import os, gc, random, re, inspect from contextlib import nullcontext import gradio as gr import torch, spaces from PIL import Image, ImageFilter, ImageOps import numpy as np import qrcode from qrcode.constants import ERROR_CORRECT_H from diffusers import ( StableDiffusionControlNetPipeline, StableDiffusionControlNetImg2ImgPipeline, # for Hi-Res Fix ControlNetModel, DPMSolverMultistepScheduler, ) # ------------------- env / runtime ------------------- # Quiet matplotlib cache warning on Spaces os.environ.setdefault("MPLCONFIGDIR", "/tmp/mpl") # Optional: faster model downloads on Spaces os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1") os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1") # Hugging Face token (add it in Space Settings → Variables and secrets) HF_TOKEN = os.environ.get("HUGGINGFACE_HUB_TOKEN") or os.environ.get("HUGGINGFACEHUB_API_TOKEN") # Device / dtype (CPU-safe) IS_CUDA = torch.cuda.is_available() IS_MPS = getattr(torch.backends, "mps", None) and torch.backends.mps.is_available() DTYPE = torch.float16 if (IS_CUDA or IS_MPS) else torch.float32 DEV_TORCH = "cuda" if IS_CUDA else ("mps" if IS_MPS else "cpu") def autocast_ctx(): if IS_CUDA: return torch.autocast(device_type="cuda", dtype=torch.float16) if IS_MPS: # MPS autocast uses fp16 path; acceptable for SD 1.5 on macOS return torch.autocast(device_type="mps", dtype=torch.float16) return nullcontext() # CPU: no autocast # ------------------- models ------------------- BASE_MODELS = { "stable-diffusion-v1-5": "runwayml/stable-diffusion-v1-5", "dream": "Lykon/dreamshaper-8", } # ControlNets CN_QRMON = "monster-labs/control_v1p_sd15_qrcode_monster" CN_BRIGHT = "latentcat/control_v1p_sd15_brightness" # ---------- helpers ---------- def resize_like(im: Image.Image, width: int, height: int, method=Image.NEAREST) -> Image.Image: if im.size == (width, height): return im return im.resize((int(width), int(height)), method) def ensure_rgb_img(x): if isinstance(x, Image.Image): return x.convert("RGB") if isinstance(x, np.ndarray): a = x if a.dtype != np.uint8: a = np.clip(a, 0, 255).astype(np.uint8) if a.ndim == 2: return Image.fromarray(a, mode="L").convert("RGB") return Image.fromarray(a).convert("RGB") if torch.is_tensor(x): t = x.detach().cpu() if t.ndim == 3 and t.shape[0] in (1, 3): t = t.permute(1, 2, 0) arr = t.numpy() if arr.max() <= 1.0: arr = arr * 255.0 arr = np.clip(arr, 0, 255).astype(np.uint8) if arr.ndim == 2: return Image.fromarray(arr, mode="L").convert("RGB") return Image.fromarray(arr).convert("RGB") raise ValueError(f"Unsupported image type for ensure_rgb_img: {type(x)}") def snap8(x: int) -> int: x = max(256, min(1024, int(x))) return x - (x % 8) def normalize_color(c): if c is None: return "white" if isinstance(c, (tuple, list)): r, g, b = (int(max(0, min(255, round(float(x))))) for x in c[:3]); return (r, g, b) if isinstance(c, str): s = c.strip() if s.startswith("#"): return s m = re.match(r"rgba?\(\s*([0-9.]+)\s*,\s*([0-9.]+)\s*,\s*([0-9.]