Update app.py

app.py

@@ -2,48 +2,94 @@ import gradio as gr
 import numpy as np
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
+from peft import PeftModel, PeftConfig
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-# default parameter
-# model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-model_repo_id = "CompVis/stable-diffusion-v1-4"
-model_dropdown = ['stabilityai/sdxl-turbo', 'CompVis/stable-diffusion-v1-4' ]
+
+# 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",
+    "m4r4k0s23/hw5_lora_raccoon",
+]
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+# Cache to avoid re-initializing pipelines repeatedly
+model_cache = {}
+
+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 == "m4r4k0s23/hw5_lora_raccoon":
+        # 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
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
+    model_id,
     prompt,
     negative_prompt,
+    seed,
     randomize_seed,
     width,
     height,
-    model_repo_id=model_repo_id,
-    seed=42,
-    guidance_scale=7,
-    num_inference_steps=20,
+    guidance_scale,
+    num_inference_steps,
+    lora_scale,  # New parameter for adjusting LoRA scale
     progress=gr.Progress(track_tqdm=True),
 ):
+    # Load the pipeline for the chosen model
+    pipe = load_pipeline(model_id)
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator(device=device).manual_seed(seed)
 
-    pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-    pipe = pipe.to(device)
+    # If using the LoRA model, update the LoRA scale if supported.
+    if model_id == "m4r4k0s23/hw5_lora_raccoon":
+        # This assumes your pipeline's unet has a method to update the LoRA scale.
+        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,
@@ -57,12 +103,7 @@ def infer(
 
     return image, seed
 
-
 examples = [
-    "A young lady in a Russian embroidered kaftan is sitting on a beautiful carved veranda, holding a cup to her mouth and drinking tea from the cup. With her other hand, the girl holds a saucer. The cup and saucer are painted with gzhel. Next to the girl on the table stands a samovar, and steam can be seen above it.",
-    "Puss in Boots wearing a sombrero crosses the Grand Canyon on a tightrope with a guitar.",
-    "A cat is playing a song called ""About the Cat"" on an accordion by the sea at sunset. The sun is quickly setting behind the horizon, and the light is fading.",
-    "A cat walks through the grass on the streets of an abandoned city. The camera view is always focused on the cat's face.",
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
     "A delicious ceviche cheesecake slice",
@@ -77,7 +118,15 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image SemaSci Template")
+        gr.Markdown(" # Text-to-Image Gradio Template")
+
+        with gr.Row():
+            # 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(
@@ -93,27 +142,10 @@ with gr.Blocks(css=css) as demo:
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-#            model_repo_id = gr.Text(
-#                label="Model Id",
-#                max_lines=1,
-#                placeholder="Choose model",
-#                visible=True,
-#                value=model_repo_id,
-#            )
-            model_repo_id = gr.Dropdown(
-                label="Model Id",
-                choices=model_dropdown,
-                info="Choose model",
-                visible=True,
-                allow_custom_value=True,
-                value=model_repo_id,
-            )            
-            
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=True,
             )
 
             seed = gr.Slider(
@@ -121,10 +153,10 @@ with gr.Blocks(css=css) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=42,
+                value=42,  # Default seed
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
             with gr.Row():
                 width = gr.Slider(
@@ -132,7 +164,7 @@ 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(
@@ -140,40 +172,51 @@ 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=7.0,  # 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=20,  # 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_id,
             prompt,
             negative_prompt,
+            seed,
             randomize_seed,
             width,
             height,
-            model_repo_id,
-            seed,
             guidance_scale,
             num_inference_steps,
+            lora_scale,  # Pass the new slider value
         ],
         outputs=[result, seed],
     )