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Running on Zero
Running on Zero
Update app.py
Browse files
app.py
CHANGED
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@@ -4,57 +4,88 @@ 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 typing import Iterable
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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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from deep_translator import GoogleTranslator
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from transformers import pipeline
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c950="#1E3450",
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)
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print("Loading Safety Checker...")
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safety_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=-1)
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try:
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results = safety_classifier(image)
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for result in results:
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if result['label'] == 'nsfw' and result['score'] > 0.75:
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return True
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return False
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except Exception as e:
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print(f"Safety check error: {e}")
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return False
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# --- بارگذاری مدل اصلی ---
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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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device = "cuda" if torch.cuda.is_available() else "cpu"
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print("Loading pipeline...")
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pipe = QwenImageEditPlusPipeline.from_pretrained(
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"Qwen/Qwen-Image-Edit-2509",
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transformer=QwenImageTransformer2DModel.from_pretrained(
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@@ -66,74 +97,52 @@ pipe = QwenImageEditPlusPipeline.from_pretrained(
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torch_dtype=dtype
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).to(device)
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pipe.load_lora_weights("
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pipe.
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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")
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pipe.load_lora_weights("tlennon-ie/qwen-edit-skin", weight_name="qwen-edit-skin_1.1_000002750.safetensors", adapter_name="edit-skin")
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pipe.load_lora_weights("lovis93/next-scene-qwen-image-lora-2509", weight_name="next-scene_lora-v2-3000.safetensors", adapter_name="next-scene")
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pipe.load_lora_weights("vafipas663/Qwen-Edit-2509-Upscale-LoRA", weight_name="qwen-edit-enhance_64-v3_000001000.safetensors", adapter_name="upscale-image")
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MAX_SEED = np.iinfo(np.int32).max
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"اصلاح نور و سایه": "light-restoration",
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"نورپردازی مجدد (Relight)": "relight",
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"نورپردازی چند زاویهای": "multi-angle-lighting",
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"روتوش پوست": "edit-skin",
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"صحنه بعدی (سینمایی)": "next-scene",
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"افزایش کیفیت (Upscale)": "upscale-image"
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}
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"۱۶:۹ (افقی - 1344x768)",
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"۹:۱۶ (عمودی - 768x1344)",
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"شخصیسازی (Custom)"
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]
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"lingerie", "bikini", "swimwear", "underwear", "fetish", "topless",
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"exhibitionism", "hentai", "ecchi", "18+"
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]
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if f" {word} " in f" {text_lower} ":
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return False
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return True
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print(f"Translation Error: {e}")
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return text
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def update_dimensions_on_upload(image):
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if image is None:
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return 1024, 1024
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original_width, original_height = image.size
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if original_width > original_height:
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new_width = 1024
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aspect_ratio = original_height / original_width
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@@ -142,560 +151,166 @@ def update_dimensions_on_upload(image):
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new_height = 1024
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aspect_ratio = original_width / original_height
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new_width = int(new_height * aspect_ratio)
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return new_width, new_height
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def get_error_html(message):
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return f"""
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<div style="background-color: #fee2e2; border: 1px solid #ef4444; color: #b91c1c; padding: 12px; border-radius: 8px; text-align: center; margin-bottom: 10px; font-weight: bold; display: flex; align-items: center; justify-content: center; gap: 8px;">
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<span style="font-size: 1.2em;">⛔</span>
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{message}
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</div>
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"""
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def get_success_html(message):
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return f"""
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<div style="background-color: #dcfce7; border: 1px solid #22c55e; color: #15803d; padding: 12px; border-radius: 8px; text-align: center; margin-bottom: 10px; font-weight: bold; display: flex; align-items: center; justify-content: center; gap: 8px;">
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<span style="font-size: 1.2em;">✅</span>
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{message}
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</div>
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"""
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@spaces.GPU(duration=30)
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def infer(
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prompt,
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seed,
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randomize_seed,
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guidance_scale,
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steps,
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aspect_ratio_selection,
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custom_width,
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custom_height,
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progress=gr.Progress(track_tqdm=True)
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):
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if
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if is_image_nsfw(input_image):
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return None, seed, get_error_html("تصویر ورودی دارای محتوای نامناسب است و پردازش نمیشود.")
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english_prompt = translate_prompt(prompt)
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if not check_text_safety(english_prompt):
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return None, seed, get_error_html("متن درخواست شامل کلمات غیرمجاز یا غیراخلاقی است.")
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adapter_internal_name = LORA_MAPPING.get(lora_adapter_persian)
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if adapter_internal_name:
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pipe.set_adapters([adapter_internal_name], adapter_weights=[1.0])
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator(device=device).manual_seed(seed)
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safety_negative = "nsfw, nude, naked, porn, sexual, xxx, breast, nipple, genital, vagina, penis, ass, lingerie, bikini, swimwear, underwear, fetish, topless, gore, violence, blood"
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base_negative = "worst quality, low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry"
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final_negative_prompt = f"{safety_negative}, {base_negative}"
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original_image = input_image.convert("RGB")
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if is_image_nsfw(result):
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return None, seed, get_error_html("تصویر تولید شده حاوی محتوای نامناسب بود و حذف شد.")
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return result, seed, get_success_html("تصویر با موفقیت ویرایش شد.")
