Update app.py from anycoder

app.py

@@ -7,6 +7,16 @@ import torch
 import random
 from PIL import Image
 from typing import Iterable
+from diffusers import FlowMatchEulerDiscreteScheduler
+
+# --- Custom Local Imports ---
+# Note: Ensure these files (pipeline_qwenimage_edit_plus.py, etc.) 
+# are present in the same directory or installed in the environment.
+from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
+from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
+from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
+
+# --- Theme Imports ---
 from gradio.themes import Soft
 from gradio.themes.utils import colors, fonts, sizes
 
@@ -94,11 +104,6 @@ if torch.cuda.is_available():
 print("Using device:", device)
 
 # --- Model Loading ---
-from diffusers import FlowMatchEulerDiscreteScheduler
-from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
-from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
-from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
-
 dtype = torch.bfloat16
 
 pipe = QwenImageEditPlusPipeline.from_pretrained(
@@ -122,11 +127,11 @@ except Exception as e:
 MAX_SEED = np.iinfo(np.int32).max
 
 # --- Dynamic LoRA Configuration ---
-# This dictionary defines the available adapters. 
-# The application uses lazy-loading to download these only when selected.
+# These are architectural placeholders. To make the styles work, update 'repo' and 'weights' 
+# to point to actual HuggingFace repositories containing valid LoRA weights.
 ADAPTER_SPECS = {
     "Cinematic-DSLR": {
-        "repo": "prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast", # Placeholder for base repo structure
+        "repo": "prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast",
         "weights": "placeholder_weights.safetensors", 
         "adapter_name": "cinematic-dslr",
         "description": "High-end cinema look with professional color grading."
@@ -155,6 +160,7 @@ ADAPTER_SPECS = {
 LOADED_ADAPTERS = set()
 
 def update_dimensions_on_upload(image):
+    """Calculates optimal dimensions based on image aspect ratio."""
     if image is None:
         return 1024, 1024
     
@@ -169,7 +175,7 @@ def update_dimensions_on_upload(image):
         aspect_ratio = original_width / original_height
         new_width = int(new_height * aspect_ratio)
         
-    # Ensure dimensions are multiples of 8
+    # Ensure dimensions are multiples of 8 (standard for diffusion models)
     new_width = (new_width // 8) * 8
     new_height = (new_height // 8) * 8
     
@@ -186,6 +192,9 @@ def infer(
     steps,
     progress=gr.Progress(track_tqdm=True)
 ):
+    """
+    Main inference function with dynamic LoRA hot-loading.
+    """
     # Cleanup memory before starting
     gc.collect()
     torch.cuda.empty_cache()
@@ -196,17 +205,17 @@ def infer(
     # 1. Get Config for Selected Adapter
     spec = ADAPTER_SPECS.get(lora_adapter)
     if not spec:
-        raise gr.Error(f"Configuration not found for: {lora_adapter}")
-
-    adapter_name = spec["adapter_name"]
+        # Fallback to base model if config missing
+        print(f"Configuration not found for: {lora_adapter}. Using base model.")
+        adapter_name = "base"
+    else:
+        adapter_name = spec["adapter_name"]
 
     # 2. Lazy Loading Logic (Hot Swapping)
     # Only loads if not currently in memory to save bandwidth/startup time
-    if adapter_name not in LOADED_ADAPTERS:
+    if spec and adapter_name not in LOADED_ADAPTERS:
         print(f"--- Hot Loading Adapter: {lora_adapter} ---")
         try:
-            # NOTE: Replace this logic with actual HuggingFace Hub calls 
-            # for your specific dynamic endpoints
             pipe.load_lora_weights(
                 spec["repo"], 
                 weight_name=spec["weights"], 
@@ -215,15 +224,19 @@ def infer(
             LOADED_ADAPTERS.add(adapter_name)
         except Exception as e:
             # Fallback for demonstration if placeholder weights don't exist
-            print(f"Info: Could not load placeholder weights for {lora_adapter}: {e}")
-            # In a real scenario, you might load a default or alert the user
-            pass
+            print(f"Info: Could not load weights for {lora_adapter}: {e}")
+            gr.Warning(f"Could not load specific style weights for '{lora_adapter}'. Using base model instead.")
+            # Ensure we don't try to set this adapter if it failed to load
+            adapter_name = "base" 
     else:
-        print(f"--- Adapter {lora_adapter} already active in memory. ---")
+        print(f"--- Adapter {lora_adapter} already active in memory or using base model. ---")
 
     # 3. Activate the specific adapter
-    # Unload others by exclusively setting this one to weight 1.0
-    pipe.set_adapters([adapter_name], adapter_weights=[1.0])
+    # If 'base' (or fallback), we disable adapters. Otherwise, set the specific one.
+    if adapter_name == "base":
+        pipe.disable_lora()
+    else:
+        pipe.set_adapters([adapter_name], adapter_weights=[1.0])
 
