[ADD] Add LoRa

app.py

@@ -6,14 +6,60 @@ import random
 from diffusers import DiffusionPipeline
 import torch
 
+
+# Model list including your LoRA model
+MODEL_LIST = [
+    "CompVis/stable-diffusion-v1-4",
+    "stabilityai/sdxl-turbo",
+    "runwayml/stable-diffusion-v1-5",
+    "stabilityai/stable-diffusion-2-1",
+    "YaArtemNosenko/dino_stickers",
+]
+
 device = "cuda" if torch.cuda.is_available() else "cpu"
-# model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
+    
+def load_pipeline(model_id: str):
+    """
+    Loads or retrieves a cached DiffusionPipeline.
+    
+    If the chosen model is your LoRA adapter, then load the base model 
+    (CompVis/stable-diffusion-v1-4) and apply the LoRA weights.
+    """
+    if model_id in model_cache:
+        return model_cache[model_id]
+    
+    if model_id == "YaArtemNosenko/dino_stickers":
+        # Use the specified base model for your LoRA adapter.
+        base_model = "CompVis/stable-diffusion-v1-4"
+        pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch_dtype)
+        # Load the LoRA weights
+        pipe.unet = PeftModel.from_pretrained(
+            pipe.unet, 
+            model_id, 
+            subfolder="unet", 
+            torch_dtype=torch_dtype
+        )
+        pipe.text_encoder = PeftModel.from_pretrained(
+            pipe.text_encoder, 
+            model_id, 
+            subfolder="text_encoder", 
+            torch_dtype=torch_dtype
+        )
+    else:
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+    
+    pipe.to(device)
+    model_cache[model_id] = pipe
+    
+    return pipe
+
+# model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 # pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 # pipe = pipe.to(device)
 
@@ -37,10 +83,14 @@ def infer(
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
-    pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-    pipe = pipe.to(device)
+    pipe = load_pipeline(model_id)
     
     generator = torch.Generator().manual_seed(seed)
+    if model_id == "YaArtemNosenko/dino_stickers":
+        if hasattr(pipe.unet, "set_lora_scale"):
+            pipe.unet.set_lora_scale(lora_scale)
+        else:
+            print("Warning: LoRA scale adjustment method not found on UNet.")
 
     image = pipe(
         prompt=prompt,
@@ -73,13 +123,14 @@ with gr.Blocks(css=css) as demo:
         gr.Markdown(" # Text-to-Image Gradio Template")
 
         with gr.Row():
-            model_repo_id = gr.Text(
-                label="model_repo_id",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your model_repo_id",
-                container=False,
+            # Dropdown to select the model from Hugging Face
+            model_id = gr.Dropdown(
+                label="Model",
+                choices=MODEL_LIST,
+                value=MODEL_LIST[0],  # Default model
             )
+
+        with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -97,7 +148,6 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
             )
 
             seed = gr.Slider(
@@ -105,7 +155,7 @@ with gr.Blocks(css=css) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
+                value=42,  # Default seed
             )
 
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
@@ -116,7 +166,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
                 height = gr.Slider(
@@ -124,32 +174,42 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
             with gr.Row():
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    maximum=20.0,
+                    step=0.5,
+                    value=7.0,
                 )
 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=100,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=20,
                 )
 
+            # New slider for LoRA scale.
+            lora_scale = gr.Slider(
+                label="LoRA Scale",
+                minimum=0.0,
+                maximum=2.0,
+                step=0.1,
+                value=1.0,
+                info="Adjust the influence of the LoRA weights",
+            )
+
         gr.Examples(examples=examples, inputs=[prompt])
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
         inputs=[
-            model_repo_id,
+            model_id,
             prompt,
             negative_prompt,
             seed,
@@ -158,6 +218,7 @@ with gr.Blocks(css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
+            lora_scale,  # Pass the new slider value
         ],
         outputs=[result, seed],
     )