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except Exception as e:
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return None, seed, get_error_html(f"خطا در پردازش: {str(e)}")
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@spaces.GPU(duration=30)
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def infer_example(input_image, prompt, lora_adapter):
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res, s, status = infer(input_image, prompt, lora_adapter, 0, True, 1.0, 4, "خودکار (پیشفرض)", 1024, 1024)
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return res, s, status
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# --- جاوااسکریپت (اصلاح شده و قدرتمند) ---
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js_code = """
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<script>
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// تابع فورس کردن تم روشن
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function forceLightMode() {
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const body = document.querySelector('body');
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if (body) {
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body.classList.remove('dark');
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body.style.backgroundColor = '#f5f7fa';
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body.style.color = '#333333';
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}
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document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
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}
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// تابع بستن مودال
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function closeQuotaModal() {
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const modal = document.getElementById('custom-quota-modal');
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if (modal) modal.style.display = 'none';
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document.querySelectorAll('.toast-wrap').forEach(el => {
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if (el.innerText.includes('GPU quota')) el.remove();
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});
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}
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// آبزرور قدرتمند برای شکار و جایگزینی خطا
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const observer = new MutationObserver((mutations) => {
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let quotaErrorFound = false;
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const text = node.innerText || node.textContent;
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// بررسی متنهای مربوط به Quota
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if (text && (text.includes('exceeded your GPU quota') || text.includes('GPU quota'))) {
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// مخفی کردن المان اصلی
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node.style.display = 'none';
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node.style.visibility = 'hidden';
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node.style.opacity = '0';
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quotaErrorFound = true;
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}
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}
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});
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});
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if (el.innerText.includes('GPU quota')) {
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el.style.display = 'none';
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quotaErrorFound = true;
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}
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});
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if (modal && modal.style.display !== 'flex') {
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modal.style.display = 'flex';
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}
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}
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});
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setInterval(forceLightMode, 1000);
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observer.observe(document.body, {
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childList: true,
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subtree: true,
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characterData: true
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});
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});
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</script>
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"""
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# جاوااسکریپت دانلود
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js_dl = """
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async (image) => {
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if (!image) { alert("لطفاً ابتدا تصویر را تولید کنید."); return; }
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let fileUrl = image.url;
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if (fileUrl && !fileUrl.startsWith('http')) {
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fileUrl = window.location.origin + fileUrl;
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} else if (!fileUrl && image.path) {
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fileUrl = window.location.origin + "/file=" + image.path;
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}
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console.log("Sending download request for:", fileUrl);
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window.parent.postMessage({ type: 'DOWNLOAD_REQUEST', url: fileUrl }, '*');
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}
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"""
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# --- تنظیمات HTML و CSS ---
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html_code = """
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Vazirmatn:wght@300;400;500;700&display=swap');
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/* تنظیمات کلی */
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:root, .dark, body, .gradio-container {
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--body-background-fill: #f5f7fa !important;
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--body-text-color: #1f2937 !important;
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--background-fill-primary: #ffffff !important;
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--background-fill-secondary: #f3f4f6 !important;
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--border-color-primary: #e5e7eb !important;
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--block-background-fill: #ffffff !important;
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--block-label-text-color: #374151 !important;
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--block-title-text-color: #111827 !important;
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--input-background-fill: #ffffff !important;
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color-scheme: light !important;
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}
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body {
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font-family: 'Vazirmatn', sans-serif !important;
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background-color: #f5f7fa !important;
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margin: 0;
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padding: 10px;
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}
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/* استایل مودال سفارشی */
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#custom-quota-modal {
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position: fixed;
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top: 0;
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left: 0;
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width: 100vw;
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height: 100vh;
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background-color: rgba(0, 0, 0, 0.85); /* تیرهتر برای تمرکز بیشتر */
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backdrop-filter: blur(8px);
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z-index: 2147483647; /* بالاترین لایه ممکن */
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display: none;
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justify-content: center;
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align-items: center;
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direction: rtl;
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}
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.quota-modal-content {
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background-color: white;
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padding: 40px;
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border-radius: 24px;
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width: 90%;
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max-width: 450px;
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text-align: center;
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box-shadow: 0 20px 60px rgba(0,0,0,0.4);
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animation: slideIn 0.4s cubic-bezier(0.175, 0.885, 0.32, 1.275);
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border: 1px solid rgba(255,255,255,0.2);
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}
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.quota-icon {
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font-size: 72px;
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margin-bottom: 25px;
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display: block;
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animation: float 3s ease-in-out infinite;
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}
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.quota-title {
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font-size: 1.6em;
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font-weight: 900;
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-
color: #e11d48;
|
| 395 |
-
margin-bottom: 15px;
|
| 396 |
-
}
|
| 397 |
|
| 398 |
-
|
| 399 |
-
|
| 400 |
-
|
| 401 |
-
|
| 402 |
-
|
| 403 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 404 |
|
| 405 |
-
|
| 406 |
-
background: linear-gradient(135deg, #e11d48 0%, #be123c 100%);
|
| 407 |
-
color: white;
|
| 408 |
-
border: none;
|
| 409 |
-
padding: 14px 35px;
|
| 410 |
-
border-radius: 14px;
|
| 411 |
-
font-weight: bold;
|
| 412 |
-
font-family: 'Vazirmatn', sans-serif;
|
| 413 |
-
font-size: 1.1em;
|
| 414 |
-
cursor: pointer;
|
| 415 |
-
box-shadow: 0 4px 15px rgba(225, 29, 72, 0.4);
|
| 416 |
-
transition: transform 0.2s;
|
| 417 |
-
width: 100%;
|
| 418 |
-
}
|
| 419 |
|
| 420 |
-
.
