Usage: switch Generation examples to vLLM-Omni (Nano/Super convention)

README.md

@@ -460,167 +460,280 @@ python -m cosmos_framework.inference.prompt_upsampling \
 
 For image-to-video, provide either one shared image via `--image-url` or one image per prompt via `--image-list` (the image-list file must have the same number of non-empty lines as the prompt file). Accepted image formats: local paths, HTTP(S) URLs, and `data:` URLs.
 
-### Setup
+### vLLM-Omni
 
-Install cosmos-framework:
+#### Container
 
-```shell
-sudo apt-get install -y --no-install-recommends curl ffmpeg git-lfs libx11-dev tree wget
+```
+docker pull vllm/vllm-omni:cosmos3
+```
 
-git clone https://github.com/NVIDIA/cosmos-framework.git
-cd cosmos-framework
+#### General Invocation
 
-# Pick the dependency group that matches your CUDA toolkit (see docs/setup.md)
-uv sync --all-extras --group=cu130-train   # or --group=cu128-train for CUDA 12.8
-source .venv/bin/activate && export LD_LIBRARY_PATH=
+You can use the release-tested `vllm-omni` package for deploying an OpenAI-compatible API inference endpoint.
+The recommended vLLM-Omni serving configuration for nvidia/Cosmos3-Edge on a single GPU is:
+
+```bash
+vllm serve nvidia/Cosmos3-Edge \
+  --omni \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --init-timeout 1800
 ```
 
-Cosmos3-Edge is a compact omni model that fits on a single GPU and is the recommended starting point for single-GPU inference. The examples below run offline batch inference via `cosmos_framework.scripts.inference`. Each example is self-contained and points at the same conditioning inputs used in this model card (hosted under the repo's `assets/`), so the shown outputs correspond to the shown inputs. Each sample's outputs are written under `-o <output_dir>/<sample_name>/` (e.g. `vision.mp4`, `sample_outputs.json`, `reasoner_text.txt`).
+To speed up inference with additional GPUs, enable context parallelism with `--ulysses-degree` or switch to tensor parallelism with `--tensor-parallel-size`. Setting `--enable-layerwise-offload` can help reduce memory usage on GPUs with less available memory.
 
-### Generation (image / video / action)
+#### Examples
 
-#### Image to video
+##### Download example prompts
 
-```shell
-BASE="https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets"
-python - "$BASE" <<'PY'
-import json, sys, urllib.request
-base = sys.argv[1]
-prompt = urllib.request.urlopen(f"{base}/example_i2v_prompt.json").read().decode()
-json.dump(
-    {"model_mode": "image2video", "prompt": prompt, "vision_path": f"{base}/example_i2v_input.jpg"},
-    open("i2v_edge.json", "w"), indent=2,
-)
-PY
+The example inputs (`assets/`) live in this model repo. Download just this folder with the Hugging Face CLI:
 
-python -m cosmos_framework.scripts.inference \
-    --parallelism-preset=latency \
-    -i i2v_edge.json \
-    -o outputs/omni_edge \
-    --checkpoint-path Cosmos3-Edge \
-    --resolution 480 \
-    --num-frames 121 \
-    --fps 24 \
-    --num-steps 50 \
-    --guidance 5.0 \
-    --shift 3.0 \
-    --sampler unipc \
-    --seed 0
+```bash
+pip install -U "huggingface_hub[cli]"
+hf download nvidia/Cosmos3-Edge assets/ --local-dir Cosmos3-Edge
+cd Cosmos3-Edge
 ```
 
-The generated video is written to `outputs/omni_edge/<sample_name>/vision.mp4`.
+Run all commands below from the downloaded repo root.
+
+---
+
+##### Image to video generation
+
+```python
+import json
+import mimetypes
+from pathlib import Path
+
+import requests
+
+# 1. Read JSON-upsampled prompt and negative prompt
+json_prompt = json.load(open("assets/example_i2v_prompt.json"))
+negative_prompt = json.load(open("assets/negative_prompt.json"))
+
+# 2. Build and send the multipart API request
+url = "http://localhost:8000/v1/videos/sync"
+image_path = Path("assets/example_i2v_input.jpg")
+mime_type = mimetypes.guess_type(image_path)[0] or "image/png"
+data = {
+    "prompt": json.dumps(json_prompt),
+    "negative_prompt": json.dumps(negative_prompt),
+    "size": "832x480",
+    "num_frames": "121",
+    "fps": "24",
+    "num_inference_steps": "50",
+    "guidance_scale": "5.0",
+    "max_sequence_length": "4096",
+    "flow_shift": "3.0",
+    "extra_params": json.dumps(
+        {
+          "use_resolution_template": False,
+          "use_duration_template": False,
+          "guardrails": True,
+        }
+    ),
+    "seed": "0",
+}
+
+with image_path.open("rb") as image_file:
+    files = {
+        "input_reference": (image_path.name, image_file, mime_type),
+    }
+    print("Sending request to server...")
+    response = requests.post(
+        url,
+        data=data,
+        files=files,
+        headers={"Accept": "video/mp4"},
+    )
+    response.raise_for_status()
+
+# 3. Save the generated video
+output_path = Path("/tmp/cosmos3_edge_i2v.mp4")
+output_path.write_bytes(response.content)
+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/edge_i2v_output.mp4"></video>
 
