    Args:
+        frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    frames: torch.Tensor

src/diffusers/pipelines/z_image/pipeline_z_image_controlnet.py

@@ -19,7 +19,7 @@ import torch
 from transformers import AutoTokenizer, PreTrainedModel
 
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import FromSingleFileMixin
+from ...loaders import FromSingleFileMixin, ZImageLoraLoaderMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.controlnets import ZImageControlNetModel
 from ...models.transformers import ZImageTransformer2DModel
@@ -185,7 +185,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class ZImageControlNetPipeline(DiffusionPipeline, FromSingleFileMixin):
+class ZImageControlNetPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFileMixin):
     model_cpu_offload_seq = "text_encoder->transformer->vae"
     _optional_components = []
     _callback_tensor_inputs = ["latents", "prompt_embeds"]
@@ -365,7 +365,7 @@ class ZImageControlNetPipeline(DiffusionPipeline, FromSingleFileMixin):
 
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
+        return self._guidance_scale > 0
 
     @property
     def joint_attention_kwargs(self):

src/diffusers/pipelines/z_image/pipeline_z_image_controlnet_inpaint.py

@@ -20,7 +20,7 @@ import torch.nn.functional as F
 from transformers import AutoTokenizer, PreTrainedModel
 
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import FromSingleFileMixin
+from ...loaders import FromSingleFileMixin, ZImageLoraLoaderMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.controlnets import ZImageControlNetModel
 from ...models.transformers import ZImageTransformer2DModel
@@ -185,7 +185,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class ZImageControlNetInpaintPipeline(DiffusionPipeline, FromSingleFileMixin):
+class ZImageControlNetInpaintPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFileMixin):
     model_cpu_offload_seq = "text_encoder->transformer->vae"
     _optional_components = []
     _callback_tensor_inputs = ["latents", "prompt_embeds"]
@@ -372,7 +372,7 @@ class ZImageControlNetInpaintPipeline(DiffusionPipeline, FromSingleFileMixin):
 
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
+        return self._guidance_scale > 0
 
     @property
     def joint_attention_kwargs(self):

src/diffusers/pipelines/z_image/pipeline_z_image_img2img.py

@@ -347,7 +347,7 @@ class ZImageImg2ImgPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingle
 
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
+        return self._guidance_scale > 0
 
     @property
     def joint_attention_kwargs(self):

src/diffusers/pipelines/z_image/pipeline_z_image_inpaint.py

@@ -462,7 +462,7 @@ class ZImageInpaintPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingle
 
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
+        return self._guidance_scale > 0
 
     @property
     def joint_attention_kwargs(self):

src/diffusers/pipelines/z_image/pipeline_z_image_omni.py

@@ -339,7 +339,7 @@ class ZImageOmniPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFil
 
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
+        return self._guidance_scale > 0
 
     @property
     def joint_attention_kwargs(self):

src/diffusers/schedulers/__init__.py

@@ -61,6 +61,8 @@ else:
     _import_structure["scheduling_flow_match_euler_discrete"] = ["FlowMatchEulerDiscreteScheduler"]
     _import_structure["scheduling_flow_match_heun_discrete"] = ["FlowMatchHeunDiscreteScheduler"]
     _import_structure["scheduling_flow_match_lcm"] = ["FlowMatchLCMScheduler"]
+    _import_structure["scheduling_helios"] = ["HeliosScheduler"]
+    _import_structure["scheduling_helios_dmd"] = ["HeliosDMDScheduler"]
     _import_structure["scheduling_heun_discrete"] = ["HeunDiscreteScheduler"]
     _import_structure["scheduling_ipndm"] = ["IPNDMScheduler"]
     _import_structure["scheduling_k_dpm_2_ancestral_discrete"] = ["KDPM2AncestralDiscreteScheduler"]
@@ -164,6 +166,8 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
         from .scheduling_flow_match_heun_discrete import FlowMatchHeunDiscreteScheduler
         from .scheduling_flow_match_lcm import FlowMatchLCMScheduler
+        from .scheduling_helios import HeliosScheduler
+        from .scheduling_helios_dmd import HeliosDMDScheduler
         from .scheduling_heun_discrete import HeunDiscreteScheduler
         from .scheduling_ipndm import IPNDMScheduler
         from .scheduling_k_dpm_2_ancestral_discrete import KDPM2AncestralDiscreteScheduler

