Upload app.py

app.py

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+import os
+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
+
+colors.steel_blue = colors.Color(
+    name="steel_blue",
+    c50="#EBF3F8",
+    c100="#D3E5F0",
+    c200="#A8CCE1",
+    c300="#7DB3D2",
+    c400="#529AC3",
+    c500="#4682B4",
+    c600="#3E72A0",
+    c700="#36638C",
+    c800="#2E5378",
+    c900="#264364",
+    c950="#1E3450",
+)
+
+class SteelBlueTheme(Soft):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.gray,
+        secondary_hue: colors.Color | str = colors.steel_blue,
+        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_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()
+
+from diffusers import FlowMatchEulerDiscreteScheduler
+from optimization import optimize_pipeline_
+from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
+from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
+from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+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)
+
+# Load all LoRA adapters
+pipe.load_lora_weights("autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime",
+                       weight_name="Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors",
+                       adapter_name="anime")
+pipe.load_lora_weights("dx8152/Qwen-Edit-2509-Multiple-angles",
+                       weight_name="镜头转换.safetensors",
+                       adapter_name="multiple-angles")
+pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Light_restoration",
+                       weight_name="移除光影.safetensors",
+                       adapter_name="light-restoration")
+pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Relight",
+                       weight_name="Qwen-Edit-Relight.safetensors",
+                       adapter_name="relight")
+
+pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
+# It's recommended to run optimization after loading all weights
+# optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")
+
+MAX_SEED = np.iinfo(np.int32).max
+
+# --- 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
+
+# --- Main Inference Function ---
+@spaces.GPU
+def infer(
+    input_image,
+    prompt,
+    lora_adapter,
+    seed,
+    randomize_seed,
+    guidance_scale,
+    steps,
+    width,
+    height,
+    progress=gr.Progress(track_tqdm=True)
+):
+    if input_image is None:
+        raise gr.Error("Please upload an image to edit.")
+
+    # Dynamically set the adapter
+    if lora_adapter == "Photo-to-Anime":
+        pipe.set_adapters(["anime"], adapter_weights=[1.0])
+    elif lora_adapter == "Multiple-Angles":
+        pipe.set_adapters(["multiple-angles"], adapter_weights=[1.0])
+    elif lora_adapter == "Light-Restoration":
+        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)
+    
+    # *** 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
+        height=height,
+        width=width,
+        num_inference_steps=steps,
+        generator=generator,
+        true_cfg_scale=guidance_scale,
+        num_images_per_prompt=1,
+    ).images[0]
+
+    return result, seed, gr.Button(visible=True)
+
+# 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
+
+# --- UI Layout ---
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
+}
+#main-title h1 {font-size: 2.1em !important;}
+"""
+
+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():
+            with gr.Column():
+                input_image = gr.Image(label="Upload Image", type="pil")
+
+                prompt = gr.Text(
+                    label="Edit Prompt",
+                    show_label=True,
+                    placeholder="e.g., transform into anime",
+                )
+                
+                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)
+                    # 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)
+                    
+        gr.Examples(
+            examples=[
+                ["examples/1.jpg", "Transform into anime.", "Photo-to-Anime"],
+                ["examples/2.jpg", "Move the camera left.", "Multiple-Angles"],
+                ["examples/2.jpg", "Move the camera right.", "Multiple-Angles"],
+                ["examples/2.jpg", "Move the camera down.", "Multiple-Angles"],
+                ["examples/2.jpg", "Rotate the camera 45 degrees to the left.", "Multiple-Angles"],
+                ["examples/3.jpg", "Rotate the camera 45 degrees to the right.", "Multiple-Angles"],
+                ["examples/3.jpg", "Switch the camera to a top-down view.", "Multiple-Angles"],
+                ["examples/3.jpg", "Switch the camera to a wide-angle lens.", "Multiple-Angles"],
+                ["examples/3.jpg", "Switch the camera to a close-up lens.", "Multiple-Angles"],
+                ["examples/shadow_example.jpg", "Remove shadows and relight the image using soft lighting.", "Light-Restoration"],
+                ["examples/relight_example.jpg", "Relight the image using soft, diffused lighting that simulates sunlight filtering through curtains.", "Relight"],
+            ],
+            inputs=[input_image, prompt, lora_adapter],
+            outputs=[output_image, seed],
+            fn=infer_example,
+            cache_examples=False,
+            label="Examples"
+        )
+        
+    run_button.click(
+        fn=infer,
+        inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, width, height],
+        outputs=[output_image, seed]
+    )
+    
+    input_image.upload(
+        fn=update_dimensions_on_upload,
+        inputs=[input_image],
+        outputs=[width, height]
+    )
+
+demo.launch(mcp_server=True, ssr_mode=False, show_error=True)