Testing 2 Stable Diffusion

app.py

@@ -12,22 +12,31 @@ from diffusers import (
     DPMSolverMultistepScheduler,
 )
 
+# --- gradio_client bool-schema hotfix (prevents blank page on Spaces) ---
+try:
+    import gradio_client.utils as _gcu
+    _orig_get_type = _gcu.get_type
+    def _get_type_safe(schema):
+        if isinstance(schema, bool):   # handle JSON Schema True/False
+            return "any"
+        return _orig_get_type(schema)
+    _gcu.get_type = _get_type_safe
+except Exception:
+    pass
+# -----------------------------------------------------------------------
+
 # Quiet matplotlib cache warning on Spaces
 os.environ.setdefault("MPLCONFIGDIR", "/tmp/mpl")
 
-# ---- HF auth helper (works for public & private/gated repos) ----
+# Token helper: add a Secret in your Space named HUGGINGFACE_HUB_TOKEN
 def _hf_auth():
-    # Add this in Space Settings → Variables → New secret
-    # Name: HUGGINGFACE_HUB_TOKEN   Value: <your HF token>
     tok = os.getenv("HUGGINGFACE_HUB_TOKEN") or os.getenv("HF_TOKEN")
-    if not tok:
-        return {}
-    return {"token": tok, "use_auth_token": tok}
+    return {"token": tok, "use_auth_token": tok} if tok else {}
 
 # ---- base models for the two tabs ----
 BASE_MODELS = {
     "stable-diffusion-v1-5": "runwayml/stable-diffusion-v1-5",
-    "dream": "Lykon/dreamshaper-8",
+    "dream":                 "Lykon/dreamshaper-8",
 }
 
 # ControlNet (QR Monster v2 for SD15)
@@ -56,9 +65,6 @@ def normalize_color(c):
     return "white"
 
 def make_qr(url="https://example.com", size=768, border=12, back_color="#FFFFFF", blur_radius=0.0):
-    """
-    IMPORTANT for Method 1: give ControlNet a sharp, black-on-WHITE QR (no blur).
-    """
     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")
@@ -68,7 +74,6 @@ def make_qr(url="https://example.com", size=768, border=12, back_color="#FFFFFF"
     return img
 
 def enforce_qr_contrast(stylized: Image.Image, qr_img: Image.Image, strength: float = 0.0, feather: float = 1.0) -> Image.Image:
-    """Optional gentle repair. Default OFF for Method 1."""
     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)))
@@ -97,12 +102,7 @@ def _base_scheduler_for(pipe):
 def get_cn():
     global _CN
     if _CN is None:
-        _CN = ControlNetModel.from_pretrained(
-            CN_QRMON,
-            torch_dtype=DTYPE,
-            use_safetensors=True,
-            **_hf_auth()
-        )
+        _CN = ControlNetModel.from_pretrained(CN_QRMON, torch_dtype=DTYPE, use_safetensors=True, **_hf_auth())
     return _CN
 
 def get_qrmon_txt2img_pipe(model_id: str):
@@ -114,7 +114,7 @@ def get_qrmon_txt2img_pipe(model_id: str):
             safety_checker=None,
             use_safetensors=True,
             low_cpu_mem_usage=True,
-            **_hf_auth()
+            **_hf_auth(),
         )
         _CN_TXT2IMG[model_id] = _base_scheduler_for(pipe)
     return _CN_TXT2IMG[model_id]
@@ -128,7 +128,7 @@ def get_qrmon_img2img_pipe(model_id: str):
             safety_checker=None,
             use_safetensors=True,
             low_cpu_mem_usage=True,
-            **_hf_auth()
+            **_hf_auth(),
         )
         _CN_IMG2IMG[model_id] = _base_scheduler_for(pipe)
     return _CN_IMG2IMG[model_id]
@@ -143,15 +143,12 @@ def _qr_txt2img_core(model_id: str,
 
     s = snap8(size)
 
-    # Control image: crisp black-on-white QR
     qr_img = make_qr(url=url, size=s, border=int(border), back_color="#FFFFFF", blur_radius=0.0)
 
-    # Seed / generator
     if int(seed) < 0:
         seed = random.randint(0, 2**31 - 1)
     gen = torch.Generator(device="cuda").manual_seed(int(seed))
 