src/diffusers/schedulers/scheduling_helios.py

@@ -0,0 +1,867 @@
+# Copyright 2025 The Helios Team 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.
+
+import math
+from dataclasses import dataclass
+from typing import Literal
+
+import numpy as np
+import torch
+
+from ..configuration_utils import ConfigMixin, register_to_config
+from ..schedulers.scheduling_utils import SchedulerMixin
+from ..utils import BaseOutput, deprecate
+
+
+@dataclass
+class HeliosSchedulerOutput(BaseOutput):
+    prev_sample: torch.FloatTensor
+    model_outputs: torch.FloatTensor | None = None
+    last_sample: torch.FloatTensor | None = None
+    this_order: int | None = None
+
+
+class HeliosScheduler(SchedulerMixin, ConfigMixin):
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        shift: float = 1.0,  # Following Stable diffusion 3,
+        stages: int = 3,
+        stage_range: list = [0, 1 / 3, 2 / 3, 1],
+        gamma: float = 1 / 3,
+        # For UniPC
+        thresholding: bool = False,
+        prediction_type: str = "flow_prediction",
+        solver_order: int = 2,
+        predict_x0: bool = True,
+        solver_type: str = "bh2",
+        lower_order_final: bool = True,
+        disable_corrector: list[int] = [],
+        solver_p: SchedulerMixin = None,
+        use_flow_sigmas: bool = True,
+        scheduler_type: str = "unipc",  # ["euler", "unipc"]
+        use_dynamic_shifting: bool = False,
+        time_shift_type: Literal["exponential", "linear"] = "exponential",
+    ):
+        self.timestep_ratios = {}  # The timestep ratio for each stage
+        self.timesteps_per_stage = {}  # The detailed timesteps per stage (fix max and min per stage)
+        self.sigmas_per_stage = {}  # always uniform [1000, 0]
+        self.start_sigmas = {}  # for start point / upsample renoise
+        self.end_sigmas = {}  # for end point
+        self.ori_start_sigmas = {}
+
+        # self.init_sigmas()
+        self.init_sigmas_for_each_stage()
+        self.sigma_min = self.sigmas[-1].item()
+        self.sigma_max = self.sigmas[0].item()
+        self.gamma = gamma
+
+        if solver_type not in ["bh1", "bh2"]:
+            if solver_type in ["midpoint", "heun", "logrho"]:
+                self.register_to_config(solver_type="bh2")
+            else:
+                raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
+
+        self.predict_x0 = predict_x0
+        self.model_outputs = [None] * solver_order
+        self.timestep_list = [None] * solver_order
+        self.lower_order_nums = 0
+        self.disable_corrector = disable_corrector
+        self.solver_p = solver_p
+        self.last_sample = None
+        self._step_index = None
+        self._begin_index = None
+
+    def init_sigmas(self):
+        """
+        initialize the global timesteps and sigmas
+        """
+        num_train_timesteps = self.config.num_train_timesteps
+        shift = self.config.shift
+
+        alphas = np.linspace(1, 1 / num_train_timesteps, num_train_timesteps + 1)
+        sigmas = 1.0 - alphas
+        sigmas = np.flip(shift * sigmas / (1 + (shift - 1) * sigmas))[:-1].copy()
+        sigmas = torch.from_numpy(sigmas)
+        timesteps = (sigmas * num_train_timesteps).clone()
+
+        self._step_index = None
+        self._begin_index = None
+        self.timesteps = timesteps
+        self.sigmas = sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    def init_sigmas_for_each_stage(self):
+        """
+        Init the timesteps for each stage
+        """
+        self.init_sigmas()
+
+        stage_distance = []
+        stages = self.config.stages
+        training_steps = self.config.num_train_timesteps
+        stage_range = self.config.stage_range
+
+        # Init the start and end point of each stage
+        for i_s in range(stages):
+            # To decide the start and ends point
+            start_indice = int(stage_range[i_s] * training_steps)
+            start_indice = max(start_indice, 0)
+            end_indice = int(stage_range[i_s + 1] * training_steps)
+            end_indice = min(end_indice, training_steps)
+            start_sigma = self.sigmas[start_indice].item()
+            end_sigma = self.sigmas[end_indice].item() if end_indice < training_steps else 0.0
+            self.ori_start_sigmas[i_s] = start_sigma
+
+            if i_s != 0:
+                ori_sigma = 1 - start_sigma
+                gamma = self.config.gamma
+                corrected_sigma = (1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma)) * ori_sigma
+                # corrected_sigma = 1 / (2 - ori_sigma) * ori_sigma
+                start_sigma = 1 - corrected_sigma
+
+            stage_distance.append(start_sigma - end_sigma)
+            self.start_sigmas[i_s] = start_sigma
+            self.end_sigmas[i_s] = end_sigma
+
+        # Determine the ratio of each stage according to flow length
+        tot_distance = sum(stage_distance)
+        for i_s in range(stages):
+            if i_s == 0:
+                start_ratio = 0.0
+            else:
+                start_ratio = sum(stage_distance[:i_s]) / tot_distance
+            if i_s == stages - 1:
+                end_ratio = 0.9999999999999999
+            else:
+                end_ratio = sum(stage_distance[: i_s + 1]) / tot_distance
+
+            self.timestep_ratios[i_s] = (start_ratio, end_ratio)
+
+        # Determine the timesteps and sigmas for each stage
+        for i_s in range(stages):
+            timestep_ratio = self.timestep_ratios[i_s]
+            # timestep_max = self.timesteps[int(timestep_ratio[0] * training_steps)]
+            timestep_max = min(self.timesteps[int(timestep_ratio[0] * training_steps)], 999)
+            timestep_min = self.timesteps[min(int(timestep_ratio[1] * training_steps), training_steps - 1)]
+            timesteps = np.linspace(timestep_max, timestep_min, training_steps + 1)
+            self.timesteps_per_stage[i_s] = (
+                timesteps[:-1] if isinstance(timesteps, torch.Tensor) else torch.from_numpy(timesteps[:-1])
+            )
+            stage_sigmas = np.linspace(0.999, 0, training_steps + 1)
+            self.sigmas_per_stage[i_s] = torch.from_numpy(stage_sigmas[:-1])
+
+    @property
+    def step_index(self):
+        """
+        The index counter for current timestep. It will increase 1 after each scheduler step.
+        """
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        """
+        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+        """
+        return self._begin_index
+
+    def set_begin_index(self, begin_index: int = 0):
+        """
+        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
+
+        Args:
+            begin_index (`int`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def _sigma_to_t(self, sigma):
+        return sigma * self.config.num_train_timesteps
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int,
+        stage_index: int | None = None,
+        device: str | torch.device = None,
+        sigmas: bool | None = None,
+        mu: bool | None = None,
+        is_amplify_first_chunk: bool = False,
+    ):
+        """
+        Setting the timesteps and sigmas for each stage
+        """
+        if self.config.scheduler_type == "dmd":
+            if is_amplify_first_chunk:
+                num_inference_steps = num_inference_steps * 2 + 1
+            else:
+                num_inference_steps = num_inference_steps + 1
+
+        self.num_inference_steps = num_inference_steps
+        self.init_sigmas()
+
+        if self.config.stages == 1:
+            if sigmas is None:
+                sigmas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)[:-1].astype(
+                    np.float32
+                )
+                if self.config.shift != 1.0:
+                    assert not self.config.use_dynamic_shifting
+                    sigmas = self.time_shift(self.config.shift, 1.0, sigmas)
+            timesteps = (sigmas * self.config.num_train_timesteps).copy()
+            sigmas = torch.from_numpy(sigmas)
+        else:
+            stage_timesteps = self.timesteps_per_stage[stage_index]
+            timesteps = np.linspace(
+                stage_timesteps[0].item(),
+                stage_timesteps[-1].item(),
+                num_inference_steps,
+            )
+
+            stage_sigmas = self.sigmas_per_stage[stage_index]
+            ratios = np.linspace(stage_sigmas[0].item(), stage_sigmas[-1].item(), num_inference_steps)
+            sigmas = torch.from_numpy(ratios)
+
+        self.timesteps = torch.from_numpy(timesteps).to(device=device)
+        self.sigmas = torch.cat([sigmas, torch.zeros(1)]).to(device=device)
+
+        self._step_index = None
+        self.reset_scheduler_history()
+
+        if self.config.scheduler_type == "dmd":
+            self.timesteps = self.timesteps[:-1]
+            self.sigmas = torch.cat([self.sigmas[:-2], self.sigmas[-1:]])
+
+        if self.config.use_dynamic_shifting:
+            assert self.config.shift == 1.0
+            self.sigmas = self.time_shift(mu, 1.0, self.sigmas)
+            if self.config.stages == 1:
+                self.timesteps = self.sigmas[:-1] * self.config.num_train_timesteps
+            else:
+                self.timesteps = self.timesteps_per_stage[stage_index].min() + self.sigmas[:-1] * (
+                    self.timesteps_per_stage[stage_index].max() - self.timesteps_per_stage[stage_index].min()
+                )
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.time_shift
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+        """
+        Apply time shifting to the sigmas.
+
+        Args:
+            mu (`float`):
+                The mu parameter for the time shift.
+            sigma (`float`):
+                The sigma parameter for the time shift.
+            t (`torch.Tensor`):
+                The input timesteps.
+
+        Returns:
+            `torch.Tensor`:
+                The time-shifted timesteps.
+        """
+        if self.config.time_shift_type == "exponential":
+            return self._time_shift_exponential(mu, sigma, t)
+        elif self.config.time_shift_type == "linear":
+            return self._time_shift_linear(mu, sigma, t)
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_exponential
+    def _time_shift_exponential(self, mu, sigma, t):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_linear
+    def _time_shift_linear(self, mu, sigma, t):
+        return mu / (mu + (1 / t - 1) ** sigma)
+
+    # ---------------------------------- Euler ----------------------------------
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        pos = 1 if len(indices) > 1 else 0
+
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step_euler(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: float | torch.FloatTensor = None,
+        sample: torch.FloatTensor = None,
+        generator: torch.Generator | None = None,
+        sigma: torch.FloatTensor | None = None,
+        sigma_next: torch.FloatTensor | None = None,
+        return_dict: bool = True,
+    ) -> HeliosSchedulerOutput | tuple:
+        assert (sigma is None) == (sigma_next is None), "sigma and sigma_next must both be None or both be not None"
+
+        if sigma is None and sigma_next is None:
+            if (
+                isinstance(timestep, int)
+                or isinstance(timestep, torch.IntTensor)
+                or isinstance(timestep, torch.LongTensor)
+            ):
+                raise ValueError(
+                    (
+                        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                        " one of the `scheduler.timesteps` as a timestep."
+                    ),
+                )
+
+        if self.step_index is None:
+            self._step_index = 0
+
+        # Upcast to avoid precision issues when computing prev_sample
+        sample = sample.to(torch.float32)
+
+        if sigma is None and sigma_next is None:
+            sigma = self.sigmas[self.step_index]
+            sigma_next = self.sigmas[self.step_index + 1]
+
+        prev_sample = sample + (sigma_next - sigma) * model_output
+
+        # Cast sample back to model compatible dtype
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        # upon completion increase step index by one
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return HeliosSchedulerOutput(prev_sample=prev_sample)
+
+    # ---------------------------------- UniPC ----------------------------------
+    def _sigma_to_alpha_sigma_t(self, sigma):
+        if self.config.use_flow_sigmas:
+            alpha_t = 1 - sigma
+            sigma_t = torch.clamp(sigma, min=1e-8)
+        else:
+            alpha_t = 1 / ((sigma**2 + 1) ** 0.5)
+            sigma_t = sigma * alpha_t
+
+        return alpha_t, sigma_t
+
+    def convert_model_output(
+        self,
+        model_output: torch.Tensor,
+        *args,
+        sample: torch.Tensor = None,
+        sigma: torch.Tensor = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Convert the model output to the corresponding type the UniPC algorithm needs.
+
+        Args:
+            model_output (`torch.Tensor`):
+                The direct output from the learned diffusion model.
+            timestep (`int`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.Tensor`):
+                A current instance of a sample created by the diffusion process.
+
+        Returns:
+            `torch.Tensor`:
+                The converted model output.
+        """
+        timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
+        if sample is None:
+            if len(args) > 1:
+                sample = args[1]
+            else:
+                raise ValueError("missing `sample` as a required keyword argument")
+        if timestep is not None:
+            deprecate(
+                "timesteps",
+                "1.0.0",
+                "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+            )
+
+        flag = False
+        if sigma is None:
+            flag = True
+            sigma = self.sigmas[self.step_index]
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
+
+        if self.predict_x0:
+            if self.config.prediction_type == "epsilon":
+                x0_pred = (sample - sigma_t * model_output) / alpha_t
+            elif self.config.prediction_type == "sample":
+                x0_pred = model_output
+            elif self.config.prediction_type == "v_prediction":
+                x0_pred = alpha_t * sample - sigma_t * model_output
+            elif self.config.prediction_type == "flow_prediction":
+                if flag:
+                    sigma_t = self.sigmas[self.step_index]
+                else:
+                    sigma_t = sigma
+                x0_pred = sample - sigma_t * model_output
+            else:
+                raise ValueError(
+                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, "
+                    "`v_prediction`, or `flow_prediction` for the UniPCMultistepScheduler."
+                )
+
+            if self.config.thresholding:
+                x0_pred = self._threshold_sample(x0_pred)
+
+            return x0_pred
+        else:
+            if self.config.prediction_type == "epsilon":
+                return model_output
+            elif self.config.prediction_type == "sample":
+                epsilon = (sample - alpha_t * model_output) / sigma_t
+                return epsilon
+            elif self.config.prediction_type == "v_prediction":
+                epsilon = alpha_t * model_output + sigma_t * sample
+                return epsilon
+            else:
+                raise ValueError(
+                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
+                    " `v_prediction` for the UniPCMultistepScheduler."
+                )
+
+    def multistep_uni_p_bh_update(
+        self,
+        model_output: torch.Tensor,
+        *args,
+        sample: torch.Tensor = None,
+        order: int = None,
+        sigma: torch.Tensor = None,
+        sigma_next: torch.Tensor = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified.
+
+        Args:
+            model_output (`torch.Tensor`):
+                The direct output from the learned diffusion model at the current timestep.
+            prev_timestep (`int`):
+                The previous discrete timestep in the diffusion chain.
+            sample (`torch.Tensor`):
+                A current instance of a sample created by the diffusion process.
+            order (`int`):
+                The order of UniP at this timestep (corresponds to the *p* in UniPC-p).
+
+        Returns:
+            `torch.Tensor`:
+                The sample tensor at the previous timestep.
+        """
+        prev_timestep = args[0] if len(args) > 0 else kwargs.pop("prev_timestep", None)
+        if sample is None:
+            if len(args) > 1:
+                sample = args[1]
+            else:
+                raise ValueError("missing `sample` as a required keyword argument")
+        if order is None:
+            if len(args) > 2:
+                order = args[2]
+            else:
+                raise ValueError("missing `order` as a required keyword argument")
+        if prev_timestep is not None:
+            deprecate(
+                "prev_timestep",
+                "1.0.0",
+                "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+            )
+        model_output_list = self.model_outputs
+
+        s0 = self.timestep_list[-1]
+        m0 = model_output_list[-1]
+        x = sample
+
+        if self.solver_p:
+            x_t = self.solver_p.step(model_output, s0, x).prev_sample
+            return x_t
+
+        if sigma_next is None and sigma is None:
+            sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
+        else:
+            sigma_t, sigma_s0 = sigma_next, sigma
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
+        alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
+
+        lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
+        lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
+
+        h = lambda_t - lambda_s0
+        device = sample.device
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            si = self.step_index - i
+            mi = model_output_list[-(i + 1)]
+            alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
+            lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
+            rk = (lambda_si - lambda_s0) / h
+            rks.append(rk)
+            D1s.append((mi - m0) / rk)
+
+        rks.append(1.0)
+        rks = torch.tensor(rks, device=device)
+
+        R = []
+        b = []
+
+        hh = -h if self.predict_x0 else h
+        h_phi_1 = torch.expm1(hh)  # h\phi_1(h) = e^h - 1
+        h_phi_k = h_phi_1 / hh - 1
+
+        factorial_i = 1
+
+        if self.config.solver_type == "bh1":
+            B_h = hh
+        elif self.config.solver_type == "bh2":
+            B_h = torch.expm1(hh)
+        else:
+            raise NotImplementedError()
+
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= i + 1
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
+
+        R = torch.stack(R)
+        b = torch.tensor(b, device=device)
+
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1)  # (B, K)
+            # for order 2, we use a simplified version
+            if order == 2:
+                rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
+            else:
+                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)
+        else:
+            D1s = None
+
+        if self.predict_x0:
+            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
+            if D1s is not None:
+                pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
+            else:
+                pred_res = 0
+            x_t = x_t_ - alpha_t * B_h * pred_res
+        else:
+            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
+            if D1s is not None:
+                pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
+            else:
+                pred_res = 0
+            x_t = x_t_ - sigma_t * B_h * pred_res
+
+        x_t = x_t.to(x.dtype)
+        return x_t
+
+    def multistep_uni_c_bh_update(
+        self,
+        this_model_output: torch.Tensor,
+        *args,
+        last_sample: torch.Tensor = None,
+        this_sample: torch.Tensor = None,
+        order: int = None,
+        sigma_before: torch.Tensor = None,
+        sigma: torch.Tensor = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        One step for the UniC (B(h) version).
+
+        Args:
+            this_model_output (`torch.Tensor`):
+                The model outputs at `x_t`.
+            this_timestep (`int`):
+                The current timestep `t`.
+            last_sample (`torch.Tensor`):
+                The generated sample before the last predictor `x_{t-1}`.
+            this_sample (`torch.Tensor`):
+                The generated sample after the last predictor `x_{t}`.
+            order (`int`):
+                The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`.
+
+        Returns:
+            `torch.Tensor`:
+                The corrected sample tensor at the current timestep.
+        """
+        this_timestep = args[0] if len(args) > 0 else kwargs.pop("this_timestep", None)
+        if last_sample is None:
+            if len(args) > 1:
+                last_sample = args[1]
+            else:
+                raise ValueError("missing `last_sample` as a required keyword argument")
+        if this_sample is None:
+            if len(args) > 2:
+                this_sample = args[2]
+            else:
+                raise ValueError("missing `this_sample` as a required keyword argument")
+        if order is None:
+            if len(args) > 3:
+                order = args[3]
+            else:
+                raise ValueError("missing `order` as a required keyword argument")
+        if this_timestep is not None:
+            deprecate(
+                "this_timestep",
+                "1.0.0",
+                "Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+            )
+
+        model_output_list = self.model_outputs
+
+        m0 = model_output_list[-1]
+        x = last_sample
+        x_t = this_sample
+        model_t = this_model_output
+
+        if sigma_before is None and sigma is None:
+            sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[self.step_index - 1]
+        else:
+            sigma_t, sigma_s0 = sigma, sigma_before
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
+        alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
+
+        lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
+        lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
+
+        h = lambda_t - lambda_s0
+        device = this_sample.device
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            si = self.step_index - (i + 1)
+            mi = model_output_list[-(i + 1)]
+            alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
+            lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
+            rk = (lambda_si - lambda_s0) / h
+            rks.append(rk)
+            D1s.append((mi - m0) / rk)
+
+        rks.append(1.0)
+        rks = torch.tensor(rks, device=device)
+
+        R = []
+        b = []
+
+        hh = -h if self.predict_x0 else h
+        h_phi_1 = torch.expm1(hh)  # h\phi_1(h) = e^h - 1
+        h_phi_k = h_phi_1 / hh - 1
+
+        factorial_i = 1
+
+        if self.config.solver_type == "bh1":
+            B_h = hh
+        elif self.config.solver_type == "bh2":
+            B_h = torch.expm1(hh)
+        else:
+            raise NotImplementedError()
+
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= i + 1
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
+
+        R = torch.stack(R)
+        b = torch.tensor(b, device=device)
+
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1)
+        else:
+            D1s = None
+
+        # for order 1, we use a simplified version
+        if order == 1:
+            rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
+        else:
+            rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)
+
+        if self.predict_x0:
+            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
+            if D1s is not None:
+                corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
+            else:
+                corr_res = 0
+            D1_t = model_t - m0
+            x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
+        else:
+            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
+            if D1s is not None:
+                corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
+            else:
+                corr_res = 0
+            D1_t = model_t - m0
+            x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)
+        x_t = x_t.to(x.dtype)
+        return x_t
+
+    def step_unipc(
+        self,
+        model_output: torch.Tensor,
+        timestep: int | torch.Tensor = None,
+        sample: torch.Tensor = None,
+        return_dict: bool = True,
+        model_outputs: list = None,
+        timestep_list: list = None,
+        sigma_before: torch.Tensor = None,
+        sigma: torch.Tensor = None,
+        sigma_next: torch.Tensor = None,
+        cus_step_index: int = None,
+        cus_lower_order_num: int = None,
+        cus_this_order: int = None,
+        cus_last_sample: torch.Tensor = None,
+    ) -> HeliosSchedulerOutput | tuple:
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
+        if cus_step_index is None:
+            if self.step_index is None:
+                self._step_index = 0
+        else:
+            self._step_index = cus_step_index
+
+        if cus_lower_order_num is not None:
+            self.lower_order_nums = cus_lower_order_num
+
+        if cus_this_order is not None:
+            self.this_order = cus_this_order
+
+        if cus_last_sample is not None:
+            self.last_sample = cus_last_sample
+
+        use_corrector = (
+            self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None
+        )
+
+        # Convert model output using the proper conversion method
+        model_output_convert = self.convert_model_output(model_output, sample=sample, sigma=sigma)
+
+        if model_outputs is not None and timestep_list is not None:
+            self.model_outputs = model_outputs[:-1]
+            self.timestep_list = timestep_list[:-1]
+
+        if use_corrector:
+            sample = self.multistep_uni_c_bh_update(
+                this_model_output=model_output_convert,
+                last_sample=self.last_sample,
+                this_sample=sample,
+                order=self.this_order,
+                sigma_before=sigma_before,
+                sigma=sigma,
+            )
+
+        if model_outputs is not None and timestep_list is not None:
+            model_outputs[-1] = model_output_convert
+            self.model_outputs = model_outputs[1:]
+            self.timestep_list = timestep_list[1:]
+        else:
+            for i in range(self.config.solver_order - 1):
+                self.model_outputs[i] = self.model_outputs[i + 1]
+                self.timestep_list[i] = self.timestep_list[i + 1]
+            self.model_outputs[-1] = model_output_convert
+            self.timestep_list[-1] = timestep
+
+        if self.config.lower_order_final:
+            this_order = min(self.config.solver_order, len(self.timesteps) - self.step_index)
+        else:
+            this_order = self.config.solver_order
+        self.this_order = min(this_order, self.lower_order_nums + 1)  # warmup for multistep
+        assert self.this_order > 0
+
+        self.last_sample = sample
+        prev_sample = self.multistep_uni_p_bh_update(
+            model_output=model_output,  # pass the original non-converted model output, in case solver-p is used
+            sample=sample,
+            order=self.this_order,
+            sigma=sigma,
+            sigma_next=sigma_next,
+        )
+
+        if cus_lower_order_num is None:
+            if self.lower_order_nums < self.config.solver_order:
+                self.lower_order_nums += 1
+
+        # upon completion increase step index by one
+        if cus_step_index is None:
+            self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample, model_outputs, self.last_sample, self.this_order)
+
+        return HeliosSchedulerOutput(
+            prev_sample=prev_sample,
+            model_outputs=model_outputs,
+            last_sample=self.last_sample,
+            this_order=self.this_order,
+        )
+
+    # ---------------------------------- Merge ----------------------------------
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: float | torch.FloatTensor = None,
+        sample: torch.FloatTensor = None,
+        generator: torch.Generator | None = None,
+        return_dict: bool = True,
+    ) -> HeliosSchedulerOutput | tuple:
+        if self.config.scheduler_type == "euler":
+            return self.step_euler(
+                model_output=model_output,
+                timestep=timestep,
+                sample=sample,
+                generator=generator,
+                return_dict=return_dict,
+            )
+        elif self.config.scheduler_type == "unipc":
+            return self.step_unipc(
+                model_output=model_output,
+                timestep=timestep,
+                sample=sample,
+                return_dict=return_dict,
+            )
+        else:
+            raise NotImplementedError
+
+    def reset_scheduler_history(self):
+        self.model_outputs = [None] * self.config.solver_order
+        self.timestep_list = [None] * self.config.solver_order
+        self.lower_order_nums = 0
+        self.disable_corrector = self.config.disable_corrector
+        self.solver_p = self.config.solver_p
+        self.last_sample = None
+        self._step_index = None
+        self._begin_index = None
+
+    def __len__(self):
+        return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_helios_dmd.py

