Update app.py

app.py

@@ -1,109 +1,5 @@
-import torch, gradio as gr
-from PIL import Image
-from diffusers import StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-
-# --- Configuration ---
-BASE_MODEL_ID = "sd-legacy/stable-diffusion-v1-5"  # modèle de base
-LORA_PATH = "./wanostyle_2_offset.safetensors"      # LoRA que tu as uploadé à la racine du Space
-
-device = "cuda"
-dtype = torch.float16
-
-# --- Chargement pipeline SD1.5 ---
-print("🔹 Chargement du modèle de base...")
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    BASE_MODEL_ID,
-    torch_dtype=dtype,
-    safety_checker=None,
-    use_safetensors=True,
-)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-
-# Optimisations mémoire stables (GPU T4)
-pipe.enable_attention_slicing()
-pipe.enable_vae_slicing()
-pipe.enable_vae_tiling()
-pipe.to(device)
-
-# Chargement du LoRA
-print("🔹 Chargement du LoRA...")
-pipe.load_lora_weights(LORA_PATH, adapter_name="onepiece")
-
-# Negative prompt par défaut
-DEFAULT_NEG = "low quality, worst quality, extra fingers, deformed, blurry, text, watermark, logo"
-
-def stylize(
-    image,
-    prompt,
-    lora_scale=0.9,
-    strength=0.45,
-    cfg=7.0,
-    steps=30,
-    width=640,
-    height=640,
-    seed=-1
-):
-    if image is None:
-        return None
-
-    # Générateur de seed
-    generator = None
-    if seed is not None and int(seed) >= 0:
-        generator = torch.Generator(device=device).manual_seed(int(seed))
-
-    # Redimensionne l'image d'entrée
-    image = image.convert("RGB").resize((int(width), int(height)), Image.Resampling.LANCZOS)
-
-    # Active le LoRA
-    pipe.set_adapters(["onepiece"], adapter_weights=[float(lora_scale)])
-
-    # Génération
-    result = pipe(
-        prompt=prompt.strip(),
-        image=image,
-        strength=float(strength),
-        guidance_scale=float(cfg),
-        num_inference_steps=int(steps),
-        negative_prompt=DEFAULT_NEG,
-        generator=generator
-    )
-
-    return result.images[0]
-
-
-# --- Interface Gradio ---
-EX_PROMPT = (
-    "portrait buste, pirate stylisé One Piece, cel shading, ligne claire, "
-    "détails anime, expression confiante, fond simple"
-)
-
-with gr.Blocks(css="footer{display:none !important}") as demo:
-    gr.Markdown("# 🏴‍☠️ One Piece (LoRA) – Génération d'image stylisée")
-
-    with gr.Row():
-        with gr.Column():
-            in_image = gr.Image(type="pil", label="Photo de la personne")
-            prompt = gr.Textbox(label="Prompt", value=EX_PROMPT)
-            lora_scale = gr.Slider(0.0, 1.5, value=0.9, step=0.05, label="Force du LoRA (style One Piece)")
-            strength = gr.Slider(0.1, 0.9, value=0.45, step=0.05, label="Denoise strength (garde du visage)")
-            cfg = gr.Slider(1, 12, value=7, step=0.5, label="CFG (guidance scale)")
-            steps = gr.Slider(10, 60, value=30, step=1, label="Steps")
-            with gr.Row():
-                width = gr.Dropdown(choices=[512, 640, 768], value=640, label="Largeur")
-                height = gr.Dropdown(choices=[512, 640, 768], value=640, label="Hauteur")
-            seed = gr.Number(value=-1, label="Seed (-1 aléatoire)")
-            btn = gr.Button("🎨 Générer l'image", variant="primary")
-        with gr.Column():
-            out_image = gr.Image(label="Résultat", interactive=False)
-
-    btn.click(
-        stylize,
-        inputs=[in_image, prompt, lora_scale, strength, cfg, steps, width, height, seed],
-        outputs=out_image
-    )
-
-# Empêche les OOM liés aux multiples requêtes
-demo.queue(concurrency_count=1, max_size=4)
-
-if __name__ == "__main__":
-    demo.launch()
+import torch
+print(f"Is CUDA available: {torch.cuda.is_available()}")
+# True
+print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
+# Tesla T4