Add Cosmos3 video2video generation support

docs/source/en/api/pipelines/cosmos3.md

@@ -77,6 +77,8 @@ python -m cosmos_framework.inference.prompt_upsampling \
 
 Switch `--mode` to match the workflow you are targeting (`text2image`, `text2video`, `image2video`). The command writes the upsampled prompt(s) to the `--output` file as a JSON array (one object per non-empty line in `--input`); pass a `.jsonl` path instead to get one JSON object per line. For `image2video`, you must also supply the conditioning image via `--image-url` (a URL or local path) or `--image-list` (one image per prompt).
 
+<!-- TODO: Add prompt upsampling support for video inputs (video-to-video) to the upsampler CLI. -->
+
 A pre-upsampled positive prompt (`assets/example_t2v_prompt.json`) and negative prompt (`assets/negative_prompt.json`) are provided for convenience, and are used by the generation examples below. The examples load these JSON files and pass them to the pipeline as JSON strings via `json.dumps(...)`.
 
 ## Text-to-video
@@ -276,6 +278,200 @@ export_to_video(result.video, "cosmos3_i2v.mp4", fps=24, macro_block_size=1)
 </hfoption>
 </hfoptions>
 
+## Video-to-video
+
+Pass a conditioning clip via `video=` (e.g. from `load_video`). The pipeline anchors the leading latent frames given by `condition_frame_indexes_vision` (default `[0, 1]`) to the clip and denoises the rest. Use `condition_video_keep` (`"first"` or `"last"`) to choose which end of a longer source clip the conditioning frames are taken from. As with the other modes, the prompt should follow the descriptive JSON structure described in [Prompt upsampling](#prompt-upsampling).
+
+<!-- TODO: Add prompt upsampling support for video inputs (video-to-video) to the upsampler CLI. -->
+
+<hfoptions id="model">
+<hfoption id="Nano">
+
+```python
+import json
+import torch
+from diffusers import Cosmos3OmniPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from diffusers.utils import export_to_video, load_video
+
+# JSON-upsampled positive and negative prompts (see "Prompt upsampling" above).
+json_prompt = json.load(open("assets/example_v2v_prompt.json"))
+negative_prompt = json.load(open("assets/negative_prompt_i2v.json"))
+
+pipe = Cosmos3OmniPipeline.from_pretrained(
+    "nvidia/Cosmos3-Nano", torch_dtype=torch.bfloat16, device_map="cuda"
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(
+    pipe.scheduler.config, flow_shift=10.0, use_karras_sigmas=False
+)
+
+video = load_video(
+    "https://github.com/nvidia-cosmos/cosmos-dependencies/raw/refs/heads/assets/cosmos3/inputs/vision/robot_pouring.mp4"
+)
+
+result = pipe(
+    prompt=json.dumps(json_prompt),
+    negative_prompt=json.dumps(negative_prompt),
+    video=video,
+    condition_frame_indexes_vision=[0, 1],
+    condition_video_keep="first",
+    num_frames=189,
+    height=720,
+    width=1280,
+    num_inference_steps=35,
+    guidance_scale=6.0,
+    fps=24.0,
+)
+# macro_block_size=1 allows arbitrary frame sizes (Cosmos3 outputs are not always divisible by 16).
+export_to_video(result.video, "cosmos3_v2v.mp4", fps=24, macro_block_size=1)
+```
+
+</hfoption>
+<hfoption id="Super">
+
+```python
+import json
+import torch
+from diffusers import Cosmos3OmniPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from diffusers.utils import export_to_video, load_video
+
+# JSON-upsampled positive and negative prompts (see "Prompt upsampling" above).
+json_prompt = json.load(open("assets/example_v2v_prompt.json"))
+negative_prompt = json.load(open("assets/negative_prompt_i2v.json"))
+
+pipe = Cosmos3OmniPipeline.from_pretrained(
+    "nvidia/Cosmos3-Super", torch_dtype=torch.bfloat16, device_map="cuda"
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(