@@ -0,0 +1,331 @@
+# Copyright 2025 The Helios Team 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.
+
+import math
+from dataclasses import dataclass
+from typing import Literal
+
+import numpy as np
+import torch
+
+from ..configuration_utils import ConfigMixin, register_to_config
+from ..schedulers.scheduling_utils import SchedulerMixin
+from ..utils import BaseOutput
+
+
+@dataclass
+class HeliosDMDSchedulerOutput(BaseOutput):
+    prev_sample: torch.FloatTensor
+    model_outputs: torch.FloatTensor | None = None
+    last_sample: torch.FloatTensor | None = None
+    this_order: int | None = None
+
+
+class HeliosDMDScheduler(SchedulerMixin, ConfigMixin):
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        shift: float = 1.0,  # Following Stable diffusion 3,
+        stages: int = 3,
+        stage_range: list = [0, 1 / 3, 2 / 3, 1],
+        gamma: float = 1 / 3,
+        prediction_type: str = "flow_prediction",
+        use_flow_sigmas: bool = True,
+        use_dynamic_shifting: bool = False,
+        time_shift_type: Literal["exponential", "linear"] = "linear",
+    ):
+        self.timestep_ratios = {}  # The timestep ratio for each stage
+        self.timesteps_per_stage = {}  # The detailed timesteps per stage (fix max and min per stage)
+        self.sigmas_per_stage = {}  # always uniform [1000, 0]
+        self.start_sigmas = {}  # for start point / upsample renoise
+        self.end_sigmas = {}  # for end point
+        self.ori_start_sigmas = {}
+
+        # self.init_sigmas()
+        self.init_sigmas_for_each_stage()
+        self.sigma_min = self.sigmas[-1].item()
+        self.sigma_max = self.sigmas[0].item()
+        self.gamma = gamma
+
+        self.last_sample = None
+        self._step_index = None
+        self._begin_index = None
+
+    def init_sigmas(self):
+        """
+        initialize the global timesteps and sigmas
+        """
+        num_train_timesteps = self.config.num_train_timesteps
+        shift = self.config.shift
+
+        alphas = np.linspace(1, 1 / num_train_timesteps, num_train_timesteps + 1)
+        sigmas = 1.0 - alphas
+        sigmas = np.flip(shift * sigmas / (1 + (shift - 1) * sigmas))[:-1].copy()
+        sigmas = torch.from_numpy(sigmas)
+        timesteps = (sigmas * num_train_timesteps).clone()
+
+        self._step_index = None
+        self._begin_index = None
+        self.timesteps = timesteps
+        self.sigmas = sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    def init_sigmas_for_each_stage(self):
+        """
+        Init the timesteps for each stage
+        """
+        self.init_sigmas()
+
+        stage_distance = []
+        stages = self.config.stages
+        training_steps = self.config.num_train_timesteps
+        stage_range = self.config.stage_range
+
+        # Init the start and end point of each stage
+        for i_s in range(stages):
+            # To decide the start and ends point
+            start_indice = int(stage_range[i_s] * training_steps)
+            start_indice = max(start_indice, 0)
+            end_indice = int(stage_range[i_s + 1] * training_steps)
+            end_indice = min(end_indice, training_steps)
+            start_sigma = self.sigmas[start_indice].item()
+            end_sigma = self.sigmas[end_indice].item() if end_indice < training_steps else 0.0
+            self.ori_start_sigmas[i_s] = start_sigma
+
+            if i_s != 0:
+                ori_sigma = 1 - start_sigma
+                gamma = self.config.gamma
+                corrected_sigma = (1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma)) * ori_sigma
+                # corrected_sigma = 1 / (2 - ori_sigma) * ori_sigma
+                start_sigma = 1 - corrected_sigma
+
+            stage_distance.append(start_sigma - end_sigma)
+            self.start_sigmas[i_s] = start_sigma
+            self.end_sigmas[i_s] = end_sigma
+
+        # Determine the ratio of each stage according to flow length
+        tot_distance = sum(stage_distance)
+        for i_s in range(stages):
+            if i_s == 0:
+                start_ratio = 0.0
+            else:
+                start_ratio = sum(stage_distance[:i_s]) / tot_distance
+            if i_s == stages - 1:
+                end_ratio = 0.9999999999999999
+            else:
+                end_ratio = sum(stage_distance[: i_s + 1]) / tot_distance
+
+            self.timestep_ratios[i_s] = (start_ratio, end_ratio)
+
+        # Determine the timesteps and sigmas for each stage
+        for i_s in range(stages):
+            timestep_ratio = self.timestep_ratios[i_s]
+            # timestep_max = self.timesteps[int(timestep_ratio[0] * training_steps)]
+            timestep_max = min(self.timesteps[int(timestep_ratio[0] * training_steps)], 999)
+            timestep_min = self.timesteps[min(int(timestep_ratio[1] * training_steps), training_steps - 1)]
+            timesteps = np.linspace(timestep_max, timestep_min, training_steps + 1)
+            self.timesteps_per_stage[i_s] = (
+                timesteps[:-1] if isinstance(timesteps, torch.Tensor) else torch.from_numpy(timesteps[:-1])
+            )
+            stage_sigmas = np.linspace(0.999, 0, training_steps + 1)
+            self.sigmas_per_stage[i_s] = torch.from_numpy(stage_sigmas[:-1])
+
+    @property
+    def step_index(self):
+        """
+        The index counter for current timestep. It will increase 1 after each scheduler step.
+        """
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        """
+        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+        """
+        return self._begin_index
+
+    def set_begin_index(self, begin_index: int = 0):
+        """
+        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
+
+        Args:
+            begin_index (`int`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def _sigma_to_t(self, sigma):
+        return sigma * self.config.num_train_timesteps
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int,
+        stage_index: int | None = None,
+        device: str | torch.device = None,
+        sigmas: bool | None = None,
+        mu: bool | None = None,
+        is_amplify_first_chunk: bool = False,
+    ):
+        """
+        Setting the timesteps and sigmas for each stage
+        """
+        if is_amplify_first_chunk:
+            num_inference_steps = num_inference_steps * 2 + 1
+        else:
+            num_inference_steps = num_inference_steps + 1
+
+        self.num_inference_steps = num_inference_steps
+        self.init_sigmas()
+
+        if self.config.stages == 1:
+            if sigmas is None:
+                sigmas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)[:-1].astype(
+                    np.float32
+                )
+                if self.config.shift != 1.0:
+                    assert not self.config.use_dynamic_shifting
+                    sigmas = self.time_shift(self.config.shift, 1.0, sigmas)
+            timesteps = (sigmas * self.config.num_train_timesteps).copy()
+            sigmas = torch.from_numpy(sigmas)
+        else:
+            stage_timesteps = self.timesteps_per_stage[stage_index]
+            timesteps = np.linspace(
+                stage_timesteps[0].item(),
+                stage_timesteps[-1].item(),
+                num_inference_steps,
+            )
+
+            stage_sigmas = self.sigmas_per_stage[stage_index]
+            ratios = np.linspace(stage_sigmas[0].item(), stage_sigmas[-1].item(), num_inference_steps)
+            sigmas = torch.from_numpy(ratios)
+
+        self.timesteps = torch.from_numpy(timesteps).to(device=device)
+        self.sigmas = torch.cat([sigmas, torch.zeros(1)]).to(device=device)
+
+        self._step_index = None
+        self.reset_scheduler_history()
+
+        self.timesteps = self.timesteps[:-1]
+        self.sigmas = torch.cat([self.sigmas[:-2], self.sigmas[-1:]])
+
+        if self.config.use_dynamic_shifting:
+            assert self.config.shift == 1.0
+            self.sigmas = self.time_shift(mu, 1.0, self.sigmas)
+            if self.config.stages == 1:
+                self.timesteps = self.sigmas[:-1] * self.config.num_train_timesteps
+            else:
+                self.timesteps = self.timesteps_per_stage[stage_index].min() + self.sigmas[:-1] * (
+                    self.timesteps_per_stage[stage_index].max() - self.timesteps_per_stage[stage_index].min()
+                )
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.time_shift
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+        """
+        Apply time shifting to the sigmas.
+
+        Args:
+            mu (`float`):
+                The mu parameter for the time shift.
+            sigma (`float`):
+                The sigma parameter for the time shift.
+            t (`torch.Tensor`):
+                The input timesteps.
+
+        Returns:
+            `torch.Tensor`:
+                The time-shifted timesteps.
+        """
+        if self.config.time_shift_type == "exponential":
+            return self._time_shift_exponential(mu, sigma, t)
+        elif self.config.time_shift_type == "linear":
+            return self._time_shift_linear(mu, sigma, t)
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_exponential
+    def _time_shift_exponential(self, mu, sigma, t):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_linear
+    def _time_shift_linear(self, mu, sigma, t):
+        return mu / (mu + (1 / t - 1) ** sigma)
+
+    # ---------------------------------- For DMD ----------------------------------
+    def add_noise(self, original_samples, noise, timestep, sigmas, timesteps):
+        sigmas = sigmas.to(noise.device)
+        timesteps = timesteps.to(noise.device)
+        timestep_id = torch.argmin((timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
+        sigma = sigmas[timestep_id].reshape(-1, 1, 1, 1, 1)
+        sample = (1 - sigma) * original_samples + sigma * noise
+        return sample.type_as(noise)
+
+    def convert_flow_pred_to_x0(self, flow_pred, xt, timestep, sigmas, timesteps):
+        # use higher precision for calculations
+        original_dtype = flow_pred.dtype
+        device = flow_pred.device
+        flow_pred, xt, sigmas, timesteps = (x.double().to(device) for x in (flow_pred, xt, sigmas, timesteps))
+
+        timestep_id = torch.argmin((timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
+        sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1, 1)
+        x0_pred = xt - sigma_t * flow_pred
+        return x0_pred.to(original_dtype)
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: float | torch.FloatTensor = None,
+        sample: torch.FloatTensor = None,
+        generator: torch.Generator | None = None,
+        return_dict: bool = True,
+        cur_sampling_step: int = 0,
+        dmd_noisy_tensor: torch.FloatTensor | None = None,
+        dmd_sigmas: torch.FloatTensor | None = None,
+        dmd_timesteps: torch.FloatTensor | None = None,
+        all_timesteps: torch.FloatTensor | None = None,
+    ) -> HeliosDMDSchedulerOutput | tuple:
+        pred_image_or_video = self.convert_flow_pred_to_x0(
+            flow_pred=model_output,
+            xt=sample,
+            timestep=torch.full((model_output.shape[0],), timestep, dtype=torch.long, device=model_output.device),
+            sigmas=dmd_sigmas,
+            timesteps=dmd_timesteps,
+        )
+        if cur_sampling_step < len(all_timesteps) - 1:
+            prev_sample = self.add_noise(
+                pred_image_or_video,
+                dmd_noisy_tensor,
+                torch.full(
+                    (model_output.shape[0],),
+                    all_timesteps[cur_sampling_step + 1],
+                    dtype=torch.long,
+                    device=model_output.device,
+                ),
+                sigmas=dmd_sigmas,
+                timesteps=dmd_timesteps,
+            )
+        else:
+            prev_sample = pred_image_or_video
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return HeliosDMDSchedulerOutput(prev_sample=prev_sample)
+
+    def reset_scheduler_history(self):
+        self._step_index = None
+        self._begin_index = None
+
+    def __len__(self):
+        return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_ipndm.py

