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>
48 lines
2.1 KiB
Python
48 lines
2.1 KiB
Python
from fastvideo import VideoGenerator
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def main():
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# Point this to your local diffusers model dir (or replace with a HF model ID).
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model_path = "KyleShao/Cosmos-Predict2.5-2B-Diffusers"
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generator = VideoGenerator.from_pretrained(
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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, # set True if GPU is out of memory
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"offload": {
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"dit": False,
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"vae": False,
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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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prompt = (
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"A high-definition video captures the precision of robotic welding in an industrial setting. "
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"The first frame showcases a robotic arm, equipped with a welding torch, positioned over a large metal structure. "
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"The welding process is in full swing, with bright sparks and intense light illuminating the scene, "
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"creating a vivid display of blue and white hues. "
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"A significant amount of smoke billows around the welding area, partially obscuring the view but emphasizing the heat and activity. "
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"The background reveals parts of the workshop environment, including a ventilation system and various pieces of machinery, "
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"indicating a busy and functional industrial workspace. "
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"As the video progresses, the robotic arm maintains its steady position, continuing the welding process and moving to its left. "
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"The welding torch consistently emits sparks and light, and the smoke continues to rise, diffusing slightly as it moves upward. "
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"The metal surface beneath the torch shows ongoing signs of heating and melting. "
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"The scene retains its industrial ambiance, with the welding sparks and smoke dominating the visual field, "
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"underscoring the ongoing nature of the welding operation.")
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generator.generate({
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"prompt": prompt,
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"output": {"output_path": "outputs_video/cosmos2_5_t2w.mp4", "save_video": True},
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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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