|
| 421 |
-
|
| 422 |
-
|
| 423 |
-
|
| 424 |
-
|
| 425 |
-
|
| 426 |
-
|
| 427 |
-
|
| 428 |
-
|
| 429 |
-
|
| 430 |
-
|
| 431 |
-
|
| 432 |
-
|
| 433 |
-
|
| 434 |
-
|
| 435 |
-
/* مخفی کردن پیامهای خطای پیشفرض Gradio */
|
| 436 |
-
.toast-wrap, .toast-error, .error {
|
| 437 |
-
display: none !important;
|
| 438 |
-
opacity: 0 !important;
|
| 439 |
-
visibility: hidden !important;
|
| 440 |
-
pointer-events: none !important;
|
| 441 |
-
}
|
| 442 |
|
| 443 |
-
|
| 444 |
#col-container {
|
| 445 |
margin: 0 auto;
|
| 446 |
-
max-width:
|
| 447 |
-
direction: rtl;
|
| 448 |
-
text-align: right;
|
| 449 |
-
padding: 30px;
|
| 450 |
-
background: #ffffff !important;
|
| 451 |
-
border-radius: 24px;
|
| 452 |
-
box-shadow: 0 10px 40px -10px rgba(0,0,0,0.08);
|
| 453 |
-
border: 1px solid rgba(255,255,255,0.8);
|
| 454 |
-
}
|
| 455 |
-
|
| 456 |
-
#main-title h1 {
|
| 457 |
-
font-size: 2.4em !important;
|
| 458 |
-
text-align: center;
|
| 459 |
-
color: #1a202c !important;
|
| 460 |
-
margin-bottom: 15px;
|
| 461 |
-
font-weight: 800;
|
| 462 |
-
background: -webkit-linear-gradient(45deg, #2563eb, #1e40af);
|
| 463 |
-
-webkit-background-clip: text;
|
| 464 |
-
-webkit-text-fill-color: transparent;
|
| 465 |
-
}
|
| 466 |
-
|
| 467 |
-
#main-description {
|
| 468 |
-
text-align: center;
|
| 469 |
-
font-size: 1.15em;
|
| 470 |
-
color: #4b5563 !important;
|
| 471 |
-
margin-bottom: 40px;
|
| 472 |
-
line-height: 1.6;
|
| 473 |
-
}
|
| 474 |
-
|
| 475 |
-
.gr-input-label, span.label-wrap, label span {
|
| 476 |
-
font-weight: 700 !important;
|
| 477 |
-
color: #374151 !important;
|
| 478 |
-
font-size: 0.95em !important;
|
| 479 |
-
margin-bottom: 8px !important;
|
| 480 |
}
|
| 481 |
-
|
| 482 |
-
textarea, input[type="text"] {
|
| 483 |
-
border: 2px solid #e2e8f0 !important;
|
| 484 |
-
border-radius: 12px !important;
|
| 485 |
-
background-color: #ffffff !important;
|
| 486 |
-
color: #111827 !important;
|
| 487 |
-
padding: 12px !important;
|
| 488 |
-
font-family: 'Vazirmatn', sans-serif !important;
|
| 489 |
-
}
|
| 490 |
-
|
| 491 |
-
textarea:focus, input[type="text"]:focus {
|
| 492 |
-
border-color: #3b82f6 !important;
|
| 493 |
-
box-shadow: 0 0 0 4px rgba(59, 130, 246, 0.1) !important;
|
| 494 |
-
outline: none;
|
| 495 |
-
}
|
| 496 |
-
|
| 497 |
-
.gr-dropdown {
|
| 498 |
-
background: #ffffff !important;
|
| 499 |
-
border-radius: 12px !important;
|
| 500 |
-
}
|
| 501 |
-
|
| 502 |
-
.primary-btn, button.primary {
|
| 503 |
-
background: linear-gradient(135deg, #10b981 0%, #059669 100%) !important;
|
| 504 |
-
border: none !important;
|
| 505 |
-
color: white !important;
|
| 506 |
-
font-weight: 700 !important;
|
| 507 |
-
font-size: 1.1em !important;
|
| 508 |
-
padding: 14px 28px !important;
|
| 509 |
-
border-radius: 14px !important;
|
| 510 |
-
box-shadow: 0 4px 15px rgba(16, 185, 129, 0.3) !important;
|
| 511 |
-
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
|
| 512 |
-
cursor: pointer !important;
|
| 513 |
-
width: 100%;
|
| 514 |
-
margin-top: 15px;
|
| 515 |
-
}
|
| 516 |
-
|
| 517 |
-
.primary-btn:hover, button.primary:hover {
|
| 518 |
-
transform: translateY(-2px);
|
| 519 |
-
box-shadow: 0 8px 25px rgba(16, 185, 129, 0.45) !important;
|
| 520 |
-
}
|
| 521 |
-
|
| 522 |
-
#download-btn {
|
| 523 |
-
background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%) !important;
|
| 524 |
-
box-shadow: 0 4px 15px rgba(59, 130, 246, 0.3) !important;
|
| 525 |
-
}
|
| 526 |
-
#download-btn:hover {
|
| 527 |
-
box-shadow: 0 8px 25px rgba(59, 130, 246, 0.45) !important;
|
| 528 |
-
}
|
| 529 |
-
|
| 530 |
-
.gradio-container .prose table,
|
| 531 |
-
.gradio-container table {
|
| 532 |
-
background-color: #ffffff !important;
|
| 533 |
-
color: #111827 !important;
|
| 534 |
-
border: 1px solid #e5e7eb !important;
|
| 535 |
-
border-radius: 12px !important;
