Update app.py

app.py

@@ -69,7 +69,7 @@ steel_blue_theme = SteelBlueTheme()
 
 # --- Model Loading ---
 from diffusers import FlowMatchEulerDiscreteScheduler
-from optimization import optimize_pipeline_ # Assuming this is a custom file
+# from optimization import optimize_pipeline_ # Assuming this is a custom file, if not available, comment out the call
 from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
 from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
 from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
@@ -102,42 +102,10 @@ 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())
+MAX_SEED = np.iinfo(np.int32).max
 
-optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")
-
-# --- Helper Function for Aspect Ratio (Corrected) ---
-@torch.no_grad()
-def update_dimensions_on_upload(image):
-    # *** FIX: This function now correctly preserves aspect ratio for all image sizes. ***
-    if image is None:
-        return 1024, 1024  # Default for no image
-
-    original_width, original_height = image.size
-    max_dim = 1024
-
-    if original_width > max_dim or original_height > max_dim:
-        # If the image is larger than the max dimension, scale it down
-        if original_width > original_height:
-            new_width = max_dim
-            new_height = int(max_dim * original_height / original_width)
-        else:
-            new_height = max_dim
-            new_width = int(max_dim * original_width / original_height)
-    else:
-        # If the image is smaller, use its original dimensions
-        new_width = original_width
-        new_height = original_height
-
-    # Ensure final dimensions are multiples of 8 for model compatibility
-    final_width = (new_width // 8) * 8
-    final_height = (new_height // 8) * 8
-
-    return final_width, final_height
-
-
-# --- Main Inference Function ---
+# --- Main Inference Function (Corrected) ---
 @spaces.GPU
 def infer(
     input_image,
@@ -147,8 +115,6 @@ def infer(
     randomize_seed,
     guidance_scale,
     steps,
-    width,
-    height,
     progress=gr.Progress(track_tqdm=True)
 ):
     if input_image is None:
@@ -163,16 +129,19 @@ def infer(
         pipe.set_adapters(["light-restoration"], adapter_weights=[1.0])
     elif lora_adapter == "Relight":
         pipe.set_adapters(["relight"], adapter_weights=[1.0])
-        
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    
+
     generator = torch.Generator(device=device).manual_seed(seed)
-    
     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"
-    
+
+    # *** FIX: Get dimensions directly from the input image to preserve aspect ratio ***
+    original_image = input_image.convert("RGB")
+    width, height = original_image.size
+
     result = pipe(
-        image=input_image.convert("RGB"),
+        image=original_image,
         prompt=prompt,
         negative_prompt=negative_prompt,
         height=height,
@@ -180,22 +149,19 @@ def infer(
         num_inference_steps=steps,
         generator=generator,
         true_cfg_scale=guidance_scale,
-        num_images_per_prompt=1,
     ).images[0]
 
     return result, seed
 
-# --- Wrapper for Examples ---
+# --- Wrapper for Examples (Corrected) ---
 @spaces.GPU
 def infer_example(input_image, prompt, lora_adapter):
     input_pil = input_image.convert("RGB")
-    # Calculate correct aspect ratio for the example image using the corrected function
-    width, height = update_dimensions_on_upload(input_pil)
     # Set reasonable default values for example inference
-    guidance_scale = 1.0
-    steps = 4
+    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)
+    result, seed = infer(input_pil, prompt, lora_adapter, 0, True, guidance_scale, steps)
     return result, seed
 
 # --- UI Layout ---
@@ -211,36 +177,33 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
     with gr.Column(elem_id="col-container"):
         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(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,
                     placeholder="e.g., transform into anime",
                 )
-                
+
                 run_button = gr.Button("Run", variant="primary")
-                
+
                 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)
-                    # 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)
-                    
+                    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)
+
             with gr.Column():
-                output_image = gr.Image(label="Output Image", interactive=False, format="png", height=290)
+                output_image = gr.Image(label="Output Image", interactive=False, format="png", height=400)
 
         gr.Examples(
             examples=[
@@ -259,21 +222,17 @@ 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,
+            cache_examples="lazy",
             label="Examples"
         )
-        
-    # --- Event Handlers ---
+
+    # --- Event Handlers (Corrected) ---
     run_button.click(
         fn=infer,
-        inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, width, height],
+        inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
         outputs=[output_image, seed]
     )
     
-    input_image.upload(
-        fn=update_dimensions_on_upload,
-        inputs=[input_image],
-        outputs=[width, height]
-    )
+    # No longer need the upload handler for dimensions
 
 demo.launch()