Update app.py

app.py

@@ -3,21 +3,14 @@ import json
 import os
 import torch
 import gradio as gr
-
 from diffusers import AutoPipelineForText2Image
 from PIL import PngImagePlugin
-from huggingface_hub import hf_hub_download
 
 BASE_MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
 
-# LoRA cuerpo hourglass (Muapi)
-LORA_HOURGLASS_REPO = "Muapi/hourglass-body-shape-sd1.5-sdxl-pony-flux-olaz"
-LORA_HOURGLASS_FILE = "hourglass-body-shape-sd1.5-sdxl-pony-flux-olaz.safetensors"
-LORA_HOURGLASS_ADAPTER = "hourglass_body"
-
-# LoRA visages / détails (Stable Yogi)
+# SEULEMENT LoRA face/detail compatible
 LORA_FACE_REPO = "akash-guptag/Detailers_By_Stable_Yogi"
-LORA_FACE_ADAPTER = "face_helper"
+LORA_FACE_ADAPTER = "face_detail"
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
@@ -31,37 +24,11 @@ pipe = AutoPipelineForText2Image.from_pretrained(
 pipe.to(DEVICE)
 pipe.set_progress_bar_config(disable=True)
 
-# Téléchargement explicite du LoRA hourglass (debug + cache)
-try:
-    local_path = hf_hub_download(
-        repo_id=LORA_HOURGLASS_REPO,
-        filename=LORA_HOURGLASS_FILE,
-        local_dir="./lora_cache"
-    )
-    print(f"Fichier LoRA hourglass OK : {local_path}")
-except Exception as e:
-    print(f"Erreur repo/fichier hourglass : {e}")
-
-print("Chargement LoRA hourglass body...")
-pipe.load_lora_weights(
-    LORA_HOURGLASS_REPO,
-    weight_name=LORA_HOURGLASS_FILE,
-    adapter_name=LORA_HOURGLASS_ADAPTER,
-    low_cpu_mem_usage=False,  # important pour certains LoRA hybrides
-)
-
 print("Chargement LoRA face/detail (Stable Yogi)...")
-pipe.load_lora_weights(
-    LORA_FACE_REPO,
-    adapter_name=LORA_FACE_ADAPTER,
-)
+pipe.load_lora_weights(LORA_FACE_REPO, adapter_name=LORA_FACE_ADAPTER)
 
 os.makedirs("outputs", exist_ok=True)
 
-
-# -----------------------
-# Fonction de génération
-# -----------------------
 @spaces.GPU()
 def generate(
     prompt: str,
@@ -73,32 +40,16 @@ def generate(
     height: float,
     face_enabled: bool,
     face_weight: float,
-    hourglass_enabled: bool,
-    hourglass_weight: float,
     script_name: str,
 ):
     if not prompt:
         return None, "Prompt vide.", ""
 
-    # Seed
     seed_int = int(seed) if seed is not None else 0
-    if seed_int < 0:
-        generator = torch.Generator(device=DEVICE)
-    else:
-        generator = torch.Generator(device=DEVICE).manual_seed(seed_int)
-
-    # Gestion des LoRA : liste d’adapters + poids
-    adapters = []
-    weights = []
-
-    # LoRA visages / détails
-    adapters.append(LORA_FACE_ADAPTER)
-    weights.append(float(face_weight) if face_enabled else 0.0)
-
-    # LoRA hourglass
-    adapters.append(LORA_HOURGLASS_ADAPTER)
-    weights.append(float(hourglass_weight) if hourglass_enabled else 0.0)
+    generator = torch.Generator(device=DEVICE).manual_seed(seed_int) if seed_int >= 0 else torch.Generator(device=DEVICE)
 
+    adapters = [LORA_FACE_ADAPTER]
+    weights = [float(face_weight) if face_enabled else 0.0]
     pipe.set_adapters(adapters, adapter_weights=weights)
 
     try:
@@ -112,24 +63,16 @@ def generate(
             generator=generator,
         )
     except Exception as e:
-        err_txt = f"Erreur pendant la génération SDXL : {repr(e)}"
-        return None, err_txt, ""
+        return None, f"Erreur SDXL : {repr(e)}", ""
 
     image = result.images[0]
-
+    
     metadata = {
         "base_model": BASE_MODEL_ID,
         "face_lora_repo": LORA_FACE_REPO,
-        "face_lora_adapter": LORA_FACE_ADAPTER,
         "face_enabled": bool(face_enabled),
         "face_weight": float(face_weight) if face_enabled else 0.0,
-        "hourglass_lora_repo": LORA_HOURGLASS_REPO,
-        "hourglass_lora_file": LORA_HOURGLASS_FILE,
-        "hourglass_lora_adapter": LORA_HOURGLASS_ADAPTER,
-        "hourglass_enabled": bool(hourglass_enabled),
-        "hourglass_weight": float(hourglass_weight) if hourglass_enabled else 0.0,
         "prompt": prompt,
-        "negative_prompt": negative_prompt,
         "seed": seed_int,
         "steps": int(steps),
         "guidance_scale": float(guidance),
@@ -137,10 +80,9 @@ def generate(
         "height": int(height),
     }
 