@@ -31,14 +31,18 @@ class IPNDMScheduler(SchedulerMixin, ConfigMixin):
     Args:
         num_train_timesteps (`int`, defaults to 1000):
             The number of diffusion steps to train the model.
-        trained_betas (`np.ndarray`, *optional*):
+        trained_betas (`np.ndarray` or `List[float]`, *optional*):
             Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
     """
 
     order = 1
 
     @register_to_config
-    def __init__(self, num_train_timesteps: int = 1000, trained_betas: np.ndarray | list[float] | None = None):
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        trained_betas: np.ndarray | list[float] | None = None,
+    ):
         # set `betas`, `alphas`, `timesteps`
         self.set_timesteps(num_train_timesteps)
 
@@ -56,21 +60,29 @@ class IPNDMScheduler(SchedulerMixin, ConfigMixin):
         self._begin_index = None
 
     @property
-    def step_index(self):
+    def step_index(self) -> int | None:
         """
         The index counter for current timestep. It will increase 1 after each scheduler step.
+
+        Returns:
+            `int` or `None`:
+                The index counter for current timestep.
         """
         return self._step_index
 
     @property
-    def begin_index(self):
+    def begin_index(self) -> int | None:
         """
         The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+
+        Returns:
+            `int` or `None`:
+                The index for the first timestep.
         """
         return self._begin_index
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
-    def set_begin_index(self, begin_index: int = 0):
+    def set_begin_index(self, begin_index: int = 0) -> None:
         """
         Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
 
@@ -169,7 +181,7 @@ class IPNDMScheduler(SchedulerMixin, ConfigMixin):
         Args:
             model_output (`torch.Tensor`):
                 The direct output from learned diffusion model.
-            timestep (`int`):
+            timestep (`int` or `torch.Tensor`):
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
@@ -228,7 +240,30 @@ class IPNDMScheduler(SchedulerMixin, ConfigMixin):
         """
         return sample
 
-    def _get_prev_sample(self, sample, timestep_index, prev_timestep_index, ets):
+    def _get_prev_sample(
+        self,
+        sample: torch.Tensor,
+        timestep_index: int,
+        prev_timestep_index: int,
+        ets: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Predicts the previous sample based on the current sample, timestep indices, and running model outputs.
+
+        Args:
+            sample (`torch.Tensor`):
+                The current sample.
+            timestep_index (`int`):
+                Index of the current timestep in the schedule.
+            prev_timestep_index (`int`):
+                Index of the previous timestep in the schedule.
+            ets (`torch.Tensor`):
+                The running sequence of model outputs.
+
+        Returns:
+            `torch.Tensor`:
+                The predicted previous sample.
+        """
         alpha = self.alphas[timestep_index]
         sigma = self.betas[timestep_index]
 
@@ -240,5 +275,5 @@ class IPNDMScheduler(SchedulerMixin, ConfigMixin):
 
         return prev_sample
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.config.num_train_timesteps

src/diffusers/utils/dummy_pt_objects.py

@@ -656,6 +656,21 @@ class AutoencoderOobleck(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class AutoencoderRAE(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class AutoencoderTiny(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -1031,6 +1046,21 @@ class GlmImageTransformer2DModel(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class HeliosTransformer3DModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class HiDreamImageTransformer2DModel(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -2743,6 +2773,36 @@ class FlowMatchLCMScheduler(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class HeliosDMDScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
+class HeliosScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class HeunDiscreteScheduler(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1352,6 +1352,36 @@ class GlmImagePipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class HeliosPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
+class HeliosPyramidPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class HiDreamImagePipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

src/diffusers/utils/dynamic_modules_utils.py

@@ -299,7 +299,10 @@ def get_cached_module_file(
     # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
     pretrained_model_name_or_path = str(pretrained_model_name_or_path)
 
-    module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
+    if subfolder is not None:
+        module_file_or_url = os.path.join(pretrained_model_name_or_path, subfolder, module_file)
+    else:
+        module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
 
     if os.path.isfile(module_file_or_url):
         resolved_module_file = module_file_or_url
@@ -384,7 +387,11 @@ def get_cached_module_file(
                 if not os.path.exists(submodule_path / module_folder):
                     os.makedirs(submodule_path / module_folder)
             module_needed = f"{module_needed}.py"
-            shutil.copyfile(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
+            if subfolder is not None:
+                source_path = os.path.join(pretrained_model_name_or_path, subfolder, module_needed)
+            else:
+                source_path = os.path.join(pretrained_model_name_or_path, module_needed)
+            shutil.copyfile(source_path, submodule_path / module_needed)
     else:
         # Get the commit hash
         # TODO: we will get this info in the etag soon, so retrieve it from there and not here.

src/diffusers/utils/hub_utils.py

@@ -107,6 +107,7 @@ def load_or_create_model_card(
     widget: list[dict] | None = None,
     inference: bool | None = None,
     is_modular: bool = False,
+    update_model_card: bool = False,
 ) -> ModelCard:
     """
     Loads or creates a model card.
@@ -133,6 +134,9 @@ def load_or_create_model_card(
             `load_or_create_model_card` from a training script.
         is_modular: (`bool`, optional): Boolean flag to denote if the model card is for a modular pipeline.
             When True, uses model_description as-is without additional template formatting.
+        update_model_card: (`bool`, optional): When True, regenerates the model card content even if one
+            already exists on the remote repo. Existing card metadata (tags, license, etc.) is preserved. Only
+            supported for modular pipelines (i.e., `is_modular=True`).
     """
     if not is_jinja_available():
         raise ValueError(
@@ -141,9 +145,17 @@ def load_or_create_model_card(
             " To install it, please run `pip install Jinja2`."
         )
 
+    if update_model_card and not is_modular:
+        raise ValueError("`update_model_card=True` is only supported for modular pipelines (`is_modular=True`).")
+
     try:
         # Check if the model card is present on the remote repo
         model_card = ModelCard.load(repo_id_or_path, token=token)
+        # For modular pipelines, regenerate card content when requested (preserve existing metadata)
+        if update_model_card and is_modular and model_description is not None:
+            existing_data = model_card.data
+            model_card = ModelCard(model_description)
+            model_card.data = existing_data
     except (EntryNotFoundError, RepositoryNotFoundError):
         # Otherwise create a model card from template
         if from_training:

tests/hooks/test_group_offloading.py

@@ -566,3 +566,127 @@ class GroupOffloadTests(unittest.TestCase):
             "layers_per_block": 1,
         }
         return init_dict
+
+
+# Model with conditionally-executed modules, simulating Helios patch_short/patch_mid/patch_long behavior.
+# These modules are only called when optional inputs are provided, which means the lazy prefetch
+# execution order tracer may not see them on the first forward pass. This can cause a device mismatch
+# on subsequent calls when the modules ARE invoked but their weights were never onloaded.
+# See: https://github.com/huggingface/diffusers/pull/13211
+class DummyModelWithConditionalModules(ModelMixin):
+    def __init__(self, in_features: int, hidden_features: int, out_features: int, num_layers: int) -> None:
+        super().__init__()
+
+        self.linear_1 = torch.nn.Linear(in_features, hidden_features)
+        self.activation = torch.nn.ReLU()
+        self.blocks = torch.nn.ModuleList(
+            [DummyBlock(hidden_features, hidden_features, hidden_features) for _ in range(num_layers)]
+        )
+        self.linear_2 = torch.nn.Linear(hidden_features, out_features)
+
+        # These modules are only invoked when optional_input is not None.
+        # Output dimension matches hidden_features so they can be added after linear_1.
+        self.optional_proj_1 = torch.nn.Linear(in_features, hidden_features)
+        self.optional_proj_2 = torch.nn.Linear(in_features, hidden_features)
+
+    def forward(self, x: torch.Tensor, optional_input: torch.Tensor | None = None) -> torch.Tensor:
+        x = self.linear_1(x)
+        x = self.activation(x)
+        if optional_input is not None:
+            # Add optional projections after linear_1 so dimensions match (both hidden_features)
+            x = x + self.optional_proj_1(optional_input)
+            x = x + self.optional_proj_2(optional_input)
+        for block in self.blocks:
+            x = block(x)
+        x = self.linear_2(x)
+        return x
+
+
+class ConditionalModuleGroupOffloadTests(GroupOffloadTests):
+    """Tests for conditionally-executed modules under group offloading with streams.
+
+    Regression tests for the case where a module is not executed during the first forward pass
+    (when the lazy prefetch execution order is traced), but IS executed on subsequent passes.
+    Without the fix, the weights of such modules remain on CPU while the input is on GPU,
+    causing a RuntimeError about tensor device mismatch.
+    """
+
+    def get_model(self):
+        torch.manual_seed(0)
+        return DummyModelWithConditionalModules(
+            in_features=self.in_features,
+            hidden_features=self.hidden_features,
+            out_features=self.out_features,
+            num_layers=self.num_layers,
+        )
+
+    @parameterized.expand([("leaf_level",), ("block_level",)])
+    @unittest.skipIf(
+        torch.device(torch_device).type not in ["cuda", "xpu"],
+        "Test requires a CUDA or XPU device.",
+    )
+    def test_conditional_modules_with_stream(self, offload_type: str):
+        """Regression test: conditionally-executed modules must not cause device mismatch when using streams.
+
+        The model contains two optional Linear layers (optional_proj_1, optional_proj_2) that are only
+        executed when `optional_input` is provided. This simulates modules like patch_short/patch_mid/
+        patch_long in HeliosTransformer3DModel, which are only called when history latents are present.
+
+        When using streams, `LazyPrefetchGroupOffloadingHook` traces the execution order on the first
+        forward pass and sets up a prefetch chain so each module pre-loads the next one's weights.
+        Modules not executed during this tracing pass are excluded from the prefetch chain.
+
+        The bug: if a module was absent from the first (tracing) pass, its `onload_self` flag gets set
+        to False (meaning "someone else will onload me"). But since it's not in the prefetch chain,
+        nobody ever does — so its weights remain on CPU. When the module is eventually called in a
+        subsequent pass, the input is on GPU but the weights are on CPU, causing a RuntimeError.
+
+        We therefore must invoke the model multiple times:
+        1. First pass WITHOUT optional_input: triggers the lazy prefetch tracing. optional_proj_1/2
+            are absent, so they are excluded from the prefetch chain.
+        2. Second pass WITH optional_input: the regression case. Without the fix, this raises a
+            RuntimeError because optional_proj_1/2 weights are still on CPU.
+        3. Third pass WITHOUT optional_input: verifies the model remains stable after having seen
+            both code paths.
+        """
+
+        model = self.get_model()
+        model_ref = self.get_model()
+        model_ref.load_state_dict(model.state_dict(), strict=True)
+        model_ref.to(torch_device)
+
+        model.enable_group_offload(
+            torch_device,
+            offload_type=offload_type,
+            num_blocks_per_group=1,
+            use_stream=True,
+        )
+
+        x = torch.randn(4, self.in_features).to(torch_device)
+        optional_input = torch.randn(4, self.in_features).to(torch_device)
+
+        with torch.no_grad():
+            # First forward pass WITHOUT optional_input — this is when the lazy prefetch
+            # execution order is traced. optional_proj_1/2 are NOT in the traced order.
+            out_ref_no_opt = model_ref(x, optional_input=None)
+            out_no_opt = model(x, optional_input=None)
+            self.assertTrue(
+                torch.allclose(out_ref_no_opt, out_no_opt, atol=1e-5),
+                f"[{offload_type}] Outputs do not match on first pass (no optional_input).",
+            )
+
+            # Second forward pass WITH optional_input — optional_proj_1/2 ARE now called.
+            out_ref_with_opt = model_ref(x, optional_input=optional_input)
+            out_with_opt = model(x, optional_input=optional_input)
+            self.assertTrue(
+                torch.allclose(out_ref_with_opt, out_with_opt, atol=1e-5),
+                f"[{offload_type}] Outputs do not match on second pass (with optional_input).",
+            )
+
+            # Third pass again without optional_input — verify stable behavior.
+            out_ref_no_opt2 = model_ref(x, optional_input=None)
+            out_no_opt2 = model(x, optional_input=None)
+            self.assertTrue(
+                torch.allclose(out_ref_no_opt2, out_no_opt2, atol=1e-5),
+                f"[{offload_type}] Outputs do not match on third pass (back to no optional_input).",
+            )

