Delete main.py

main.py

@@ -1,111 +0,0 @@
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from tqdm.auto import tqdm
-from huggingface_hub import hf_hub_url, login, HfApi, create_repo
-import os
-import traceback
-from peft import PeftModel
-
-def display_image(image):
-    """Replace this with your actual image display logic."""
-    image.show() 
-
-def load_and_merge_lora(base_model_id, lora_id, lora_adapter_name):
-    try:
-        pipe = DiffusionPipeline.from_pretrained(
-            base_model_id,
-            torch_dtype=torch.float16,
-            variant="fp16",
-            use_safetensors=True,
-        ).to("cuda")
-
-        pipe.scheduler = DPMSolverMultistepScheduler.from_config(
-            pipe.scheduler.config
-        )
-
-        # Get the UNet model from the pipeline
-        unet = pipe.unet
-
-        # Apply PEFT to the UNet model 
-        unet = PeftModel.from_pretrained(
-            unet, 
-            lora_id, 
-            torch_dtype=torch.float16, 
-            adapter_name=lora_adapter_name
-        )
-
-        # Replace the original UNet in the pipeline with the PEFT-loaded one
-        pipe.unet = unet
-
-        print("LoRA merged successfully!")
-        return pipe
-
-    except Exception as e:
-        error_msg = traceback.format_exc()  
-        print(f"Error merging LoRA: {e}\n\nFull traceback saved to errors.txt")
-
-        with open("errors.txt", "w") as f:
-            f.write(error_msg)
-
-        return None
-
-def save_merged_model(pipe, save_path, push_to_hub=False, hf_token=None):
-    """Saves and optionally pushes the merged model to Hugging Face Hub."""
-    try:
-        pipe.save_pretrained(save_path)
-        print(f"Merged model saved successfully to: {save_path}")
-
-        if push_to_hub:
-            if hf_token is None:
-                hf_token = input("Enter your Hugging Face write token: ")
-                login(token=hf_token)
-
-            repo_name = input("Enter the Hugging Face repository name "
-                              "(e.g., your_username/your_model_name): ")
-
-            # Create the repository if it doesn't exist
-            create_repo(repo_name, token=hf_token, exist_ok=True) 
-
-            api = HfApi()
-            api.upload_folder(
-                folder_path=save_path,
-                repo_id=repo_name,
-                token=hf_token,
-                repo_type="model",
-            )
-            print(f"Model pushed successfully to Hugging Face Hub: {repo_name}")
-
-    except Exception as e:
-        print(f"Error saving/pushing the merged model: {e}")
-
-if __name__ == "__main__":
-    base_model_id = input("Enter the base model ID: ") 
-    lora_id = "Tech-Meld/life-fx" 
-    lora_adapter_name = input("Enter the LoRA adapter name: ")
-
-    pipe = load_and_merge_lora(base_model_id, lora_id, lora_adapter_name)
-
-    if pipe:
-        prompt = input("Enter your prompt: ")
-        lora_scale = float(input("Enter the LoRA scale (e.g., 0.9): ")) 
-
-        image = pipe(
-            prompt, 
-            num_inference_steps=30, 
-            cross_attention_kwargs={"scale": lora_scale}, 
-            generator=torch.manual_seed(0)
-        ).images[0]
-
-        # Display the image (make sure you have a display function)
-        display_image(image)
-
-        # Save the image
-        image.save("generated_image.png") 
-        print(f"Image saved to: generated_image.png")
-
-        save_path = input("Enter the directory where you want to save the merged model: ")
-
-        push_to_hub = input("Do you want to push the model to Hugging Face Hub? (yes/no): ")
-        push_to_hub = push_to_hub.lower() == "yes"
-
-        save_merged_model(pipe, save_path, push_to_hub=push_to_hub)