-#### Action — forward dynamics
+---
 
-Forward dynamics rolls out one action chunk at a time. This self-contained script fetches the UMI conditioning frame and action chunks, runs `cosmos_framework.scripts.inference` per chunk, feeds each chunk's final generated frame in as the next chunk's conditioning image, and stitches the 2-chunk rollout. Install `imageio[ffmpeg]` first.
+##### Action generation
+
+The forward-dynamics example uses UMI robotics action trajectories, and the inverse-dynamics examples use autonomous-vehicle (AV) action trajectories. Source files:
+
+- Forward dynamics first frame: `assets/example_action_fd_umi_first_frame.png`
+- Forward dynamics action chunks: `assets/example_action_fd_umi_action_chunks.json`
+- Forward dynamics output video: `assets/edge_action_fd_umi_2chunk_output.mp4`
+- Inverse dynamics source videos: `assets/example_action_id_av_0_input.mp4`, `assets/example_action_id_av_1_input.mp4`
+- Inverse dynamics predicted actions: `assets/edge_action_id_av_0_output.json`, `assets/edge_action_id_av_1_output.json`
+
+###### Action forward dynamics
+
+The example below performs a 2-chunk UMI robotics rollout with the vLLM-Omni `/v1/videos/sync` inference endpoint. Each request sends one conditioning frame through `input_reference` and one 16-step normalized action chunk through `extra_params["action"]`. The request also sets the top-level `size` field to the input image resolution, so vLLM-Omni returns each chunk at the same resolution as the conditioning image without reflection padding. The stitched output drops each chunk's conditioning frame, producing 32 generated frames. The script extracts the last generated frame from each chunk and uses it as the next chunk's conditioning frame.
 
 ```python
-import json, subprocess, urllib.request
+import json
+import mimetypes
 from pathlib import Path
 
 import imageio.v3 as iio
 import numpy as np
-
-BASE = "https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets"
-work = Path("umi_fd"); work.mkdir(exist_ok=True)
-
-# 1. Fetch the UMI conditioning frame + action chunks from the model repo
-first_frame = work / "first_frame.png"
-urllib.request.urlretrieve(f"{BASE}/example_action_fd_umi_first_frame.png", first_frame)
-spec = json.loads(urllib.request.urlopen(f"{BASE}/example_action_fd_umi_action_chunks.json").read())
-chunks = spec["action_chunks"]            # shape (2, 16, 10)
-
-current_frame, stitched = first_frame, []
-for i, chunk in enumerate(chunks):
-    # 2a. Write this chunk as a flat (16, 10) action_path file
-    action_path = work / f"action_{i}.json"
-    action_path.write_text(json.dumps(chunk))
-    # 2b. Build the forward_dynamics input for this chunk
-    #     (fields match inputs/omni/action_forward_dynamics_*.json)
-    sample = {
-        "name": f"umi_fd_{i}",
-        "model_mode": "forward_dynamics",
-        "domain_name": spec["domain_name"],              # "umi"
-        "view_point": spec["view_point"],                # "ego_view"
-        "fps": spec["fps"],                              # 20
-        "image_size": spec["image_size"],                # 256
-        "action_chunk_size": spec["action_chunk_size"],  # 16
-        "prompt": spec["prompt"],                        # "mouse arrangement"
-        "vision_path": str(current_frame),
-        "action_path": str(action_path),
-        "seed": i,
+import requests
+from PIL import Image
+
+url = "http://localhost:8000/v1/videos/sync"
+first_frame_path = Path("assets/example_action_fd_umi_first_frame.png")
+action_spec = json.loads(Path("assets/example_action_fd_umi_action_chunks.json").read_text())
+action_chunks = action_spec["action_chunks"]
+
+prompt = action_spec.get("prompt", "mouse arrangement")
+fps = int(action_spec.get("fps", 20))
+action_chunk_size = int(action_spec.get("action_chunk_size", 16))
+current_frame_path = first_frame_path
+input_width, input_height = Image.open(first_frame_path).size
+chunk_video_paths = []
+stitch_frames = []
+
+for chunk_idx, action_chunk in enumerate(action_chunks):
+    mime_type = mimetypes.guess_type(current_frame_path)[0] or "image/png"
+    extra_params = {
+        "action_mode": "forward_dynamics",
+        "domain_name": action_spec.get("domain_name", "umi"),
+        "action_chunk_size": action_chunk_size,
+        "image_size": action_spec.get("image_size", 256),
+        "view_point": action_spec.get("view_point", "ego_view"),
+        "action": action_chunk,
+        "guardrails": True,
+    }
+    data = {
+        "prompt": prompt,
+        "num_frames": str(action_chunk_size + 1),  # conditioning frame + generated frames
+        "fps": str(fps),
+        "size": f"{input_width}x{input_height}",  # return chunks at input resolution
+        "num_inference_steps": "30",
+        "guidance_scale": "1.0",
+        "flow_shift": "10.0",
+        "seed": str(chunk_idx),
+        "extra_params": json.dumps(extra_params),
     }
-    sample_path = work / f"sample_{i}.json"
-    sample_path.write_text(json.dumps(sample))
-    # 2c. Run cosmos-framework inference for this chunk
-    subprocess.run([
-        "python", "-m", "cosmos_framework.scripts.inference",
-        "--parallelism-preset=latency",
-        "-i", str(sample_path),
-        "-o", str(work / "out"),
-        "--checkpoint-path", "Cosmos3-Edge",
-    ], check=True)
-    # 2d. Read the rollout; drop the conditioning frame (index 0), keep the generated frames
-    frames = iio.imread(work / "out" / f"umi_fd_{i}" / "vision.mp4")
-    stitched.extend(frames[1:])
-    # 2e. Autoregressive conditioning: last generated frame -> next chunk's input image
-    next_frame = work / f"ar_frame_{i + 1}.png"
-    iio.imwrite(next_frame, frames[-1])
-    current_frame = next_frame
-
-# 3. Stitch the generated-only rollout
-out_mp4 = work / "umi_fd_2chunk.mp4"
-iio.imwrite(out_mp4, np.asarray(stitched), fps=spec["fps"])
-print("Saved stitched rollout:", out_mp4)
+
+    with current_frame_path.open("rb") as image_file:
+        files = {"input_reference": (current_frame_path.name, image_file, mime_type)}
+        print(f"Sending action FD chunk {chunk_idx} to vLLM-Omni...")
+        response = requests.post(
+            url,
+            data=data,
+            files=files,
+            headers={"Accept": "video/mp4"},
+            timeout=600,
+        )
+        response.raise_for_status()
+
+    chunk_video_path = Path(f"/tmp/cosmos3_edge_action_fd_chunk_{chunk_idx:02d}.mp4")
+    chunk_video_path.write_bytes(response.content)
+    chunk_video_paths.append(chunk_video_path)
+
+    # The returned chunk contains the conditioning frame followed by generated frames.
+    # Drop the conditioning frame when stitching the generated-only rollout.
+    frames = iio.imread(chunk_video_path)
+    stitch_frames.extend(frames[1:])
+
+    # Autoregressive conditioning: use the final generated frame from this chunk
+    # as the input image for the next vLLM-Omni request.
+    if chunk_idx + 1 < len(action_chunks):
+        current_frame_path = Path(f"/tmp/cosmos3_edge_action_fd_ar_frame_{chunk_idx + 1:02d}.png")
+        iio.imwrite(current_frame_path, frames[-1])
+
+stitched_path = Path("/tmp/cosmos3_edge_action_fd_umi_2chunk.mp4")
+iio.imwrite(stitched_path, np.asarray(stitch_frames), fps=fps)
+print("Generated chunk videos:", chunk_video_paths)
+print("Saved stitched rollout:", stitched_path)
+print("stitched resolution:", f"{input_width}x{input_height}")
 ```
 
+
 Example output:
 
 <video width="512" controls src="https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets/edge_action_fd_umi_2chunk_output.mp4"></video>
 
-#### Action — inverse dynamics
+###### Action inverse dynamics
 
-Inverse dynamics predicts the action sequence from an observation video (no action input). Repeat with `example_action_id_av_1_input.mp4` (and `name: av_inverse_1`) for the second example.
+```python
+import json
+import time
+from pathlib import Path
 
