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Running on Zero
Running on Zero
| """English ZeroGPU interface with separate CPU/GPU/CPU stages.""" | |
| import spaces # Must precede any imports that can initialize CUDA. | |
| import logging | |
| import os | |
| import gradio as gr | |
| from cloud_backend import CloudEngine | |
| from cloud_catalog import CloudCatalog | |
| from studio_ui import build_studio, launch_style | |
| logger = logging.getLogger(__name__) | |
| ENGINE = CloudEngine(catalog=CloudCatalog()) | |
| ENGINE.eagerly_load(print) | |
| def prepare_motion(source, style_name, reference_mode, reference_id, upload, caption, frames, | |
| seed, steps, text_weight, style_weight, style_guidance, content, progress=gr.Progress()): | |
| def report(message): | |
| progress(0, desc=message) | |
| try: | |
| if source == "Dataset reference": | |
| if reference_mode == "Specific ID" and not str(reference_id or "").strip(): | |
| raise ValueError("Choose or enter a reference ID for the selected style.") | |
| if reference_mode not in {"Random reference", "Specific ID"}: | |
| raise ValueError("Choose a reference selection mode.") | |
| selected = ENGINE.catalog.select(style_name, | |
| reference_id if reference_mode == "Specific ID" else "", seed=int(seed)) | |
| style_path, reference_info = selected.path, selected.metadata() | |
| elif source == "Upload .npy": | |
| style_path, reference_info = upload, None | |
| else: | |
| raise ValueError("Choose a style reference source.") | |
| prepared = ENGINE.prepare( | |
| style_path, caption, frames=frames, seed=seed, steps=steps, | |
| text_weight=text_weight, style_weight=style_weight, | |
| style_guidance=style_guidance, content_path=content, report=report, | |
| reference_info=reference_info, | |
| ) | |
| except ValueError as error: | |
| raise gr.Error(str(error)) from error | |
| except Exception as error: | |
| logger.exception("Motion preparation failed") | |
| raise gr.Error(f"Reference preparation failed ({type(error).__name__}): {str(error)[:500]}") from error | |
| return prepared, "Reference validated. Waiting for ZeroGPU...", gr.Button(interactive=False) | |
| def generate_motion(prepared, progress=gr.Progress(track_tqdm=True)): | |
| """The registered callback performs actual CUDA sampling, never rendering.""" | |
| def report(message): | |
| progress(0, desc=message) | |
| try: | |
| generated = ENGINE.infer(prepared, report=report) | |
| except ValueError as error: | |
| raise gr.Error(str(error)) from error | |
| except Exception as error: | |
| logger.exception("ZeroGPU motion inference failed") | |
| raise gr.Error(f"GPU inference failed ({type(error).__name__}): {str(error)[:500]}") from error | |
| return generated, "Inference finished. Rendering MP4 and BVH..." | |
| def render_motion(generated, progress=gr.Progress()): | |
| def report(message): | |
| progress(0, desc=message) | |
| try: | |
| result = ENGINE.finish(generated, report=report) | |
| except ValueError as error: | |
| raise gr.Error(str(error)) from error | |
| except Exception as error: | |
| logger.exception("CPU motion export failed") | |
| raise gr.Error(f"Motion export failed ({type(error).__name__}): {str(error)[:500]}") from error | |
| info = result["info"] | |
| selected = info["style_reference"] | |
| reference_label = (f"{selected['style']} / {selected['id']}" if "id" in selected | |
| else selected.get("filename", "uploaded reference")) | |
| message = (f"**Reference: {reference_label}** \n" | |
| f"Motion: {info['frames'] / info['fps']:.1f} s 路 " | |
| f"Inference: {info['inference_seconds']:.2f} s 路 " | |
| f"Processing: {info['processing_seconds']:.2f} s 路 Seed: {info['seed']} \n" | |
| "Generated with Hugging Face ZeroGPU.") | |
| return (result["video"], result["reference"], result["content_video"], | |
| [result["video"], result["bvh"], result["features"], result["motion"], result["metadata"]], message) | |
| def build_demo(): | |
| return build_studio(ENGINE.catalog, prepare_motion, generate_motion, render_motion) | |
| if __name__ == "__main__": | |
| logging.basicConfig(level=logging.INFO) | |
| build_demo().launch(server_name=os.environ.get("GRADIO_SERVER_NAME", "127.0.0.1"), | |
| server_port=int(os.environ.get("PORT", "7860")), | |
| share=False, max_file_size="20mb", show_error=False, **launch_style()) | |