Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py — InstantID SDXL (robuste: OMP + InsightFace fallback + downloads sûrs)
+# app.py — InstantID SDXL (stable) : ControlNet objet unique, téléchargements sûrs, InsightFace fallback
 
 # 0) Environnement AVANT TOUT IMPORT
 import os, sys
@@ -19,7 +19,7 @@ DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 DTYPE  = torch.float16 if DEVICE == "cuda" else torch.float32
 
 # 2) Chemins & Hub
-ASSETS_REPO = "InstantX/InstantID"
+ASSETS_REPO = "InstantX/InstantID"       # repo Model public qui contient les poids
 CHECKPOINTS_DIR  = "./checkpoints"
 CN_LOCAL_DIR     = os.path.join(CHECKPOINTS_DIR, "ControlNetModel")
 IP_ADAPTER_LOCAL = os.path.join(CHECKPOINTS_DIR, "ip-adapter.bin")
@@ -52,11 +52,13 @@ def ensure_assets_or_download():
     safe_download(ASSETS_REPO, "ControlNetModel/diffusion_pytorch_model.safetensors", CHECKPOINTS_DIR, 100_000_000, "IdentityNet weights")
     safe_download(ASSETS_REPO, "ip-adapter.bin", CHECKPOINTS_DIR, 100_000_000, "ip-adapter")
 
-# 4) Import dynamique de la pipeline InstantID
+# 4) Import dynamique de la pipeline InstantID (fichier texte local)
 def import_pipeline_or_fail():
     pipeline_file = "./instantid/pipeline_stable_diffusion_xl_instantid.py"
     if not os.path.exists(pipeline_file):
         raise RuntimeError("❌ Fichier pipeline manquant : ./instantid/pipeline_stable_diffusion_xl_instantid.py")
+    if os.path.getsize(pipeline_file) < 1024:
+        raise RuntimeError("❌ Fichier pipeline trop petit (vide ?). Colle le contenu depuis GitHub InstantID.")
     spec = importlib.util.spec_from_file_location("instantid_pipeline", pipeline_file)
     mod  = importlib.util.module_from_spec(spec)
     spec.loader.exec_module(mod)
@@ -64,7 +66,8 @@ def import_pipeline_or_fail():
         if isinstance(obj, type) and "InstantID" in name and hasattr(obj, "from_pretrained"):
             print(f"✅ Pipeline trouvée : {name}")
             return obj
-    raise RuntimeError("❌ Aucune classe pipeline InstantID trouvée dans la pipeline.")
+    avail = [n for n, o in vars(mod).items() if isinstance(o, type)]
+    raise RuntimeError("❌ Aucune classe pipeline InstantID trouvée. Classes dispo: " + ", ".join(avail))
 
 # 5) draw_kps local (remplace la dépendance au draw_kps du fichier)
 def draw_kps_local(img_pil, kps):
@@ -76,7 +79,7 @@ def draw_kps_local(img_pil, kps):
         d.ellipse((x - r, y - r, x + r, y + r), fill="black")
     return out
 
-# 6) Chargement pipeline
+# 6) Chargement pipeline (ControlNet en OBJET, pas liste)
 load_logs = []
 try:
     SDXLInstantID = import_pipeline_or_fail()
@@ -86,9 +89,11 @@ try:
     load_logs.append(f"Chargement base: {BASE_MODEL}")
 
     controlnet_identitynet = ControlNetModel.from_pretrained(CN_LOCAL_DIR, torch_dtype=DTYPE)
+
+    # 🔧 Ici: controlnet=controlnet_identitynet (objet unique)
     pipe = SDXLInstantID.from_pretrained(
         BASE_MODEL,
-        controlnet=[controlnet_identitynet],
+        controlnet=controlnet_identitynet,
         torch_dtype=DTYPE,
         safety_checker=None,
         feature_extractor=None,
@@ -140,13 +145,19 @@ def generate(face_image, prompt, negative_prompt, identity_strength, adapter_str
     try:
         if face_image is None:
             return None, "Merci d'ajouter une photo visage.", "\n".join(load_logs)
+
         gen = None if seed is None or int(seed) < 0 else torch.Generator(device=DEVICE).manual_seed(int(seed))
+
         face = ImageOps.exif_transpose(face_image).convert("RGB")
         ms = min(face.size); x=(face.width-ms)//2; y=(face.height-ms)//2
         face_sq = face.crop((x, y, x+ms, y+ms)).resize((512,512), Image.Resampling.LANCZOS)
+
         kps_img = extract_kps_image(face_sq)
+
         if hasattr(pipe, "set_ip_adapter_scale"):
             pipe.set_ip_adapter_scale(float(adapter_strength))
+
+        # Ici, controlnet est un objet unique → image et scale ne sont PAS en liste
         images = pipe(
             prompt=prompt.strip(),
             negative_prompt=(negative_prompt or "").strip(),
@@ -158,7 +169,9 @@ def generate(face_image, prompt, negative_prompt, identity_strength, adapter_str
             height=int(height),
             generator=gen,
         ).images
+
         return images[0], "", "\n".join(load_logs)
+
     except torch.cuda.OutOfMemoryError as oom:
         return None, "CUDA OOM: baisse la résolution/steps.", "\n".join(load_logs)
     except Exception:
@@ -176,15 +189,15 @@ EX_NEG = (
 )
 
 with gr.Blocks(css="footer{display:none !important}") as demo:
-    gr.Markdown("# 🏴‍☠️ One Piece – InstantID SDXL (robuste)")
+    gr.Markdown("# 🏴‍☠️ One Piece – InstantID SDXL (stable)")
 
     with gr.Row():
         with gr.Column():
             face_image = gr.Image(type="pil", label="Photo visage", height=360)
-            prompt = gr.Textbox(label="Prompt", value=EX_PROMPT)
-            negative = gr.Textbox(label="Negative Prompt", value=EX_NEG)
-            identity_strength = gr.Slider(0.2, 1.5, 0.95, 0.05, label="Fidélité visage")
-            adapter_strength  = gr.Slider(0.2, 1.5, 0.85, 0.05, label="Détails anime")
+            prompt = gr.Textbox(label="Prompt", value=EX_PROMPT, lines=3)
+            negative = gr.Textbox(label="Negative Prompt", value=EX_NEG, lines=3)
+            identity_strength = gr.Slider(0.2, 1.5, 0.95, 0.05, label="Fidélité visage (IdentityNet)")
+            adapter_strength  = gr.Slider(0.2, 1.5, 0.85, 0.05, label="Détails anime (Adapter)")
             steps  = gr.Slider(10, 60, 30, 1, label="Steps")
             cfg    = gr.Slider(0.1, 12.0, 5.5, 0.1, label="CFG")
             width  = gr.Dropdown(choices=[576, 640, 704, 768, 896], value=704, label="Largeur")