tests/lora/test_lora_layers_helios.py

@@ -0,0 +1,120 @@
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import unittest
+
+import torch
+from transformers import AutoTokenizer, T5EncoderModel
+
+from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, HeliosPipeline, HeliosTransformer3DModel
+
+from ..testing_utils import floats_tensor, require_peft_backend, skip_mps
+
+
+sys.path.append(".")
+
+from .utils import PeftLoraLoaderMixinTests  # noqa: E402
+
+
+@require_peft_backend
+@skip_mps
+class HeliosLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
+    pipeline_class = HeliosPipeline
+    scheduler_cls = FlowMatchEulerDiscreteScheduler
+    scheduler_kwargs = {}
+
+    transformer_kwargs = {
+        "patch_size": (1, 2, 2),
+        "num_attention_heads": 2,
+        "attention_head_dim": 12,
+        "in_channels": 16,
+        "out_channels": 16,
+        "text_dim": 32,
+        "freq_dim": 256,
+        "ffn_dim": 32,
+        "num_layers": 2,
+        "cross_attn_norm": True,
+        "qk_norm": "rms_norm_across_heads",
+        "rope_dim": (4, 4, 4),
+        "has_multi_term_memory_patch": True,
+        "guidance_cross_attn": True,
+        "zero_history_timestep": True,
+        "is_amplify_history": False,
+    }
+    transformer_cls = HeliosTransformer3DModel
+    vae_kwargs = {
+        "base_dim": 3,
+        "z_dim": 16,
+        "dim_mult": [1, 1, 1, 1],
+        "num_res_blocks": 1,
+        "temperal_downsample": [False, True, True],
+    }
+    vae_cls = AutoencoderKLWan
+    has_two_text_encoders = True
+    tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
+    text_encoder_cls, text_encoder_id = T5EncoderModel, "hf-internal-testing/tiny-random-t5"
+
+    text_encoder_target_modules = ["q", "k", "v", "o"]
+
+    supports_text_encoder_loras = False
+
+    @property
+    def output_shape(self):
+        return (1, 33, 32, 32, 3)
+
+    def get_dummy_inputs(self, with_generator=True):
+        batch_size = 1
+        sequence_length = 16
+        num_channels = 4
+        num_frames = 9
+        num_latent_frames = 3  # (num_frames - 1) // temporal_compression_ratio + 1
+        sizes = (4, 4)
+
+        generator = torch.manual_seed(0)
+        noise = floats_tensor((batch_size, num_latent_frames, num_channels) + sizes)
+        input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
+
+        pipeline_inputs = {
+            "prompt": "",
+            "num_frames": num_frames,
+            "num_inference_steps": 1,
+            "guidance_scale": 6.0,
+            "height": 32,
+            "width": 32,
+            "max_sequence_length": sequence_length,
+            "output_type": "np",
+        }
+        if with_generator:
+            pipeline_inputs.update({"generator": generator})
+
+        return noise, input_ids, pipeline_inputs
+
+    def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
+        super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
+
+    def test_simple_inference_with_text_denoiser_lora_unfused(self):
+        super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
+
+    @unittest.skip("Not supported in Helios.")
+    def test_simple_inference_with_text_denoiser_block_scale(self):
+        pass
+
+    @unittest.skip("Not supported in Helios.")
+    def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
+        pass
+
+    @unittest.skip("Not supported in Helios.")
+    def test_modify_padding_mode(self):
+        pass

tests/models/autoencoders/test_models_autoencoder_rae.py

@@ -0,0 +1,300 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+
+import pytest
+import torch
+import torch.nn.functional as F
+from torchvision.transforms.functional import to_tensor
+
+import diffusers.models.autoencoders.autoencoder_rae as _rae_module
+from diffusers.models.autoencoders.autoencoder_rae import (
+    _ENCODER_FORWARD_FNS,
+    AutoencoderRAE,
+    _build_encoder,
+)
+from diffusers.utils import load_image
+
+from ...testing_utils import (
+    backend_empty_cache,
+    enable_full_determinism,
+    slow,
+    torch_all_close,
+    torch_device,
+)
+from ..testing_utils import BaseModelTesterConfig, ModelTesterMixin
+from .testing_utils import AutoencoderTesterMixin
+
+
+enable_full_determinism()
+
+
+# ---------------------------------------------------------------------------
+# Tiny test encoder for fast unit tests (no transformers dependency)
+# ---------------------------------------------------------------------------
+
+
+class _TinyTestEncoderModule(torch.nn.Module):
+    """Minimal encoder that mimics the patch-token interface without any HF model."""
+
+    def __init__(self, hidden_size: int = 16, patch_size: int = 8, **kwargs):
+        super().__init__()
+        self.patch_size = patch_size
+        self.hidden_size = hidden_size
+
+    def forward(self, images: torch.Tensor) -> torch.Tensor:
+        pooled = F.avg_pool2d(images.mean(dim=1, keepdim=True), kernel_size=self.patch_size, stride=self.patch_size)
+        tokens = pooled.flatten(2).transpose(1, 2).contiguous()
+        return tokens.repeat(1, 1, self.hidden_size)
+
+
+def _tiny_test_encoder_forward(model, images):
+    return model(images)
+
+
+def _build_tiny_test_encoder(encoder_type, hidden_size, patch_size, num_hidden_layers):
+    return _TinyTestEncoderModule(hidden_size=hidden_size, patch_size=patch_size)
+
+
+# Monkey-patch the dispatch tables so "tiny_test" is recognised by AutoencoderRAE
+_ENCODER_FORWARD_FNS["tiny_test"] = _tiny_test_encoder_forward
+_original_build_encoder = _build_encoder
+
+
+def _patched_build_encoder(encoder_type, hidden_size, patch_size, num_hidden_layers):
+    if encoder_type == "tiny_test":
+        return _build_tiny_test_encoder(encoder_type, hidden_size, patch_size, num_hidden_layers)
+    return _original_build_encoder(encoder_type, hidden_size, patch_size, num_hidden_layers)
+
+
+_rae_module._build_encoder = _patched_build_encoder
+
+
+# ---------------------------------------------------------------------------
+# Test config
+# ---------------------------------------------------------------------------
+
+
+class AutoencoderRAETesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return AutoencoderRAE
+
+    @property
+    def output_shape(self):
+        return (3, 16, 16)
+
+    def get_init_dict(self):
+        return {
+            "encoder_type": "tiny_test",
+            "encoder_hidden_size": 16,
+            "encoder_patch_size": 8,
+            "encoder_input_size": 32,
+            "patch_size": 4,
+            "image_size": 16,
+            "decoder_hidden_size": 32,
+            "decoder_num_hidden_layers": 1,
+            "decoder_num_attention_heads": 4,
+            "decoder_intermediate_size": 64,
+            "num_channels": 3,
+            "encoder_norm_mean": [0.5, 0.5, 0.5],
+            "encoder_norm_std": [0.5, 0.5, 0.5],
+            "noise_tau": 0.0,
+            "reshape_to_2d": True,
+            "scaling_factor": 1.0,
+        }
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_dummy_inputs(self):
+        return {"sample": torch.randn(2, 3, 32, 32, generator=self.generator, device="cpu").to(torch_device)}
+
+    # Bridge for AutoencoderTesterMixin which still uses the old interface
+    def prepare_init_args_and_inputs_for_common(self):
+        return self.get_init_dict(), self.get_dummy_inputs()
+
+    def _make_model(self, **overrides) -> AutoencoderRAE:
+        config = self.get_init_dict()
+        config.update(overrides)
+        return AutoencoderRAE(**config).to(torch_device)
+
+
+class TestAutoEncoderRAE(AutoencoderRAETesterConfig, ModelTesterMixin):
+    """Core model tests for AutoencoderRAE."""
+
+    @pytest.mark.skip(reason="AutoencoderRAE does not support torch dynamo yet")
+    def test_from_save_pretrained_dynamo(self): ...
+
+    def test_fast_encode_decode_and_forward_shapes(self):
+        model = self._make_model().eval()
+        x = torch.rand(2, 3, 32, 32, device=torch_device)
+
+        with torch.no_grad():
+            z = model.encode(x).latent
+            decoded = model.decode(z).sample
+            recon = model(x).sample
+
+        assert z.shape == (2, 16, 4, 4)
+        assert decoded.shape == (2, 3, 16, 16)
+        assert recon.shape == (2, 3, 16, 16)
+        assert torch.isfinite(recon).all().item()
+
+    def test_fast_scaling_factor_encode_and_decode_consistency(self):
+        torch.manual_seed(0)
+        model_base = self._make_model(scaling_factor=1.0).eval()
+        torch.manual_seed(0)
+        model_scaled = self._make_model(scaling_factor=2.0).eval()
+
+        x = torch.rand(2, 3, 32, 32, device=torch_device)
+        with torch.no_grad():
+            z_base = model_base.encode(x).latent
+            z_scaled = model_scaled.encode(x).latent
+            recon_base = model_base.decode(z_base).sample
+            recon_scaled = model_scaled.decode(z_scaled).sample
+
+        assert torch.allclose(z_scaled, z_base * 2.0, atol=1e-5, rtol=1e-4)
+        assert torch.allclose(recon_scaled, recon_base, atol=1e-5, rtol=1e-4)
+
+    def test_fast_latents_normalization_matches_formula(self):
+        latents_mean = torch.full((1, 16, 1, 1), 0.25, dtype=torch.float32)
+        latents_std = torch.full((1, 16, 1, 1), 2.0, dtype=torch.float32)
+
+        model_raw = self._make_model().eval()
+        model_norm = self._make_model(latents_mean=latents_mean, latents_std=latents_std).eval()
+        x = torch.rand(1, 3, 32, 32, device=torch_device)
+
+        with torch.no_grad():
+            z_raw = model_raw.encode(x).latent
+            z_norm = model_norm.encode(x).latent
+
+        expected = (z_raw - latents_mean.to(z_raw.device, z_raw.dtype)) / (
+            latents_std.to(z_raw.device, z_raw.dtype) + 1e-5
+        )
+        assert torch.allclose(z_norm, expected, atol=1e-5, rtol=1e-4)
+
+    def test_fast_slicing_matches_non_slicing(self):
+        model = self._make_model().eval()
+        x = torch.rand(3, 3, 32, 32, device=torch_device)
+
+        with torch.no_grad():
+            model.use_slicing = False
+            z_no_slice = model.encode(x).latent
+            out_no_slice = model.decode(z_no_slice).sample
+
+            model.use_slicing = True
+            z_slice = model.encode(x).latent
+            out_slice = model.decode(z_slice).sample
+
+        assert torch.allclose(z_slice, z_no_slice, atol=1e-6, rtol=1e-5)
+        assert torch.allclose(out_slice, out_no_slice, atol=1e-6, rtol=1e-5)
+
+    def test_fast_noise_tau_applies_only_in_train(self):
+        model = self._make_model(noise_tau=0.5).to(torch_device)
+        x = torch.rand(2, 3, 32, 32, device=torch_device)
+
+        model.train()
+        torch.manual_seed(0)
+        z_train_1 = model.encode(x).latent
+        torch.manual_seed(1)
+        z_train_2 = model.encode(x).latent
+
+        model.eval()
+        torch.manual_seed(0)
+        z_eval_1 = model.encode(x).latent
+        torch.manual_seed(1)
+        z_eval_2 = model.encode(x).latent
+
+        assert z_train_1.shape == z_eval_1.shape
+        assert not torch.allclose(z_train_1, z_train_2)
+        assert torch.allclose(z_eval_1, z_eval_2, atol=1e-6, rtol=1e-5)
+
+
+class TestAutoEncoderRAESlicingTiling(AutoencoderRAETesterConfig, AutoencoderTesterMixin):
+    """Slicing and tiling tests for AutoencoderRAE."""
+
+
+@slow
+@pytest.mark.skip(reason="Not enough model usage to justify slow tests yet.")
+class AutoencoderRAEEncoderIntegrationTests:
+    def teardown_method(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_dinov2_encoder_forward_shape(self):
+        encoder = _build_encoder("dinov2", hidden_size=768, patch_size=14, num_hidden_layers=12).to(torch_device)
+        x = torch.rand(1, 3, 224, 224, device=torch_device)
+        y = _ENCODER_FORWARD_FNS["dinov2"](encoder, x)
+
+        assert y.ndim == 3
+        assert y.shape[0] == 1
+        assert y.shape[1] == 256  # (224/14)^2 - 5 (CLS + 4 register) = 251?  Actually dinov2 has 256 patches
+        assert y.shape[2] == 768
+
+    def test_siglip2_encoder_forward_shape(self):
+        encoder = _build_encoder("siglip2", hidden_size=768, patch_size=16, num_hidden_layers=12).to(torch_device)
+        x = torch.rand(1, 3, 224, 224, device=torch_device)
+        y = _ENCODER_FORWARD_FNS["siglip2"](encoder, x)
+
+        assert y.ndim == 3
+        assert y.shape[0] == 1
+        assert y.shape[1] == 196  # (224/16)^2
+        assert y.shape[2] == 768
+
+    def test_mae_encoder_forward_shape(self):
+        encoder = _build_encoder("mae", hidden_size=768, patch_size=16, num_hidden_layers=12).to(torch_device)
+        x = torch.rand(1, 3, 224, 224, device=torch_device)
+        y = _ENCODER_FORWARD_FNS["mae"](encoder, x, patch_size=16)
+
+        assert y.ndim == 3
+        assert y.shape[0] == 1
+        assert y.shape[1] == 196  # (224/16)^2
+        assert y.shape[2] == 768
+
+
+@slow
+@pytest.mark.skip(reason="Not enough model usage to justify slow tests yet.")
+class AutoencoderRAEIntegrationTests:
+    def teardown_method(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_autoencoder_rae_from_pretrained_dinov2(self):
+        model = AutoencoderRAE.from_pretrained("nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08").to(torch_device)
+        model.eval()
+
+        image = load_image(
+            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"
+        )
+        image = image.convert("RGB").resize((224, 224))
+        x = to_tensor(image).unsqueeze(0).to(torch_device)
+
+        with torch.no_grad():
+            latents = model.encode(x).latent
+            assert latents.shape == (1, 768, 16, 16)
+
+            recon = model.decode(latents).sample
+            assert recon.shape == (1, 3, 256, 256)
+            assert torch.isfinite(recon).all().item()
+
+            # fmt: off
+            expected_latent_slice = torch.tensor([0.7617, 0.8824, -0.4891])
+            expected_recon_slice = torch.tensor([0.1263, 0.1355, 0.1435])
+            # fmt: on
+
+            assert torch_all_close(latents[0, :3, 0, 0].float().cpu(), expected_latent_slice, atol=1e-3)
+            assert torch_all_close(recon[0, 0, 0, :3].float().cpu(), expected_recon_slice, atol=1e-3)

tests/models/test_models_auto.py

@@ -1,9 +1,15 @@
+import json
+import os
+import tempfile
 import unittest
 from unittest.mock import MagicMock, patch
 