|
| 536 |
-
overflow: hidden !important;
|
| 537 |
-
width: 100% !important;
|
| 538 |
-
margin-top: 20px !important;
|
| 539 |
-
}
|
| 540 |
-
|
| 541 |
-
.gradio-container thead th {
|
| 542 |
-
background-color: #f3f4f6 !important;
|
| 543 |
-
color: #374151 !important;
|
| 544 |
-
font-weight: 700 !important;
|
| 545 |
-
border-bottom: 2px solid #e5e7eb !important;
|
| 546 |
-
padding: 12px !important;
|
| 547 |
-
text-align: right !important;
|
| 548 |
-
}
|
| 549 |
-
|
| 550 |
-
.gradio-container tbody tr {
|
| 551 |
-
background-color: #ffffff !important;
|
| 552 |
-
border-bottom: 1px solid #f3f4f6 !important;
|
| 553 |
-
}
|
| 554 |
-
|
| 555 |
-
.gradio-container tbody tr:hover {
|
| 556 |
-
background-color: #f9fafb !important;
|
| 557 |
-
}
|
| 558 |
-
|
| 559 |
-
.gradio-container tbody td {
|
| 560 |
-
background-color: #ffffff !important;
|
| 561 |
-
color: #374151 !important;
|
| 562 |
-
padding: 10px !important;
|
| 563 |
-
}
|
| 564 |
-
|
| 565 |
-
footer { display: none !important; }
|
| 566 |
-
.flagging { display: none !important; }
|
| 567 |
-
|
| 568 |
-
@media (prefers-color-scheme: dark) {
|
| 569 |
-
body, .gradio-container, .prose, table, tr, td, th {
|
| 570 |
-
background-color: #ffffff !important;
|
| 571 |
-
color: #333333 !important;
|
| 572 |
-
}
|
| 573 |
-
}
|
| 574 |
-
</style>
|
| 575 |
-
"""
|
| 576 |
-
|
| 577 |
-
modal_html = """
|
| 578 |
-
<div id="custom-quota-modal">
|
| 579 |
-
<div class="quota-modal-content">
|
| 580 |
-
<span class="quota-icon">✈️</span>
|
| 581 |
-
<div class="quota-title">محدودیت استفاده</div>
|
| 582 |
-
<div class="quota-text">
|
| 583 |
-
سهمیه استفاده از گرافیک پر شده است.
|
| 584 |
-
<br><br>
|
| 585 |
-
برای ادامه، لطفاً <b>حالت هواپیما (Airplane Mode)</b> گوشی خود را یکبار روشن و خاموش کنید تا IP شما تغییر کند و سپس مجدد تلاش کنید.
|
| 586 |
-
</div>
|
| 587 |
-
<button class="quota-btn" onclick="closeQuotaModal()">متوجه شدم</button>
|
| 588 |
-
</div>
|
| 589 |
-
</div>
|
| 590 |
"""
|
| 591 |
|
| 592 |
with gr.Blocks() as demo:
|
| 593 |
-
# تزریق کدها به ترتیب
|
| 594 |
-
gr.HTML(html_code + modal_html + js_code)
|
| 595 |
-
|
| 596 |
with gr.Column(elem_id="col-container"):
|
| 597 |
-
gr.Markdown("# **
|
| 598 |
-
gr.Markdown(
|
| 599 |
-
"با هوش مصنوعی آلفا تصاویر تونو به مدل های مختلف ویرایش کنید.",
|
| 600 |
-
elem_id="main-description"
|
| 601 |
-
)
|
| 602 |
-
|
| 603 |
with gr.Row(equal_height=True):
|
| 604 |
-
|
| 605 |
-
|
| 606 |
-
|
|
|
|
|
|
|
|
|
|
| 607 |
prompt = gr.Text(
|
| 608 |
-
label="
|
| 609 |
show_label=True,
|
| 610 |
-
placeholder="
|
| 611 |
-
rtl=True,
|
| 612 |
-
lines=3
|
| 613 |
)
|
| 614 |
|
| 615 |
-
|
| 616 |
-
|
| 617 |
-
run_button = gr.Button("✨ شروع پردازش و ساخت تصویر", variant="primary", elem_classes="primary-btn")
|
| 618 |
|
| 619 |
-
|
| 620 |
-
|
| 621 |
-
|
| 622 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 623 |
|
| 624 |
with gr.Row():
|
| 625 |
lora_adapter = gr.Dropdown(
|
| 626 |
-
label="
|
| 627 |
-
choices=
|
| 628 |
-
value="
|
| 629 |
-
)
|
| 630 |
-
|
| 631 |
-
with gr.Accordion("تنظیمات پیشرفته", open=False, visible=True):
|
| 632 |
-
aspect_ratio_selection = gr.Dropdown(
|
| 633 |
-
label="ابعاد تصویر خروجی",
|
| 634 |
-
choices=ASPECT_RATIOS_LIST,
|
| 635 |
-
value="خودکار (پیشفرض)",
|
| 636 |
-
interactive=True
|
| 637 |
-
)
|
| 638 |
-
|
| 639 |
-
with gr.Row(visible=False) as custom_dims_row:
|
| 640 |
-
custom_width = gr.Slider(
|
| 641 |
-
label="عرض دلخواه (Width)",
|
| 642 |
-
minimum=256, maximum=2048, step=8, value=1024
|
| 643 |
-
)
|
| 644 |
-
custom_height = gr.Slider(
|
| 645 |
-
label="ارتفاع دلخواه (Height)",
|
| 646 |
-
minimum=256, maximum=2048, step=8, value=1024
|
| 647 |
-
)
|
| 648 |
-
|
| 649 |
-
seed = gr.Slider(label="دانه تصادفی (Seed)", minimum=0, maximum=MAX_SEED, step=1, value=0)
|
| 650 |