-```shell
-cat > av_inverse_0.json <<'JSON'
-{
-    "name": "av_inverse_0",
-    "model_mode": "inverse_dynamics",
-    "domain_name": "av",
-    "view_point": "ego_view",
-    "image_size": 480,
-    "fps": 10,
-    "action_chunk_size": 60,
-    "prompt": "You are an autonomous vehicle planning system.",
-    "vision_path": "https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets/example_action_id_av_0_input.mp4"
-}
-JSON
+import requests
 
-python -m cosmos_framework.scripts.inference \
-    --parallelism-preset=latency \
-    -i av_inverse_0.json \
-    -o outputs/omni_edge \
-    --checkpoint-path Cosmos3-Edge \
-    --seed 0
-```
+base_url = "http://localhost:8000"
+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"),
+}
 
-The predicted action is written to `outputs/omni_edge/<sample_name>/sample_outputs.json`.
+for name, video_path in input_videos.items():
+    extra_params = {
+        "action_mode": "inverse_dynamics",
+        "domain_name": "av",
+        "action_chunk_size": 60,
+        "image_size": 480,
+        "view_point": "ego_view",
+        "raw_action_dim": 9,
+        "guardrails": True,
+    }
+    data = {
+        "prompt": "You are an autonomous vehicle planning system.",
+        "num_frames": "61",
+        "fps": "10",
+        "num_inference_steps": "30",
+        "guidance_scale": "1.0",
+        "flow_shift": "10.0",
+        "seed": "0",
+        "extra_params": json.dumps(extra_params),
+    }
 
-Example outputs (predicted action trajectory, 60 steps × 9 raw action dims = translation + rot6d):
+    with video_path.open("rb") as video_file:
+        files = {
+            "input_reference": (video_path.name, video_file, "video/mp4"),
+        }
+        print(f"Submitting {name} request to server...")
+        response = requests.post(f"{base_url}/v1/videos", data=data, files=files)
+        response.raise_for_status()
+    initial = response.json()
+
+    while True:
+        response = requests.get(f"{base_url}/v1/videos/{initial['id']}", timeout=30)
+        response.raise_for_status()
+        final = response.json()
+        print(initial["id"], final.get("status"), f"{final.get('progress', 0)}%")
+        if final.get("status") == "completed":
+            break
+        if final.get("status") in {"failed", "cancelled"}:
+            raise RuntimeError(json.dumps(final, indent=2))
+        time.sleep(2)
+
+    action = final.get("action")
+    if not action or "data" not in action:
+        raise RuntimeError(f"Response did not include action data: {json.dumps(final, indent=2)}")
+
+    output_path = Path(f"/tmp/cosmos3_edge_action_id_{name}.json")
+    output_path.write_text(json.dumps(action, indent=2))
+    print(f"Saved predicted action to {output_path}")
+    print("action shape:", action.get("shape"), "dtype:", action.get("dtype"))
+```
 
-- [av_inverse_0 predicted action JSON](assets/edge_action_id_av_0_output.json)
-- [av_inverse_1 predicted action JSON](assets/edge_action_id_av_1_output.json)
+Example outputs:
 
-The predicted per-frame relative poses are integrated into an absolute camera trajectory (`pose_rel_to_abs`, `backward_framewise`) and visualized below — 3D path with camera frustums (left) and top-down bird's-eye (right), colored by frame index:
+- [av_inverse_0 predicted action JSON](https://huggingface.co/nvidia/Cosmos3-Edge/blob/main/assets/edge_action_id_av_0_output.json)
+- [av_inverse_1 predicted action JSON](https://huggingface.co/nvidia/Cosmos3-Edge/blob/main/assets/edge_action_id_av_1_output.json)
 
-<img width="1280" src="assets/edge_action_id_av_0_output.png">
+<img width="1280" src="https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets/edge_action_id_av_0_output.png">
 
-<img width="1280" src="assets/edge_action_id_av_1_output.png">
+<img width="1280" src="https://huggingface.co/nvidia/Cosmos3-Edge/resolve/main/assets/edge_action_id_av_1_output.png">
 
 ### Reasoning
 

assets/edge_action_id_av_0_output.json

@@ -1,671 +1,672 @@
 {
-  "action_mode": "inverse_dynamics",
-  "domain": "av",
+  "data": [
+    [
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+      0.0
+    ],
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+      0.9921875,
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+      1.015625,
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+      0.001953125,
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 }

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+  "domain_id": 1
 }

assets/edge_i2v_output.mp4

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