Fix bug

README.md

@@ -1,12 +1,17 @@
 ---
-title: QRBlend ZeroGPU
-emoji: 🧩
+title: GenImages ControlNet
+emoji: 🐢
+colorFrom: green
+colorTo: green
 sdk: gradio
-sdk_version: 4.44.0
-python_version: 3.10.13
+sdk_version: 5.42.0
+app_file: app.py
+pinned: false
+license: openrail
+short_description: Testing generate Images from Controlnet
 preload_from_hub:
   - runwayml/stable-diffusion-v1-5
   - monster-labs/control_v1p_sd15_qrcode_monster
-  - latentcat/control_v1p_sd15_brightness
-license: openrail++
 ---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,291 +1,203 @@
-import os, re, gc, random
-import numpy as np
-from contextlib import nullcontext
-from typing import Tuple
-
+import os, gc, random
 import gradio as gr
-from PIL import Image, ImageFilter
+import numpy as np
+from PIL import Image
 import qrcode
 from qrcode.constants import ERROR_CORRECT_H
 
 import torch
+import spaces  # <- ZeroGPU decorator
+
 from diffusers import (
     StableDiffusionPipeline,
-    StableDiffusionControlNetImg2ImgPipeline,
+    StableDiffusionControlNetPipeline,
     ControlNetModel,
-    DPMSolverMultistepScheduler,
 )
-import spaces  # ZeroGPU decorator
-
-# =========================================================
-# Auth (optional for private models)
-# =========================================================
-hf_token = os.getenv("HF_TOKEN")
-AUTH_KW = {"token": hf_token} if hf_token else {}
-
-# =========================================================
-# Helpers (untouched logic)
-# =========================================================
-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 strengthen_qr_prompts(pos: str, neg: str) -> Tuple[str, str]:
-    # DON’T say “QR code” here – let ControlNet impose it
-    pos = (pos or "").strip()
-    neg = (neg or "").strip()
-    pos2 = f"{pos}, high contrast lighting, clean details, cohesive composition".strip(", ")
-    add_neg = "frame, border, ornate frame, watermark, text, numbers, checkerboard, mosaic, halftone, repeated pattern, glitch"
-    neg2 = (neg + (", " if neg else "") + add_neg).strip(", ").strip()
-    return pos2, neg2
-
-def enforce_qr_contrast(stylized: Image.Image, qr_img: Image.Image, strength: float = 0.6, 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")
-
-# =========================================================
-# Models & loading (ZeroGPU-friendly lazy load)
-# =========================================================
-BASE_15        = "runwayml/stable-diffusion-v1-5"
-QR_MONSTER_15  = "monster-labs/control_v1p_sd15_qrcode_monster"  # v2 subfolder is handled by authors; base path is fine
-BRIGHTNESS_15  = "latentcat/control_v1p_sd15_brightness"          # optional helper
-
-_sd = {"pipe": None}
-_cn = {"pipe": None}
-
-def _setup_scheduler(pipe):
-    pipe.scheduler = DPMSolverMultistepScheduler.from_config(
-        pipe.scheduler.config,
-        use_karras_sigmas=True,
-        algorithm_type="dpmsolver++"
-    )
+from diffusers.schedulers.scheduling_euler_discrete import EulerDiscreteScheduler
+from controlnet_aux import CannyDetector
+
+# -----------------------------
+# Versions / env
+# -----------------------------
+TORCH_DTYPE = torch.float16  # Spaces GPU slice supports fp16 well
+
+# Optional (private models): set HF_TOKEN in Space secrets
+HF_TOKEN = os.getenv("HF_TOKEN")
+AUTH = {"token": HF_TOKEN} if HF_TOKEN else {}
+
+# -----------------------------
+# Global caches (lazy)
+# -----------------------------
+_sd_txt = {"pipe": None}
+_sd_cn  = {"pipe": None, "canny": None}
+
+BASE_15     = "runwayml/stable-diffusion-v1-5"
+CN_CANNY_15 = "lllyasviel/sd-controlnet-canny"
+CN_TILE_15  = "lllyasviel/control_v11f1e_sd15_tile"
+
+NEG_DEFAULT = "lowres, low contrast, blurry, jpeg artifacts, worst quality, bad anatomy, extra digits"
+
+
+# -----------------------------
+# QR maker (unchanged behavior)
+# -----------------------------
+def make_qr(url: str = "http://www.mybirdfire.com", size: int = 512, border: int = 4) -> Image.Image:
+    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="white").convert("RGB")
+    return img.resize((int(size), int(size)), resample=Image.NEAREST)
 
