Update app.py

app.py

@@ -1,97 +1,97 @@
-import os
-import sys
-import subprocess
-
-# Install deps au démarrage (ignore erreurs si déjà installé)
-def install_deps():
-    deps = [
-        "torch==2.5.1",
-        "torchaudio==2.5.1",
-        "torchvision==0.20.1",
-        "diffusers==0.32.2",
-        "transformers==4.50.3",
-        "accelerate==1.1.1",
-        "huggingface-hub==0.26.1",
-        "safetensors==0.4.6",
-        "pillow==10.4.0",
-        "peft==0.13.2"
-    ]
-    for dep in deps:
-        subprocess.check_call([sys.executable, "-m", "pip", "install", "--quiet", dep])
-
-install_deps()  # Lance une fois
-
 import torch
 import gradio as gr
 from diffusers import FluxPipeline
-# ... reste du code identique
-# Chargement du modèle Flux.1-schnell (Apache 2.0, NSFW-friendly)
+from huggingface_hub import hf_hub_download
+import random
+
+# Chargement du modèle Flux.1-schnell
 model_id = "black-forest-labs/FLUX.1-schnell"
-pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()
-pipe.enable_sequential_cpu_offload()  # Optimise VRAM pour Space gratuit
 
 # Variables globales pour LoRA
 lora_repo = None
-lora_strength = 0.8
+lora_path = None
 
 def load_lora(repo_id):
-    global pipe, lora_repo
+    global lora_repo, lora_path
     try:
         lora_path = hf_hub_download(repo_id=repo_id, filename="flux-lora.safetensors")
-        pipe.load_lora_weights(lora_path)
-        pipe.fuse_lora()
         lora_repo = repo_id
-        return f"LoRA chargé : {repo_id}"
+        return f"✅ LoRA chargé : {repo_id}"
     except Exception as e:
-        return f"Erreur LoRA : {str(e)}"
+        return f"❌ Erreur LoRA : {str(e)}"
 
-@spaces.GPU(duration=120)
 def generate(prompt, negative_prompt, width=1024, height=1024, steps=4, seed=-1, lora_scale=0.8):
-    # Chargement pipe À L'INTÉRIEUR pour stateless GPU
-    pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-    if lora_repo:
-        pipe.load_lora_weights(lora_path)
-        pipe.fuse_lora(lora_scale=lora_scale)
-    
-    generator = torch.Generator("cuda").manual_seed(seed if seed != -1 else random.randint(0, 2**32))
-    
-    image = pipe(
-        prompt, negative_prompt=negative_prompt,
-        height=height, width=width, num_inference_steps=steps,
-        guidance_scale=0.0, generator=generator
-    ).images[0]
-    
-    del pipe  # Nettoyage VRAM
-    torch.cuda.empty_cache()
-    return image
+    try:
+        # Chargement du pipe
+        pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+        pipe.enable_model_cpu_offload()
+        
+        # Chargement LoRA si disponible
+        if lora_repo and lora_path:
+            pipe.load_lora_weights(lora_path)
+            pipe.fuse_lora(lora_scale=lora_scale)
+        
+        generator = torch.Generator("cuda").manual_seed(seed if seed != -1 else random.randint(0, 2**32))
+        
+        image = pipe(
+            prompt, 
+            negative_prompt=negative_prompt,
+            height=height, 
+            width=width, 
+            num_inference_steps=steps,
+            guidance_scale=0.0, 
+            generator=generator
+        ).images[0]
+        
+        # Nettoyage VRAM
+        del pipe
+        torch.cuda.empty_cache()
+        return image
+    except Exception as e:
+        return None
 
-# Interface Gradio favorisant NSFW
-with gr.Blocks(title="Flux Schnell NSFW LoRA Space", theme=gr.themes.Dark()) as demo:
-    gr.Markdown("# Flux.1 Schnell + LoRA NSFW\nGénérateur rapide (4 steps) pour images explicites. Ajoutez LoRA NSFW de HF/Civitai.")
+# Interface Gradio
+with gr.Blocks(title="Flux Schnell + LoRA", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🎨 Flux.1 Schnell + LoRA\nGénérateur rapide (4 steps) avec support LoRA personnalisé")
     
     with gr.Row():
         with gr.Column(scale=1):
-            lora_input = gr.Textbox(label="Repo HF LoRA (ex: XLabs-AI/flux-lora-collection ou NSFW)", value="")
-            load_btn = gr.Button("Charger LoRA")
-            status = gr.Textbox(label="Status LoRA")
+            lora_input = gr.Textbox(
+                label="Repo HuggingFace LoRA", 
+                placeholder="ex: XLabs-AI/flux-lora-collection",
+                value=""
+            )
+            load_btn = gr.Button("Charger LoRA", variant="secondary")
+            status = gr.Textbox(label="Status", interactive=False)
         
         with gr.Column(scale=4):
-            prompt = gr.Textbox(label="Prompt NSFW", placeholder="Fille nue réaliste, pose sexy, éclairage studio, haut détail, 8k", lines=3, value="beautiful naked woman, detailed anatomy, erotic pose, realistic skin, soft lighting")
-            neg_prompt = gr.Textbox(label="Negative", value="blurry, deformed, ugly, lowres, text, watermark")
+            prompt = gr.Textbox(
+                label="Prompt", 
+                placeholder="une belle image artistique, haute qualité, détaillée",
+                lines=3,
+                value="beautiful artistic portrait, high quality, detailed"
+            )
+            neg_prompt = gr.Textbox(
+                label="Prompt négatif", 
+                value="blurry, deformed, ugly, lowres, text, watermark"
+            )
     
     with gr.Row():
-        steps = gr.Slider(1, 20, value=4, label="Steps (4=rapide)")
+        steps = gr.Slider(1, 20, value=4, label="Steps", step=1)
         width = gr.Slider(512, 2048, value=1024, step=128, label="Largeur")
         height = gr.Slider(512, 2048, value=1024, step=128, label="Hauteur")
-        lora_scale_slider = gr.Slider(0, 2, value=0.8, label="Force LoRA")
-        seed = gr.Number(value=-1, label="Seed (-1=random)")
+        lora_scale_slider = gr.Slider(0, 2, value=0.8, step=0.1, label="Force LoRA")
+        seed = gr.Number(value=-1, label="Seed (-1=random)", precision=0)
     
-    generate_btn = gr.Button("Générer Image NSFW", variant="primary")
-    output = gr.Image(label="Résultat")
+    generate_btn = gr.Button("🚀 Générer Image", variant="primary", size="lg")
+    output = gr.Image(label="Résultat", type="pil")
     
     load_btn.click(load_lora, inputs=lora_input, outputs=status)
-    generate_btn.click(generate, inputs=[prompt, neg_prompt, width, height, steps, seed, lora_scale_slider], outputs=output)
+    generate_btn.click(
+        generate, 
+        inputs=[prompt, neg_prompt, width, height, steps, seed, lora_scale_slider], 
+        outputs=output
+    )
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch()