Davids048andClaude Fable 5.1
a5aa64ab6b
VideoGenerator.from_pretrained(model_path, config) takes the same nested
settings as from_config; the 24 flat keywords, from_pretrained_kwargs_to_config,
FROM_PRETRAINED_KWARGS, and every flat_field in the schema are deleted.
nvfp4_fa4 is the typed field engine.attention.nvfp4_fa4, applied by a
resolution step. 65 call sites and the docs use the nested form; the three
kwargs golden cases are config cases with byte-identical results.
Every value is decided during resolution; no runtime code calls
with_override. The device offload policy (unified memory, MPS, layerwise
conflicts, lazy module load) runs as resolution steps in the main process
(fastvideo/api/device_policy.py), and the worker runs on the config it
receives. The LTX-2 refine defaults from model_index.json and the MiniMax-H3
schedule from fastvideo_inference.json are resolution steps
(fastvideo/api/checkpoint_defaults.py) that read local checkpoints or download
only those files; the H3 pipeline validates the schedule against the loaded
schedulers but no longer writes it. The preprocessing entry point passes the
downloaded local path as run state instead of a config override. Teacher and
critic models load with override_transformer_cls_name as a load argument.
The test isolation turns the device policy off, as it blocks downloads, so
that the goldens do not depend on the machine.
Against the 3f6893a0 goldens, every field still matches except boundary_ratio
and ltx2_vae_tiling (DESIGN section 9) and the fields that no longer exist.
The launcher and trainer YAML verifications report 0 unexplained differences.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
103 lines
4.2 KiB
Python
103 lines
4.2 KiB
Python
"""
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GEN3C: 3D-aware camera-controlled video generation.
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This example generates a video from a single input image with camera control.
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The pipeline uses MoGe depth estimation, 3D point cloud forward warping,
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and the GEN3C diffusion model.
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Requirements:
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1. Install MoGe:
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uv pip install git+https://github.com/microsoft/MoGe.git
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If you hit `ImportError: libGL.so.1`, install:
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sudo apt-get update && sudo apt-get install -y libgl1 libglib2.0-0 libsm6 libxext6 libxrender1
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2. Download and convert weights:
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huggingface-cli download nvidia/GEN3C-Cosmos-7B --local-dir official_weights/GEN3C-Cosmos-7B
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python scripts/checkpoint_conversion/convert_gen3c_to_fastvideo.py \
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--source ./official_weights/GEN3C-Cosmos-7B/model.pt \
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--output ./converted_weights/GEN3C-Cosmos-7B \
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--components-source nvidia/Cosmos-Predict2-2B-Video2World
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3. Provide an input image for 3D-conditioned generation.
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"""
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import argparse
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from fastvideo import VideoGenerator
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def main():
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parser = argparse.ArgumentParser(description="GEN3C video generation")
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parser.add_argument("--model_path", type=str, default="converted_weights/GEN3C-Cosmos-7B")
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parser.add_argument("--image_path", type=str, default=None, help="Input image for 3D cache conditioning")
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parser.add_argument("--prompt", type=str, default="A slow camera pan over a sunlit landscape.")
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parser.add_argument(
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"--negative_prompt",
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type=str,
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default=("The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
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"over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
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"underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, "
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"jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special "
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"effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and "
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"flickering. Overall, the video is of poor quality."),
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)
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parser.add_argument(
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"--trajectory",
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type=str,
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default="left",
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choices=["left", "right", "up", "down", "zoom_in", "zoom_out", "clockwise", "counterclockwise", "none"])
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parser.add_argument("--movement_distance", type=float, default=0.3)
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parser.add_argument("--camera_rotation",
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type=str,
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default="center_facing",
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choices=["center_facing", "no_rotation", "trajectory_aligned"])
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parser.add_argument("--height", type=int, default=704)
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parser.add_argument("--width", type=int, default=1280)
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parser.add_argument("--num_frames", type=int, default=121)
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parser.add_argument("--num_inference_steps", type=int, default=35)
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parser.add_argument("--guidance_scale", type=float, default=1.0)
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parser.add_argument("--output_path", type=str, default="outputs_video/gen3c.mp4")
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parser.add_argument("--seed", type=int, default=42)
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args = parser.parse_args()
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generator = VideoGenerator.from_pretrained(
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args.model_path,
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{
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"engine": {
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"num_gpus": 1,
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"use_fsdp_inference": False,
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"offload": {
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"dit": False,
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"vae": True,
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"text_encoder": True,
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"pin_cpu_memory": True,
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},
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},
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},
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)
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video = generator.generate({
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"prompt": args.prompt,
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"negative_prompt": args.negative_prompt,
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"inputs": {"image_path": args.image_path},
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"sampling": {
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"height": args.height,
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"width": args.width,
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"num_frames": args.num_frames,
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"num_inference_steps": args.num_inference_steps,
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"guidance_scale": args.guidance_scale,
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"fps": 24,
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"seed": args.seed,
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},
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"output": {"output_path": args.output_path, "save_video": True},
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"extensions": {
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"trajectory_type": args.trajectory,
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"movement_distance": args.movement_distance,
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"camera_rotation": args.camera_rotation,
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},
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})
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generator.shutdown()
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if __name__ == "__main__":
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main()
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