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README.md

@@ -1,14 +1,20 @@
 ---
-title: Controllnet SDLX
-emoji: 📈
-colorFrom: blue
-colorTo: purple
+title: ControlNet SDXL Canny
+emoji: 🔥
+colorFrom: yellow
+colorTo: red
 sdk: gradio
-sdk_version: 6.19.0
-python_version: '3.12'
+sdk_version: 5.28.0
 app_file: app.py
 pinned: false
-short_description: Controllnet_SDLX
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ControlNet · SDXL Canny (ZeroGPU)
+
+Hochwertige Bildgenerierung mit SDXL + xinsir Canny ControlNet, läuft auf ZeroGPU.
+
+## Wichtig: Hardware in Settings auf "ZeroGPU" stellen
+Dieser Space ist für ZeroGPU gebaut (benötigt einen Pro-Account oder eine
+ZeroGPU-fähige Org). In den Space-Settings unter "Hardware" -> ZeroGPU wählen.
+
+Der @spaces.GPU Decorator im Code weist die GPU nur während der Generierung zu.

app.py

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+import spaces
+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+from diffusers import (
+    StableDiffusionXLControlNetPipeline,
+    ControlNetModel,
+    AutoencoderKL,
+    EulerAncestralDiscreteScheduler,
+)
+
+DTYPE = torch.float16
+
+# ---------------------------------------------------------------------------
+# Model loading (runs once on startup, stays on GPU via ZeroGPU)
+# ---------------------------------------------------------------------------
+controlnet = ControlNetModel.from_pretrained(
+    "xinsir/controlnet-canny-sdxl-1.0",
+    torch_dtype=DTYPE,
+)
+vae = AutoencoderKL.from_pretrained(
+    "madebyollin/sdxl-vae-fp16-fix",
+    torch_dtype=DTYPE,
+)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    vae=vae,
+    torch_dtype=DTYPE,
+    safety_checker=None,
+)
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+# ---------------------------------------------------------------------------
+# Helper: extract Canny edges (resized to ~1024 for best SDXL performance)
+# ---------------------------------------------------------------------------
+def extract_canny(image: Image.Image, low: int, high: int):
+    img = np.array(image.convert("RGB"))
+    h, w, _ = img.shape
+    ratio = np.sqrt(1024.0 * 1024.0 / (w * h))
+    new_w, new_h = int(w * ratio), int(h * ratio)
+    img = cv2.resize(img, (new_w, new_h))
+    edges = cv2.Canny(img, low, high)
+    edges = np.concatenate([edges[:, :, None]] * 3, axis=2)
+    return Image.fromarray(edges), new_w, new_h
+
+# ---------------------------------------------------------------------------
+# Main generation function — @spaces.GPU activates ZeroGPU during the call
+# ---------------------------------------------------------------------------
+@spaces.GPU(duration=90)
+def generate(input_image, prompt, negative_prompt, canny_low, canny_high,
+             guidance_scale, steps, cn_scale, seed):
+    if input_image is None:
+        raise gr.Error("Bitte lade ein Bild hoch.")
+    if not prompt.strip():
+        raise gr.Error("Bitte gib einen Prompt ein.")
+
+    pil_image = Image.fromarray(input_image)
+    control_image, new_w, new_h = extract_canny(pil_image, int(canny_low), int(canny_high))
+
+    generator = torch.manual_seed(int(seed)) if seed >= 0 else None
+
+    result = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt or None,
+        image=control_image,
+        controlnet_conditioning_scale=float(cn_scale),
+        num_inference_steps=int(steps),
+        guidance_scale=float(guidance_scale),
+        width=new_w,
+        height=new_h,
+        generator=generator,
+    ).images[0]
+
+    return control_image, result
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+css = """
+body { font-family: 'Inter', sans-serif; background: #0f0f11; color: #e8e8f0; }
+.gradio-container { max-width: 1100px; margin: 0 auto; }
+#title { text-align: center; padding: 2rem 0 0.5rem; }
+#title h1 { font-size: 2rem; font-weight: 700; letter-spacing: -0.5px;
+             background: linear-gradient(90deg, #f59e0b, #ef4444);
+             -webkit-background-clip: text; -webkit-text-fill-color: transparent; }
+#title p  { color: #9090a8; font-size: 0.95rem; margin-top: 0.25rem; }
+.panel    { background: #1a1a22; border: 1px solid #2a2a38; border-radius: 12px; padding: 1.25rem; }
+.generate-btn { background: linear-gradient(135deg, #f59e0b, #ef4444) !important;
+                color: white !important; border: none !important;
+                font-weight: 600 !important; font-size: 1rem !important;
+                border-radius: 8px !important; height: 48px !important; }
+.generate-btn:hover { opacity: 0.9 !important; }
+"""
+
+with gr.Blocks(css=css, title="ControlNet SDXL Canny") as demo:
+    gr.HTML("""
+        <div id="title">
+            <h1>🔥 ControlNet · SDXL Canny</h1>
+            <p>Hochwertige Bildgenerierung mit SDXL auf ZeroGPU. Lade ein Bild hoch, schreib einen Prompt – die Struktur deines Originals bleibt erhalten.</p>
+        </div>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1, elem_classes="panel"):
+            gr.Markdown("### 📥 Eingabe")
+            input_image = gr.Image(label="Referenzbild", type="numpy", height=300)
+            prompt = gr.Textbox(label="Prompt", lines=3,
+                placeholder="a rugged pirate on a wooden ship, photorealistic, cinematic, 8k")
+            negative_prompt = gr.Textbox(label="Negative Prompt (optional)", lines=2,
+                placeholder="blurry, low quality, deformed, extra limbs")
+
+            with gr.Accordion("⚙️ Erweiterte Einstellungen", open=False):
+                with gr.Row():
+                    canny_low  = gr.Slider(0, 255, value=100, step=1, label="Canny Low")
+                    canny_high = gr.Slider(0, 255, value=200, step=1, label="Canny High")
+                with gr.Row():
+                    guidance_scale = gr.Slider(1, 15, value=6.0, step=0.5, label="Guidance Scale")
+                    steps          = gr.Slider(15, 50, value=30, step=1, label="Inference Steps")
+                cn_scale = gr.Slider(0.1, 2.0, value=0.8, step=0.05,
+                    label="ControlNet Stärke (niedriger = mehr Freiheit)")
+                seed = gr.Number(value=42, label="Seed (-1 = zufällig)", precision=0)
+
+            run_btn = gr.Button("🎨  Generieren", elem_classes="generate-btn")
+
+        with gr.Column(scale=1, elem_classes="panel"):
+            gr.Markdown("### 📤 Ergebnis")
+            canny_out  = gr.Image(label="Canny-Kantenbild", height=250)
+            result_out = gr.Image(label="Generiertes Bild", height=400)
+
+    run_btn.click(
+        fn=generate,
+        inputs=[input_image, prompt, negative_prompt, canny_low, canny_high,
+                guidance_scale, steps, cn_scale, seed],
+        outputs=[canny_out, result_out],
+    )
+
+demo.queue().launch()

controlnet_zerogpu_space.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:28e58f133d78916497255fb34300bd2b09b667b4ae40b524c8c5fbe6a3f3f700
+size 3526

requirements.txt

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+torch
+torchvision
+diffusers
+transformers
+accelerate
+opencv-python-headless
+Pillow
+numpy