     # 4. Standard Inference Setup
     if randomize_seed:
@@ -250,7 +263,7 @@ def infer(
         return result, seed
 
     except Exception as e:
-        raise e
+        raise gr.Error(f"Error during inference: {e}")
     finally:
         # Cleanup
         gc.collect()
@@ -258,6 +271,7 @@ def infer(
 
 @spaces.GPU
 def infer_example(input_image, prompt, lora_adapter):
+    """Helper function for Gradio Examples."""
     if input_image is None:
         return None, 0
     
@@ -271,60 +285,61 @@ def infer_example(input_image, prompt, lora_adapter):
 # Gradio 6 Syntax: gr.Blocks() takes NO parameters. All config goes in demo.launch()
 
 with gr.Blocks() as demo:
-    with gr.Column(elem_id="col-container"):
-        # Header
-        gr.HTML("""
-        <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;">
-            <h1 style="margin: 0;">Qwen-Image-Edit-2509-LoRAs-Fast</h1>
-            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="text-decoration: none; color: inherit;">
-                <small>Built with anycoder</small>
-            </a>
-        </div>
-        """)
-        
-        gr.Markdown("Perform diverse image edits using specialized [LoRA](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image-Edit-2509) adapters for the [Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) model.")
-
-        with gr.Row(equal_height=True):
-            with gr.Column():
-                input_image = gr.Image(label="Upload Image", type="pil", height=290)
-                
-                prompt = gr.Text(
-                    label="Edit Prompt",
-                    show_label=True,
-                    placeholder="e.g., apply cinematic lighting...",
-                )
+    gr.HTML("""
+    <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;">
+        <h1 style="margin: 0;">Qwen-Image-Edit-2509-LoRAs-Fast</h1>
+    </div>
+    """)
+    
+    gr.Markdown(
+        "Perform diverse image edits using specialized [LoRA](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image-Edit-2509) "
+        "adapters for the [Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) model. "
+        "This demo features **dynamic hot-loading**, downloading LoRA weights only when you select them."
+    )
 
-                run_button = gr.Button("Edit Image", variant="primary")
-
-            with gr.Column():
-                output_image = gr.Image(label="Output Image", interactive=False, format="png", height=353)
-                
-                with gr.Row():
-                    # Dynamic keys based on the config dict
-                    lora_adapter = gr.Dropdown(
-                        label="Choose Editing Style",
-                        choices=list(ADAPTER_SPECS.keys()),
-                        value="Cinematic-DSLR"
-                    )
-                
-                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)
-        
-        gr.Examples(
-            examples=[
-                ["examples/1.jpg", "Apply cinematic dslr style.", "Cinematic-DSLR"],
-                ["examples/5.jpg", "Enhance portrait lighting.", "Portrait-Pro"],
-                ["examples/4.jpg", "Switch to high key lighting.", "High-Key-Lighting"],
-            ],
-            inputs=[input_image, prompt, lora_adapter],
-            outputs=[output_image, seed],
-            fn=infer_example,
-            cache_examples=False,
-            label="Examples"
-        )
+    with gr.Row(equal_height=True):
+        with gr.Column():
+            input_image = gr.Image(label="Upload Image", type="pil", height=290)
+            
+            prompt = gr.Text(
+                label="Edit Prompt",
+                show_label=True,
+                placeholder="e.g., apply cinematic lighting...",
+                lines=2
+            )
+
+            run_button = gr.Button("Edit Image", variant="primary", size="lg")
+
+        with gr.Column():
+            output_image = gr.Image(label="Output Image", interactive=False, format="png", height=353)
+            
+            with gr.Row():
+                # Dynamic keys based on the config dict
+                lora_adapter = gr.Dropdown(
+                    label="Choose Editing Style",
+                    choices=list(ADAPTER_SPECS.keys()),
+                    value="Cinematic-DSLR",
+                    info="Select a style to hot-load"
+                )
+            
+            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)
+    
+    gr.Examples(
+        examples=[
+            ["examples/1.jpg", "Apply cinematic dslr style.", "Cinematic-DSLR"],
+            ["examples/5.jpg", "Enhance portrait lighting.", "Portrait-Pro"],
+            ["examples/4.jpg", "Switch to high key lighting.", "High-Key-Lighting"],
+        ],
+        inputs=[input_image, prompt, lora_adapter],
+        outputs=[output_image, seed],
+        fn=infer_example,
+        cache_examples=False,
+        label="Examples"
+    )
 
     # Gradio 6 Event Listeners
     run_button.click(
@@ -337,13 +352,16 @@ with gr.Blocks() as demo:
 css="""
 #col-container {
     margin: 0 auto;
-    max-width: 960px;
+    max-width: 1000px;
+}
+.gradio-container {
+    font-family: 'Outfit', sans-serif !important;
 }
-#main-title h1 {font-size: 2.1em !important;}
 """
 
 if __name__ == "__main__":
     # Gradio 6 Launch Syntax
+    # All app-level parameters (theme, css, footer_links) go here.
     demo.queue(max_size=30).launch(
         css=css, 
         theme=orange_red_theme,