Add Diffusers user guide for Cosmos3 Edge

.gitattributes

@@ -51,3 +51,4 @@ assets/edge_i2v_output.mp4 filter=lfs diff=lfs merge=lfs -text
 assets/edge_action_fd_umi_2chunk_output.mp4 filter=lfs diff=lfs merge=lfs -text
 assets/edge_action_id_av_0_output.png filter=lfs diff=lfs merge=lfs -text
 assets/edge_action_id_av_1_output.png filter=lfs diff=lfs merge=lfs -text
+assets/diffusers_outputs/edge_i2v_diffusers.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -174,6 +174,7 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
 - [vLLM-Omni](https://github.com/vllm-project/vllm-omni)
 - [vLLM](https://github.com/vllm-project/vllm)
 - [PyTorch](https://github.com/nvidia/cosmos3)
+- [Hugging Face Diffusers](https://github.com/huggingface/diffusers)
 
 **Supported Hardware Microarchitecture Compatibility:**
 
@@ -737,6 +738,234 @@ Example outputs:
 
 <img width="1280" src="https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets/edge_action_id_av_1_output.png">
 
+### Diffusers
+
+Cosmos3-Edge is supported in the Hugging Face Diffusers library through [`Cosmos3OmniPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cosmos3). The pipeline supports image-to-video generation and action-conditioned forward and inverse dynamics.
+
+#### Container
+
+To install Diffusers with `Cosmos3OmniPipeline`:
+
+```bash
+uv venv --python 3.12 --seed --managed-python
+source .venv/bin/activate
+uv pip install \
+  "diffusers @ git+https://github.com/huggingface/diffusers.git" \
+  accelerate \
+  av \
+  cosmos_guardrail \
+  huggingface_hub \
+  imageio \
+  imageio-ffmpeg \
+  torch \
+  torchvision \
+  transformers
+```
+
+#### Examples
+
+The examples use the inputs in this repository's `assets/` directory. Run them from the model repository root.
+
+##### Image to video generation
+
+```python
+import json
+from pathlib import Path
+
+import torch
+from diffusers import Cosmos3OmniPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from diffusers.utils import export_to_video, load_image
+
+# Read the JSON-upsampled positive and negative prompts.
+json_prompt = json.loads(Path("assets/example_i2v_prompt.json").read_text())
+negative_prompt = json.loads(Path("assets/negative_prompt.json").read_text())
+
+pipe = Cosmos3OmniPipeline.from_pretrained(
+    "nvidia/Cosmos3-Edge",
+    torch_dtype=torch.bfloat16,
+    enable_safety_checker=True,
+)
+pipe.to("cuda")
+pipe.scheduler = UniPCMultistepScheduler.from_config(
+    pipe.scheduler.config, flow_shift=3.0, use_karras_sigmas=False
+)
+
+result = pipe(
+    prompt=json.dumps(json_prompt),
+    negative_prompt=json.dumps(negative_prompt),
+    image=load_image("assets/example_i2v_input.jpg"),
+    num_frames=121,
+    height=480,
+    width=832,
+    fps=24.0,
+    num_inference_steps=50,
+    guidance_scale=5.0,
+    generator=torch.Generator(device="cuda").manual_seed(0),
+    add_resolution_template=False,
+    add_duration_template=False,
+)
+
+output_path = Path("assets/diffusers_outputs/edge_i2v_diffusers.mp4")
+output_path.parent.mkdir(parents=True, exist_ok=True)
+# macro_block_size=1 preserves the requested 832x480 resolution.
+export_to_video(result.video, str(output_path), fps=24, macro_block_size=1)
+print(f"Saved video to {output_path}")
+```
+
+Example output:
+
+<video controls width="832" height="480" src="https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets/diffusers_outputs/edge_i2v_diffusers.mp4"></video>
+
+---
+
+##### Action generation
+
+The forward-dynamics example uses UMI robotics action trajectories, and the inverse-dynamics examples use autonomous-vehicle (AV) action trajectories. The pipeline's `CosmosActionCondition` groups the action task, embodiment, action chunk, and conditioning image or video. Unlike the image-to-video example, action prompts are plain task descriptions and should not be prompt-upsampled.
+
+###### Action forward dynamics
+
+This two-chunk UMI rollout conditions the first chunk on `assets/example_action_fd_umi_first_frame.png`, then conditions each subsequent chunk on the final generated frame of the previous chunk. Dropping each chunk's conditioning frame produces a stitched 32-frame rollout.
+
+```python
+import json
+from pathlib import Path
+
+import torch
+from diffusers import Cosmos3OmniPipeline, CosmosActionCondition
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from diffusers.utils import export_to_video, load_image
+
+action_spec = json.loads(Path("assets/example_action_fd_umi_action_chunks.json").read_text())
+output_dir = Path("assets/diffusers_outputs")
+output_dir.mkdir(parents=True, exist_ok=True)
+
+pipe = Cosmos3OmniPipeline.from_pretrained(
+    "nvidia/Cosmos3-Edge",
+    torch_dtype=torch.bfloat16,
