Create app.py

app.py

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+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from tqdm.auto import tqdm
+from huggingface_hub import cached_download, hf_hub_url
+import os
+
+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_weight_name, lora_adapter_name):
+    try: 
+        pipe = DiffusionPipeline.from_pretrained(
+            base_model_id, 
+            torch_dtype=torch.float16, 
+            scheduler=DPMSolverMultistepScheduler.from_config(
+                pipe.scheduler.config),
+            variant="fp16",
+            use_safetensors=True,
+        ).to("cuda")
+
+        lora_url = hf_hub_url(lora_id, revision="main", filename=lora_weight_name)
+        lora_path = cached_download(lora_url) 
+
+        with tqdm(desc="Loading LoRA weights", unit="step") as pbar:
+            pipe.load_lora_weights(
+                lora_path,
+                weight_name=lora_weight_name,
+                adapter_name=lora_adapter_name,
+                progress_callback=lambda step, max_steps: pbar.update(1)
+            )
+
+        print("LoRA merged successfully!")
+        return pipe
+    except Exception as e:
+        print(f"Error merging LoRA: {e}")
+        return None
+
+def save_merged_model(pipe, save_path):
+    """Saves the merged model to the specified path."""
+    try:
+        pipe.save_pretrained(save_path)
+        print(f"Merged model saved successfully to: {save_path}")
+    except Exception as e:
+        print(f"Error saving the merged model: {e}")
+
+if __name__ == "__main__":
+    base_model_id = input("Enter the base model ID: ") 
+    lora_id = input("Enter the LoRA Hugging Face Hub ID: ") 
+    lora_weight_name = input("Enter the LoRA weight file name: ")
+    lora_adapter_name = input("Enter the LoRA adapter name: ")
+
+    pipe = load_and_merge_lora(base_model_id, lora_id, lora_weight_name, 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_image(image)
+
+        # Ask the user for a directory to save the model
+        save_path = input(
+            "Enter the directory where you want to save the merged model: "
+        )
+        save_merged_model(pipe, save_path)