Del

app.py

@@ -1,160 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
-import os
-import time
-
-# Create output directory if it doesn't exist
-os.makedirs("outputs", exist_ok=True)
-
-# Function to load model (only once)
-@torch.inference_mode()
-def load_model():
-    print("Loading SkyReels V2 model...")
-    pipe = DiffusionPipeline.from_pretrained(
-        "Skywork/SkyReels-V2-DF-1.3B-540P",
-        torch_dtype=torch.float16,
-        variant="fp16"
-    )
-    
-    # Move to GPU and optimize
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    pipe.to(device)
-    
-    # Apply memory optimizations
-    pipe.enable_model_cpu_offload()
-    pipe.enable_vae_slicing()
-    
-    # Use DPMSolver for faster generation
-    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-    
-    print(f"Model loaded successfully on {device}!")
-    return pipe
-
-# Generate video function
-@torch.inference_mode()
-def generate_video(
-    prompt,
-    negative_prompt,
-    num_frames,
-    steps,
-    guidance_scale,
-    width,
-    height,
-    progress=gr.Progress()
-):
-    # Load the model if not already loaded
-    if "pipe" not in generate_video.__dict__:
-        generate_video.pipe = load_model()
-    
-    # Update progress
-    progress(0, desc="Starting generation...")
-    
-    # Generate video frames
-    video_frames = generate_video.pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=steps,
-        num_frames=num_frames,
-        height=height,
-        width=width,
-        guidance_scale=guidance_scale,
-        callback=lambda i, t, latents: progress((i + 1) / steps)
-    ).frames
-    
-    # Save the video
-    timestamp = int(time.time())
-    output_path = f"outputs/generated_video_{timestamp}.mp4"
-    export_to_video(video_frames, output_path)
-    
-    return output_path
-
-# Create Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# SkyReels V2 Video Generation")
-    gr.Markdown("Generate videos using the Skywork/SkyReels-V2-DF-1.3B-540P model")
-    
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(
-                label="Prompt",
-                placeholder="A panda playing guitar on a mountain top, cinematic lighting",
-                value="A panda playing guitar on a mountain top, cinematic lighting"
-            )
-            
-            negative_prompt = gr.Textbox(
-                label="Negative Prompt",
-                placeholder="poor quality, blurry, distorted",
-                value="poor quality, blurry, distorted"
-            )
-            
-            with gr.Row():
-                num_frames = gr.Slider(
-                    minimum=8,
-                    maximum=24,
-                    value=16,
-                    step=1,
-                    label="Number of Frames"
-                )
-                
-                steps = gr.Slider(
-                    minimum=15,
-                    maximum=50,
-                    value=25,
-                    step=1,
-                    label="Inference Steps"
-                )
-            
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    minimum=1.0,
-                    maximum=15.0,
-                    value=7.5,
-                    step=0.5,
-                    label="Guidance Scale"
-                )
-            
-            with gr.Row():
-                width = gr.Dropdown(
-                    choices=[576, 640, 704],
-                    value=576,
-                    label="Width"
-                )
-                
-                height = gr.Dropdown(
-                    choices=[320, 384, 448],
-                    value=320,
-                    label="Height"
-                )
-            
-            generate_btn = gr.Button("Generate Video")
-        
-        with gr.Column():
-            output_video = gr.Video(label="Generated Video")
-    
-    generate_btn.click(
-        fn=generate_video,
-        inputs=[
-            prompt,
-            negative_prompt,
-            num_frames,
-            steps,
-            guidance_scale,
-            width,
-            height
-        ],
-        outputs=output_video
-    )
-    
-    gr.Markdown("""
-    ## Usage Tips
-    - Use detailed prompts for better results
-    - Higher guidance scale = more prompt adherence
-    - More inference steps = better quality but slower
-    - First generation will take longer as it loads the model
-    - GPU with 10GB+ VRAM recommended
-    """)
-
-# Run the app
-demo.queue().launch()