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app.py

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+import os
+import gc
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+import random
+from PIL import Image
+from typing import Iterable
+from gradio.themes import Soft
+from gradio.themes.utils import colors, fonts, sizes
+
+# --- Custom Theme Definition ---
+colors.orange_red = colors.Color(
+    name="orange_red",
+    c50="#FFF0E5",
+    c100="#FFE0CC",
+    c200="#FFC299",
+    c300="#FFA366",
+    c400="#FF8533",
+    c500="#FF4500",
+    c600="#E63E00",
+    c700="#CC3700",
+    c800="#B33000",
+    c900="#992900",
+    c950="#802200",
+)
+
+class OrangeRedTheme(Soft):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.gray,
+        secondary_hue: colors.Color | str = colors.orange_red,
+        neutral_hue: colors.Color | str = colors.slate,
+        text_size: sizes.Size | str = sizes.text_lg,
+        font: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
+        ),
+        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            background_fill_primary="*primary_50",
+            background_fill_primary_dark="*primary_900",
+            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
+            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
+            button_primary_text_color="white",
+            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_700)",
+            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
+            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",
+        )
+
+orange_red_theme = OrangeRedTheme()
+
+# --- Device Setup ---
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
+print("torch.__version__ =", torch.__version__)
+print("torch.version.cuda =", torch.version.cuda)
+print("cuda available:", torch.cuda.is_available())
+print("cuda device count:", torch.cuda.device_count())
+if torch.cuda.is_available():
+    print("current device:", torch.cuda.current_device())
+    print("device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
+
+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(
+    "Qwen/Qwen-Image-Edit-2509",
+    transformer=QwenImageTransformer2DModel.from_pretrained(
+        "linoyts/Qwen-Image-Edit-Rapid-AIO", 
+        subfolder='transformer',
+        torch_dtype=dtype,
+        device_map='cuda'
+    ),
+    torch_dtype=dtype
+).to(device)
+
+# Apply FA3 Optimization
+try:
+    pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
+    print("Flash Attention 3 Processor set successfully.")
+except Exception as e:
+    print(f"Warning: Could not set FA3 processor: {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.
+ADAPTER_SPECS = {
+    "Cinematic-DSLR": {
+        "repo": "prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast", # Placeholder for base repo structure
+        "weights": "placeholder_weights.safetensors", 
+        "adapter_name": "cinematic-dslr",
+        "description": "High-end cinema look with professional color grading."
+    },
+    "Portrait-Pro": {
+        "repo": "prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast",
+        "weights": "placeholder_weights.safetensors",
+        "adapter_name": "portrait-pro",
+        "description": "Optimized for studio portrait lighting and skin detail."
+    },
+    "High-Key-Lighting": {
+        "repo": "prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast",
+        "weights": "placeholder_weights.safetensors",
+        "adapter_name": "high-key",
+        "description": "Bright, even lighting typical of commercial photography."
+    },
+    "Editorial-Style": {
+        "repo": "prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast",
+        "weights": "placeholder_weights.safetensors",
+        "adapter_name": "editorial",
+        "description": "Magazine-style composition and contrast."
+    }
+}
+
+# Track what is currently loaded in memory for hot-swapping
+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
+        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
+    new_width = (new_width // 8) * 8
+    new_height = (new_height // 8) * 8
+    
+    return new_width, new_height
+
+@spaces.GPU
+def infer(
+    input_image,
+    prompt,
+    lora_adapter,
+    seed,
+    randomize_seed,
+    guidance_scale,
+    steps,
+    progress=gr.Progress(track_tqdm=True)
+):
+    # Cleanup memory before starting
+    gc.collect()
+    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 (Hot Swapping)
+    # Only loads if not currently in memory to save bandwidth/startup time
+    if 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"], 
+                adapter_name=adapter_name
+            )
+            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
+    else:
+        print(f"--- Adapter {lora_adapter} already active in memory. ---")
+
+    # 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. 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"
+
+    original_image = input_image.convert("RGB")
+    width, height = update_dimensions_on_upload(original_image)
+
+    try:
+        result = pipe(
+            image=original_image,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            height=height,
+            width=width,
+            num_inference_steps=steps,
+            generator=generator,
+            true_cfg_scale=guidance_scale,
+        ).images[0]
+        
+        return result, seed
+
+    except Exception as e:
+        raise e
+    finally:
+        # Cleanup
+        gc.collect()
+        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
+
+# --- Gradio 6 Application ---
+# 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...",
+                )
+
+                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"
+        )
+
+    # Gradio 6 Event Listeners
+    run_button.click(
+        fn=infer,
+        inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
+        outputs=[output_image, seed],
+        api_visibility="public"
+    )
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
+}
+#main-title h1 {font-size: 2.1em !important;}
+"""
+
+if __name__ == "__main__":
+    # Gradio 6 Launch Syntax
+    demo.queue(max_size=30).launch(
+        css=css, 
+        theme=orange_red_theme, 
+        mcp_server=True, 
+        ssr_mode=False, 
+        show_error=True,
+        footer_links=[{"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}]
+    )

requirements.txt

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+Pillow
+diffusers
+gc
+gradio
+numpy
+qwenimage
+spaces
+torch