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import os |
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import gc |
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import gradio as gr |
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import numpy as np |
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import spaces |
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import torch |
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import random |
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from PIL import Image |
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from gradio.themes import Soft |
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from gradio.themes.utils import colors, fonts, sizes |
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colors.orange_red = colors.Color( |
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name="orange_red", |
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c50="#FFF0E5", |
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c100="#FFE0CC", |
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c200="#FFC299", |
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c300="#FFA366", |
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c400="#FF8533", |
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c500="#FF4500", |
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c600="#E63E00", |
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c700="#CC3700", |
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c800="#B33000", |
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c900="#992900", |
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c950="#802200", |
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) |
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class OrangeRedTheme(Soft): |
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def __init__( |
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self, |
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*, |
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primary_hue: colors.Color | str = colors.gray, |
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secondary_hue: colors.Color | str = colors.orange_red, |
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neutral_hue: colors.Color | str = colors.slate, |
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text_size: sizes.Size | str = sizes.text_lg, |
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font=(fonts.GoogleFont("Outfit"), "Arial", "sans-serif"), |
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font_mono=(fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace"), |
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): |
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super().__init__( |
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primary_hue=primary_hue, |
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secondary_hue=secondary_hue, |
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neutral_hue=neutral_hue, |
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text_size=text_size, |
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font=font, |
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font_mono=font_mono, |
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) |
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super().set( |
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background_fill_primary="*primary_50", |
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background_fill_primary_dark="*primary_900", |
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body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)", |
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body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)", |
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button_primary_text_color="white", |
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button_primary_text_color_hover="white", |
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button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)", |
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button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)", |
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button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)", |
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button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)", |
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button_secondary_text_color="black", |
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button_secondary_text_color_hover="white", |
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button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)", |
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button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)", |
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button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)", |
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button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)", |
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slider_color="*secondary_500", |
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slider_color_dark="*secondary_600", |
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block_title_text_weight="600", |
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block_border_width="3px", |
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block_shadow="*shadow_drop_lg", |
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button_primary_shadow="*shadow_drop_lg", |
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button_large_padding="11px", |
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color_accent_soft="*primary_100", |
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block_label_background_fill="*primary_200", |
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) |
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orange_red_theme = OrangeRedTheme() |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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from diffusers import FlowMatchEulerDiscreteScheduler |
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from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline |
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from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel |
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from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3 |
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dtype = torch.bfloat16 |
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pipe = QwenImageEditPlusPipeline.from_pretrained( |
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"Qwen/Qwen-Image-Edit-2511", |
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transformer=QwenImageTransformer2DModel.from_pretrained( |
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"linoyts/Qwen-Image-Edit-Rapid-AIO", |
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subfolder='transformer', |
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torch_dtype=dtype, |
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device_map='cuda' |
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), |
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torch_dtype=dtype |
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).to(device) |
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try: |
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pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3()) |
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except Exception as e: |
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print(f"Warning: FA3 processor error: {e}") |
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MAX_SEED = np.iinfo(np.int32).max |
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LOADED_ADAPTERS = set() |
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ADAPTER_SPECS = { |
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"Multiple-Angles": {"repo": "dx8152/Qwen-Edit-2509-Multiple-angles", "weights": "镜头转换.safetensors", "adapter_name": "multiple-angles"}, |
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"MNCL": {"repo": "sanetium/Testing", "weights": "qwen_MCNL_v1.0.safetensors", "adapter_name": "MNCL"}, |
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"MMystic": {"repo": "sanetium/mystic", "weights": "Qwen-MysticXXX-v1.safetensors", "adapter_name": "Mystic"}, |
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"next scene": {"repo": "aiqwen/next-scene-qwen-image-lora-2509", "weights": "next-scene_lora_v1-3000.safetensors", "adapter_name": "Next scene"}, |
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"LilSeven": {"repo": "sanetium/Lilseven", "weights": "lilseven8000.safetensors", "adapter_name": "LilSeven"}, |
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"URP": {"repo": "prithivMLmods/Qwen-Image-Edit-2511-Ultra-Realistic-Portrait", "weights": "URP_20.safetensors", "adapter_name": "URP"}, |
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"IEI": {"repo": "peteromallet/Qwen-Image-Edit-InSubject", "weights": "InSubject-0.5.safetensors", "adapter_name": "IEI"}, |
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} |
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def update_dimensions_on_upload(image): |
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if image is None: return 1024, 1024 |
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w, h = image.size |
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aspect = h / w if w > h else w / h |
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new_w, new_h = (1024, int(1024 * aspect)) if w > h else (int(1024 * aspect), 1024) |
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return (new_w // 8) * 8, (new_h // 8) * 8 |
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@spaces.GPU |
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def infer(images, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, lora_strength): |
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gc.collect() |
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torch.cuda.empty_cache() |
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if not images: raise gr.Error("Please upload images.") |
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pil_images = [] |
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for item in images: |
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try: |
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path = item[0] if isinstance(item, (tuple, list)) else item |
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pil_images.append(Image.open(path).convert("RGB") if isinstance(path, str) else path.convert("RGB")) |
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except: continue |
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spec = ADAPTER_SPECS.get(lora_adapter) |
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adapter_name = spec["adapter_name"] |
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if adapter_name not in LOADED_ADAPTERS: |
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pipe.load_lora_weights(spec["repo"], weight_name=spec["weights"], adapter_name=adapter_name) |
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LOADED_ADAPTERS.add(adapter_name) |
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pipe.set_adapters([adapter_name], adapter_weights=[lora_strength]) |
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if randomize_seed: seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator(device=device).manual_seed(seed) |
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w, h = update_dimensions_on_upload(pil_images[0]) |
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result = pipe( |
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image=pil_images, |
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prompt=prompt, |
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negative_prompt="worst quality, low quality, blurry, text, watermark,extra hands,bad anatomy,blurry face,", |
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height=h, width=w, |
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num_inference_steps=steps, |
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generator=generator, |
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true_cfg_scale=guidance_scale, |
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).images[0] |
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return result, seed |
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css = "#col-container { margin: 0 auto; max-width: 1000px; } #main-title h1 { font-size: 2.3em !important; }" |
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with gr.Blocks(theme=orange_red_theme, css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown("# **Qwen2511**", elem_id="main-title") |
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with gr.Row(): |
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with gr.Column(): |
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images = gr.Gallery(label="Upload Images", type="filepath", columns=2, height=300) |
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prompt = gr.Text(label="Edit Prompt", placeholder="e.g., transform into anime..") |
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run_button = gr.Button("Edit Image", variant="primary") |
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with gr.Column(): |
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output_image = gr.Image(label="Output Image", interactive=False) |
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lora_adapter = gr.Dropdown(label="Style", choices=list(ADAPTER_SPECS.keys()), value="Multiple-Angles") |
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with gr.Accordion("Advanced Settings", open=False): |
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lora_strength = gr.Slider(label="LoRA Strength", minimum=0.0, maximum=2.0, step=0.01, value=1.0) |
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, value=0) |
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randomize_seed = gr.Checkbox(label="Randomize Seed", value=True) |
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guidance_scale = gr.Slider(label="Guidance", minimum=1.0, maximum=10.0, value=1.0) |
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=4) |
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gr.Markdown("[*](https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast) Experimental Space.") |
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run_button.click( |
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fn=infer, |
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inputs=[images, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, lora_strength], |
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outputs=[output_image, seed] |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=30).launch(ssr_mode=False, show_error=True) |
