Delete app.py

app.py

@@ -1,280 +0,0 @@
-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=4096, step=8, value=1024, visible=True)
-                    width = gr.Slider(label="Width", minimum=256, maximum=4096, step=8, value=1024, visible=True)
-                    
-        gr.Examples(
-            examples=[
-                ["examples/1.jpg", "Transform into anime.", "Photo-to-Anime"],
-                ["examples/2.jpeg", "Move the camera left.", "Multiple-Angles"],
-                ["examples/2.jpeg", "Move the camera right.", "Multiple-Angles"],
-                ["examples/2.jpeg", "Move the camera down.", "Multiple-Angles"],
-                ["examples/2.jpeg", "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)