-def _enable_memory_savers(pipe):
-    # Good defaults for Spaces/ZeroGPU
-    pipe.enable_attention_slicing()
-    pipe.enable_vae_slicing()
-    pipe.enable_vae_tiling()
-    pipe.enable_model_cpu_offload()
 
-def _load_sd_txt2img():
-    if _sd["pipe"] is None:
+# -----------------------------
+# Lazy loaders (Spaces-safe)
+# -----------------------------
+def _get_sd15_txt2img():
+    if _sd_txt["pipe"] is None:
         pipe = StableDiffusionPipeline.from_pretrained(
             BASE_15,
-            torch_dtype=torch.float16,
+            torch_dtype=TORCH_DTYPE,
             safety_checker=None,
             use_safetensors=True,
             low_cpu_mem_usage=True,
-            **AUTH_KW
+            **AUTH
         )
-        _setup_scheduler(pipe)
-        _enable_memory_savers(pipe)
-        _sd["pipe"] = pipe
-    return _sd["pipe"]
-
-def _load_cn_img2img():
-    if _cn["pipe"] is None:
-        qrnet  = ControlNetModel.from_pretrained(
-            QR_MONSTER_15, torch_dtype=torch.float16, use_safetensors=True, **AUTH_KW
-        )
-        bright = ControlNetModel.from_pretrained(
-            BRIGHTNESS_15, torch_dtype=torch.float16, use_safetensors=True, **AUTH_KW
-        )
-        pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+        # Memory savers — ok to call before GPU is attached
+        pipe.enable_attention_slicing()
+        pipe.enable_vae_slicing()
+        pipe.enable_model_cpu_offload()
+        _sd_txt["pipe"] = pipe
+    return _sd_txt["pipe"]
+
+def _get_sd15_canny_tile():
+    if _sd_cn["pipe"] is None:
+        canny = ControlNetModel.from_pretrained(CN_CANNY_15, torch_dtype=TORCH_DTYPE, use_safetensors=True, **AUTH)
+        tile  = ControlNetModel.from_pretrained(CN_TILE_15,  torch_dtype=TORCH_DTYPE, use_safetensors=True, **AUTH)
+
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
             BASE_15,
-            controlnet=[qrnet, bright],
-            torch_dtype=torch.float16,
+            controlnet=[canny, tile],
+            torch_dtype=TORCH_DTYPE,
             safety_checker=None,
             use_safetensors=True,
             low_cpu_mem_usage=True,
-            **AUTH_KW
+            **AUTH
         )
-        _setup_scheduler(pipe)
-        _enable_memory_savers(pipe)
-        _cn["pipe"] = pipe
-    return _cn["pipe"]
-
-# =========================================================
-# Generation utilities (use inside @spaces.GPU)
-# =========================================================
-def sd_generate(prompt, negative, steps, guidance, seed, size=512):
-    pipe = _load_sd_txt2img()
-    # Reproducible generator — on GPU if available
-    gen = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu")
-    if int(seed) != 0:
-        gen = gen.manual_seed(int(seed))
-    else:
-        gen = gen.manual_seed(random.randint(0, 2**31 - 1))
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+        pipe.enable_attention_slicing()
+        pipe.enable_vae_slicing()
+        pipe.enable_model_cpu_offload()
+
+        _sd_cn["pipe"] = pipe
+        _sd_cn["canny"] = CannyDetector()
+    return _sd_cn["pipe"], _sd_cn["canny"]
+
+
+# -----------------------------
+# SD 1.5 (prompt-only)
+# -----------------------------
+@spaces.GPU(duration=120)
+def sd_generate(prompt, negative, steps, guidance, seed):
+    pipe = _get_sd15_txt2img()
+
+    # Reproducible generator on CUDA (available during @GPU call)
+    g = torch.Generator(device="cuda")
+    g = g.manual_seed(int(seed)) if int(seed) != 0 else g.manual_seed(random.randint(0, 2**31 - 1))
 
