Lora

app.py

@@ -2,16 +2,18 @@ import gradio as gr
 import numpy as np
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# 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",
+    "akaUNik/hw5-futurama-lora",  # Your LoRA model option
 ]
 
 if torch.cuda.is_available():
@@ -19,24 +21,36 @@ if torch.cuda.is_available():
 else:
     torch_dtype = torch.float32
 
-# To avoid re-initializing pipelines repeatedly, we can cache them:
+# Cache to avoid re-initializing pipelines repeatedly
 model_cache = {}
 
 def load_pipeline(model_id: str):
-    """Loads or retrieves a cached DiffusionPipeline."""
+    """
+    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 == "akaUNik/hw5-futurama-lora":
+        # 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 into the U-Net.
+        # This assumes that load_attn_procs loads the LoRA weights.
+        pipe.unet.load_attn_procs(model_id)
     else:
         pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
-        pipe.to(device)
-        model_cache[model_id] = pipe
-        return pipe
+    
+    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,
@@ -47,6 +61,7 @@ def infer(
     height,
     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
@@ -55,7 +70,15 @@ def infer(
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    # If using the LoRA model, update the LoRA scale if supported.
+    if model_id == "akaUNik/hw5-futurama-lora":
+        # 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,
@@ -69,7 +92,6 @@ def infer(
 
     return image, seed
 
-
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
@@ -113,7 +135,6 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                # visible=False,
             )
 
             seed = gr.Slider(
@@ -132,7 +153,7 @@ 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(
@@ -140,7 +161,7 @@ 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,
                 )
 
             with gr.Row():
@@ -149,7 +170,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=0.0,
                     maximum=20.0,
                     step=0.5,
-                    value=7.0,  # Common default for SD
+                    value=7.0,
                 )
 
                 num_inference_steps = gr.Slider(
@@ -157,9 +178,19 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=100,
                     step=1,
-                    value=20,  # Common default for SD
+                    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],
@@ -174,6 +205,7 @@ with gr.Blocks(css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
+            lora_scale,  # Pass the new slider value
         ],
         outputs=[result, seed],
     )