+    enable_safety_checker=True,
+)
+pipe.to("cuda")
+pipe.scheduler = UniPCMultistepScheduler.from_config(
+    pipe.scheduler.config, flow_shift=10.0, use_karras_sigmas=False
+)
+
+prompt = action_spec.get("prompt", "mouse arrangement")
+fps = int(action_spec.get("fps", 20))
+chunk_size = int(action_spec.get("action_chunk_size", 16))
+current_frame = load_image("assets/example_action_fd_umi_first_frame.png")
+stitched_frames = []
+chunk_paths = []
+
+for chunk_idx, action_chunk in enumerate(action_spec["action_chunks"]):
+    result = pipe(
+        prompt=prompt,
+        action=CosmosActionCondition(
+            mode="forward_dynamics",
+            chunk_size=chunk_size,
+            domain_name=action_spec.get("domain_name", "umi"),
+            resolution_tier=int(action_spec.get("image_size", 256)),
+            raw_actions=torch.tensor(action_chunk, dtype=torch.float32),
+            image=current_frame,
+            view_point=action_spec.get("view_point", "ego_view"),
+        ),
+        fps=fps,
+        num_inference_steps=30,
+        guidance_scale=1.0,
+        generator=torch.Generator(device="cuda").manual_seed(chunk_idx),
+        use_system_prompt=False,
+    )
+
+    chunk_path = output_dir / f"edge_action_fd_diffusers_chunk_{chunk_idx:02d}.mp4"
+    export_to_video(result.video, str(chunk_path), fps=fps, macro_block_size=1)
+    chunk_paths.append(chunk_path)
+    stitched_frames.extend(result.video[1:])
+    current_frame = result.video[-1]
+
+stitched_path = output_dir / "edge_action_fd_umi_2chunk_diffusers.mp4"
+export_to_video(stitched_frames, str(stitched_path), fps=fps, macro_block_size=1)
+print("Generated chunk videos:", chunk_paths)
+print("Saved stitched rollout:", stitched_path)
+```
+
+Example output:
+
+<video controls width="256" height="256" src="https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets/diffusers_outputs/edge_action_fd_umi_2chunk_diffusers.mp4"></video>
+
+###### Action inverse dynamics
+
+This example predicts the 60-step, 9-D AV action sequence that connects each 61-frame conditioning video. It writes one JSON output per input video.
+
+```python
+import json
+from pathlib import Path
+
+import torch
+from diffusers import Cosmos3OmniPipeline, CosmosActionCondition
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from diffusers.utils import load_video
+
+input_videos = {
+    "av_inverse_0": Path("assets/example_action_id_av_0_input.mp4"),
+    "av_inverse_1": Path("assets/example_action_id_av_1_input.mp4"),
+}
+output_dir = Path("assets/diffusers_outputs")
+output_dir.mkdir(parents=True, exist_ok=True)
+
+pipe = Cosmos3OmniPipeline.from_pretrained(
+    "nvidia/Cosmos3-Edge",
+    torch_dtype=torch.bfloat16,
+    enable_safety_checker=True,
+)
+pipe.to("cuda")
+pipe.scheduler = UniPCMultistepScheduler.from_config(
+    pipe.scheduler.config, flow_shift=10.0, use_karras_sigmas=False
+)
+
+for name, video_path in input_videos.items():
+    result = pipe(
+        prompt="You are an autonomous vehicle planning system.",
+        action=CosmosActionCondition(
+            mode="inverse_dynamics",
+            chunk_size=60,
+            domain_name="av",
+            resolution_tier=480,
+            video=load_video(str(video_path)),
+            view_point="ego_view",
+        ),
+        fps=10,
+        num_inference_steps=30,
+        guidance_scale=1.0,
+        generator=torch.Generator(device="cuda").manual_seed(0),
+        use_system_prompt=False,
+    )
+    if result.action is None:
+        raise RuntimeError(f"{name} did not return an action tensor")
+
+    action = result.action[0].cpu()
+    output_path = output_dir / f"edge_action_id_{name}_diffusers.json"
+    output_path.write_text(
+        json.dumps(
+            {
+                "data": action.tolist(),
+                "shape": list(action.shape),
+                "dtype": str(action.dtype),
+                "raw_action_dim": 9,
+                "action_mode": "inverse_dynamics",
+                "domain_id": 1,
+            },
+            indent=2,
+        )
+        + "\n"
+    )
+    print(f"Saved predicted action to {output_path}; shape={tuple(action.shape)}")
+```
+
+Example outputs:
+
+- [av_inverse_0 predicted action JSON](https://huggingface.co/nvidia/Cosmos3-Edge/blob/main/assets/diffusers_outputs/edge_action_id_av_inverse_0_diffusers.json)
+- [av_inverse_1 predicted action JSON](https://huggingface.co/nvidia/Cosmos3-Edge/blob/main/assets/diffusers_outputs/edge_action_id_av_inverse_1_diffusers.json)
+
 ### vLLM
 
 #### Container

assets/diffusers_outputs/edge_action_id_av_inverse_0_diffusers.json

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