-    base_name = script_name.strip() or "sdxl_lora_generation"
-    safe_name = base_name.replace(" ", "_")
-    img_path = os.path.join("outputs", f"{safe_name}.png")
-    json_path = os.path.join("outputs", f"{safe_name}.json")
+    base_name = script_name.strip().replace(" ", "_") or "sdxl_detailer"
+    img_path = os.path.join("outputs", f"{base_name}.png")
+    json_path = os.path.join("outputs", f"{base_name}.json")
 
     pnginfo = PngImagePlugin.PngInfo()
     pnginfo.add_text("generation_params", json.dumps(metadata, ensure_ascii=False))
@@ -149,133 +91,33 @@ def generate(
     with open(json_path, "w", encoding="utf-8") as f:
         json.dump(metadata, f, ensure_ascii=False, indent=2)
 
-    script_txt = json.dumps(metadata, ensure_ascii=False, indent=2)
-
-    return image, script_txt, json_path
-
-
-# -----------------------
-# UI Gradio
-# -----------------------
-with gr.Blocks(title="SDXL + LoRA (Diffusers)") as demo:
-    gr.Markdown(
-        "## SDXL base + LoRA SDXL (Diffusers)\n"
-        "Génération SDXL 1.0 avec LoRA detailer + hourglass body."
-    )
+    return image, json.dumps(metadata, ensure_ascii=False, indent=2), json_path
 
+# UI simplifiée
+with gr.Blocks(title="SDXL + Face Detail LoRA") as demo:
+    gr.Markdown("## SDXL 1.0 + LoRA Detailer (Stable Yogi)")
+    
     with gr.Row():
         with gr.Column():
-            prompt = gr.Textbox(
-                label="Prompt",
-                lines=4,
-                placeholder="Décris la scène à générer avec SDXL...",
-                value=(
-                    "masterpiece, best quality, 1girl, looking at viewer, "
-                    "sharp face, detailed skin, realistic eyes, hourglass figure, slim waist, wide hips"
-                ),
-            )
-            negative = gr.Textbox(
-                label="Negative prompt",
-                lines=3,
-                value="blurry face, deformed face, bad anatomy, bad proportions, low quality",
-            )
-
-            seed = gr.Number(
-                label="Seed (-1 = random)",
-                value=-1,
-            )
-            steps = gr.Slider(
-                minimum=10,
-                maximum=60,
-                value=30,
-                step=1,
-                label="Steps",
-            )
-            guidance = gr.Slider(
-                minimum=1.0,
-                maximum=20.0,
-                value=7.0,
-                step=0.5,
-                label="CFG / Guidance scale",
-            )
-
-            width = gr.Slider(
-                minimum=512,
-                maximum=1536,
-                value=1024,
-                step=64,
-                label="Width (SDXL natif 1024)",
-            )
-            height = gr.Slider(
-                minimum=512,
-                maximum=1536,
-                value=1024,
-                step=64,
-                label="Height (SDXL natif 1024)",
-            )
-
-            face_enabled = gr.Checkbox(
-                label="Activer LoRA face/detail (Stable Yogi)",
-                value=True,
-            )
-            face_weight = gr.Slider(
-                minimum=0.0,
-                maximum=1.5,
-                value=0.8,  # recommandé <= 0.8 pour ce LoRA[web:60][web:63]
-                step=0.05,
-                label="Force LoRA face/detail",
-            )
-
-            hourglass_enabled = gr.Checkbox(
-                label="Activer LoRA hourglass body shape",
-                value=True,
-            )
-
-            hourglass_weight = gr.Slider(
-                minimum=0.0,
-                maximum=2.0,
-                value=0.9,
-                step=0.05,
-                label="Force LoRA hourglass body",
-            )
-
-            script_name = gr.Textbox(
-                label="Nom base pour l'image / script",
-                value="sdxl_lora_generation",
-            )
-
-            run_btn = gr.Button("Générer", variant="primary")
-
-        with gr.Column():
-            out_img = gr.Image(
-                label="Image générée (SDXL)",
-            )
-            out_script = gr.Textbox(
-                label="Script (JSON des paramètres)",
-                lines=20,
+            prompt = gr.Textbox(label="Prompt", lines=4, value="masterpiece, 1girl, detailed face, sharp eyes, realistic skin")
+            negative = gr.Textbox(label="Negative", value="blurry, deformed, low quality")
+            
+            seed = gr.Number(label="Seed (-1=random)", value=-1)
+            steps = gr.Slider(10, 60, 30, step=1, label="Steps")
+            guidance = gr.Slider(1.0, 20.0, 7.0, step=0.5, label="Guidance")
+            
+            width = gr.Slider(512, 1536, 1024, step=64, label="Width")
+            height = gr.Slider(512, 1536, 1024, step=64, label="Height")
+            
+            face_enabled = gr.Checkbox("Activer LoRA face/detail", value=True)
+            face_weight = gr.Slider(0.0, 1.5, 0.8, step=0.05, label="Poids LoRA")
+            
+            script_name = gr.Textbox("Nom sortie", value="test_detailer")
+            gr.Button("Générer", variant="primary").click(
+                generate,
+                inputs=[prompt, negative, seed, steps, guidance, width, height, face_enabled, face_weight, script_name],
+                outputs=[gr.Image(label="Image"), gr.Textbox(label="JSON", lines=15), gr.File(label="Télécharger JSON")]
             )
-            out_file = gr.File(
-                label="Fichier JSON des paramètres (téléchargeable)",
-            )
-
-    run_btn.click(
-        fn=generate,
-        inputs=[
-            prompt,
-            negative,
-            seed,
-            steps,
-            guidance,
-            width,
-            height,
-            face_enabled,
-            face_weight,
-            hourglass_enabled,
-            hourglass_weight,
-            script_name,
-        ],
-        outputs=[out_img, out_script, out_file],
-    )
 
 if __name__ == "__main__":
     demo.launch()