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18c998c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | 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()
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