Create app.py

app.py

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+!pip install diffusers torch moviepy pillow
+import torch
+from diffusers import StableDiffusionImg2ImgPipeline
+from PIL import Image
+from moviepy.editor import ImageSequenceClip
+import os
+
+# Step 1: Set up Stable Diffusion img2img pipeline
+def setup_pipeline(model_name="CompVis/stable-diffusion-v1-4"):
+    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_name, torch_dtype=torch.float16)
+    pipe.to("cuda")  # Use GPU for faster generation
+    return pipe
+
+# Step 2: Generate frames from the single image
+def generate_frames(pipe, input_image_path, prompt, num_frames=30, guidance_scale=7.5, strength=0.5, output_folder="frames"):
+    os.makedirs(output_folder, exist_ok=True)
+    frames = []
+    
+    # Load the input image
+    input_image = Image.open(input_image_path).convert("RGB")
+    
+    for i in range(num_frames):
+        # Slightly modify the prompt or strength for variation
+        current_prompt = f"{prompt}, frame {i+1} of {num_frames}"
+        current_strength = strength + (0.01 * i)  # Gradual change in strength
+        
+        # Generate a new image
+        generated_image = pipe(
+            prompt=current_prompt,
+            image=input_image,
+            strength=current_strength,
+            guidance_scale=guidance_scale
+        ).images[0]
+        
+        # Save the frame
+        frame_path = os.path.join(output_folder, f"frame_{i:03d}.png")
+        generated_image.save(frame_path)
+        frames.append(frame_path)
+        print(f"Generated frame {i+1}/{num_frames}")
+    
+    return frames
+
+# Step 3: Create video from frames
+def create_video(frames, output_file="output_video.mp4", fps=24):
+    clip = ImageSequenceClip(frames, fps=fps)
+    clip.write_videofile(output_file, codec="libx264")
+    print(f"Video saved as {output_file}")
+
+# Step 4: Main script
+if __name__ == "__main__":
+    # Model and prompt configuration
+    input_image_path = "/mnt/data/Screenshot 2025-01-03 171727.png"  # Use the uploaded image
+    prompt = "A child riding a bicycle through a magical forest, dynamic and cinematic lighting"
+    num_frames = 30
+    fps = 24
+    
+    # Initialize Stable Diffusion img2img pipeline
+    pipe = setup_pipeline()
+
+    # Generate frames from the single image
+    print("Generating frames...")
+    frames = generate_frames(pipe, input_image_path, prompt, num_frames=num_frames)
+
+    # Create video
+    print("Creating video...")
+    create_video(frames, output_file="image_to_video_diffusion.mp4", fps=fps)
+