-    # --- Stage A: txt2img with ControlNet
     pipe = get_qrmon_txt2img_pipe(model_id)
     if torch.cuda.is_available(): torch.cuda.empty_cache()
     gc.collect()
@@ -159,8 +156,8 @@ def _qr_txt2img_core(model_id: str,
         out = pipe(
             prompt=str(style_prompt),
             negative_prompt=str(negative or ""),
-            image=qr_img,                                   # control image for txt2img
-            controlnet_conditioning_scale=float(qr_weight), # ~1.0–1.2 works well
+            image=qr_img,
+            controlnet_conditioning_scale=float(qr_weight),
             control_guidance_start=0.0,
             control_guidance_end=1.0,
             num_inference_steps=int(steps),
@@ -171,7 +168,6 @@ def _qr_txt2img_core(model_id: str,
     lowres = out.images[0]
     lowres = enforce_qr_contrast(lowres, qr_img, strength=float(repair_strength), feather=float(feather))
 
-    # --- Optional Stage B: Hi-Res Fix (img2img with same QR)
     final = lowres
     if use_hires:
         up = max(1.0, min(2.0, float(hires_upscale)))
@@ -183,9 +179,9 @@ def _qr_txt2img_core(model_id: str,
             out2 = pipe2(
                 prompt=str(style_prompt),
                 negative_prompt=str(negative or ""),
-                image=lowres,                      # init image
-                control_image=qr_img,              # same QR
-                strength=float(hires_strength),    # ~0.7 like "Hires Fix"
+                image=lowres,
+                control_image=qr_img,
+                strength=float(hires_strength),
                 controlnet_conditioning_scale=float(qr_weight),
                 control_guidance_start=0.0,
                 control_guidance_end=1.0,
@@ -199,7 +195,6 @@ def _qr_txt2img_core(model_id: str,
     final = enforce_qr_contrast(final, qr_img, strength=float(repair_strength), feather=float(feather))
     return final, lowres, qr_img
 
-# Wrappers for each tab (so Gradio can bind without passing the model id)
 @spaces.GPU(duration=120)
 def qr_txt2img_anything(*args):
     return _qr_txt2img_core(BASE_MODELS["stable-diffusion-v1-5"], *args)
@@ -212,7 +207,6 @@ def qr_txt2img_dream(*args):
 with gr.Blocks() as demo:
     gr.Markdown("# ZeroGPU • Method 1: QR Control (two base models)")
 
-    # ---- Tab 1: stable-diffusion-v1-5 ----
     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")
@@ -223,18 +217,14 @@ with gr.Blocks() as demo:
         border1    = gr.Slider(2, 16, value=4, step=1, label="QR border (quiet zone)")
         qr_w1      = gr.Slider(0.6, 1.6, value=1.5, step=0.05, label="QR control weight")
         seed1      = gr.Number(value=-1, precision=0, label="Seed (-1 random)")
-
         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(0.3, 0.9, value=0.7, 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)")
-
         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 stable-diffusion-v1-5").click(
             qr_txt2img_anything,
             [url1, s_prompt1, s_negative1, steps1, cfg1, size1, border1, qr_w1, seed1,
@@ -242,7 +232,6 @@ with gr.Blocks() as demo:
             [final_img1, low_img1, ctrl_img1]
         )
 
-    # ---- Tab 2: DreamShaper (general art/painterly) ----
     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")
@@ -253,18 +242,14 @@ with gr.Blocks() as demo:
         border2    = gr.Slider(2, 16, value=8, step=1, label="QR border (quiet zone)")
         qr_w2      = gr.Slider(0.6, 1.6, value=1.5, step=0.05, label="QR control weight")
         seed2      = gr.Number(value=-1, precision=0, label="Seed (-1 random)")
-
         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(0.3, 0.9, value=0.7, 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)")
-
         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,
@@ -273,5 +258,9 @@ with gr.Blocks() as demo:
         )
 
 if __name__ == "__main__":
-    # Private Spaces: no share=True
-    demo.queue(max_size=12).launch()
+    # Keep launch simple on Spaces
+    demo.queue(max_size=12).launch(
+        server_name="0.0.0.0",
+        server_port=int(os.environ.get("PORT", 7860)),
+        show_error=True,
+    )

requirements.txt

@@ -1,13 +1,16 @@
+# Core
 numpy==1.26.4
 torch==2.2.0
 diffusers==0.30.2
-accelerate>=0.25.0
-transformers>=4.40.0
-safetensors
-gradio>=4.44.1
-pydantic==2.10.6
+accelerate==0.25.0
+transformers==4.40.2
+safetensors==0.4.3
 huggingface_hub==0.29.3
-spaces
-qrcode[pil]
-Pillow
 
+# UI / HF Spaces
+gradio==4.44.1
+spaces==0.27.1
+
+# QR + imaging
+qrcode[pil]==7.4.2
+Pillow==10.3.0