Update app.py

app.py

@@ -101,12 +101,12 @@ dtype = torch.bfloat16
 pipe = QwenImageEditPlusPipeline.from_pretrained(
     "Qwen/Qwen-Image-Edit-2511",
     transformer=QwenImageTransformer2DModel.from_pretrained(
-        "Pr0f3ssi0n4ln00b/Phr00t-Qwen-Rapid-AIO", # -> diffusers compatiable transformer weights extracted from [Phr00t/Qwen-Image-Edit-Rapid-AIO]
-        subfolder='transformer',
+        "Pr0f3ssi0n4ln00b/Phr00t-Qwen-Rapid-AIO",  # diffusers-compatible transformer weights
+        subfolder="transformer",
         torch_dtype=dtype,
-        device_map='cuda'
+        device_map="cuda",
     ),
-    torch_dtype=dtype
+    torch_dtype=dtype,
 ).to(device)
 
 # Apply FA3 Optimization
@@ -118,84 +118,95 @@ except Exception as e:
 
 MAX_SEED = np.iinfo(np.int32).max
 
+# -------------------------
+# LoRA adapters + presets
+# -------------------------
+
+NONE_LORA = "None"
+
 # Define the config for all adapters
 ADAPTER_SPECS = {
     "Photo-to-Anime": {
         "repo": "autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime",
         "weights": "Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors",
-        "adapter_name": "anime"
+        "adapter_name": "anime",
     },
     "Semirealistic-photo-detailer": {
         "repo": "rzgar/Qwen-Image-Edit-semi-realistic-detailer",
         "weights": "Qwen-Image-Edit-Anime-Semi-Realistic-Detailer-v1.safetensors",
-        "adapter_name": "semirealistic"
+        "adapter_name": "semirealistic",
     },
     "Any2Real_Alpha": {
         "repo": "lrzjason/QwenEdit-Anything2Real_Alpha",
         "weights": "Anything2RealAlpha.safetensors",
-        "adapter_name": "photorealpha"
+        "adapter_name": "photorealpha",
     },
     "Any2Real_2601": {
         "repo": "lrzjason/Anything2Real_2601",
         "weights": "anything2real_2601_A_final_patched.safetensors",
-        "adapter_name": "photoreal"
+        "adapter_name": "photoreal",
     },
     "Multiple-Angles": {
         "repo": "dx8152/Qwen-Edit-2509-Multiple-angles",
         "weights": "镜头转换.safetensors",
-        "adapter_name": "multiple-angles"
+        "adapter_name": "multiple-angles",
     },
     "Light-Restoration": {
         "repo": "dx8152/Qwen-Image-Edit-2509-Light_restoration",
         "weights": "移除光影.safetensors",
-        "adapter_name": "light-restoration"
+        "adapter_name": "light-restoration",
     },
     "Relight": {
         "repo": "dx8152/Qwen-Image-Edit-2509-Relight",
         "weights": "Qwen-Edit-Relight.safetensors",
-        "adapter_name": "relight"
+        "adapter_name": "relight",
     },
     "Multi-Angle-Lighting": {
         "repo": "dx8152/Qwen-Edit-2509-Multi-Angle-Lighting",
         "weights": "多角度灯光-251116.safetensors",
-        "adapter_name": "multi-angle-lighting"
+        "adapter_name": "multi-angle-lighting",
     },
     "Edit-Skin": {
         "repo": "tlennon-ie/qwen-edit-skin",
         "weights": "qwen-edit-skin_1.1_000002750.safetensors",
-        "adapter_name": "edit-skin"
+        "adapter_name": "edit-skin",
     },
     "Next-Scene": {
         "repo": "lovis93/next-scene-qwen-image-lora-2509",
         "weights": "next-scene_lora-v2-3000.safetensors",
-        "adapter_name": "next-scene"
+        "adapter_name": "next-scene",
     },
     "Flat-Log": {
         "repo": "tlennon-ie/QwenEdit2509-FlatLogColor",
         "weights": "QwenEdit2509-FlatLogColor.safetensors",
-        "adapter_name": "flat-log"
+        "adapter_name": "flat-log",
     },
     "Upscale-Image": {
         "repo": "vafipas663/Qwen-Edit-2509-Upscale-LoRA",
         "weights": "qwen-edit-enhance_64-v3_000001000.safetensors",
-        "adapter_name": "upscale-image"
+        "adapter_name": "upscale-image",
     },
     "Upscale2K": {
         "repo": "valiantcat/Qwen-Image-Edit-2509-Upscale2K",
         "weights": "qwen_image_edit_2509_upscale.safetensors",
-        "adapter_name": "upscale-2k"
+        "adapter_name": "upscale-2k",
     },
 }
 