+    pipe.scheduler.config, flow_shift=10.0, use_karras_sigmas=False
+)
+
+video = load_video(
+    "https://github.com/nvidia-cosmos/cosmos-dependencies/raw/refs/heads/assets/cosmos3/inputs/vision/robot_pouring.mp4"
+)
+
+result = pipe(
+    prompt=json.dumps(json_prompt),
+    negative_prompt=json.dumps(negative_prompt),
+    video=video,
+    condition_frame_indexes_vision=[0, 1],
+    condition_video_keep="first",
+    num_frames=189,
+    height=720,
+    width=1280,
+    num_inference_steps=35,
+    guidance_scale=6.0,
+    fps=24.0,
+)
+# macro_block_size=1 allows arbitrary frame sizes (Cosmos3 outputs are not always divisible by 16).
+export_to_video(result.video, "cosmos3_v2v.mp4", fps=24, macro_block_size=1)
+```
+
+</hfoption>
+</hfoptions>
+
+## Video-to-video with sound
+
+When the checkpoint carries a `sound_tokenizer`, add `enable_sound=True` to the video-to-video call to jointly generate a synchronized audio track. The waveform is returned alongside the video and can be muxed into the MP4 with [`~utils.encode_video`].
+
+<hfoptions id="model">
+<hfoption id="Nano">
+
+```python
+import json
+import torch
+from diffusers import Cosmos3OmniPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from diffusers.utils import encode_video, load_video
+
+# JSON-upsampled positive and negative prompts (see "Prompt upsampling" above).
+json_prompt = json.load(open("assets/example_v2v_prompt.json"))
+negative_prompt = json.load(open("assets/negative_prompt_i2v.json"))
+
+pipe = Cosmos3OmniPipeline.from_pretrained(
+    "nvidia/Cosmos3-Nano", torch_dtype=torch.bfloat16, device_map="cuda"
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(
+    pipe.scheduler.config, flow_shift=10.0, use_karras_sigmas=False
+)
+
+video = load_video(
+    "https://github.com/nvidia-cosmos/cosmos-dependencies/raw/refs/heads/assets/cosmos3/inputs/vision/robot_pouring.mp4"
+)
+
+result = pipe(
+    prompt=json.dumps(json_prompt),
+    negative_prompt=json.dumps(negative_prompt),
+    video=video,
+    condition_frame_indexes_vision=[0, 1],
+    condition_video_keep="first",
+    num_frames=189,
+    height=720,
+    width=1280,
+    fps=24.0,
+    enable_sound=True,
+)
+
+encode_video(
+    result.video,
+    fps=24,
+    audio=result.sound,
+    audio_sample_rate=pipe.sound_tokenizer.config.sampling_rate,
+    output_path="cosmos3_v2v_with_sound.mp4",
+)
+```
+
+</hfoption>
+<hfoption id="Super">
+
+```python
+import json
+import torch
+from diffusers import Cosmos3OmniPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from diffusers.utils import encode_video, load_video
+
+# JSON-upsampled positive and negative prompts (see "Prompt upsampling" above).
+json_prompt = json.load(open("assets/example_v2v_prompt.json"))
+negative_prompt = json.load(open("assets/negative_prompt_i2v.json"))
+
+pipe = Cosmos3OmniPipeline.from_pretrained(
+    "nvidia/Cosmos3-Super", torch_dtype=torch.bfloat16, device_map="cuda"
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(
+    pipe.scheduler.config, flow_shift=10.0, use_karras_sigmas=False
+)
+
+video = load_video(
+    "https://github.com/nvidia-cosmos/cosmos-dependencies/raw/refs/heads/assets/cosmos3/inputs/vision/robot_pouring.mp4"
+)
+
+result = pipe(
+    prompt=json.dumps(json_prompt),
+    negative_prompt=json.dumps(negative_prompt),
+    video=video,
+    condition_frame_indexes_vision=[0, 1],
+    condition_video_keep="first",
+    num_frames=189,
+    height=720,
+    width=1280,
+    fps=24.0,
+    enable_sound=True,
+)
+
+encode_video(
+    result.video,
+    fps=24,
+    audio=result.sound,
+    audio_sample_rate=pipe.sound_tokenizer.config.sampling_rate,
+    output_path="cosmos3_v2v_with_sound.mp4",
+)
+```
+
+</hfoption>
+</hfoptions>
+
 ## Text-to-video with sound
 
