Update app.py

app.py

@@ -108,4 +108,166 @@ try:
             weight_name=IP_STYLE_WEIGHT,
             adapter_name="style",
         )
-        load_logs.append("✅ IP-Adapter Style (SDXL) chargé (adapter_na
+        load_logs.append("✅ IP-Adapter Style (SDXL) chargé (adapter_name='style').")
+        HAS_STYLE_ADAPTER = True
+    except Exception as e:
+        load_logs.append(f"ℹ️ IP-Adapter Style non chargé: {e}")
+
+    if DEVICE == "cuda":
+        if hasattr(pipe, "image_proj_model"): pipe.image_proj_model.to("cuda")
+        if hasattr(pipe, "unet"):             pipe.unet.to("cuda")
+
+    load_logs.append("✅ InstantID prêt.")
+except Exception:
+    load_logs += ["❌ ERREUR au chargement:", traceback.format_exc()]
+    pipe = None
+
+if pipe is None:
+    raise RuntimeError("Échec de chargement du pipeline.\n" + "\n".join(load_logs))
+
+def load_face_analyser():
+    errors = []
+    for name in ("antelopev2", "buffalo_l"):
+        try:
+            fa = FaceAnalysis(name=name, root="./models", providers=["CPUExecutionProvider"])
+            fa.prepare(ctx_id=0, det_size=(640, 640))
+            print(f"✅ InsightFace chargé: {name}")
+            return fa
+        except Exception as e:
+            errors.append(f"{name}: {e}")
+            print(f"⚠️ InsightFace échec {name} → {e}")
+    raise RuntimeError("Echec chargement InsightFace. Détails: " + " | ".join(errors))
+
+fa = load_face_analyser()
+
+def extract_face_embed_and_kps(pil_img):
+    import numpy as np, cv2
+    img_cv2 = cv2.cvtColor(np.array(pil_img.convert("RGB")), cv2.COLOR_RGB2BGR)
+    faces = fa.get(img_cv2)
+    if not faces:
+        raise ValueError("Aucun visage détecté dans la photo.")
+    face = faces[-1]
+    emb_np = face["embedding"]
+    if not isinstance(emb_np, np.ndarray):
+        emb_np = np.asarray(emb_np, dtype="float32")
+    if emb_np.ndim == 1:
+        emb_np = emb_np[None, ...]  # (1, D)
+    face_emb = torch.from_numpy(emb_np).to(device=DEVICE, dtype=DTYPE)  # ← Tensor [1,D] sur bon device/dtype
+    kps_img  = draw_kps_local(pil_img, face["kps"])
+    return face_emb, kps_img
+
+def generate(face_image, style_image, prompt, negative_prompt,
+             identity_strength, adapter_strength, style_strength,
+             steps, cfg, width, height, seed):
+    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)
+
+        face_emb, kps_img = extract_face_embed_and_kps(face_sq)
+
+        try:
+            if HAS_STYLE_ADAPTER and style_image is not None:
+                pipe.set_ip_adapter_scale({"instantid": float(adapter_strength), "style": float(style_strength)})
+            else:
+                pipe.set_ip_adapter_scale(float(adapter_strength))
+        except Exception as e:
+            print(f"ℹ️ set_ip_adapter_scale ignoré: {e}")
+
+        cn = getattr(pipe, "controlnet", None)
+        if isinstance(cn, (list, tuple)):
+            n_cn = len(cn)
+        else:
+            try: n_cn = len(cn)
+            except Exception: n_cn = 1
+
+        image_arg = [kps_img] * n_cn if n_cn > 1 else ([kps_img] if isinstance(cn, (list, tuple)) else kps_img)
+        scale_val = float(identity_strength)
+        scale_arg = [scale_val] * n_cn if n_cn > 1 else ([scale_val] if isinstance(cn, (list, tuple)) else scale_val)
+
+       # ✅ Construction des kwargs d’inférence — compat 0.29/0.30
+gen_kwargs = dict(
+    prompt=(prompt or "").strip(),
+    negative_prompt=(negative_prompt or "").strip(),
+    image=image_arg,                         # IdentityNet (landmarks)
+    image_embeds=face_emb,                   # compat pipeline
+    # --- CLÉ : passer aux deux noms pour couvrir 0.29 (added_cond_kwargs) et 0.30 (added_conditions)