+# Preset prompt per LoRA (leave empty for others)
+LORA_PRESET_PROMPTS = {
+    "Any2Real_2601": "change the picture 1 to realistic photograph",
+}
+
 # Track what is currently loaded in memory
 LOADED_ADAPTERS = set()
 
 def update_dimensions_on_upload(image):
     if image is None:
         return 1024, 1024
-    
+
     original_width, original_height = image.size
-    
+
     if original_width > original_height:
         new_width = 1024
         aspect_ratio = original_height / original_width
@@ -204,13 +215,29 @@ def update_dimensions_on_upload(image):
         new_height = 1024
         aspect_ratio = original_width / original_height
         new_width = int(new_height * aspect_ratio)
-        
+
     # Ensure dimensions are multiples of 8
     new_width = (new_width // 8) * 8
     new_height = (new_height // 8) * 8
-    
+
     return new_width, new_height
 
+def on_lora_change(selected_lora, current_prompt):
+    """
+    - If user selects a LoRA with a preset prompt:
+        - Fill prompt ONLY if it's currently empty or matches the previous preset behavior.
+      (To keep this simple and non-destructive: only fill when empty.)
+    - If user selects None: don't touch prompt.
+    """
+    if selected_lora == NONE_LORA:
+        return gr.update(value=current_prompt)
+
+    preset = LORA_PRESET_PROMPTS.get(selected_lora, "")
+    if preset and (current_prompt is None or str(current_prompt).strip() == ""):
+        return gr.update(value=preset)
+
+    return gr.update(value=current_prompt)
+
 @spaces.GPU
 def infer(
     input_image,
@@ -220,47 +247,60 @@ def infer(
     randomize_seed,
     guidance_scale,
     steps,
-    progress=gr.Progress(track_tqdm=True)
+    progress=gr.Progress(track_tqdm=True),
 ):
     # Cleanup memory before starting
     gc.collect()
-    torch.cuda.empty_cache()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
 
     if input_image is None:
         raise gr.Error("Please upload an image to edit.")
 
-    # 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"]
-
-    # 2. Lazy Loading Logic
-    if adapter_name not in LOADED_ADAPTERS:
-        print(f"--- Downloading and Loading Adapter: {lora_adapter} ---")
+    # 1) Handle "None" LoRA: disable adapters
+    if lora_adapter == NONE_LORA:
+        # Ensure no adapters are active
         try:
-            pipe.load_lora_weights(
-                spec["repo"], 
-                weight_name=spec["weights"], 
-                adapter_name=adapter_name
-            )
-            LOADED_ADAPTERS.add(adapter_name)
-        except Exception as e:
-            raise gr.Error(f"Failed to load adapter {lora_adapter}: {e}")
+            pipe.set_adapters([], adapter_weights=[])
+        except Exception:
+            # Some versions may not like empty lists; as a fallback set weights of all loaded adapters to 0
+            if LOADED_ADAPTERS:
+                pipe.set_adapters(list(LOADED_ADAPTERS), adapter_weights=[0.0] * len(LOADED_ADAPTERS))
     else:
-        print(f"--- Adapter {lora_adapter} is already loaded. ---")
+        # 2) 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"]
+
+        # 3) Lazy Loading Logic
+        if adapter_name not in LOADED_ADAPTERS:
+            print(f"--- Downloading and Loading Adapter: {lora_adapter} ---")
+            try:
+                pipe.load_lora_weights(
+                    spec["repo"],
+                    weight_name=spec["weights"],
+                    adapter_name=adapter_name,
+                )
+                LOADED_ADAPTERS.add(adapter_name)
+            except Exception as e:
+                raise gr.Error(f"Failed to load adapter {lora_adapter}: {e}")
+        else:
+            print(f"--- Adapter {lora_adapter} is already loaded. ---")
 