+)", s, re.IGNORECASE) if m: r = int(max(0, min(255, round(float(m.group(1)))))) g = int(max(0, min(255, round(float(m.group(2)))))) b = int(max(0, min(255, round(float(m.group(3)))))) return (r, g, b) return s return "white" def make_qr(url="https://example.com", size=768, border=12, back_color="#FFFFFF", blur_radius=0.0): qr = qrcode.QRCode(version=None, error_correction=ERROR_CORRECT_H, box_size=10, border=int(border)) qr.add_data(url.strip()); qr.make(fit=True) img = qr.make_image(fill_color="black", back_color=normalize_color(back_color)).convert("RGB") img = img.resize((int(size), int(size)), Image.NEAREST) if blur_radius and blur_radius > 0: img = img.filter(ImageFilter.GaussianBlur(radius=float(blur_radius))) return img def enforce_qr_contrast(stylized: Image.Image, qr_img: Image.Image, strength: float = 0.0, feather: float = 1.0) -> Image.Image: if strength <= 0: return stylized q = qr_img.convert("L") black_mask = q.point(lambda p: 255 if p < 128 else 0).filter(ImageFilter.GaussianBlur(radius=float(feather))) black = np.asarray(black_mask, dtype=np.float32) / 255.0 white = 1.0 - black s = np.asarray(stylized.convert("RGB"), dtype=np.float32) / 255.0 s = s * (1.0 - float(strength) * black[..., None]) s = s + (1.0 - s) * (float(strength) * 0.85 * white[..., None]) s = np.clip(s, 0.0, 1.0) return Image.fromarray((s * 255.0).astype(np.uint8), mode="RGB") # ----- Brightness map preprocessing & mixing ----- def prep_brightness_map(img: Image.Image, size: int, source: str, blur_px: float = 3.0, gamma: float = 0.9, autocontrast: bool = True) -> Image.Image: method = Image.NEAREST if source == "qr" else Image.LANCZOS im = img.resize((size, size), method).convert("L") if source != "qr": if autocontrast: im = ImageOps.autocontrast(im, cutoff=2) if blur_px and blur_px > 0: im = im.filter(ImageFilter.GaussianBlur(radius=float(blur_px))) if gamma and gamma != 1.0: arr = np.asarray(im, dtype=np.float32) / 255.0 arr = np.clip(arr ** float(gamma), 0.0, 1.0) im = Image.fromarray((arr * 255.0).astype(np.uint8), "L") return im.convert("RGB") def blend_brightness_maps(qr_img: Image.Image, init_img: Image.Image, size: int, alpha: float, blur_px: float = 2.5, gamma: float = 0.9, autocontrast: bool = True) -> Image.Image: qr_map = prep_brightness_map(qr_img, size, "qr") init_map = prep_brightness_map(init_img, size, "init", blur_px=blur_px, gamma=gamma, autocontrast=autocontrast) qa = np.asarray(qr_map, dtype=np.float32) ia = np.asarray(init_map, dtype=np.float32) a = float(alpha) mix = np.clip((1.0 - a) * ia + a * qa, 0, 255).astype(np.uint8) return Image.fromarray(mix, mode="RGB") # ---------- lazy pipelines / models ---------- _CN_QR = None _CN_BR = None _CN_TXT2IMG = {} _CN_IMG2IMG = {} def _base_scheduler_for(pipe): pipe.scheduler = DPMSolverMultistepScheduler.from_config( pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="dpmsolver++" ) pipe.enable_attention_slicing() pipe.enable_vae_slicing() pipe.enable_model_cpu_offload() return pipe def get_qr_cn(): global _CN_QR if _CN_QR is None: _CN_QR = ControlNetModel.from_pretrained( CN_QRMON, torch_dtype=DTYPE, use_safetensors=True, token=HF_TOKEN ) return _CN_QR def get_bright_cn(): global _CN_BR if _CN_BR is None: _CN_BR = ControlNetModel.from_pretrained( CN_BRIGHT, torch_dtype=DTYPE, use_safetensors=True, token=HF_TOKEN ) return _CN_BR def get_controlnets(use_brightness: bool): return [get_qr_cn(), get_bright_cn()] if use_brightness else get_qr_cn() def get_txt2img_pipe(model_id: str, use_brightness: bool): key = (model_id, "2cn" if use_brightness else "1cn") if key not in _CN_TXT2IMG: pipe = StableDiffusionControlNetPipeline.from_pretrained( model_id, controlnet=get_controlnets(use_brightness), torch_dtype=DTYPE, safety_checker=None, use_safetensors=True, low_cpu_mem_usage=True, token=HF_TOKEN, ) _CN_TXT2IMG[key] = _base_scheduler_for(pipe) return _CN_TXT2IMG[key] def get_img2img_pipe(model_id: str, use_brightness: bool): key = (model_id, "2cn" if use_brightness else "1cn") if key not in _CN_IMG2IMG: pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( model_id, controlnet=get_controlnets(use_brightness), torch_dtype=DTYPE, safety_checker=None, use_safetensors=True, low_cpu_mem_usage=True, token=HF_TOKEN, ) _CN_IMG2IMG[key] = _base_scheduler_for(pipe) return _CN_IMG2IMG[key] # -------- core helpers -------- def _pick_brightness_image(mode: str, qr_img: Image.Image, init_img: Image.Image | None, custom_img: Image.Image | None) -> Image.Image: if mode == "init" and init_img is not None: return init_img if mode == "custom" and custom_img is not None: return custom_img return qr_img # -------- Method 1: QR control model in text-to-image (+ optional Hi-Res Fix) -------- def _qr_txt2img_core(model_id: str, url: str, style_prompt: str, negative: str, steps: int, cfg: float, size: int, border: int, qr_weight: float, seed: int, use_hires: bool, hires_upscale: float, hires_strength: float, repair_strength: float, feather: float, control_start: float, control_end: float, use_brightness: bool, bright_weight: float, bright_start: float, bright_end: float, bright_mode: str, bright_custom: Image.Image | None): s = snap8(size) # --- Build base-size control images (s x s) qr_img = make_qr(url=url, size=s, border=int(border), back_color="#FFFFFF", blur_radius=0.0) if use_brightness: raw_bright_s = _pick_brightness_image(bright_mode, qr_img, None, bright_custom) bright_img_s = prep_brightness_map(raw_bright_s, s, bright_mode) control_images_s = [ensure_rgb_img(qr_img), ensure_rgb_img(bright_img_s)] scales_s = [float(qr_weight), float(bright_weight)] starts_s = [float(control_start), float(bright_start)] ends_s = [float(control_end), float(bright_end)] else: control_images_s = ensure_rgb_img(qr_img) scales_s = float(qr_weight) starts_s = float(control_start) ends_s = float(control_end) # Seed / generator if int(seed) < 0: seed = random.randint(0, 2**31 - 1) gen = torch.Generator(device=DEV_TORCH).manual_seed(int(seed)) # --- Stage A: txt2img at s x s pipe = get_txt2img_pipe(model_id, use_brightness) if torch.cuda.is_available(): torch.cuda.empty_cache() gc.collect() kwargs = dict( prompt=str(style_prompt), negative_prompt=str(negative or ""), num_inference_steps=int(steps), guidance_scale=float(cfg), width=s, height=s, generator=gen, controlnet_conditioning_scale=scales_s, control_guidance_start=starts_s, control_guidance_end=ends_s, ) # detect which argument the pipeline supports sig = inspect.signature(pipe.__call__) if "control_image" in sig.parameters: kwargs["control_image"] = control_images_s elif "image" in sig.parameters: kwargs["image"] = control_images_s else: raise RuntimeError("Pipeline does not accept controlnet images") with autocast_ctx(): out = pipe(**kwargs) lowres = out.images[0] # --- Stage B: optional hi-res final = lowres qr_for_repair = qr_img if use_hires: up = max(1.0, min(2.0, float(hires_upscale))) W = snap8(int(s * up)); H = W qr_img_hi = resize_like(qr_img, W, H, method=Image.NEAREST) if use_brightness: raw_bright_hi = _pick_brightness_image(bright_mode, qr_img_hi, None, bright_custom) bright_img_hi = prep_brightness_map(raw_bright_hi, W, bright_mode) control_images_hi = [ensure_rgb_img(qr_img_hi), ensure_rgb_img(bright_img_hi)] scales_hi = scales_s; starts_hi = starts_s; ends_hi = ends_s else: control_images_hi = ensure_rgb_img(qr_img_hi) scales_hi = scales_s; starts_hi = starts_s; ends_hi = ends_s pipe2 = get_img2img_pipe(model_id, use_brightness) if torch.cuda.is_available(): torch.cuda.empty_cache() gc.collect() kwargs2 = dict( prompt=str(style_prompt), negative_prompt=str(negative or ""), image=lowres, strength=float(hires_strength), num_inference_steps=int(steps), guidance_scale=float(cfg), width=W, height=H, generator=gen, controlnet_conditioning_scale=scales_hi, control_guidance_start=starts_hi, control_guidance_end=ends_hi, ) sig2 = inspect.signature(pipe2.__call__) if "control_image" in sig2.parameters: kwargs2["control_image"] = control_images_hi elif "image" in sig2.parameters: kwargs2["image"] = control_images_hi else: raise RuntimeError("Img2Img pipeline does not accept controlnet images") with autocast_ctx(): out2 = pipe2(**kwargs2) final = out2.images[0] qr_for_repair = qr_img_hi final = enforce_qr_contrast(final, qr_for_repair, strength=float(repair_strength), feather=float(feather)) return final, lowres, qr_img # ===================== helpers for img2img ===================== def center_square(im: Image.Image) -> Image.Image: w, h = im.size if w == h: return im if w > h: off = (w - h) // 2 return im.crop((off, 0, off + h, h)) else: off = (h - w) // 2 return im.crop((0, off, w, off + w)) def prep_init_image(init_img: Image.Image, target: int) -> Image.Image: s = snap8(target) im = center_square(init_img.convert("RGB")) return im.resize((s, s), Image.LANCZOS) # ================== img2img + QR Control core ================== def _qr_img2img_core(model_id: str, init_image: Image.Image, url: str, style_prompt: str, negative: str, steps: int, cfg: float, size: int, border: int, qr_weight: float, seed: int, strength: float, repair_strength: float, feather: float, control_start: float, control_end: float, use_brightness: bool, bright_weight: float, bright_start: float, bright_end: float, bright_mode: str, bright_custom: Image.Image | None, bright_blur_px: float = 2.5, bright_gamma: float = 0.9, bright_autocontrast: bool = True, bright_mix_alpha: float = 0.65): s = snap8(size) init = ensure_rgb_img(prep_init_image(init_image, s)) qr_img = ensure_rgb_img(make_qr(url=url, size=s, border=int(border), back_color="#FFFFFF", blur_radius=0.0)) if int(seed) < 0: seed = random.randint(0, 2**31 - 1) gen = torch.Generator(device=DEV_TORCH).manual_seed(int(seed)) if use_brightness: if bright_mode == "mix": bright_img = blend_brightness_maps(qr_img, init, s, alpha=float(bright_mix_alpha), blur_px=float(bright_blur_px), gamma=float(bright_gamma), autocontrast=bool(bright_autocontrast)) else: raw_bright = _pick_brightness_image(bright_mode, qr_img, init, bright_custom) bright_img = prep_brightness_map(raw_bright, s, bright_mode, blur_px=float(bright_blur_px), gamma=float(bright_gamma), autocontrast=bool(bright_autocontrast)) control_images = [ensure_rgb_img(qr_img), ensure_rgb_img(bright_img)] scales = [float(qr_weight), float(bright_weight)] starts = [float(control_start), float(bright_start)] ends = [float(control_end), float(bright_end)] else: control_images = ensure_rgb_img(qr_img) scales = float(qr_weight) starts = float(control_start) ends = float(control_end) pipe = get_img2img_pipe(model_id, use_brightness) if torch.cuda.is_available(): torch.cuda.empty_cache() gc.collect() kwargs = dict( prompt=str(style_prompt), negative_prompt=str(negative or ""), image=init, strength=float(strength), num_inference_steps=int(steps), guidance_scale=float(cfg), width=s, height=s, generator=gen, controlnet_conditioning_scale=scales, control_guidance_start=starts, control_guidance_end=ends, ) sig = inspect.signature(pipe.