 When the checkpoint carries a `sound_tokenizer`, pass `enable_sound=True` to jointly generate a synchronized audio track. The waveform is returned alongside the video and can be muxed into the MP4 with [`~utils.encode_video`].

examples/cosmos3/README.md

@@ -40,6 +40,16 @@ python examples/cosmos3/inference_cosmos3.py \
     --vision-path https://github.com/nvidia-cosmos/cosmos-dependencies/releases/download/assets/robot_153.jpg
 ```
 
+Video-to-video (condition on the leading frames of a clip and continue it):
+
+```bash
+python examples/cosmos3/inference_cosmos3.py \
+    --prompt "A robotic arm finishes pouring liquid into the glass." \
+    --video-path "https://github.com/nvidia-cosmos/cosmos-dependencies/raw/refs/heads/assets/cosmos3/inputs/vision/robot_pouring.mp4" \
+    --condition-frame-indexes-vision 0,1 \
+    --condition-video-keep first
+```
+
 Text-to-video-with-sound (sound-capable checkpoint only):
 
 ```bash

examples/cosmos3/inference_cosmos3.py

@@ -18,6 +18,9 @@
 Image-to-video:
     python inference_cosmos3.py --prompt "..." --vision-path /path/to/image.jpg
 
+Video-to-video:
+    python inference_cosmos3.py --prompt "..." --video-path /path/to/video.mp4
+
 Text-to-video-with-sound (requires a sound-capable checkpoint):
     python inference_cosmos3.py --prompt "..." --enable-sound
 """
@@ -70,6 +73,22 @@ def main():
         default=None,
         help="Optional URL or local path for an image-conditioning frame, or an action conditioning video.",
     )
+    parser.add_argument(
+        "--video-path",
+        default=None,
+        help="Optional URL or local path to a conditioning video for video-to-video generation.",
+    )
+    parser.add_argument(
+        "--condition-frame-indexes-vision",
+        default=None,
+        help="Comma-separated latent frame indexes kept clean for video-to-video (default: 0,1).",
+    )
+    parser.add_argument(
+        "--condition-video-keep",
+        choices=["first", "last"],
+        default="first",
+        help="Take the video-to-video conditioning frames from the first or last of the source clip (default: first).",
+    )
     parser.add_argument("--output", default=".", help="Directory to save generated video/image/audio files.")
     parser.add_argument(
         "--height",
@@ -206,6 +225,30 @@ def main():
             add_duration_template=args.add_duration_template,
             enable_safety_check=not args.no_safety_check,
         )
+    elif args.video_path is not None:
+        video = load_video(args.video_path)
+        condition_frame_indexes_vision = (
+            [int(i) for i in args.condition_frame_indexes_vision.split(",") if i.strip()]
+            if args.condition_frame_indexes_vision is not None
+            else [0, 1]
+        )
+        result = pipeline(
+            prompt=args.prompt,
+            video=video,
+            condition_frame_indexes_vision=condition_frame_indexes_vision,
+            condition_video_keep=args.condition_video_keep,
+            num_frames=args.num_frames,
+            height=args.height,
+            width=args.width,
+            fps=args.fps,
+            num_inference_steps=args.num_inference_steps,
+            enable_sound=args.enable_sound,
+            guidance_scale=args.guidance_scale,
+            generator=generator,
+            add_resolution_template=args.add_resolution_template,
+            add_duration_template=args.add_duration_template,
+            enable_safety_check=not args.no_safety_check,
+        )
     else:
         image = load_image(args.vision_path) if args.vision_path is not None else None
         result = pipeline(