Create DF.py

DF.py

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+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()