__call__) if "control_image" in sig.parameters: kwargs["control_image"] = control_images elif "image" in sig.parameters and isinstance(control_images, list): kwargs["image"] = [init] + control_images else: raise RuntimeError("Img2Img pipeline does not accept controlnet images") with autocast_ctx(): out = pipe(**kwargs) final = out.images[0] final = enforce_qr_contrast(final, qr_img, strength=float(repair_strength), feather=float(feather)) return final, init, qr_img # ============== wrappers for Gradio ============== @spaces.GPU(duration=120) def qr_img2img_blend(model_key: str, init_image: Image.Image, url: str, style_prompt: str, negative: str, steps: int, cfg: float, size: int, border: int, qr_weight: float, seed: int, strength: float, repair_strength: float, feather: float, control_start: float, control_end: float, use_brightness: bool, bright_weight: float, bright_start: float, bright_end: float, bright_mode: str, bright_custom: Image.Image | None, bright_blur_px: float, bright_gamma: float, bright_autocontrast: bool, bright_mix_alpha: float): model_id = BASE_MODELS.get(model_key, BASE_MODELS["stable-diffusion-v1-5"]) return _qr_img2img_core(model_id, init_image, url, style_prompt, negative, steps, cfg, size, border, qr_weight, seed, strength, repair_strength, feather, control_start, control_end, use_brightness, bright_weight, bright_start, bright_end, bright_mode, bright_custom, bright_blur_px, bright_gamma, bright_autocontrast, bright_mix_alpha) @spaces.GPU(duration=120) def qr_txt2img_sd15(*args): return _qr_txt2img_core(BASE_MODELS["stable-diffusion-v1-5"], *args) @spaces.GPU(duration=120) def qr_txt2img_dream(*args): return _qr_txt2img_core(BASE_MODELS["dream"], *args) # ---------- UI ---------- with gr.Blocks() as demo: gr.Markdown("# ZeroGPU • QR Control (with optional Brightness ControlNet)") # ---- Tab 1: stable-diffusion-v1-5 (Brightness forced ON) ---- with gr.Tab("stable-diffusion-v1-5"): url1 = gr.Textbox(label="URL/Text", value="http://www.mybirdfire.com") s_prompt1 = gr.Textbox(label="Style prompt", value="japanese painting, elegant shrine and torii, distant mount fuji, autumn maple trees, warm sunlight, 1girl in kimono, highly detailed, intricate patterns, anime key visual, dramatic composition") s_negative1 = gr.Textbox(label="Negative prompt", value="ugly, low quality, blurry, nsfw, watermark, text, low contrast, deformed, extra digits") size1 = gr.Slider(384, 1024, value=640, step=64, label="Canvas (px)") steps1 = gr.Slider(10, 50, value=30, step=1, label="Steps") cfg1 = gr.Slider(1.0, 12.0, value=6.0, step=0.1, label="CFG") border1 = gr.Slider(2, 20, value=12, step=1, label="QR border (quiet zone)") qr_w1 = gr.Slider(0.8, 1.8, value=1.6, step=0.05, label="QR control weight") seed1 = gr.Number(value=-1, precision=0, label="Seed (-1 random)") cstart1 = gr.Slider(0.0, 0.6, value=0.0, step=0.05, label="QR control start") cend1 = gr.Slider(0.4, 1.0, value=1.0, step=0.05, label="QR control end") use_hires1 = gr.Checkbox(value=True, label="Hi-Res