[UPD] Upd. app.py

app.py

@@ -2,10 +2,11 @@ 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"
 
 # Model list including your LoRA model
 MODEL_LIST = [
@@ -16,14 +17,14 @@ MODEL_LIST = [
     "YaArtemNosenko/dino_stickers",
 ]
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
-    
+# Cache to avoid re-initializing pipelines repeatedly
+model_cache = {}
+
 def load_pipeline(model_id: str):
     """
     Loads or retrieves a cached DiffusionPipeline.
@@ -56,20 +57,13 @@ def load_pipeline(model_id: str):
     
     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)
-
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
-    model_repo_id, 
+    model_id,
     prompt,
     negative_prompt,
     seed,
@@ -78,15 +72,20 @@ 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
+    pipe = load_pipeline(model_id)
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
-    pipe = load_pipeline(model_id)
-    
-    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 == "YaArtemNosenko/dino_stickers":
+        # 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:
@@ -104,7 +103,6 @@ def infer(
 
     return image, seed
 
-
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
@@ -224,4 +222,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()