+import torch
 from transformers import CLIPTextModel, LongformerModel
 
+from diffusers import ConfigMixin
 from diffusers.models import AutoModel, UNet2DConditionModel
+from diffusers.models.modeling_utils import ModelMixin
 
 
 class TestAutoModel(unittest.TestCase):
@@ -35,6 +41,45 @@ class TestAutoModel(unittest.TestCase):
         )
         assert isinstance(model, CLIPTextModel)
 
+    def test_load_dynamic_module_from_local_path_with_subfolder(self):
+        CUSTOM_MODEL_CODE = (
+            "import torch\n"
+            "from diffusers import ModelMixin, ConfigMixin\n"
+            "from diffusers.configuration_utils import register_to_config\n"
+            "\n"
+            "class CustomModel(ModelMixin, ConfigMixin):\n"
+            "    @register_to_config\n"
+            "    def __init__(self, hidden_size=8):\n"
+            "        super().__init__()\n"
+            "        self.linear = torch.nn.Linear(hidden_size, hidden_size)\n"
+            "\n"
+            "    def forward(self, x):\n"
+            "        return self.linear(x)\n"
+        )
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            subfolder = "custom_model"
+            model_dir = os.path.join(tmpdir, subfolder)
+            os.makedirs(model_dir)
+
+            with open(os.path.join(model_dir, "modeling.py"), "w") as f:
+                f.write(CUSTOM_MODEL_CODE)
+
+            config = {
+                "_class_name": "CustomModel",
+                "_diffusers_version": "0.0.0",
+                "auto_map": {"AutoModel": "modeling.CustomModel"},
+                "hidden_size": 8,
+            }
+            with open(os.path.join(model_dir, "config.json"), "w") as f:
+                json.dump(config, f)
+
+            torch.save({}, os.path.join(model_dir, "diffusion_pytorch_model.bin"))
+
+            model = AutoModel.from_pretrained(tmpdir, subfolder=subfolder, trust_remote_code=True)
+            assert model.__class__.__name__ == "CustomModel"
+            assert model.config["hidden_size"] == 8
+
 
 class TestAutoModelFromConfig(unittest.TestCase):
     @patch(
@@ -100,3 +145,51 @@ class TestAutoModelFromConfig(unittest.TestCase):
     def test_from_config_raises_on_none(self):
         with self.assertRaises(ValueError, msg="Please provide a `pretrained_model_name_or_path_or_dict`"):
             AutoModel.from_config(None)
+
+
+class TestRegisterForAutoClass(unittest.TestCase):
+    def test_register_for_auto_class_sets_attribute(self):
+        class DummyModel(ModelMixin, ConfigMixin):
+            config_name = "config.json"
+
+        DummyModel.register_for_auto_class("AutoModel")
+        self.assertEqual(DummyModel._auto_class, "AutoModel")
+
+    def test_register_for_auto_class_rejects_unsupported(self):
+        class DummyModel(ModelMixin, ConfigMixin):
+            config_name = "config.json"
+
+        with self.assertRaises(ValueError, msg="Only 'AutoModel' is supported"):
+            DummyModel.register_for_auto_class("AutoPipeline")
+
+    def test_auto_map_in_saved_config(self):
+        class DummyModel(ModelMixin, ConfigMixin):
+            config_name = "config.json"
+
+        DummyModel.register_for_auto_class("AutoModel")
+        model = DummyModel()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model.save_config(tmpdir)
+            config_path = os.path.join(tmpdir, "config.json")
+            with open(config_path, "r") as f:
+                config = json.load(f)
+
+        self.assertIn("auto_map", config)
+        self.assertIn("AutoModel", config["auto_map"])
+        module_name = DummyModel.__module__.split(".")[-1]
+        self.assertEqual(config["auto_map"]["AutoModel"], f"{module_name}.DummyModel")
+
+    def test_no_auto_map_without_register(self):
+        class DummyModel(ModelMixin, ConfigMixin):
+            config_name = "config.json"
+
+        model = DummyModel()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model.save_config(tmpdir)
+            config_path = os.path.join(tmpdir, "config.json")
+            with open(config_path, "r") as f:
+                config = json.load(f)
+
+        self.assertNotIn("auto_map", config)

tests/models/transformers/test_models_transformer_helios.py

@@ -0,0 +1,168 @@
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import pytest
+import torch
+
+from diffusers import HeliosTransformer3DModel
+from diffusers.utils.torch_utils import randn_tensor
+
+from ...testing_utils import enable_full_determinism, torch_device
+from ..testing_utils import (
+    AttentionTesterMixin,
+    BaseModelTesterConfig,
+    MemoryTesterMixin,
+    ModelTesterMixin,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
+)
+
+
+enable_full_determinism()
+
+
+class HeliosTransformer3DTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return HeliosTransformer3DModel
+
+    @property
+    def pretrained_model_name_or_path(self):
+        return "hf-internal-testing/tiny-helios-base-transformer"
+
+    @property
+    def output_shape(self) -> tuple[int, ...]:
+        return (4, 2, 16, 16)
+
+    @property
+    def input_shape(self) -> tuple[int, ...]:
+        return (4, 2, 16, 16)
+
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict[str, int | list[int] | tuple | str | bool]:
+        return {
+            "patch_size": (1, 2, 2),
+            "num_attention_heads": 2,
+            "attention_head_dim": 12,
+            "in_channels": 4,
+            "out_channels": 4,
+            "text_dim": 16,
+            "freq_dim": 256,
+            "ffn_dim": 32,
+            "num_layers": 2,
+            "cross_attn_norm": True,
+            "qk_norm": "rms_norm_across_heads",
+            "rope_dim": (4, 4, 4),
+            "has_multi_term_memory_patch": True,
+            "guidance_cross_attn": True,
+            "zero_history_timestep": True,
+            "is_amplify_history": False,
+        }
+
+    def get_dummy_inputs(self) -> dict[str, torch.Tensor]:
+        batch_size = 1
+        num_channels = 4
+        num_frames = 2
+        height = 16
+        width = 16
+        text_encoder_embedding_dim = 16
+        sequence_length = 12
+
+        hidden_states = randn_tensor(
+            (batch_size, num_channels, num_frames, height, width),
+            generator=self.generator,
+            device=torch_device,
+        )
+        timestep = torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device)
+        encoder_hidden_states = randn_tensor(
+            (batch_size, sequence_length, text_encoder_embedding_dim),
+            generator=self.generator,
+            device=torch_device,
+        )
+        indices_hidden_states = torch.ones((batch_size, num_frames)).to(torch_device)
+        indices_latents_history_short = torch.ones((batch_size, num_frames - 1)).to(torch_device)
+        indices_latents_history_mid = torch.ones((batch_size, num_frames - 1)).to(torch_device)
+        indices_latents_history_long = torch.ones((batch_size, (num_frames - 1) * 4)).to(torch_device)
+        latents_history_short = randn_tensor(
+            (batch_size, num_channels, num_frames - 1, height, width),
+            generator=self.generator,
+            device=torch_device,
+        )
+        latents_history_mid = randn_tensor(
+            (batch_size, num_channels, num_frames - 1, height, width),
+            generator=self.generator,
+            device=torch_device,
+        )
+        latents_history_long = randn_tensor(
+            (batch_size, num_channels, (num_frames - 1) * 4, height, width),
+            generator=self.generator,
+            device=torch_device,
+        )
+
+        return {
+            "hidden_states": hidden_states,
+            "timestep": timestep,
+            "encoder_hidden_states": encoder_hidden_states,
+            "indices_hidden_states": indices_hidden_states,
+            "indices_latents_history_short": indices_latents_history_short,
+            "indices_latents_history_mid": indices_latents_history_mid,
+            "indices_latents_history_long": indices_latents_history_long,
+            "latents_history_short": latents_history_short,
+            "latents_history_mid": latents_history_mid,
+            "latents_history_long": latents_history_long,
+        }
+
+
+class TestHeliosTransformer3D(HeliosTransformer3DTesterConfig, ModelTesterMixin):
+    """Core model tests for Helios Transformer 3D."""
+
+    @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
+    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
+        # Skip: fp16/bf16 require very high atol to pass, providing little signal.
+        # Dtype preservation is already tested by test_from_save_pretrained_dtype and test_keep_in_fp32_modules.
+        pytest.skip("Tolerance requirements too high for meaningful test")
+
+
+class TestHeliosTransformer3DMemory(HeliosTransformer3DTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for Helios Transformer 3D."""
+
+
+class TestHeliosTransformer3DTraining(HeliosTransformer3DTesterConfig, TrainingTesterMixin):
+    """Training tests for Helios Transformer 3D."""
+
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {"HeliosTransformer3DModel"}
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+
+class TestHeliosTransformer3DAttention(HeliosTransformer3DTesterConfig, AttentionTesterMixin):
+    """Attention processor tests for Helios Transformer 3D."""
+
+
+class TestHeliosTransformer3DCompile(HeliosTransformer3DTesterConfig, TorchCompileTesterMixin):
+    """Torch compile tests for Helios Transformer 3D."""
+
+    @pytest.mark.xfail(
+        reason="Helios DiT does not compile when deterministic algorithms are used due to https://github.com/pytorch/pytorch/issues/170079"
+    )
+    def test_torch_compile_recompilation_and_graph_break(self):
+        super().test_torch_compile_recompilation_and_graph_break()

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -10,6 +10,11 @@ import torch
 import diffusers
 from diffusers import AutoModel, ComponentsManager, ModularPipeline, ModularPipelineBlocks
 from diffusers.guiders import ClassifierFreeGuidance
+from diffusers.modular_pipelines import (
+    ConditionalPipelineBlocks,
+    LoopSequentialPipelineBlocks,
+    SequentialPipelineBlocks,
+)
 from diffusers.modular_pipelines.modular_pipeline_utils import (
     ComponentSpec,
     ConfigSpec,
@@ -19,7 +24,13 @@ from diffusers.modular_pipelines.modular_pipeline_utils import (
 )
 from diffusers.utils import logging
 
-from ..testing_utils import backend_empty_cache, numpy_cosine_similarity_distance, require_accelerator, torch_device
+from ..testing_utils import (
+    CaptureLogger,
+    backend_empty_cache,
+    numpy_cosine_similarity_distance,
+    require_accelerator,
+    torch_device,
+)
 
 
 class ModularPipelineTesterMixin:
@@ -429,6 +440,117 @@ class ModularGuiderTesterMixin:
         assert max_diff > expected_max_diff, "Output with CFG must be different from normal inference"
 
 
+class TestCustomBlockRequirements:
+    def get_dummy_block_pipe(self):
+        class DummyBlockOne:
+            # keep two arbitrary deps so that we can test warnings.
+            _requirements = {"xyz": ">=0.8.0", "abc": ">=10.0.0"}
+
+        class DummyBlockTwo:
+            # keep two dependencies that will be available during testing.
+            _requirements = {"transformers": ">=4.44.0", "diffusers": ">=0.2.0"}
+
+        pipe = SequentialPipelineBlocks.from_blocks_dict(
+            {"dummy_block_one": DummyBlockOne, "dummy_block_two": DummyBlockTwo}
+        )
+        return pipe
+
+    def get_dummy_conditional_block_pipe(self):
+        class DummyBlockOne:
+            _requirements = {"xyz": ">=0.8.0", "abc": ">=10.0.0"}
+
+        class DummyBlockTwo:
+            _requirements = {"transformers": ">=4.44.0", "diffusers": ">=0.2.0"}
+
+        class DummyConditionalBlocks(ConditionalPipelineBlocks):
+            block_classes = [DummyBlockOne, DummyBlockTwo]
+            block_names = ["block_one", "block_two"]
+            block_trigger_inputs = []
+
+            def select_block(self, **kwargs):
+                return "block_one"
+
+        return DummyConditionalBlocks()
+
+    def get_dummy_loop_block_pipe(self):
+        class DummyBlockOne:
+            _requirements = {"xyz": ">=0.8.0", "abc": ">=10.0.0"}
+
+        class DummyBlockTwo:
+            _requirements = {"transformers": ">=4.44.0", "diffusers": ">=0.2.0"}
+
+        return LoopSequentialPipelineBlocks.from_blocks_dict({"block_one": DummyBlockOne, "block_two": DummyBlockTwo})
+
+    def test_sequential_block_requirements_save_load(self, tmp_path):
+        pipe = self.get_dummy_block_pipe()
+        pipe.save_pretrained(tmp_path)
+
+        config_path = tmp_path / "modular_config.json"
+
+        with open(config_path, "r") as f:
+            config = json.load(f)
+
+        assert "requirements" in config
+        requirements = config["requirements"]
+
+        expected_requirements = {
+            "xyz": ">=0.8.0",
+            "abc": ">=10.0.0",
+            "transformers": ">=4.44.0",
+            "diffusers": ">=0.2.0",
+        }
+        assert expected_requirements == requirements
+
+    def test_sequential_block_requirements_warnings(self, tmp_path):
+        pipe = self.get_dummy_block_pipe()
+
+        logger = logging.get_logger("diffusers.modular_pipelines.modular_pipeline_utils")
+        logger.setLevel(30)
+
+        with CaptureLogger(logger) as cap_logger:
+            pipe.save_pretrained(tmp_path)
+
+        template = "{req} was specified in the requirements but wasn't found in the current environment"
+        msg_xyz = template.format(req="xyz")
+        msg_abc = template.format(req="abc")
+        assert msg_xyz in str(cap_logger.out)
+        assert msg_abc in str(cap_logger.out)
+
+    def test_conditional_block_requirements_save_load(self, tmp_path):
+        pipe = self.get_dummy_conditional_block_pipe()
+        pipe.save_pretrained(tmp_path)
+
+        config_path = tmp_path / "modular_config.json"
+        with open(config_path, "r") as f:
+            config = json.load(f)
+
+        assert "requirements" in config
+        expected_requirements = {
+            "xyz": ">=0.8.0",
+            "abc": ">=10.0.0",
+            "transformers": ">=4.44.0",
+            "diffusers": ">=0.2.0",
+        }
+        assert expected_requirements == config["requirements"]
+
+    def test_loop_block_requirements_save_load(self, tmp_path):
+        pipe = self.get_dummy_loop_block_pipe()
+        pipe.save_pretrained(tmp_path)
+
+        config_path = tmp_path / "modular_config.json"
+        with open(config_path, "r") as f:
+            config = json.load(f)
+
+        assert "requirements" in config
+        expected_requirements = {
+            "xyz": ">=0.8.0",
+            "abc": ">=10.0.0",
+            "transformers": ">=4.44.0",
+            "diffusers": ">=0.2.0",
+        }
+        assert expected_requirements == config["requirements"]
+
+
 class TestModularModelCardContent:
     def create_mock_block(self, name="TestBlock", description="Test block description"):
         class MockBlock:
@@ -483,8 +605,7 @@ class TestModularModelCardContent:
             "blocks_description",
             "components_description",
             "configs_section",
-            "inputs_description",
-            "outputs_description",
+            "io_specification_section",
             "trigger_inputs_section",
             "tags",
         ]
@@ -581,18 +702,19 @@ class TestModularModelCardContent:
         blocks = self.create_mock_blocks(inputs=inputs)
         content = generate_modular_model_card_content(blocks)
 