-
randomize_seed = gr.Checkbox(label="استفاده از Seed تصادفی", value=True)
|
| 651 |
-
guidance_scale = gr.Slider(label="میزان وفاداری به متن (Guidance Scale)", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
|
| 652 |
-
steps = gr.Slider(label="تعداد مراحل پردازش (Steps)", minimum=1, maximum=50, step=1, value=4)
|
| 653 |
-
|
| 654 |
-
def toggle_row(choice):
|
| 655 |
-
if choice == "شخصیسازی (Custom)":
|
| 656 |
-
return gr.update(visible=True)
|
| 657 |
-
return gr.update(visible=False)
|
| 658 |
-
|
| 659 |
-
aspect_ratio_selection.change(
|
| 660 |
-
fn=toggle_row,
|
| 661 |
-
inputs=aspect_ratio_selection,
|
| 662 |
-
outputs=custom_dims_row
|
| 663 |
-
)
|
| 664 |
-
|
| 665 |
gr.Examples(
|
| 666 |
examples=[
|
| 667 |
-
["examples/
|
| 668 |
-
["examples/
|
| 669 |
-
["examples/
|
| 670 |
-
["examples/
|
| 671 |
-
["examples/
|
| 672 |
-
["examples/
|
| 673 |
-
["examples/
|
| 674 |
-
["examples/
|
| 675 |
-
["examples/
|
| 676 |
-
|
| 677 |
-
["examples/6.jpg", "دوربین را به نمای پایین به بالا تغییر بده.", "تغییر زاویه دید"],
|
| 678 |
],
|
| 679 |
-
inputs=[
|
| 680 |
-
outputs=[output_image, seed
|
| 681 |
fn=infer_example,
|
| 682 |
cache_examples=False,
|
| 683 |
-
label="
|
| 684 |
)
|
| 685 |
|
| 686 |
run_button.click(
|
| 687 |
fn=infer,
|
| 688 |
-
inputs=[
|
| 689 |
-
outputs=[output_image, seed
|
| 690 |
-
api_name="predict"
|
| 691 |
)
|
| 692 |
-
|
| 693 |
-
download_button.click(
|
| 694 |
-
fn=None,
|
| 695 |
-
inputs=[output_image],
|
| 696 |
-
outputs=None,
|
| 697 |
-
js=js_dl
|
| 698 |
-
)
|
| 699 |
-
|
| 700 |
if __name__ == "__main__":
|
| 701 |
-
demo.queue(max_size=
|
|
|
|
| 4 |
import spaces
|
| 5 |
import torch
|
| 6 |
import random
|
| 7 |
+
from PIL import Image
|
| 8 |
from typing import Iterable
|
| 9 |
from gradio.themes import Soft
|
| 10 |
from gradio.themes.utils import colors, fonts, sizes
|
|
|
|
|
|
|
| 11 |
|
| 12 |
+
colors.orange_red = colors.Color(
|
| 13 |
+
name="orange_red",
|
| 14 |
+
c50="#FFF0E5",
|
| 15 |
+
c100="#FFE0CC",
|
| 16 |
+
c200="#FFC299",
|
| 17 |
+
c300="#FFA366",
|
| 18 |
+
c400="#FF8533",
|
| 19 |
+
c500="#FF4500",
|
| 20 |
+
c600="#E63E00",
|
| 21 |
+
c700="#CC3700",
|
| 22 |
+
c800="#B33000",
|
| 23 |
+
c900="#992900",
|
| 24 |
+
c950="#802200",
|
|
|
|
| 25 |
)
|
| 26 |
|
| 27 |
+
class OrangeRedTheme(Soft):
|
| 28 |
+
def __init__(
|
| 29 |
+
self,
|
| 30 |
+
*,
|
| 31 |
+
primary_hue: colors.Color | str = colors.gray,
|
| 32 |
+
secondary_hue: colors.Color | str = colors.orange_red,
|
| 33 |
+
neutral_hue: colors.Color | str = colors.slate,
|
| 34 |
+
text_size: sizes.Size | str = sizes.text_lg,
|
| 35 |
+
font: fonts.Font | str | Iterable[fonts.Font | str] = (
|
| 36 |
+
fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
|
| 37 |
+
),
|
| 38 |
+
font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
|
| 39 |
+
fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
|
| 40 |
+
),
|
| 41 |
+
):
|
| 42 |
+
super().__init__(
|
| 43 |
+
primary_hue=primary_hue,
|
| 44 |
+
secondary_hue=secondary_hue,
|
| 45 |
+
neutral_hue=neutral_hue,
|
| 46 |
+
text_size=text_size,
|
| 47 |
+
font=font,
|
| 48 |
+
font_mono=font_mono,
|
| 49 |
+
)
|
| 50 |
+
super().set(
|
| 51 |
+
background_fill_primary="*primary_50",
|
| 52 |
+
background_fill_primary_dark="*primary_900",
|
| 53 |
+
body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
|
| 54 |
+
body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
|
| 55 |
+
button_primary_text_color="white",
|
| 56 |
+
button_primary_text_color_hover="white",
|
| 57 |
+
button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
|
| 58 |
+
button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
|
| 59 |
+
button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
|
| 60 |
+
button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
|
| 61 |
+