+    added_conditions={"image_embeds": face_emb},     # ← diffusers >= 0.30.0
+    added_cond_kwargs={"image_embeds": face_emb},    # ← diffusers 0.29.x (safe no-op si ignoré)
+    controlnet_conditioning_scale=scale_arg,
+    num_inference_steps=int(steps),
+    guidance_scale=float(cfg),
+    width=int(width),
+    height=int(height),
+    generator=gen,
+)
+
+
+        if HAS_STYLE_ADAPTER and style_image is not None:
+            try:
+                gen_kwargs["ip_adapter_image"] = ImageOps.exif_transpose(style_image).convert("RGB")
+            except Exception as e:
+                print(f"ℹ️ ip_adapter_image ignoré: {e}")
+
+        images = pipe(**gen_kwargs).images
+        return images[0], "", "\n".join(load_logs)
+
+    except torch.cuda.OutOfMemoryError:
+        return None, "CUDA OOM: baisse la résolution ou les steps.", "\n".join(load_logs)
+    except Exception:
+        return None, "Erreur:\n" + traceback.format_exc(), "\n".join(load_logs)
+
+EX_PROMPT = (
+    "one piece style, Eiichiro Oda style, anime portrait, upper body, pirate outfit, "
+    "clean lineart, cel shading, vibrant colors, expressive eyes, dynamic composition, simple background"
+)
+EX_NEG = (
+    "realistic, photo, photorealistic, skin pores, complex lighting, "
+    "low quality, worst quality, lowres, blurry, noisy, watermark, text, logo, jpeg artifacts, "
+    "bad anatomy, deformed, multiple faces, nsfw"
+)
+
+with gr.Blocks(css="footer{display:none !important}") as demo:
+    gr.Markdown("# 🏴‍☠️ InstantID SDXL + IP-Adapter Style (2D) — visage → perso One Piece")
+    with gr.Row():
+        with gr.Column():
+            face_image  = gr.Image(type="pil", label="Photo visage (obligatoire)", height=260)
+            style_image = gr.Image(type="pil", label="Image de style (optionnel)", height=260)
+            gr.Markdown("Astuce : poster/planche One Piece → rendu 2D renforcé via IP-Adapter Style.")
+            prompt   = gr.Textbox(label="Prompt", value=EX_PROMPT, lines=3)
+            negative = gr.Textbox(label="Negative Prompt", value=EX_NEG, lines=3)
+            with gr.Row():
+                identity_strength = gr.Slider(0.2, 1.5, 0.95, 0.05, label="Fidélité visage (IdentityNet)")
+                adapter_strength  = gr.Slider(0.1, 1.5, 0.85, 0.05, label="Détails anime (InstantID)")
+            style_strength = gr.Slider(0.1, 1.5, 0.95, 0.05, label="Force style (IP-Adapter Style)")
+            steps  = gr.Slider(10, 60, 30, 1, label="Steps")
+            cfg    = gr.Slider(0.1, 12.0, 6.5, 0.1, label="CFG")
+            width  = gr.Dropdown(choices=[576, 640, 704, 768, 896], value=704, label="Largeur")
+            height = gr.Dropdown(choices=[704, 768, 896, 1024], value=896, label="Hauteur")
+            seed   = gr.Number(value=-1, label="Seed (-1 aléatoire)")
+            btn    = gr.Button("🎨 Générer", variant="primary")
+        with gr.Column():
+            out_image = gr.Image(label="Résultat", interactive=False)
+            err_box   = gr.Textbox(label="Erreurs", visible=False)
+            log_box   = gr.Textbox(label="Logs", value="\n".join(load_logs), lines=12)
+
+    def wrap(*args):
+        img, err, logs = generate(*args)
+        return img, gr.update(visible=bool(err), value=err), gr.update(value=logs)
+
+    btn.click(
+        wrap,
+        inputs=[face_image, style_image, prompt, negative,
+                identity_strength, adapter_strength, style_strength,
+                steps, cfg, width, height, seed],
+        outputs=[out_image, err_box, log_box],
+    )
+
+demo.queue(api_open=False)
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)