SolitaryThinker
f0e727f5ee
Advertise FastVideo/DreamX-World-5B-Cam-Diffusers and FastVideo/DreamX-World-5B-Diffusers as the loadable ids in the registry (raw GD-ML path detection is kept via the pattern detectors), default the example and local tests to the hub ids so they no longer skip when local converted dirs are absent, and drop the now-redundant pipeline class override in the example (model_index.json carries DreamXWorldPipeline).
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
"""DreamX-World-5B-Cam camera-controlled video generation.
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Uses the pre-converted Diffusers checkpoint FastVideo/DreamX-World-5B-Cam-Diffusers.
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To convert the raw GD-ML/DreamX-World-5B-Cam checkpoint yourself, see
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scripts/checkpoint_conversion/dreamx_world_to_diffusers.py.
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"""
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import os
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from fastvideo import VideoGenerator
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OUTPUT_PATH = os.getenv("DREAMX_WORLD_OUTPUT_PATH", "video_samples_dreamx_world")
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def _env_int(name: str, default: int) -> int:
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return int(os.getenv(name, str(default)))
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def _env_float(name: str, default: float) -> float:
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return float(os.getenv(name, str(default)))
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def main():
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model_name = os.getenv("DREAMX_WORLD_MODEL_DIR", "FastVideo/DreamX-World-5B-Cam-Diffusers")
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generator = VideoGenerator.from_pretrained(
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model_name,
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num_gpus=1,
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use_fsdp_inference=False,
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dit_cpu_offload=False,
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vae_cpu_offload=True,
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text_encoder_cpu_offload=True,
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pin_cpu_memory=False,
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)
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prompt = os.getenv(
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"DREAMX_WORLD_PROMPT",
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"A cinematic first-person drive through a futuristic coastal city at "
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"sunrise, reflective glass towers, clean streets, soft volumetric light.",
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)
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image_path = os.getenv(
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"DREAMX_WORLD_IMAGE_PATH",
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"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
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)
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kwargs = {
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"output_path": OUTPUT_PATH,
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"save_video": os.getenv("DREAMX_WORLD_SAVE_VIDEO", "1") != "0",
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"height": _env_int("DREAMX_WORLD_HEIGHT", 480),
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"width": _env_int("DREAMX_WORLD_WIDTH", 832),
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"num_frames": _env_int("DREAMX_WORLD_NUM_FRAMES", 161),
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"num_inference_steps": _env_int("DREAMX_WORLD_STEPS", 30),
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"guidance_scale": _env_float("DREAMX_WORLD_GUIDANCE", 5.0),
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"action_list": os.getenv("DREAMX_WORLD_ACTIONS", "w,d,w").split(","),
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"action_speed_list": [
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float(value)
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for value in os.getenv("DREAMX_WORLD_ACTION_SPEEDS", "4.0,2.0,4.0").split(",")
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],
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}
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if image_path:
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kwargs["image_path"] = image_path
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try:
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generator.generate_video(prompt, **kwargs)
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finally:
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generator.shutdown()
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if __name__ == "__main__":
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main()
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