button_secondary_text_color="black",
|
| 62 |
+
button_secondary_text_color_hover="white",
|
| 63 |
+
button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
|
| 64 |
+
button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
|
| 65 |
+
button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
|
| 66 |
+
button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
|
| 67 |
+
slider_color="*secondary_500",
|
| 68 |
+
slider_color_dark="*secondary_600",
|
| 69 |
+
block_title_text_weight="600",
|
| 70 |
+
block_border_width="3px",
|
| 71 |
+
block_shadow="*shadow_drop_lg",
|
| 72 |
+
button_primary_shadow="*shadow_drop_lg",
|
| 73 |
+
button_large_padding="11px",
|
| 74 |
+
color_accent_soft="*primary_100",
|
| 75 |
+
block_label_background_fill="*primary_200",
|
| 76 |
+
)
|
| 77 |
|
| 78 |
+
orange_red_theme = OrangeRedTheme()
|
|
|
|
|
|
|
| 79 |
|
| 80 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| 81 |
+
dtype = torch.bfloat16
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 82 |
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|
| 83 |
from diffusers import FlowMatchEulerDiscreteScheduler
|
| 84 |
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
|
| 85 |
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
|
| 86 |
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
|
| 87 |
|
| 88 |
+
print("Loading Qwen Image Edit Pipeline...")
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|
| 89 |
pipe = QwenImageEditPlusPipeline.from_pretrained(
|
| 90 |
"Qwen/Qwen-Image-Edit-2509",
|
| 91 |
transformer=QwenImageTransformer2DModel.from_pretrained(
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|
| 97 |
torch_dtype=dtype
|
| 98 |
).to(device)
|
| 99 |
|
| 100 |
+
print("Loading and Fusing Lightning LoRA...")
|
| 101 |
+
pipe.load_lora_weights("lightx2v/Qwen-Image-Lightning",
|
| 102 |
+
weight_name="Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors",
|
| 103 |
+
adapter_name="lightning")
|
| 104 |
+
pipe.fuse_lora(adapter_names=["lightning"], lora_scale=1.0)
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|
| 105 |
|
| 106 |
+
print("Loading Task Adapters...")
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|
| 107 |
|
| 108 |
+
pipe.load_lora_weights("tarn59/apply_texture_qwen_image_edit_2509",
|
| 109 |
+
weight_name="apply_texture_v2_qwen_image_edit_2509.safetensors",
|
| 110 |
+
adapter_name="texture")
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|
| 111 |
|
| 112 |
+
pipe.load_lora_weights("ostris/qwen_image_edit_inpainting",
|
| 113 |
+
weight_name="qwen_image_edit_inpainting.safetensors",
|
| 114 |
+
adapter_name="fusion")
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|
| 115 |
|
| 116 |
+
pipe.load_lora_weights("ostris/qwen_image_edit_2509_shirt_design",
|
| 117 |
+
weight_name="qwen_image_edit_2509_shirt_design.safetensors",
|
| 118 |
+
adapter_name="shirt_design")
|
| 119 |
+
|
| 120 |
+
pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Fusion",
|
| 121 |
+
weight_name="溶图.safetensors",
|
| 122 |
+
adapter_name="fusion-x")
|
| 123 |
|
| 124 |
+
pipe.load_lora_weights("oumoumad/Qwen-Edit-2509-Material-transfer",
|
| 125 |
+
weight_name="material-transfer_000004769.safetensors",
|
| 126 |
+
adapter_name="material-transfer")
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|
| 127 |
|
| 128 |
+
pipe.load_lora_weights("dx8152/Qwen-Edit-2509-Light-Migration",
|
| 129 |
+
weight_name="参考色调.safetensors",
|
| 130 |
+
adapter_name="light-migration")
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|
| 131 |
|
| 132 |
+
try:
|
| 133 |
+
pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
|
| 134 |
+
print("Flash Attention 3 Processor set successfully.")
|
| 135 |
+
except Exception as e:
|
| 136 |
+
print(f"Could not set FA3 processor (likely hardware mismatch): {e}. using default attention.")
