| from __future__ import annotations
|
|
|
| import os
|
| import random
|
| import tempfile
|
| from typing import Annotated
|
|
|
| import gradio as gr
|
| from huggingface_hub import InferenceClient
|
|
|
| from app import _log_call_end, _log_call_start, _truncate_for_log
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|
|
| HF_VIDEO_TOKEN = os.getenv("HF_READ_TOKEN") or os.getenv("HF_TOKEN")
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|
|
|
|
| def _write_video_tmp(data_iter_or_bytes: object, suffix: str = ".mp4") -> str:
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| fd, fname = tempfile.mkstemp(suffix=suffix)
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| try:
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| with os.fdopen(fd, "wb") as file:
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| if isinstance(data_iter_or_bytes, (bytes, bytearray)):
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| file.write(data_iter_or_bytes)
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| elif hasattr(data_iter_or_bytes, "read"):
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| file.write(data_iter_or_bytes.read())
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| elif hasattr(data_iter_or_bytes, "content"):
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| file.write(data_iter_or_bytes.content)
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| elif hasattr(data_iter_or_bytes, "__iter__") and not isinstance(data_iter_or_bytes, (str, dict)):
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| for chunk in data_iter_or_bytes:
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| if chunk:
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| file.write(chunk)
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| else:
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| raise gr.Error("Unsupported video data type returned by provider.")
|
| except Exception:
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| try:
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| os.remove(fname)
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| except Exception:
|
| pass
|
| raise
|
| return fname
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|
|
|
|
| def Generate_Video(
|
| prompt: Annotated[str, "Text description of the video to generate (e.g., 'a red fox running through a snowy forest at sunrise')."],
|
| model_id: Annotated[str, "Hugging Face model id in the form 'creator/model-name'. Defaults to Wan-AI/Wan2.2-T2V-A14B."] = "Wan-AI/Wan2.2-T2V-A14B",
|
| negative_prompt: Annotated[str, "What should NOT appear in the video."] = "",
|
| steps: Annotated[int, "Number of denoising steps (1–100). Higher can improve quality but is slower."] = 25,
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| cfg_scale: Annotated[float, "Guidance scale (1–20). Higher = follow the prompt more closely, lower = more creative."] = 3.5,
|
| seed: Annotated[int, "Random seed for reproducibility. Use -1 for a random seed per call."] = -1,
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| width: Annotated[int, "Output width in pixels (multiples of 8 recommended)."] = 768,
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| height: Annotated[int, "Output height in pixels (multiples of 8 recommended)."] = 768,
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| fps: Annotated[int, "Frames per second of the output video (e.g., 24)."] = 24,
|
| duration: Annotated[float, "Target duration in seconds (provider/model dependent, commonly 2–6s)."] = 4.0,
|
| ) -> str:
|
| _log_call_start(
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| "Generate_Video",
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| prompt=_truncate_for_log(prompt, 160),
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| model_id=model_id,
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| steps=steps,
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| cfg_scale=cfg_scale,
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| fps=fps,
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| duration=duration,
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| size=f"{width}x{height}",
|
| )
|
| if not prompt or not prompt.strip():
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| _log_call_end("Generate_Video", "error=empty prompt")
|
| raise gr.Error("Please provide a non-empty prompt.")
|
| providers = ["auto", "replicate", "fal-ai"]
|
| last_error: Exception | None = None
|
| parameters = {
|
| "negative_prompt": negative_prompt or None,
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| "num_inference_steps": steps,
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| "guidance_scale": cfg_scale,
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| "seed": seed if seed != -1 else random.randint(1, 1_000_000_000),
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| "width": width,
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| "height": height,
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| "fps": fps,
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| "duration": duration,
|
| }
|
| for provider in providers:
|
| try:
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| client = InferenceClient(api_key=HF_VIDEO_TOKEN, provider=provider)
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| if hasattr(client, "text_to_video"):
|
| num_frames = int(duration * fps) if duration and fps else None
|
| extra_body = {}
|
| if width:
|
| extra_body["width"] = width
|
| if height:
|
| extra_body["height"] = height
|
| if fps:
|
| extra_body["fps"] = fps
|
| if duration:
|
| extra_body["duration"] = duration
|
| result = client.text_to_video(
|
| prompt=prompt,
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| model=model_id,
|
| guidance_scale=cfg_scale,
|
| negative_prompt=[negative_prompt] if negative_prompt else None,
|
| num_frames=num_frames,
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| num_inference_steps=steps,
|
| seed=parameters["seed"],
|
| extra_body=extra_body if extra_body else None,
|
| )
|
| else:
|
| result = client.post(
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| model=model_id,
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| json={"inputs": prompt, "parameters": {k: v for k, v in parameters.items() if v is not None}},
|
| )
|
| path = _write_video_tmp(result, suffix=".mp4")
|
| try:
|
| size = os.path.getsize(path)
|
| except Exception:
|
| size = -1
|
| _log_call_end("Generate_Video", f"provider={provider} path={os.path.basename(path)} bytes={size}")
|
| return path
|
| except Exception as exc:
|
| last_error = exc
|
| continue
|
| msg = str(last_error) if last_error else "Unknown error"
|
| lowered = msg.lower()
|
| if "404" in msg:
|
| raise gr.Error(f"Model not found or unavailable: {model_id}. Check the id and HF token access.")