Fix (img2img upscale)") hires_up1 = gr.Slider(1.0, 2.0, value=2.0, step=0.25, label="Hi-Res upscale (×)") hires_str1 = gr.Slider(0.30, 0.60, value=0.45, step=0.05, label="Hi-Res denoise strength") repair1 = gr.Slider(0.0, 1.0, value=0.0, step=0.05, label="Post repair strength (optional)") feather1 = gr.Slider(0.0, 3.0, value=1.0, step=0.1, label="Repair feather (px)") use_bright1 = gr.Checkbox(value=True, visible=False) bright_w1 = gr.Slider(0.0, 0.5, value=0.15, step=0.01, label="Brightness weight") bright_s1 = gr.Slider(0.0, 0.8, value=0.10, step=0.05, label="Brightness start") bright_e1 = gr.Slider(0.2, 1.0, value=0.80, step=0.05, label="Brightness end") bright_mode1 = gr.Radio(choices=["qr","custom"], value="qr", label="Brightness source") bright_ref1 = gr.Image(label="(Optional) custom brightness ref", type="pil") final_img1 = gr.Image(label="Final (or Hi-Res) image") low_img1 = gr.Image(label="Low-res (Stage A) preview") ctrl_img1 = gr.Image(label="Control QR used") gr.Button("Generate with SD 1.5").click( qr_txt2img_sd15, [url1, s_prompt1, s_negative1, steps1, cfg1, size1, border1, qr_w1, seed1, use_hires1, hires_up1, hires_str1, repair1, feather1, cstart1, cend1, use_bright1, bright_w1, bright_s1, bright_e1, bright_mode1, bright_ref1], [final_img1, low_img1, ctrl_img1], api_name="qr_txt2img_sd15" ) # ---- Tab 2: DreamShaper 8 (Brightness forced ON) ---- with gr.Tab("DreamShaper 8"): url2 = gr.Textbox(label="URL/Text", value="http://www.mybirdfire.com") s_prompt2 = gr.Textbox(label="Style prompt", value="ornate baroque palace interior, gilded details, chandeliers, volumetric light, ultra detailed, cinematic") s_negative2 = gr.Textbox(label="Negative prompt", value="lowres, low contrast, blurry, jpeg artifacts, watermark, text, bad anatomy") size2 = gr.Slider(384, 1024, value=640, step=64, label="Canvas (px)") steps2 = gr.Slider(10, 50, value=30, step=1, label="Steps") cfg2 = gr.Slider(1.0, 12.0, value=6.5, step=0.1, label="CFG") border2 = gr.Slider(2, 20, value=12, step=1, label="QR border (quiet zone)") qr_w2 = gr.Slider(0.8, 1.8, value=1.6, step=0.05, label="QR control weight") seed2 = gr.Number(value=-1, precision=0, label="Seed (-1 random)") cstart2 = gr.Slider(0.0, 0.6, value=0.0, step=0.05, label="QR control start") cend2 = gr.Slider(0.4, 1.0, value=1.0, step=0.05, label="QR control end") use_hires2 = gr.Checkbox(value=True, label="Hi-Res Fix (img2img upscale)") hires_up2 = gr.Slider(1.0, 2.0, value=2.0, step=0.25, label="Hi-Res upscale (×)") hires_str2 = gr.Slider(0.30, 0.60, value=0.45, step=0.05, label="Hi-Res denoise strength") repair2 = gr.Slider(0.0, 1.0, value=0.0, step=0.05, label="Post repair strength (optional)") feather2 = gr.Slider(0.0, 3.0, value=1.0, step=0.1, label="Repair feather (px)") use_bright2 = gr.Checkbox(value=True, visible=False) bright_w2 = gr.Slider(0.0, 0.5, value=0.15, step=0.01, label="Brightness weight") bright_s2 = gr.Slider(0.0, 0.8, value=0.10, step=0.05, label="Brightness start") bright_e2 = gr.Slider(0.2, 1.0, value=0.80, step=0.05, label="Brightness end") bright_mode2 = gr.Radio(choices=["qr","custom"], value="qr", label="Brightness source") bright_ref2 = gr.Image(label="(Optional) custom brightness ref", type="pil") final_img2 = gr.Image(label="Final (or Hi-Res) image") low_img2 = gr.Image(label="Low-res (Stage A) preview") ctrl_img2 = gr.Image(label="Control QR