|
| 137 |
+
|
| 138 |
+
MAX_SEED = np.iinfo(np.int32).max
|
|
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|
| 139 |
|
| 140 |
def update_dimensions_on_upload(image):
|
| 141 |
if image is None:
|
| 142 |
return 1024, 1024
|
| 143 |
+
|
| 144 |
original_width, original_height = image.size
|
| 145 |
+
|
| 146 |
if original_width > original_height:
|
| 147 |
new_width = 1024
|
| 148 |
aspect_ratio = original_height / original_width
|
|
|
|
| 151 |
new_height = 1024
|
| 152 |
aspect_ratio = original_width / original_height
|
| 153 |
new_width = int(new_height * aspect_ratio)
|
| 154 |
+
|
| 155 |
+
# Ensure dimensions are multiples of 16
|
| 156 |
+
new_width = (new_width // 16) * 16
|
| 157 |
+
new_height = (new_height // 16) * 16
|
| 158 |
+
|
| 159 |
return new_width, new_height
|
| 160 |
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|
| 161 |
@spaces.GPU(duration=30)
|
| 162 |
def infer(
|
| 163 |
+
image_1,
|
| 164 |
+
image_2,
|
| 165 |
prompt,
|
| 166 |
+
lora_adapter,
|
| 167 |
seed,
|
| 168 |
randomize_seed,
|
| 169 |
guidance_scale,
|
| 170 |
steps,
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|
| 171 |
progress=gr.Progress(track_tqdm=True)
|
| 172 |
):
|
| 173 |
+
if image_1 is None or image_2 is None:
|
| 174 |
+
raise gr.Error("Please upload both images for Fusion/Texture/FaceSwap tasks.")
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|
| 175 |
|
| 176 |
+
if not prompt:
|
| 177 |
+
if lora_adapter == "Cloth-Design-Fuse":
|
| 178 |
+
prompt = "Put this design on their shirt."
|
| 179 |
+
elif lora_adapter == "Texture Edit":
|
| 180 |
+
prompt = "Apply texture to object."
|
| 181 |
+
elif lora_adapter == "Fuse-Objects":
|
| 182 |
+
prompt = "Fuse object into background."
|
| 183 |
+
elif lora_adapter == "Super-Fusion":
|
| 184 |
+
prompt = "Blend the product into the background, correct its perspective and lighting, and make it naturally integrated with the scene."
|
| 185 |
+
elif lora_adapter == "Material-Transfer":
|
| 186 |
+
prompt = "change materials of image1 to match the reference in image2"
|
| 187 |
+
elif lora_adapter == "Light-Migration":
|
| 188 |
+
prompt = "Refer to the color tone, remove the original lighting from Image 1, and relight Image 1 based on the lighting and color tone of Image 2."
|
| 189 |
+
|
| 190 |
+
adapters_map = {
|
| 191 |
+
"Texture Edit": "texture",
|
| 192 |
+
"Fuse-Objects": "fusion",
|
| 193 |
+
"Cloth-Design-Fuse": "shirt_design",
|
| 194 |
+
"Super-Fusion": "fusion-x",
|
| 195 |
+
"Material-Transfer": "material-transfer",
|
| 196 |
+
"Light-Migration": "light-migration",
|
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|
| 197 |
}
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|
| 198 |
|
| 199 |
+
active_adapter = adapters_map.get(lora_adapter)
|
|
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|
|
| 200 |
|
| 201 |
+
if active_adapter:
|
| 202 |
+
pipe.set_adapters([active_adapter], adapter_weights=[1.0])
|
| 203 |
+
else:
|
| 204 |
+
pipe.set_adapters([], adapter_weights=[])
|
|
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|
| 205 |
|
| 206 |
+
if randomize_seed:
|
| 207 |
+
seed = random.randint(0, MAX_SEED)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 208 |
|
| 209 |
+
generator = torch.Generator(device=device).manual_seed(seed)
|
| 210 |
+
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"
|
|
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|
|
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|
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|
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|
|
|
|
|
| 211 |
|
| 212 |
+
img1_pil = image_1.convert("RGB")
|
| 213 |
+
img2_pil = image_2.convert("RGB")
|
|
|
|
| 214 |
|
| 215 |
+
width, height = update_dimensions_on_upload(img1_pil)
|
|
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|
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|
|
| 216 |
|
| 217 |
+
result = pipe(
|
| 218 |
+
image=[img1_pil, img2_pil],
|
| 219 |
+
prompt=prompt,
|
| 220 |
+
negative_prompt=negative_prompt,
|
| 221 |
+
height=height,
|
| 222 |
+
width=width,
|
| 223 |
+
num_inference_steps=steps,
|
| 224 |
+
generator=generator,
|
| 225 |
+
true_cfg_scale=guidance_scale,
|
| 226 |
+
).images[0]
|
| 227 |
|
| 228 |
+
return result, seed
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
| 229 |
|
| 230 |
+
@spaces.GPU(duration=30)
|
| 231 |
+
def infer_example(image_1, image_2, prompt, lora_adapter):
|
| 232 |
+
if image_1 is None or image_2 is None:
|
| 233 |
+
return None, 0
|
| 234 |
+
result, seed = infer(
|
| 235 |
+
image_1.convert("RGB"),
|
| 236 |
+
image_2.convert("RGB"),
|
| 237 |
+
prompt,
|
| 238 |
+
lora_adapter,
|
| 239 |
+
0,
|
| 240 |
+
True,
|
| 241 |
+
1.0,
|
| 242 |
+
4
|
| 243 |
+
)
|
| 244 |
+
return result, seed
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 245 |
|
| 246 |
+
css="""
|
| 247 |
#col-container {
|
| 248 |
margin: 0 auto;
|
| 249 |
+
max-width: 1100px;
|
|
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|
|
|
| 250 |
}
|
| 251 |
+
#main-title h1 {font-size: 2.1em !important;}
|
|
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|
|
| 252 |
"""
|
| 253 |
|
| 254 |
with gr.Blocks() as demo:
|
|
|
|
|
|
|
|
|
|
| 255 |
with gr.Column(elem_id="col-container"):
|
| 256 |
+
gr.Markdown("# **Qwen-Image-Edit-2509-LoRAs-Fast-Fusion**", elem_id="main-title")
|
| 257 |
+
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.")
