DF.py

import os
import time
import torch
import gradio as gr
from diffusers import WanPipeline, AutoencoderKLWan
from diffusers.utils import export_to_video
from dfloat11 import DFloat11Model
import spaces
import uuid

# Set environment variables
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "1"

# Ensure this runs on CPU or ZeroGPU
@spaces.GPU(enable_queue=True)
def generate_video(prompt, negative_prompt, width, height, num_frames,
                   guidance_scale, guidance_scale_2, num_inference_steps, fps):
    torch.cuda.empty_cache()
    start_time = time.time()

    # Load model
    vae = AutoencoderKLWan.from_pretrained(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        subfolder="vae",
        torch_dtype=torch.float32
    )

    pipe = WanPipeline.from_pretrained(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        vae=vae,
        torch_dtype=torch.bfloat16
    )

    # Load DFloat11 optimization layers
    DFloat11Model.from_pretrained(
        "DFloat11/Wan2.2-T2V-A14B-DF11",
        device="cpu",
        cpu_offload=True,
        bfloat16_model=pipe.transformer,
    )
    DFloat11Model.from_pretrained(
        "DFloat11/Wan2.2-T2V-A14B-2-DF11",
        device="cpu",
        cpu_offload=True,
        bfloat16_model=pipe.transformer_2,
    )

    pipe.enable_model_cpu_offload()

    # Run inference
    result = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=height,
        width=width,
        num_frames=num_frames,
        guidance_scale=guidance_scale,
        guidance_scale_2=guidance_scale_2,
        num_inference_steps=num_inference_steps,
    ).frames[0]

    output_path = f"/tmp/video_{uuid.uuid4().hex}.mp4"
    export_to_video(result, output_path, fps=fps)

    elapsed = time.time() - start_time
    print(f"Video generated in {elapsed:.2f} seconds")

    return output_path


# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🎥 Wan2.2 Text-to-Video Generator (ZeroGPU Ready)")

    with gr.Row():
        prompt = gr.Textbox(
            label="Prompt",
            value="A serene koi pond at night, with glowing lanterns reflecting on the rippling water. Ethereal fireflies dance above as cherry blossoms gently fall.",
            lines=3
        )
        negative_prompt = gr.Textbox(
            label="Negative Prompt",
            value="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
            lines=3
        )

    with gr.Row():
        width = gr.Slider(256, 1280, value=768, step=64, label="Width")
        height = gr.Slider(256, 720, value=432, step=64, label="Height")
        num_frames = gr.Slider(8, 81, value=40, step=1, label="Number of Frames")
        fps = gr.Slider(8, 30, value=16, step=1, label="FPS")

    with gr.Row():
        guidance_scale = gr.Slider(1.0, 10.0, value=4.0, step=0.1, label="Guidance Scale")
        guidance_scale_2 = gr.Slider(1.0, 10.0, value=3.0, step=0.1, label="Guidance Scale 2")
        num_inference_steps = gr.Slider(10, 60, value=40, step=1, label="Inference Steps")

    with gr.Row():
        btn = gr.Button("🎬 Generate Video")
        output_video = gr.Video(label="Generated Video")

    btn.click(
        generate_video,
        inputs=[prompt, negative_prompt, width, height, num_frames, guidance_scale, guidance_scale_2, num_inference_steps, fps],
        outputs=[output_video]
    )

# Launch Gradio app
demo.launch()