used") gr.Button("Generate with DreamShaper 8").click( qr_txt2img_dream, [url2, s_prompt2, s_negative2, steps2, cfg2, size2, border2, qr_w2, seed2, use_hires2, hires_up2, hires_str2, repair2, feather2, cstart2, cend2, use_bright2, bright_w2, bright_s2, bright_e2, bright_mode2, bright_ref2], [final_img2, low_img2, ctrl_img2], api_name="qr_txt2img_dream" ) # ------------------- Image Blend (img2img + QR) ------------------- with gr.Tab("Image Blend (img2img + QR)"): m_key = gr.Dropdown(choices=list(BASE_MODELS.keys()), value="stable-diffusion-v1-5", label="Base model") init_up = gr.Image(label="Upload base image", type="pil") url_b = gr.Textbox(label="URL/Text", value="http://www.mybirdfire.com") s_prompt_b = gr.Textbox(label="Style prompt", value="highly detailed, cinematic lighting, rich textures") s_negative_b = gr.Textbox(label="Negative prompt", value="ugly, low quality, blurry, watermark, text") size_b = gr.Slider(384, 1024, value=768, step=64, label="Canvas (px, target)") steps_b = gr.Slider(10, 50, value=50, step=1, label="Steps") cfg_b = gr.Slider(1.0, 12.0, value=7.0, step=0.1, label="CFG") border_b = gr.Slider(2, 20, value=12, step=1, label="QR border (quiet zone)") qr_w_b = gr.Slider(0.8, 1.8, value=1.8, step=0.05, label="QR control weight") seed_b = gr.Number(value=-1, precision=0, label="Seed (-1 random)") strength_b = gr.Slider(0.2, 0.9, value=0.75, step=0.05, label="Img2Img denoise strength (blend amount)") cstart_b = gr.Slider(0.0, 0.6, value=0.0, step=0.05, label="QR control start") cend_b = gr.Slider(0.4, 1.0, value=0.95, step=0.05, label="QR control end") repair_b = gr.Slider(0.0, 1.0, value=0.1, step=0.05, label="Post repair strength (optional)") feather_b = gr.Slider(0.0, 3.0, value=1.5, step=0.1, label="Repair feather (px)") use_bright_b = gr.Checkbox(value=True, label="Add Brightness ControlNet") bright_w_b = gr.Slider(0.0, 0.5, value=0.25, step=0.01, label="Brightness weight") bright_s_b = gr.Slider(0.0, 0.8, value=0.10, step=0.05, label="Brightness start") bright_e_b = gr.Slider(0.2, 1.0, value=0.80, step=0.05, label="Brightness end") bright_mode_b = gr.Radio(choices=["mix","qr","init","custom"], value="mix", label="Brightness source") bright_ref_b = gr.Image(label="(Optional) custom brightness ref", type="pil") bright_blur_b = gr.Slider(0.0, 6.0, value=2.5, step=0.1, label="Brightness blur (px)") bright_gamma_b = gr.Slider(0.6, 1.2, value=0.9, step=0.01, label="Brightness gamma") bright_auto_b = gr.Checkbox(value=True, label="Brightness auto-contrast") bright_mix_b = gr.Slider(0.0, 1.0, value=0.65, step=0.01, label="Brightness source mix") final_b = gr.Image(label="Final blended image") init_b = gr.Image(label="(Resized) init image used") ctrl_b = gr.Image(label="Control QR used") gr.Button("Blend Uploaded Image with QR").click( qr_img2img_blend, [m_key, init_up, url_b, s_prompt_b, s_negative_b, steps_b, cfg_b, size_b, border_b, qr_w_b, seed_b, strength_b, repair_b, feather_b, cstart_b, cend_b, use_bright_b, bright_w_b, bright_s_b, bright_e_b, bright_mode_b, bright_ref_b, bright_blur_b, bright_gamma_b, bright_auto_b, bright_mix_b], [final_b, init_b, ctrl_b], api_name="qr_img2img_blend" ) if __name__ == "__main__": demo.queue(max_size=12).launch( server_name="0.0.0.0", # 👈 required on Spaces server_port=7860, # 👈 Spaces expects 7860 show_api=False, # optional: quieter UI share=False, # Spaces provides the public URL )