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
     gc.collect()
 
-    out = pipe(
-        prompt=prompt,
-        negative_prompt=negative or "",
-        num_inference_steps=int(steps),
-        guidance_scale=float(guidance),
-        width=int(size), height=int(size),
-        generator=gen
-    )
+    with torch.autocast(device_type="cuda", dtype=TORCH_DTYPE):
+        out = pipe(
+            prompt=str(prompt),
+            negative_prompt=(negative or ""),
+            num_inference_steps=int(steps),
+            guidance_scale=float(guidance),
+            generator=g
+        )
     return out.images[0]
 
-def make_qr(url="http://www.mybirdfire.com", size=512, border=10, back_color="#808080", 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)
-    bg  = normalize_color(back_color)
-    img = qr.make_image(fill_color="black", back_color=bg).convert("RGB").resize((size, size), Image.NEAREST)
-    if blur_radius and blur_radius > 0:
-        img = img.filter(ImageFilter.GaussianBlur(radius=float(blur_radius)))
-    return img
 
-NEG_DEFAULT = "lowres, low contrast, blurry, jpeg artifacts, worst quality, bad anatomy, extra digits"
+# -----------------------------
+# Stylizer (SD1.5 + ControlNet canny + tile)
+# -----------------------------
+@spaces.GPU(duration=180)
+def stylize_qr_sd15(prompt: str, negative: str, steps: int, guidance: float, seed: int,
+                    canny_low: int, canny_high: int, border: int):
+
+    pipe, canny = _get_sd15_canny_tile()
+
+    # Fresh QR → edges
+    qr_img = make_qr("http://www.mybirdfire.com", size=512, border=int(border))
+    edges = canny(qr_img, low_threshold=int(canny_low), high_threshold=int(canny_high))
 
-# =========================================================
-# Main two-stage generator (ZeroGPU-guarded)
-# =========================================================
-@spaces.GPU(duration=120)  # allocate GPU only while generating
-def qr_art_two_stage(
-    prompt, negative,
-    base_steps, base_cfg, base_seed,
-    stylize_steps, stylize_cfg, stylize_seed,
-    size, url, border, back_color,
-    denoise, qr_weight, bright_weight,
-    qr_start, qr_end, bright_start, bright_end,
-    control_blur, repair_strength, feather_px
-):
-    size = max(384, int(size) // 8 * 8)
-
-    # Stage A: base art (txt2img)
-    p_pos, p_neg = strengthen_qr_prompts(prompt, negative)
-    base_img = sd_generate(p_pos, p_neg, base_steps, base_cfg, base_seed, size=size)
-
-    # Stage B: img2img + ControlNet
-    qr_img = make_qr(url=url, size=size, border=border, back_color=back_color, blur_radius=control_blur)
-    pipe   = _load_cn_img2img()
+    # Control weights (canny, tile). Tune to taste.
+    cn_scales = [1.2, 0.6]
+
+    g = torch.Generator(device="cuda")
+    g = g.manual_seed(int(seed)) if int(seed) != 0 else g.manual_seed(random.randint(0, 2**31 - 1))
 
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
     gc.collect()
 
