Delete app.py

app.py

@@ -1,132 +0,0 @@
-import gradio as gr
-import torch
-import numpy as np
-from PIL import Image
-import cv2
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-
-# ---------------------------------------------------------------------------
-# Device setup (works on free CPU Spaces)
-# ---------------------------------------------------------------------------
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
-
-# ---------------------------------------------------------------------------
-# Model loading (cached – runs once on Space startup)
-# ---------------------------------------------------------------------------
-def load_pipeline():
-    controlnet = ControlNetModel.from_pretrained(
-        "lllyasviel/sd-controlnet-canny",
-        torch_dtype=DTYPE,
-    )
-    pipe = StableDiffusionControlNetPipeline.from_pretrained(
-        "runwayml/stable-diffusion-v1-5",
-        controlnet=controlnet,
-        torch_dtype=DTYPE,
-        safety_checker=None,
-    )
-    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-    pipe = pipe.to(DEVICE)
-    if DEVICE == "cuda":
-        pipe.enable_model_cpu_offload()
-    return pipe
-
-pipe = load_pipeline()
-
-# ---------------------------------------------------------------------------
-# Helper: extract Canny edges
-# ---------------------------------------------------------------------------
-def extract_canny(image: Image.Image, low: int, high: int) -> Image.Image:
-    img_array = np.array(image.convert("RGB"))
-    edges = cv2.Canny(img_array, low, high)
-    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
-    return Image.fromarray(edges_rgb)
-
-# ---------------------------------------------------------------------------
-# Main generation function
-# ---------------------------------------------------------------------------
-def generate(input_image, prompt, negative_prompt, canny_low, canny_high,
-             guidance_scale, steps, 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).resize((512, 512))
-    control_image = 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,
-        num_inference_steps=int(steps),
-        guidance_scale=float(guidance_scale),
-        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, #a78bfa, #60a5fa);
-             -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, #7c3aed, #2563eb) !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 Canny") as demo:
-    gr.HTML("""
-        <div id="title">
-            <h1>⚡ ControlNet · Canny Edge</h1>
-            <p>Lade ein Bild hoch, schreib einen Prompt – und erzeuge ein neues Bild, das die Struktur deines Originals übernimmt.</p>
-        </div>
-    """)
-
-    gr.Markdown(f"> 🖥️ Läuft auf: **{DEVICE.upper()}** — auf CPU dauert eine Generierung ca. 2–5 Minuten. Bitte Geduld.")
-
-    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",
-                placeholder="a futuristic city at night, neon lights, photorealistic, 8k", lines=3)
-            negative_prompt = gr.Textbox(label="Negative Prompt (optional)",
-                placeholder="blurry, low quality, watermark, deformed", lines=2)
-
-            with gr.Accordion("⚙️ Erweiterte Einstellungen", open=False):
-                with gr.Row():
-                    canny_low  = gr.Slider(0, 255, value=100, step=1, label="Canny Low Threshold")
-                    canny_high = gr.Slider(0, 255, value=200, step=1, label="Canny High Threshold")
-                with gr.Row():
-                    guidance_scale = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")
-                    steps          = gr.Slider(10, 30, value=15, step=1, label="Inference Steps")
-                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=350)
-
-    run_btn.click(
-        fn=generate,
-        inputs=[input_image, prompt, negative_prompt, canny_low, canny_high,
-                guidance_scale, steps, seed],
-        outputs=[canny_out, result_out],
-    )
-
-demo.queue().launch()