|
| if "503" in msg:
|
| raise gr.Error("The model is warming up. Please try again shortly.")
|
| if "401" in msg or "403" in msg:
|
| raise gr.Error("Please duplicate the space and provide a `HF_READ_TOKEN` to enable Image and Video Generation.")
|
| if ("api_key" in lowered) or ("hf auth login" in lowered) or ("unauthorized" in lowered) or ("forbidden" in lowered):
|
| raise gr.Error("Please duplicate the space and provide a `HF_READ_TOKEN` to enable Image and Video Generation.")
|
| _log_call_end("Generate_Video", f"error={_truncate_for_log(msg, 200)}")
|
| raise gr.Error(f"Video generation failed: {msg}")
|
|
|
|
|
| def build_interface() -> gr.Interface:
|
| return gr.Interface(
|
| fn=Generate_Video,
|
| inputs=[
|
| gr.Textbox(label="Prompt", placeholder="Enter a prompt for the video", lines=2),
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| gr.Textbox(
|
| label="Model",
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| value="Wan-AI/Wan2.2-T2V-A14B",
|
| placeholder="creator/model-name",
|
| max_lines=1,
|
| info="<a href=\"https://huggingface.co/models?pipeline_tag=text-to-video&inference_provider=nebius,cerebras,novita,fireworks-ai,together,fal-ai,groq,featherless-ai,nscale,hyperbolic,sambanova,cohere,replicate,scaleway,publicai,hf-inference&sort=trending\" target=\"_blank\" rel=\"noopener noreferrer\">Browse models</a>",
|
| ),
|
| gr.Textbox(label="Negative Prompt", value="", lines=2),
|
| gr.Slider(minimum=1, maximum=100, value=25, step=1, label="Steps"),
|
| gr.Slider(minimum=1.0, maximum=20.0, value=3.5, step=0.1, label="CFG Scale"),
|
| gr.Slider(minimum=-1, maximum=1_000_000_000, value=-1, step=1, label="Seed (-1 = random)"),
|
| gr.Slider(minimum=64, maximum=1920, value=768, step=8, label="Width"),
|
| gr.Slider(minimum=64, maximum=1920, value=768, step=8, label="Height"),
|
| gr.Slider(minimum=4, maximum=60, value=24, step=1, label="FPS"),
|
| gr.Slider(minimum=1.0, maximum=10.0, value=4.0, step=0.5, label="Duration (s)"),
|
| ],
|
| outputs=gr.Video(label="Generated Video", show_download_button=True, format="mp4"),
|
| title="Generate Video",
|
| description=(
|
| "<div style=\"text-align:center\">Generate short videos via Hugging Face serverless inference. "
|
| "Default model is Wan2.2-T2V-A14B.</div>"
|
| ),
|
| api_description=(
|
| "Generate a short video from a text prompt using a Hugging Face model via serverless inference. "
|
| "Create dynamic scenes like 'a red fox running through a snowy forest at sunrise', 'waves crashing on a rocky shore', "
|
| "'time-lapse of clouds moving across a blue sky'. Default model: Wan2.2-T2V-A14B (2-6 second videos). "
|
| "Parameters: prompt (str), model_id (str), negative_prompt (str), steps (int), cfg_scale (float), seed (int), "
|
| "width/height (int), fps (int), duration (float in seconds). Returns MP4 file path. "
|
| "Return the generated media to the user in this format ``"
|
| ),
|
| flagging_mode="never",
|
| show_api=bool(os.getenv("HF_READ_TOKEN") or os.getenv("HF_TOKEN")),
|
| )
|
|
|
|
|
| __all__ = ["Generate_Video", "build_interface"]
|
|
|