|
|
|
|
|
|
|
|
|
|
|
|
|
| 258 |
with gr.Row(equal_height=True):
|
| 259 |
+
|
| 260 |
+
with gr.Column(scale=1):
|
| 261 |
+
with gr.Row():
|
| 262 |
+
image_1 = gr.Image(label="Base Image", type="pil", height=290)
|
| 263 |
+
image_2 = gr.Image(label="Reference Image", type="pil", height=290)
|
| 264 |
+
|
| 265 |
prompt = gr.Text(
|
| 266 |
+
label="Edit Prompt",
|
| 267 |
show_label=True,
|
| 268 |
+
placeholder="e.g., Apply wood texture to the mug...",
|
|
|
|
|
|
|
| 269 |
)
|
| 270 |
|
| 271 |
+
run_button = gr.Button("Edit Image", variant="primary")
|
|
|
|
|
|
|
| 272 |
|
| 273 |
+
with gr.Accordion("Advanced Settings", open=False, visible=False):
|
| 274 |
+
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
|
| 275 |
+
randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
|
| 276 |
+
guidance_scale = gr.Slider(label="True Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
|
| 277 |
+
steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4)
|
| 278 |
+
|
| 279 |
+
with gr.Column(scale=1):
|
| 280 |
+
output_image = gr.Image(label="Output Image", interactive=False, format="png", height=350)
|
| 281 |
|
| 282 |
with gr.Row():
|
| 283 |
lora_adapter = gr.Dropdown(
|
| 284 |
+
label="Choose Editing Style",
|
| 285 |
+
choices=["Texture Edit", "Cloth-Design-Fuse", "Fuse-Objects", "Super-Fusion", "Light-Migration", "Material-Transfer"],
|
| 286 |
+
value="Texture Edit",
|
| 287 |
+
)
|
| 288 |
+
|
|
|
|
|
|
|
|
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|
| 289 |
gr.Examples(
|
| 290 |
examples=[
|
| 291 |
+
["examples/M1.jpg", "examples/M2.jpg", "Refer to the color tone, remove the original lighting from Image 1, and relight Image 1 based on the lighting and color tone of Image 2.", "Light-Migration"],
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| 292 |
+
["examples/Cloth2.jpg", "examples/Design2.png", "Put this design on their shirt.", "Cloth-Design-Fuse"],
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| 293 |
+
["examples/Cup1.png", "examples/Wood1.png", "Apply wood texture to mug.", "Texture Edit"],
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| 294 |
+
["examples/Cloth1.jpg", "examples/Design1.png", "Put this design on their shirt.", "Cloth-Design-Fuse"],
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| 295 |
+
["examples/F3.jpg", "examples/F4.jpg", "Replace her glasses with the new glasses from image 1.", "Super-Fusion"],
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| 296 |
+
["examples/Chair.jpg", "examples/Material.jpg", "Change materials of image1 to match the reference in image2.", "Material-Transfer"],
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| 297 |
+
["examples/F1.jpg", "examples/F2.jpg", "Put the small bottle on the table.", "Super-Fusion"],
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| 298 |
+
["examples/Mug1.jpg", "examples/Texture1.jpg", "Apply the design from image 2 to the mug.", "Texture Edit"],
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| 299 |
+
["examples/Cat1.jpg", "examples/Glass1.webp", "A cat wearing glasses in image 2.", "Fuse-Objects"],
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| 300 |
+
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| 301 |
],
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| 302 |
+
inputs=[image_1, image_2, prompt, lora_adapter],
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| 303 |
+
outputs=[output_image, seed],
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| 304 |
fn=infer_example,
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| 305 |
cache_examples=False,
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| 306 |
+
label="Examples"
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| 307 |
)
|
| 308 |
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| 309 |
run_button.click(
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| 310 |
fn=infer,
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| 311 |
+
inputs=[image_1, image_2, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
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| 312 |
+
outputs=[output_image, seed]
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| 313 |
)
|
| 314 |
+
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|
| 315 |
if __name__ == "__main__":
|
| 316 |
+
demo.queue(max_size=50).launch(css=css, theme=orange_red_theme, mcp_server=True, ssr_mode=False, show_error=True)
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