-        assert "**Required:**" in content["inputs_description"]
-        assert "**Optional:**" in content["inputs_description"]
-        assert "prompt" in content["inputs_description"]
-        assert "num_steps" in content["inputs_description"]
-        assert "default: `50`" in content["inputs_description"]
+        io_section = content["io_specification_section"]
+        assert "**Inputs:**" in io_section
+        assert "prompt" in io_section
+        assert "num_steps" in io_section
+        assert "*optional*" in io_section
+        assert "defaults to `50`" in io_section
 
     def test_inputs_description_empty(self):
         """Test handling of pipelines without specific inputs."""
         blocks = self.create_mock_blocks(inputs=[])
         content = generate_modular_model_card_content(blocks)
 
-        assert "No specific inputs defined" in content["inputs_description"]
+        assert "No specific inputs defined" in content["io_specification_section"]
 
     def test_outputs_description_formatting(self):
         """Test that outputs are correctly formatted."""
@@ -602,15 +724,16 @@ class TestModularModelCardContent:
         blocks = self.create_mock_blocks(outputs=outputs)
         content = generate_modular_model_card_content(blocks)
 
-        assert "images" in content["outputs_description"]
-        assert "Generated images" in content["outputs_description"]
+        io_section = content["io_specification_section"]
+        assert "images" in io_section
+        assert "Generated images" in io_section
 
     def test_outputs_description_empty(self):
         """Test handling of pipelines without specific outputs."""
         blocks = self.create_mock_blocks(outputs=[])
         content = generate_modular_model_card_content(blocks)
 
-        assert "Standard pipeline outputs" in content["outputs_description"]
+        assert "Standard pipeline outputs" in content["io_specification_section"]
 
     def test_trigger_inputs_section_with_triggers(self):
         """Test that trigger inputs section is generated when present."""
@@ -628,35 +751,6 @@ class TestModularModelCardContent:
 
         assert content["trigger_inputs_section"] == ""
 
-    def test_blocks_description_with_sub_blocks(self):
-        """Test that blocks with sub-blocks are correctly described."""
-
-        class MockBlockWithSubBlocks:
-            def __init__(self):
-                self.__class__.__name__ = "ParentBlock"
-                self.description = "Parent block"
-                self.sub_blocks = {
-                    "child1": self.create_child_block("ChildBlock1", "Child 1 description"),
-                    "child2": self.create_child_block("ChildBlock2", "Child 2 description"),
-                }
-
-            def create_child_block(self, name, desc):
-                class ChildBlock:
-                    def __init__(self):
-                        self.__class__.__name__ = name
-                        self.description = desc
-
-                return ChildBlock()
-
-        blocks = self.create_mock_blocks()
-        blocks.sub_blocks["parent"] = MockBlockWithSubBlocks()
-
-        content = generate_modular_model_card_content(blocks)
-
-        assert "parent" in content["blocks_description"]
-        assert "child1" in content["blocks_description"]
-        assert "child2" in content["blocks_description"]
-
     def test_model_description_includes_block_count(self):
         """Test that model description includes the number of blocks."""
         blocks = self.create_mock_blocks(num_blocks=5)
@@ -715,6 +809,18 @@ class TestLoadComponentsSkipBehavior:
         assert pipe.unet is not None
         assert getattr(pipe, "vae", None) is None
 
+    def test_load_components_selective_loading_incremental(self):
+        """Loading a subset of components should not affect already-loaded components."""
+        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+
+        pipe.load_components(names="unet", torch_dtype=torch.float32)
+        pipe.load_components(names="text_encoder", torch_dtype=torch.float32)
+
+        assert hasattr(pipe, "unet")
+        assert pipe.unet is not None
+        assert hasattr(pipe, "text_encoder")
+        assert pipe.text_encoder is not None
+
     def test_load_components_skips_invalid_pretrained_path(self):
         pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
 
@@ -730,6 +836,112 @@ class TestLoadComponentsSkipBehavior:
         assert not hasattr(pipe, "test_component") or pipe.test_component is None
 
 
+class TestCustomModelSavePretrained:
+    def test_save_pretrained_updates_index_for_local_model(self, tmp_path):
+        """When a component without _diffusers_load_id (custom/local model) is saved,
+        modular_model_index.json should point to the save directory."""
+        import json
+
+        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+        pipe.load_components(torch_dtype=torch.float32)
+
+        pipe.unet._diffusers_load_id = "null"
+
+        save_dir = str(tmp_path / "my-pipeline")
+        pipe.save_pretrained(save_dir)
+
+        with open(os.path.join(save_dir, "modular_model_index.json")) as f:
+            index = json.load(f)
+
+        _library, _cls, unet_spec = index["unet"]
+        assert unet_spec["pretrained_model_name_or_path"] == save_dir
+        assert unet_spec["subfolder"] == "unet"
+
+        _library, _cls, vae_spec = index["vae"]
+        assert vae_spec["pretrained_model_name_or_path"] == "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
+
+    def test_save_pretrained_roundtrip_with_local_model(self, tmp_path):
+        """A pipeline with a custom/local model should be saveable and re-loadable with identical outputs."""
+        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+        pipe.load_components(torch_dtype=torch.float32)
+
+        pipe.unet._diffusers_load_id = "null"
+
+        original_state_dict = pipe.unet.state_dict()
+
+        save_dir = str(tmp_path / "my-pipeline")
+        pipe.save_pretrained(save_dir)
+
+        loaded_pipe = ModularPipeline.from_pretrained(save_dir)
+        loaded_pipe.load_components(torch_dtype=torch.float32)
+
+        assert loaded_pipe.unet is not None
+        assert loaded_pipe.unet.__class__.__name__ == pipe.unet.__class__.__name__
+
+        loaded_state_dict = loaded_pipe.unet.state_dict()
+        assert set(original_state_dict.keys()) == set(loaded_state_dict.keys())
+        for key in original_state_dict:
+            assert torch.equal(original_state_dict[key], loaded_state_dict[key]), f"Mismatch in {key}"
+
+    def test_save_pretrained_updates_index_for_model_with_no_load_id(self, tmp_path):
+        """testing the workflow of update the pipeline with a custom model and save the pipeline,
+        the modular_model_index.json should point to the save directory."""
+        import json
+
+        from diffusers import UNet2DConditionModel
+
+        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+        pipe.load_components(torch_dtype=torch.float32)
+
+        unet = UNet2DConditionModel.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-xl-pipe", subfolder="unet"
+        )
+        assert not hasattr(unet, "_diffusers_load_id")
+
+        pipe.update_components(unet=unet)
+
+        save_dir = str(tmp_path / "my-pipeline")
+        pipe.save_pretrained(save_dir)
+
+        with open(os.path.join(save_dir, "modular_model_index.json")) as f:
+            index = json.load(f)
+
+        _library, _cls, unet_spec = index["unet"]
+        assert unet_spec["pretrained_model_name_or_path"] == save_dir
+        assert unet_spec["subfolder"] == "unet"
+
+        _library, _cls, vae_spec = index["vae"]
+        assert vae_spec["pretrained_model_name_or_path"] == "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
+
+    def test_save_pretrained_overwrite_modular_index(self, tmp_path):
+        """With overwrite_modular_index=True, all component references should point to the save directory."""
+        import json
+
+        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+        pipe.load_components(torch_dtype=torch.float32)
+
+        save_dir = str(tmp_path / "my-pipeline")
+        pipe.save_pretrained(save_dir, overwrite_modular_index=True)
+
+        with open(os.path.join(save_dir, "modular_model_index.json")) as f:
+            index = json.load(f)
+
+        for component_name in ["unet", "vae", "text_encoder", "text_encoder_2"]:
+            if component_name not in index:
+                continue
+            _library, _cls, spec = index[component_name]
+            assert spec["pretrained_model_name_or_path"] == save_dir, (
+                f"{component_name} should point to save dir but got {spec['pretrained_model_name_or_path']}"
+            )
+            assert spec["subfolder"] == component_name
+
+        loaded_pipe = ModularPipeline.from_pretrained(save_dir)
+        loaded_pipe.load_components(torch_dtype=torch.float32)
+
+        assert loaded_pipe.unet is not None
+        assert loaded_pipe.vae is not None
+
+
 class TestModularPipelineInitFallback:
     """Test that ModularPipeline.__init__ falls back to default_blocks_name when
     _blocks_class_name is a base class (e.g. SequentialPipelineBlocks saved by from_blocks_dict)."""

tests/modular_pipelines/test_modular_pipelines_custom_blocks.py

@@ -192,6 +192,156 @@ class TestModularCustomBlocks:
         assert len(pipe.components) == 1
         assert pipe.component_names[0] == "transformer"
 