-    gen = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu")
-    if int(stylize_seed) != 0:
-        gen = gen.manual_seed(int(stylize_seed))
-    else:
-        gen = gen.manual_seed(random.randint(0, 2**31 - 1))
-
-    kwargs = dict(
-        prompt=p_pos,
-        negative_prompt=p_neg or NEG_DEFAULT,
-        image=base_img,                              # init image for img2img
-        control_image=[qr_img, qr_img],              # Monster + Brightness
-        strength=float(denoise),                     # how much we allow change
-        num_inference_steps=int(stylize_steps),
-        guidance_scale=float(stylize_cfg),
-        generator=gen,
-        controlnet_conditioning_scale=[float(qr_weight), float(bright_weight)],
-        width=size, height=size,  # (diffusers uses init image size; harmless here)
-    )
-
-    try:
-        out = pipe(
-            **kwargs,
-            control_guidance_start=[float(qr_start), float(bright_start)],
-            control_guidance_end=[float(qr_end), float(bright_end)],
-        )
-    except TypeError:
+    with torch.autocast(device_type="cuda", dtype=TORCH_DTYPE):
         out = pipe(
-            **kwargs,
-            controlnet_start=[float(qr_start), float(bright_start)],
-            controlnet_end=[float(qr_end), float(bright_end)],
+            prompt=str(prompt),
+            negative_prompt=(negative or NEG_DEFAULT),
+            image=[edges, qr_img],                         # txt2img ControlNet: control images
+            controlnet_conditioning_scale=cn_scales,
+            num_inference_steps=int(steps),
+            guidance_scale=float(guidance),
+            generator=g
         )
+    return out.images[0]
 
-    img = out.images[0]
-
-    # Optional post repair to push blacks/whites where modules demand
-    img = enforce_qr_contrast(img, qr_img, strength=float(repair_strength), feather=float(feather_px))
-    return img, base_img, qr_img
 
