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() 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) 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 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) 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()) MAX_SEED = np.iinfo(np.int32).max @spaces.GPU def infer( input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, progress=gr.Progress(track_tqdm=True) ): if input_image is None: raise gr.Error("Please upload an image to edit.") 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) 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 = original_image.size 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 @spaces.GPU def infer_example(input_image, prompt, lora_adapter): 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=""" #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](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") 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) gr.Examples( examples=[ ["examples/1.jpg", "Transform into anime.", "Photo-to-Anime"], ["examples/5.jpg", "Remove shadows and relight the image using soft lighting.", "Light-Restoration"], ["examples/4.jpg", "Use a subtle golden-hour filter with smooth light diffusion.", "Relight"], ["examples/2.jpeg", "Rotate the camera 45 degrees to the left.", "Multiple-Angles"], ["examples/2.jpeg", "Switch the camera to a top-down right corner view.", "Multiple-Angles"], ["examples/6.jpg", "Switch the camera to a bottom-up view.", "Multiple-Angles"], ["examples/6.jpg", "Rotate the camera 180 degrees upside down.", "Multiple-Angles"], ["examples/4.jpg", "Rotate the camera 45 degrees to the right.", "Multiple-Angles"], ["examples/4.jpg", "Switch the camera to a top-down view.", "Multiple-Angles"], ["examples/4.jpg", "Switch the camera to a wide-angle lens.", "Multiple-Angles"], ], 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], outputs=[output_image, seed] ) demo.launch(mcp_server=True, ssr_mode=False, show_error=True)