Delete main.py

main.py

@@ -1,58 +0,0 @@
-import os
-import torch
-from PIL import Image
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import gradio as gr
-
-# Disable oneDNN custom operations
-os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
-
-# Clear PyTorch cache
-torch.cuda.empty_cache()
-
-# Check if CUDA is available
-device = "cuda" if torch.cuda.is_available() else "cpu"
-if device == "cuda":
-    print("CUDA is available. Device count:", torch.cuda.device_count())
-    print("Current device:", torch.cuda.current_device())
-    print("Device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
-else:
-    print("CUDA is not available. Using CPU.")
-
-# Load ControlNet model with OpenPose pre-trained weights from Hugging Face
-controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16)
-
-# Load the Stable Diffusion model
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-).to(device)
-
-# Function for inference
-def generate_image(prompt, target_image, pose_image):
-    try:
-        # Resize images
-        target_image = target_image.resize((512, 512))
-        pose_image = pose_image.resize((512, 512))
-
-        # Generate image with ControlNet
-        output = pipe(prompt=prompt, image=target_image, control_image=pose_image, num_inference_steps=50)
-
-        # Return the result
-        return output["sample"][0]
-    except Exception as e:
-        print(f"Error during image generation: {e}")
-        return None
-
-# Setup Gradio Interface
-interface = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.Textbox(label="Prompt"),
-        gr.Image(label="Target Image", type="pil"),
-        gr.Image(label="Pose Image (Reference)", type="pil")
-    ],
-    outputs=gr.Image(label="Generated Image")
-)
-
-# Launch the interface
-interface.launch()