Update app.py

🧪 Test Shilin-LU/VINE-B-Enc

app.py

@@ -1,33 +1,32 @@
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
 model_repo_id = "stabilityai/sdxl-turbo"  # Base model
-lora_adapter_id = "radames/sdxl-turbo-DPO-LoRA"  # LoRA adapter
-lora_adapter_name = "dpo-lora-sdxl-turbo"
+lora_adapter_id = "Shilin-LU/VINE-B-Enc"  # LoRA adapter
+lora_adapter_name = "VINE-B-Enc"
 
+device = "cuda" if torch.cuda.is_available() else "cpu"
 if torch.cuda.is_available():
     torch_dtype = torch.float16
-    device = "cuda"
 else:
     torch_dtype = torch.float32
-    device = "cpu"
 
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype, variant="fp16" if torch_dtype==torch.float16 else None)
 pipe = pipe.to(device)
 # Charger l'adapter LoRA
-pipe.load_lora_weights(lora_adapter_id, adapter_name=lora_adapter_name)
-pipe.set_adapters([lora_adapter_name], adapter_weights=[1.0])
+try:
+    pipe.load_lora_weights(lora_adapter_id, adapter_name=lora_adapter_name)
+    pipe.set_adapters([lora_adapter_name], adapter_weights=[1.0])
+except Exception as e:
+    print("[INFO] If you see 'PEFT backend is required for this method.', please install PEFT with: pip install peft\nError details:", e)
+    print(f"Erreur lors du chargement de l'adapter LoRA: {e}")
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -41,9 +40,7 @@ def infer(
 ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-
     generator = torch.Generator().manual_seed(seed)
-
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -53,10 +50,8 @@ def infer(
         height=height,
         generator=generator,
     ).images[0]
-
     return image, seed
 
-
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
@@ -72,8 +67,7 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+        gr.Markdown(" # Text-to-Image Gradio Template (Shilin-LU/VINE-B-Enc adapter)")
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -82,11 +76,8 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-
             run_button = gr.Button("Run", scale=0, variant="primary")
-
         result = gr.Image(label="Result", show_label=False)
-
         with gr.Accordion("Advanced Settings", open=False):
             negative_prompt = gr.Text(
                 label="Negative prompt",
@@ -94,7 +85,6 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter a negative prompt",
                 visible=False,
             )
-
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -102,43 +92,37 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
             with gr.Row():
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
-
                 height = gr.Slider(
                     label="Height",
                     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
+                    value=0.0,
                 )
-
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=2,
                 )
-
         gr.Examples(examples=examples, inputs=[prompt])
     gr.on(
         triggers=[run_button.click, prompt.submit],