+    def test_trust_remote_code_not_propagated_to_external_repo(self):
+        """When a modular pipeline repo references a component from an external repo that has custom
+        code (auto_map in config), calling load_components(trust_remote_code=True) should NOT
+        propagate trust_remote_code to that external component. The external component should fail
+        to load."""
+
+        from diffusers import ModularPipeline
+
+        CUSTOM_MODEL_CODE = (
+            "import torch\n"
+            "from diffusers import ModelMixin, ConfigMixin\n"
+            "from diffusers.configuration_utils import register_to_config\n"
+            "\n"
+            "class CustomModel(ModelMixin, ConfigMixin):\n"
+            "    @register_to_config\n"
+            "    def __init__(self, hidden_size=8):\n"
+            "        super().__init__()\n"
+            "        self.linear = torch.nn.Linear(hidden_size, hidden_size)\n"
+            "\n"
+            "    def forward(self, x):\n"
+            "        return self.linear(x)\n"
+        )
+
+        with tempfile.TemporaryDirectory() as external_repo_dir, tempfile.TemporaryDirectory() as pipeline_repo_dir:
+            # Step 1: Create an external model repo with custom code (requires trust_remote_code)
+            with open(os.path.join(external_repo_dir, "modeling.py"), "w") as f:
+                f.write(CUSTOM_MODEL_CODE)
+
+            config = {
+                "_class_name": "CustomModel",
+                "_diffusers_version": "0.0.0",
+                "auto_map": {"AutoModel": "modeling.CustomModel"},
+                "hidden_size": 8,
+            }
+            with open(os.path.join(external_repo_dir, "config.json"), "w") as f:
+                json.dump(config, f)
+
+            torch.save({}, os.path.join(external_repo_dir, "diffusion_pytorch_model.bin"))
+
+            # Step 2: Create a custom block that references the external repo.
+            # Define both the class (for direct use) and its code string (for block.py).
+            class ExternalRefBlock(ModularPipelineBlocks):
+                @property
+                def expected_components(self):
+                    return [
+                        ComponentSpec(
+                            "custom_model",
+                            AutoModel,
+                            pretrained_model_name_or_path=external_repo_dir,
+                        )
+                    ]
+
+                @property
+                def inputs(self) -> List[InputParam]:
+                    return [InputParam("prompt", type_hint=str, required=True)]
+
+                @property
+                def intermediate_inputs(self) -> List[InputParam]:
+                    return []
+
+                @property
+                def intermediate_outputs(self) -> List[OutputParam]:
+                    return [OutputParam("output", type_hint=str)]
+
+                def __call__(self, components, state: PipelineState) -> PipelineState:
+                    block_state = self.get_block_state(state)
+                    block_state.output = "test"
+                    self.set_block_state(state, block_state)
+                    return components, state
+
+            EXTERNAL_REF_BLOCK_CODE_STR = (
+                "from typing import List\n"
+                "from diffusers import AutoModel\n"
+                "from diffusers.modular_pipelines import (\n"
+                "    ComponentSpec,\n"
+                "    InputParam,\n"
+                "    ModularPipelineBlocks,\n"
+                "    OutputParam,\n"
+                "    PipelineState,\n"
+                ")\n"
+                "\n"
+                "class ExternalRefBlock(ModularPipelineBlocks):\n"
+                "    @property\n"
+                "    def expected_components(self):\n"
+                "        return [\n"
+                "            ComponentSpec(\n"
+                '                "custom_model",\n'
+                "                AutoModel,\n"
+                f'                pretrained_model_name_or_path="{external_repo_dir}",\n'
+                "            )\n"
+                "        ]\n"
+                "\n"
+                "    @property\n"
+                "    def inputs(self) -> List[InputParam]:\n"
+                '        return [InputParam("prompt", type_hint=str, required=True)]\n'
+                "\n"
+                "    @property\n"
+                "    def intermediate_inputs(self) -> List[InputParam]:\n"
+                "        return []\n"
+                "\n"
+                "    @property\n"
+                "    def intermediate_outputs(self) -> List[OutputParam]:\n"
+                '        return [OutputParam("output", type_hint=str)]\n'
+                "\n"
+                "    def __call__(self, components, state: PipelineState) -> PipelineState:\n"
+                "        block_state = self.get_block_state(state)\n"
+                '        block_state.output = "test"\n'
+                "        self.set_block_state(state, block_state)\n"
+                "        return components, state\n"
+            )
+
+            # Save the block config, write block.py, then load back via from_pretrained
+            block = ExternalRefBlock()
+            block.save_pretrained(pipeline_repo_dir)
+
+            # auto_map will reference the module name derived from ExternalRefBlock.__module__,
+            # which is "test_modular_pipelines_custom_blocks". Write the code file with that name.
+            code_path = os.path.join(pipeline_repo_dir, "test_modular_pipelines_custom_blocks.py")
+            with open(code_path, "w") as f:
+                f.write(EXTERNAL_REF_BLOCK_CODE_STR)
+
+            block = ModularPipelineBlocks.from_pretrained(pipeline_repo_dir, trust_remote_code=True)
+            pipe = block.init_pipeline()
+            pipe.save_pretrained(pipeline_repo_dir)
+
+            # Step 3: Load the pipeline from the saved directory.
+            loaded_pipe = ModularPipeline.from_pretrained(pipeline_repo_dir, trust_remote_code=True)
+
+            assert loaded_pipe._pretrained_model_name_or_path == pipeline_repo_dir
+            assert loaded_pipe._component_specs["custom_model"].pretrained_model_name_or_path == external_repo_dir
+            assert getattr(loaded_pipe, "custom_model", None) is None
+
+            # Step 4a: load_components WITHOUT trust_remote_code.
+            # It should still fail
+            loaded_pipe.load_components()
+            assert getattr(loaded_pipe, "custom_model", None) is None
+
+            # Step 4b: load_components with trust_remote_code=True.
+            # trust_remote_code should be stripped for the external component, so it fails.
+            # The warning should contain guidance about manually loading with trust_remote_code.
+            loaded_pipe.load_components(trust_remote_code=True)
+            assert getattr(loaded_pipe, "custom_model", None) is None
+
+            # Step 4c: Manually load with AutoModel and update_components — this should work.
+            from diffusers import AutoModel
+
+            custom_model = AutoModel.from_pretrained(external_repo_dir, trust_remote_code=True)
+            loaded_pipe.update_components(custom_model=custom_model)
+            assert getattr(loaded_pipe, "custom_model", None) is not None
+
     def test_custom_block_loads_from_hub(self):
         repo_id = "hf-internal-testing/tiny-modular-diffusers-block"
         block = ModularPipelineBlocks.from_pretrained(repo_id, trust_remote_code=True)

tests/others/test_config.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import dataclasses
 import json
 import tempfile
 import unittest
@@ -306,96 +305,3 @@ class ConfigTester(unittest.TestCase):
         result = json.loads(json_string)
         assert result["test_file_1"] == config.config.test_file_1.as_posix()
         assert result["test_file_2"] == config.config.test_file_2.as_posix()
-
-
-class SampleObjectTyped(ConfigMixin):
-    config_name = "config.json"
-
-    @register_to_config
-    def __init__(
-        self,
-        a: int = 2,
-        b: int = 5,
-        c: str = "hello",
-    ):
-        pass
-
-
-class SampleObjectWithIgnore(ConfigMixin):
-    config_name = "config.json"
-    ignore_for_config = ["secret"]
-
-    @register_to_config
-    def __init__(
-        self,
-        a: int = 2,
-        secret: str = "hidden",
-    ):
-        pass
-
-
-class DataclassFromConfigTester(unittest.TestCase):
-    def test_get_dataclass_from_config_returns_frozen_dataclass(self):
-        obj = SampleObject()
-        tc = SampleObject._get_dataclass_from_config(dict(obj.config))
-        assert dataclasses.is_dataclass(tc)
-        with self.assertRaises(dataclasses.FrozenInstanceError):
-            tc.a = 99
-
-    def test_get_dataclass_from_config_class_name(self):
-        obj = SampleObject()
-        tc = SampleObject._get_dataclass_from_config(dict(obj.config))
-        assert type(tc).__name__ == "SampleObjectConfig"
-
-    def test_get_dataclass_from_config_values_match_config(self):
-        obj = SampleObject(a=10, b=20)
-        tc = SampleObject._get_dataclass_from_config(dict(obj.config))
-        assert tc.a == 10
-        assert tc.b == 20
-        assert tc.c == (2, 5)
-        assert tc.d == "for diffusion"
-        assert tc.e == [1, 3]
-
-    def test_get_dataclass_from_config_from_raw_dict(self):
-        tc = SampleObjectTyped._get_dataclass_from_config({"a": 7, "b": 3, "c": "world"})
-        assert tc.a == 7
-        assert tc.b == 3
-        assert tc.c == "world"
-
-    def test_get_dataclass_from_config_annotations(self):
-        tc = SampleObjectTyped._get_dataclass_from_config({"a": 1, "b": 2, "c": "hi"})
-        fields = {f.name: f.type for f in dataclasses.fields(tc)}
-        assert fields["a"] is int
-        assert fields["b"] is int
-        assert fields["c"] is str
-
-    def test_get_dataclass_from_config_asdict_roundtrip(self):
-        tc = SampleObjectTyped._get_dataclass_from_config({"a": 7, "b": 3, "c": "world"})
-        d = dataclasses.asdict(tc)
-        assert d == {"a": 7, "b": 3, "c": "world"}
-
-    def test_get_dataclass_from_config_ignores_extra_keys(self):
-        tc = SampleObjectTyped._get_dataclass_from_config(
-            {"a": 1, "b": 2, "c": "hi", "_class_name": "Foo", "extra": 99}
-        )
-        assert tc.a == 1
-        assert not hasattr(tc, "_class_name")
-        assert not hasattr(tc, "extra")
-
-    def test_get_dataclass_from_config_respects_ignore_for_config(self):
-        tc = SampleObjectWithIgnore._get_dataclass_from_config({"a": 5})
-        assert not hasattr(tc, "secret")
-        assert tc.a == 5
-
-    def test_get_dataclass_from_config_works_for_scheduler(self):
-        scheduler = DDIMScheduler()
-        tc = DDIMScheduler._get_dataclass_from_config(dict(scheduler.config))
-        assert dataclasses.is_dataclass(tc)
-        assert type(tc).__name__ == "DDIMSchedulerConfig"
-        assert tc.num_train_timesteps == scheduler.config.num_train_timesteps
-
-    def test_get_dataclass_from_config_different_values(self):
-        tc1 = SampleObjectTyped._get_dataclass_from_config({"a": 1, "b": 2, "c": "x"})
-        tc2 = SampleObjectTyped._get_dataclass_from_config({"a": 9, "b": 8, "c": "y"})
-        assert tc1.a == 1
-        assert tc2.a == 9

tests/pipelines/helios/test_helios.py

@@ -0,0 +1,172 @@
+# Copyright 2025 The HuggingFace Team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gc
+import unittest
+
+import torch
+from transformers import AutoConfig, AutoTokenizer, T5EncoderModel
+
+from diffusers import AutoencoderKLWan, HeliosPipeline, HeliosScheduler, HeliosTransformer3DModel
+
+from ...testing_utils import (
+    backend_empty_cache,
+    enable_full_determinism,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineTesterMixin
+
+
+enable_full_determinism()
+
+
+class HeliosPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = HeliosPipeline
+    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
+    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+    test_xformers_attention = False
+    supports_dduf = False
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        vae = AutoencoderKLWan(
+            base_dim=3,
+            z_dim=16,
+            dim_mult=[1, 1, 1, 1],
+            num_res_blocks=1,
+            temperal_downsample=[False, True, True],
+        )
+
+        torch.manual_seed(0)
+        scheduler = HeliosScheduler(stage_range=[0, 1], stages=1, use_dynamic_shifting=True)
+        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
+        text_encoder = T5EncoderModel(config)
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        torch.manual_seed(0)
+        transformer = HeliosTransformer3DModel(
+            patch_size=(1, 2, 2),
+            num_attention_heads=2,
+            attention_head_dim=12,
+            in_channels=16,
+            out_channels=16,
+            text_dim=32,
+            freq_dim=256,
+            ffn_dim=32,
+            num_layers=2,
+            cross_attn_norm=True,
+            qk_norm="rms_norm_across_heads",
+            rope_dim=(4, 4, 4),
+            has_multi_term_memory_patch=True,
+            guidance_cross_attn=True,
+            zero_history_timestep=True,
+            is_amplify_history=False,
+        )
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+        inputs = {
+            "prompt": "dance monkey",
+            "negative_prompt": "negative",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 1.0,
+            "height": 16,
+            "width": 16,
+            "num_frames": 9,
+            "max_sequence_length": 16,
+            "output_type": "pt",
+        }
+        return inputs
+
+    def test_inference(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        video = pipe(**inputs).frames
+        generated_video = video[0]
+        self.assertEqual(generated_video.shape, (33, 3, 16, 16))
+
+        # fmt: off
+        expected_slice = torch.tensor([0.4529, 0.4527, 0.4499, 0.4542, 0.4528, 0.4524, 0.4531, 0.4534, 0.5328,
+        0.5340, 0.5012, 0.5135, 0.5322, 0.5203, 0.5144, 0.5101])
+        # fmt: on
+
+        generated_slice = generated_video.flatten()
+        generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
+        self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
+
+    # Override to set a more lenient max diff threshold.
+    def test_save_load_float16(self):
+        super().test_save_load_float16(expected_max_diff=0.03)
+
+    @unittest.skip("Test not supported")
+    def test_attention_slicing_forward_pass(self):
+        pass
+
+    @unittest.skip("Optional components not applicable for Helios")
+    def test_save_load_optional_components(self):
+        pass
+
+
+@slow
+@require_torch_accelerator
+class HeliosPipelineIntegrationTests(unittest.TestCase):
+    prompt = "A painting of a squirrel eating a burger."
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    @unittest.skip("TODO: test needs to be implemented")
+    def test_helios(self):
+        pass

tests/quantization/torchao/test_torchao.py

@@ -74,7 +74,7 @@ if is_torchao_available():
 
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.7.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 class TorchAoConfigTest(unittest.TestCase):
     def test_to_dict(self):
         """
@@ -132,7 +132,7 @@ class TorchAoConfigTest(unittest.TestCase):
 # Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.7.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 class TorchAoTest(unittest.TestCase):
     def tearDown(self):
         gc.collect()
@@ -587,7 +587,7 @@ class TorchAoTest(unittest.TestCase):
 # Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.7.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 class TorchAoSerializationTest(unittest.TestCase):
     model_name = "hf-internal-testing/tiny-flux-pipe"
 
@@ -698,23 +698,22 @@ class TorchAoSerializationTest(unittest.TestCase):
         self._check_serialization_expected_slice(quant_method, quant_method_kwargs, expected_slice, device)
 
 
-@require_torchao_version_greater_or_equal("0.7.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 class TorchAoCompileTest(QuantCompileTests, unittest.TestCase):
     @property
     def quantization_config(self):
         return PipelineQuantizationConfig(
-            quant_mapping={
-                "transformer": TorchAoConfig(quant_type="int8_weight_only"),
-            },
+            quant_mapping={"transformer": TorchAoConfig(Int8WeightOnlyConfig())},
         )
 
-    @unittest.skip(
-        "Changing the device of AQT tensor with module._apply (called from doing module.to() in accelerate) does not work "
-        "when compiling."
-    )
     def test_torch_compile_with_cpu_offload(self):
+        pipe = self._init_pipeline(self.quantization_config, torch.bfloat16)
+        pipe.enable_model_cpu_offload()
+        # No compilation because it fails with:
         # RuntimeError: _apply(): Couldn't swap Linear.weight
-        super().test_torch_compile_with_cpu_offload()
+
+        # small resolutions to ensure speedy execution.
+        pipe("a dog", num_inference_steps=2, max_sequence_length=16, height=256, width=256)
 
     @parameterized.expand([False, True])
     @unittest.skip(
@@ -745,7 +744,7 @@ class TorchAoCompileTest(QuantCompileTests, unittest.TestCase):
 # Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.7.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 @slow
 @nightly
 class SlowTorchAoTests(unittest.TestCase):
@@ -907,7 +906,7 @@ class SlowTorchAoTests(unittest.TestCase):
 
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.7.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 @slow
 @nightly
 class SlowTorchAoPreserializedModelTests(unittest.TestCase):