update app.py

.gitignore

@@ -2,4 +2,5 @@ Include
 Lib
 Scripts
 share
-*env*
+*env*
+unico_*

app.py

@@ -2,32 +2,68 @@ import gradio as gr
 import numpy as np
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline #, FluxPipeline
+from diffusers import DiffusionPipeline
+from peft import PeftModel, PeftConfig
 import torch
-# import os
 
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-# torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
-# model_repo = "black-forest-labs/FLUX.1-dev"  # Replace to the model you would like to use
+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",
+    "macrdel/unico_proj",
+]
 
-# pipe = FluxPipeline.from_pretrained(model_repo, 
-#                                     torch_dtype=torch_dtype, 
-#                                     token=f"{os.environ.get('tkn')}",
-#                                     #force_download=True,
-#                                     ).to(device)
-#pipe.enable_model_cpu_offload()
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-model_repo = "stabilityai/sdxl-turbo"
+# 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 == "macrdel/unico_proj":
+        # 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,
@@ -36,18 +72,24 @@ def infer(
     height,
     guidance_scale,
     num_inference_steps,
-    model_repo=model_repo,
+    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,
-        torch_dtype=torch_dtype,
-    ).to(device)
+    # If using the LoRA model, update the LoRA scale if supported.
+    if model_id == "macrdel/unico_proj":
+        # 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,
@@ -61,7 +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",
@@ -77,20 +118,15 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text2Img Gradio")
-        # gr.Markdown(f" ## Model '{model_repo}'")
-
-        model_dropdown = gr.Dropdown(
-            label="Select Model", 
-            choices=[
-                "stabilityai/sdxl-turbo",
-                "stabilityai/stable-diffusion-xl-base-1.0",
-                #"runwayml/stable-diffusion-v1-5",
-                #"SG161222/Realistic_Vision_V5.1_noVAE",
-                "stabilityai/stable-diffusion-2-1",
-            ], 
-            value="stabilityai/sdxl-turbo",
-        )
+        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(
@@ -105,25 +141,22 @@ with gr.Blocks(css=css) as demo:
 
         result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=True):
-            with gr.Row():
-                negative_prompt = gr.Text(
-                    label="Negative prompt",
-                    max_lines=1,
-                    placeholder="Enter a negative prompt",
-                    visible=True,
-                )
-            
-            with gr.Row():
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=777,
-                )
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,  # Default seed
+            )
 
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
             with gr.Row():
                 width = gr.Slider(
@@ -131,7 +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(
@@ -139,32 +172,42 @@ 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 (CFG)",
-                    minimum=1.0,
-                    maximum=10.0,
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=20.0,
                     step=0.5,
-                    value=3.5,  # Replace with defaults that work for your model
+                    value=7.0,
                 )
 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=100,
                     step=1,
-                    value=1,  # 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,
@@ -173,7 +216,7 @@ with gr.Blocks(css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
-            model_dropdown,
+            lora_scale,  # Pass the new slider value
         ],
         outputs=[result, seed],
     )