-    # 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])
+        # 4) Activate ONLY the selected adapter
+        pipe.set_adapters([adapter_name], adapter_weights=[1.0])
 
-    # 4. Standard Inference Setup
+    # 5) Standard Inference Setup
     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"
+    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"
+    )
 
     original_image = input_image.convert("RGB")
     width, height = update_dimensions_on_upload(original_image)
@@ -276,7 +316,7 @@ def infer(
             generator=generator,
             true_cfg_scale=guidance_scale,
         ).images[0]
-        
+
         return result, seed
 
     except Exception as e:
@@ -284,20 +324,21 @@ def infer(
     finally:
         # Cleanup
         gc.collect()
-        torch.cuda.empty_cache()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
 
 @spaces.GPU
 def infer_example(input_image, prompt, lora_adapter):
     if input_image is None:
         return None, 0
-    
+
     input_pil = input_image.convert("RGB")
     guidance_scale = 1.0
     steps = 4
     result, seed = infer(input_pil, prompt, lora_adapter, 0, True, guidance_scale, steps)
     return result, seed
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 960px;
@@ -308,12 +349,16 @@ css="""
 with gr.Blocks() as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown("# **Qwen-Image-Edit-2511-LoRAs-Fast**", elem_id="main-title")
-        gr.Markdown("Perform diverse image edits using specialized [LoRA](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image-Edit-2511) adapters for the [Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) model.")
+        gr.Markdown(
+            "Perform diverse image edits using specialized "
+            "[LoRA](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image-Edit-2511) adapters for the "
+            "[Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) 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,
@@ -324,20 +369,28 @@ with gr.Blocks() as demo:
 
             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_choices = [NONE_LORA] + list(ADAPTER_SPECS.keys())
                     lora_adapter = gr.Dropdown(
                         label="Choose Editing Style",
-                        choices=list(ADAPTER_SPECS.keys()),
-                        value="Photo-to-Anime"
+                        choices=lora_choices,
+                        value=NONE_LORA,  # default is None
                     )
+
                 with gr.Accordion("Advanced Settings", open=False, visible=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)
-        
+
+        # When a user selects a LoRA, optionally auto-fill the prompt (only if the prompt is currently empty)
+        lora_adapter.change(
+            fn=on_lora_change,
+            inputs=[lora_adapter, prompt],
+            outputs=[prompt],
+        )
+
         gr.Examples(
             examples=[
                 ["examples/1.jpg", "Transform into anime.", "Photo-to-Anime"],
@@ -362,14 +415,20 @@ with gr.Blocks() as demo:
             outputs=[output_image, seed],
             fn=infer_example,
             cache_examples=False,
-            label="Examples"
+            label="Examples",
         )
 
     run_button.click(
         fn=infer,
         inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
-        outputs=[output_image, seed]
+        outputs=[output_image, seed],
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=30).launch(css=css, theme=orange_red_theme, mcp_server=True, ssr_mode=False, show_error=True)
+    demo.queue(max_size=30).launch(
+        css=css,
+        theme=orange_red_theme,
+        mcp_server=True,
+        ssr_mode=False,
+        show_error=True,
+    )