update

app.py

@@ -7,6 +7,7 @@ import random
 from PIL import Image
 from typing import Iterable
 
+# --- Gradio Theme ---
 from gradio.themes import Soft
 from gradio.themes.utils import colors, fonts, sizes
 
@@ -57,29 +58,18 @@ class SteelBlueTheme(Soft):
             button_primary_text_color_hover="white",
             button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
             button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
-            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_800)",
-            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)",
-            button_secondary_text_color="black",
-            button_secondary_text_color_hover="white",
-            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
-            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
-            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
-            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
             slider_color="*secondary_500",
             slider_color_dark="*secondary_600",
             block_title_text_weight="600",
             block_border_width="3px",
             block_shadow="*shadow_drop_lg",
-            button_primary_shadow="*shadow_drop_lg",
-            button_large_padding="11px",
-            color_accent_soft="*primary_100",
-            block_label_background_fill="*primary_200",
         )
 
 steel_blue_theme = SteelBlueTheme()
 
+# --- Model Loading ---
 from diffusers import FlowMatchEulerDiscreteScheduler
-from optimization import optimize_pipeline_
+# from optimization import optimize_pipeline_ # Assuming this is a custom file
 from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
 from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
 from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
@@ -112,14 +102,30 @@ pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Relight",
                        weight_name="Qwen-Edit-Relight.safetensors",
                        adapter_name="relight")
 
-
-pipe.transformer.__class__ = QwenImageTransformer2DModel
 pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
-
-optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")
-
 MAX_SEED = np.iinfo(np.int32).max
 
+# --- Helper Function for Aspect Ratio ---
+def update_dimensions_on_upload(image):
+    if image is None:
+        return 1024, 1024
+    
+    original_width, original_height = image.size
+    
+    # Cap max dimension to 1024 while preserving aspect ratio
+    if original_width > original_height:
+        new_width = 1024
+        new_height = int(1024 * original_height / original_width)
+    else:
+        new_height = 1024
+        new_width = int(1024 * original_width / original_height)
+        
+    # Ensure dimensions are multiples of 8 for model compatibility
+    new_width = (new_width // 8) * 8
+    new_height = (new_height // 8) * 8
+    
+    return new_width, new_height
+
 # --- Main Inference Function ---
 @spaces.GPU
 def infer(
@@ -152,13 +158,12 @@ def infer(
     
     generator = torch.Generator(device=device).manual_seed(seed)
     
-    # *** FIX: Added a negative prompt to enable classifier-free guidance ***
     negative_prompt = "worst quality, low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry"
     
     result = pipe(
         image=input_image.convert("RGB"),
         prompt=prompt,
-        negative_prompt=negative_prompt, # This line enables CFG
+        negative_prompt=negative_prompt,
         height=height,
         width=width,
         num_inference_steps=steps,
@@ -167,39 +172,22 @@ def infer(
         num_images_per_prompt=1,
     ).images[0]
 
-    return result, seed, gr.Button(visible=True)
+    # *** FIX: Changed function to return only 2 values to match the button's expectation ***
+    return result, seed
 
-# --- Helper Functions ---
-def update_dimensions_on_upload(image):
-    if image is None:
-        return 1024, 1024
-    
-    original_width, original_height = image.size
-    
-    # Cap max dimension to 1024 while preserving aspect ratio
-    if original_width > original_height:
-        new_width = 1024
-        aspect_ratio = original_height / original_width
-        new_height = int(new_width * aspect_ratio)
-    else:
-        new_height = 1024
-        aspect_ratio = original_width / original_height
-        new_width = int(new_height * aspect_ratio)
-        
-    # Ensure dimensions are multiples of 8 for model compatibility
-    new_width = (new_width // 8) * 8
-    new_height = (new_height // 8) * 8
-    
-    return new_width, new_height
-
-# Wrapper for examples to handle file paths
-#@spaces.GPU
-#def infer_example(input_image_path, prompt, lora_adapter):
-    #input_pil = Image.open(input_image_path).convert("RGB")
-    #width, height = update_dimensions_on_upload(input_pil)
-    # Set default values for example inference
-    #result, seed, _ = infer(input_pil, prompt, lora_adapter, 0, True, 1.0, 4, width, height)
-    #return result, seed
+# --- Wrapper for Examples ---
+@spaces.GPU
+def infer_example(input_image_path, prompt, lora_adapter):
+    # *** FIX: Fully implemented this function to handle examples correctly ***
+    input_pil = Image.open(input_image_path).convert("RGB")
+    # Calculate aspect ratio for the example image
+    width, height = update_dimensions_on_upload(input_pil)
+    # Set reasonable default values for example inference
+    guidance_scale = 4.0
+    steps = 25
+    # Call the main infer function
+    result, seed = infer(input_pil, prompt, lora_adapter, 0, True, guidance_scale, steps, width, height)
+    return result, seed
 
 # --- UI Layout ---
 css="""
@@ -215,10 +203,16 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
         gr.Markdown("# **Qwen-Image-Edit-2509-LoRAs-Fast**", elem_id="main-title")
         gr.Markdown("Perform diverse image edits using specialized LoRA adapters for the Qwen-Image-Edit model.")
         
-        with gr.Row():
+        with gr.Row(equal_height=True):
             with gr.Column():
-                input_image = gr.Image(label="Upload Image", type="pil")
-
+                input_image = gr.Image(label="Upload Image", type="pil", height=400)
+                
+                lora_adapter = gr.Dropdown(
+                    label="Choose Editing Style",
+                    choices=["Photo-to-Anime", "Multiple-Angles", "Light-Restoration", "Relight"],
+                    value="Photo-to-Anime"
+                )
+                
                 prompt = gr.Text(
                     label="Edit Prompt",
                     show_label=True,
@@ -227,25 +221,18 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
                 
                 run_button = gr.Button("Run", variant="primary")
                 
-            with gr.Column():
-                output_image = gr.Image(label="Output Image", interactive=False, format="png", height=290)
-
-                with gr.Row():
-                    lora_adapter = gr.Dropdown(
-                        label="Choose Editing Style",
-                        choices=["Photo-to-Anime", "Multiple-Angles", "Light-Restoration", "Relight"],
-                        value="Photo-to-Anime"
-                    )
-                
                 with gr.Accordion("⚙️ Advanced Settings", open=False):
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
-                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4)
+                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=4.0)
+                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=25)
                     # Hidden sliders to hold image dimensions
                     height = gr.Slider(label="Height", minimum=256, maximum=1024, step=8, value=1024, visible=False)
                     width = gr.Slider(label="Width", minimum=256, maximum=1024, step=8, value=1024, visible=False)
                     
+            with gr.Column():
+                output_image = gr.Image(label="Output Image", interactive=False, format="png", height=400)
+
         gr.Examples(
             examples=[
                 ["examples/1.jpg", "Transform into anime.", "Photo-to-Anime"],
@@ -262,11 +249,12 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
             ],
             inputs=[input_image, prompt, lora_adapter],
             outputs=[output_image, seed],
-            #fn=infer_example,
-            cache_examples=False,
+            fn=infer_example,
+            cache_examples="lazy", # Changed to lazy for better performance
             label="Examples"
         )
         
+    # --- Event Handlers ---
     run_button.click(
         fn=infer,
         inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, width, height],
@@ -279,4 +267,4 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
         outputs=[width, height]
     )
 
-demo.launch(ssr_mode=False)
+demo.launch()