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", # [transformer weights extracted from: Phr00t/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.load_lora_weights("dx8152/Qwen-Edit-2509-Multi-Angle-Lighting", weight_name="多角度灯光-251116.safetensors", adapter_name="multi-angle-lighting") pipe.load_lora_weights("tlennon-ie/qwen-edit-skin", weight_name="qwen-edit-skin_1.1_000002750.safetensors", adapter_name="edit-skin") pipe.load_lora_weights("lovis93/next-scene-qwen-image-lora-2509", weight_name="next-scene_lora-v2-3000.safetensors", adapter_name="next-scene") pipe.load_lora_weights("vafipas663/Qwen-Edit-2509-Upscale-LoRA", weight_name="qwen-edit-enhance_64-v3_000001000.safetensors", adapter_name="upscale-image") pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3()) MAX_SEED = np.iinfo(np.int32).max 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(duration=30) 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]) elif lora_adapter == "Multi-Angle-Lighting": pipe.set_adapters(["multi-angle-lighting"], adapter_weights=[1.0]) elif lora_adapter == "Edit-Skin": pipe.set_adapters(["edit-skin"], adapter_weights=[1.0]) elif lora_adapter == "Next-Scene": pipe.set_adapters(["next-scene"], adapter_weights=[1.0]) elif lora_adapter == "Upscale-Image": pipe.set_adapters(["upscale-image"], 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") # Use the new function to update dimensions width, height = update_dimensions_on_upload(original_image) 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(duration=30) 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() 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", height=290) prompt = gr.Text( label="Edit Prompt", show_label=True, placeholder="e.g., transform into anime..", ) run_button = gr.Button("Edit Image", variant="primary") with gr.Column(): output_image = gr.Image(label="Output Image", interactive=False, format="png", height=350) with gr.Row(): lora_adapter = gr.Dropdown( label="Choose Editing Style", choices=["Photo-to-Anime", "Multiple-Angles", "Light-Restoration", "Multi-Angle-Lighting", "Upscale-Image", "Relight", "Next-Scene", "Edit-Skin"], value="Photo-to-Anime" ) 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) 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/7.jpg", "Light source from the Right Rear", "Multi-Angle-Lighting"], ["examples/10.jpeg", "Upscale the image.", "Upscale-Image"], ["examples/7.jpg", "Light source from the Below", "Multi-Angle-Lighting"], ["examples/2.jpeg", "Switch the camera to a top-down right corner view.", "Multiple-Angles"], ["examples/9.jpg", "The camera moves slightly forward as sunlight breaks through the clouds, casting a soft glow around the character's silhouette in the mist. Realistic cinematic style, atmospheric depth.", "Next-Scene"], ["examples/8.jpg", "Make the subjects skin details more prominent and natural.", "Edit-Skin"], ["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] ) if __name__ == "__main__": demo.queue(max_size=30).launch(css=css, theme=steel_blue_theme, mcp_server=True, ssr_mode=False, show_error=True)