-# =========================================================
-# UI (Gradio Space)
-# =========================================================
+# -----------------------------
+# UI (same layout as yours)
+# -----------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧩 QR-Code Monster — Two-Stage (txt2img → img2img + ControlNet) — ZeroGPU")
-
-    with gr.Tab("Two-Stage QR Art"):
-        with gr.Row():
-            with gr.Column():
-                url      = gr.Textbox(label="URL/Text", value="http://www.mybirdfire.com")
-                prompt   = gr.Textbox(
-                    label="Style prompt (no 'QR code' here)",
-                    value="baroque palace interior with intricate roots, cinematic, dramatic lighting, ultra detailed"
-                )
-                negative = gr.Textbox(label="Negative", value="")
-                size     = gr.Slider(512, 1024, value=768, step=64, label="Canvas (px)")
-
-                gr.Markdown("**Stage A — Base art (txt2img)**")
-                base_steps = gr.Slider(10, 60, value=26, step=1, label="Base steps")
-                base_cfg   = gr.Slider(1.0, 12.0, value=6.0, step=0.1, label="Base CFG")
-                base_seed  = gr.Number(value=0, precision=0, label="Base seed (0=random)")
-
-                gr.Markdown("**Stage B — ControlNet img2img**")
-                stylize_steps = gr.Slider(10, 60, value=28, step=1, label="Stylize steps")
-                stylize_cfg   = gr.Slider(1.0, 12.0, value=6.0, step=0.1, label="Stylize CFG")
-                stylize_seed  = gr.Number(value=0, precision=0, label="Stylize seed (0=random)")
-                denoise       = gr.Slider(0.1, 0.8, value=0.48, step=0.01, label="Denoising strength (keep composition lower)")
-
-                qr_weight     = gr.Slider(0.5, 1.7, value=1.2, step=0.05, label="QR Monster weight")
-                bright_weight = gr.Slider(0.0, 1.0, value=0.20, step=0.05, label="Brightness weight")
-
-                qr_start      = gr.Slider(0.0, 1.0, value=0.05, step=0.01, label="QR start")
-                qr_end        = gr.Slider(0.0, 1.0, value=0.95, step=0.01, label="QR end")
-                bright_start  = gr.Slider(0.0, 1.0, value=0.40, step=0.01, label="Brightness start")
-                bright_end    = gr.Slider(0.0, 1.0, value=0.85, step=0.01, label="Brightness end")
-
-                border        = gr.Slider(4, 20, value=12, step=1, label="QR border (quiet zone)")
-                back_color    = gr.ColorPicker(value="#808080", label="QR background (mid-gray blends better)")
-                control_blur  = gr.Slider(0.0, 3.0, value=1.2, step=0.1, label="Soften control (Gaussian blur radius)")
-                repair_strength = gr.Slider(0.0, 1.0, value=0.65, step=0.05, label="Post repair strength")
-                feather_px      = gr.Slider(0.0, 3.0, value=1.0, step=0.1, label="Repair feather (px)")
-
-                go = gr.Button("Generate QR Art", variant="primary")
-
-            with gr.Column():
-                final_img = gr.Image(label="Final stylized QR")
-                base_img  = gr.Image(label="Base art (Stage A)")
-                ctrl_img  = gr.Image(label="Control image (QR used)")
-
-        go.click(
-            qr_art_two_stage,
-            inputs=[prompt, negative,
-                    base_steps, base_cfg, base_seed,
-                    stylize_steps, stylize_cfg, stylize_seed,
-                    size, url, border, back_color,
-                    denoise, qr_weight, bright_weight,
-                    qr_start, qr_end, bright_start, bright_end,
-                    control_blur, repair_strength, feather_px],
-            outputs=[final_img, base_img, ctrl_img]
+    gr.Markdown("## Stable Diffusion + QR Code + ControlNet (SD1.5) — ZeroGPU")
+
+    with gr.Tab("Stable Diffusion (prompt → image)"):
+        prompt = gr.Textbox(label="Prompt", value="Sky, Moon, Bird, Blue, In the dark, Goddess, Sweet, Beautiful, Fantasy, Art, Anime")
+        negative = gr.Textbox(label="Negative Prompt", value="lowres, bad anatomy, worst quality")
+        steps  = gr.Slider(10, 50, value=30, label="Steps", step=1)
+        cfg    = gr.Slider(1, 12, value=7.0, label="Guidance Scale", step=0.1)
+        seed   = gr.Number(value=0, label="Seed (0 = random)", precision=0)
+        out_sd = gr.Image(label="Generated Image")
+        gr.Button("Generate").click(sd_generate, [prompt, negative, steps, cfg, seed], out_sd)
+
+    with gr.Tab("QR Maker (mybirdfire)"):
+        url   = gr.Textbox(label="URL/Text", value="http://www.mybirdfire.com")
+        size  = gr.Slider(256, 1024, value=512, step=64, label="Size (px)")
+        quiet = gr.Slider(0, 8, value=4, step=1, label="Border (quiet zone)")
+        out_qr = gr.Image(label="QR Code", type="pil")
+        gr.Button("Generate QR").click(make_qr, [url, size, quiet], out_qr)
+
+    with gr.Tab("QR Stylizer (SD1.5 canny + tile, Euler)"):
+        s_prompt = gr.Textbox(label="Style Prompt", value="Sky, Moon, Bird, Blue, In the dark, Goddess, Sweet, Beautiful, Fantasy, Art, Anime")
+        s_negative = gr.Textbox(label="Negative Prompt", value=NEG_DEFAULT)
+        s_steps  = gr.Slider(10, 50, value=28, label="Steps", step=1)
+        s_cfg    = gr.Slider(1, 12, value=7.0, label="CFG", step=0.1)
+        s_seed   = gr.Number(value=1470713301, label="Seed", precision=0)
+        canny_l  = gr.Slider(0, 255, value=80, step=1, label="Canny low")
+        canny_h  = gr.Slider(0, 255, value=160, step=1, label="Canny high")
+        s_border = gr.Slider(2, 10, value=6, step=1, label="QR border")
+        out_styl = gr.Image(label="Stylized QR")
+        gr.Button("Stylize").click(
+            stylize_qr_sd15,
+            [s_prompt, s_negative, s_steps, s_cfg, s_seed, canny_l, canny_h, s_border],
+            out_styl
         )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=12, concurrency_count=1).launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        show_api=True,
+        analytics_enabled=False
+    )

requirements.txt

@@ -3,9 +3,10 @@ diffusers>=0.27.2
 transformers>=4.42.0
 accelerate>=0.31.0
 safetensors
-gradio>=4.29.0
+gradio==4.44.1
 qrcode[pil]
 Pillow
 huggingface-hub
+controlnet-aux>=0.0.8
 spaces
 numpy