Update app.py

app.py

@@ -1,218 +1,224 @@
-# app.py — ZeroGPU対応版
-import gradio as gr
-import spaces
-import torch
-import numpy as np
-from PIL import Image
-import os
-import subprocess
-import traceback
-import base64
-import io
-from pathlib import Path
-
-# FastAPI関連（ハイブリッド構成のため維持）
-from fastapi import FastAPI, UploadFile, File, Form, HTTPException
-
-# グローバル変数としてパイプラインを定義（初期値はNone）
-pipe = None
-face_app = None
-upsampler = None
-UPSCALE_OK = False
-
-# 0. Cache dir & helpers (起動時に実行)
-PERSIST_BASE = Path("/data")
-CACHE_ROOT = (PERSIST_BASE / "instantid_cache" if PERSIST_BASE.exists() and os.access(PERSIST_BASE, os.W_OK)
-              else Path.home() / ".cache" / "instantid_cache")
-MODELS_DIR, LORA_DIR, EMB_DIR, UPSCALE_DIR = CACHE_ROOT/"models", CACHE_ROOT/"models"/"Lora", CACHE_ROOT/"embeddings", CACHE_ROOT/"realesrgan"
-
-for p in (MODELS_DIR, LORA_DIR, EMB_DIR, UPSCALE_DIR):
-    p.mkdir(parents=True, exist_ok=True)
-
-def dl(url: str, dst: Path, attempts: int = 2):
-    if dst.exists(): return
-    for i in range(1, attempts + 1):
-        print(f"⬇ Downloading {dst.name} (try {i}/{attempts})")
-        if subprocess.call(["wget", "-q", "-O", str(dst), url]) == 0: return
-    raise RuntimeError(f"download failed → {url}")
-
-# 1. Asset download (起動時に実行)
-print("— Starting asset download check —")
-BASE_CKPT = MODELS_DIR / "beautiful_realistic_asians_v7_fp16.safetensors"
-dl("https://civitai.com/api/download/models/177164?type=Model&format=SafeTensor&size=pruned&fp=fp16", BASE_CKPT)
-IP_BIN_FILE = LORA_DIR / "ip-adapter-plus-face_sd15.bin"
-dl("https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.bin", IP_BIN_FILE)
-LORA_FILE = LORA_DIR / "ip-adapter-faceid-plusv2_sd15_lora.safetensors"
-dl("https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors", LORA_FILE)
-print("— Asset download check finished —")
-
-
-# 2. パイプライン初期化関数 (GPU確保後に呼び出される)
-def initialize_pipelines():
-    global pipe, face_app, upsampler, UPSCALE_OK
-    
-    # torch/diffusers/onnxruntimeなどのインポートを関数内に移動
-    from diffusers import StableDiffusionPipeline, ControlNetModel, DPMSolverMultistepScheduler, AutoencoderKL
-    from insightface.app import FaceAnalysis
-
-    print("--- Initializing Pipelines (GPU is now available) ---")
-    
-    device = torch.device("cuda") # ZeroGPUではGPUが保証されている
-    dtype = torch.float16
-
-    # FaceAnalysis
-    if face_app is None:
-        print("Initializing FaceAnalysis...")
-        providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        face_app = FaceAnalysis(name="buffalo_l", root=str(CACHE_ROOT), providers=providers)
-        face_app.prepare(ctx_id=0, det_size=(640, 640))
-        print("FaceAnalysis initialized.")
-
-    # Main Pipeline
-    if pipe is None:
-        print("Loading ControlNet...")
-        controlnet = ControlNetModel.from_pretrained("InstantX/InstantID", subfolder="ControlNetModel", torch_dtype=dtype)
-        
-        print("Loading StableDiffusionPipeline...")
-        pipe = StableDiffusionPipeline.from_single_file(BASE_CKPT, torch_dtype=dtype, safety_checker=None, use_safetensors=True, clip_skip=2)
-        
-        print("Moving pipeline to GPU...")
-        pipe.to(device) # .to(device)をここで呼ぶ
-
-        print("Loading VAE...")
-        pipe.vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=dtype).to(device)
-        pipe.controlnet = controlnet
-        
-        print("Configuring Scheduler...")
-        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
-        
-        print("Loading IP-Adapter and LoRA...")
-        pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name=IP_BIN_FILE.name)
-        pipe.load_lora_weights(str(LORA_DIR), weight_name=LORA_FILE.name)
-        
-        pipe.set_ip_adapter_scale(0.65)
-        print("Main pipeline initialized.")
-
-    # Upscaler
-    if upsampler is None and not UPSCALE_OK: # 一度失敗したら再試行しない
-        print("Checking for Upscaler...")
-        try:
-            from basicsr.archs.rrdb_arch import RRDBNet
-            from realesrgan import RealESRGAN
-            rrdb = RRDBNet(3, 3, 64, 23, 32, scale=8)
-            upsampler = RealESRGAN(device, rrdb, scale=8)
-            upsampler.load_weights(str(UPSCALE_DIR / "RealESRGAN_x8plus.pth"))
-            UPSCALE_OK = True
-            print("Upscaler initialized successfully.")
-        except Exception as e:
-            UPSCALE_OK = False # 失敗を記録
-            print(f"Real-ESRGAN disabled → {e}")
-    
-    print("--- All pipelines ready ---")
-
-
-# 4. Core generation logic
-BASE_PROMPT = ("(masterpiece:1.2), best quality, ultra-realistic, RAW photo, 8k,\n""photo of {subject},\n""cinematic lighting, golden hour, rim light, shallow depth of field,\n""textured skin, high detail, shot on Canon EOS R5, 85 mm f/1.4, ISO 200,\n""<lora:ip-adapter-faceid-plusv2_sd15_lora:0.65>, (face),\n""(aesthetic:1.1), (cinematic:0.8)")
-NEG_PROMPT = ("ng_deepnegative_v1_75t, CyberRealistic_Negative-neg, UnrealisticDream, ""(worst quality:2), (low quality:1.8), lowres, (jpeg artifacts:1.2), ""painting, sketch, illustration, drawing, cartoon, anime, cgi, render, 3d, ""monochrome, grayscale, text, logo, watermark, signature, username, ""(MajicNegative_V2:0.8), bad hands, extra digits, fused fingers, malformed limbs, ""missing arms, missing legs, (badhandv4:0.7), BadNegAnatomyV1-neg, skin blemishes, acnes, age spot, glans")
-
-
-# ZeroGPUで実行される本体。durationを60秒に設定。
-@spaces.GPU(duration=60)
-def _generate_core(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress=gr.Progress(track_tqdm=True)):
-    # 初回呼び出し時にパイプラインを初期化
-    initialize_pipelines()
-
-    progress(0, desc="Generating image...")
-    prompt = BASE_PROMPT.format(subject=(subject.strip() or "a beautiful 20yo woman"))
-    if add_prompt: prompt += ", " + add_prompt
-    neg = NEG_PROMPT + (", " + add_neg if add_neg else "")
-    pipe.set_ip_adapter_scale(ip_scale)
-
-    result = pipe(prompt=prompt, negative_prompt=neg, ip_adapter_image=face_img, image=face_img, controlnet_conditioning_scale=0.9, num_inference_steps=int(steps) + 5, guidance_scale=cfg, width=int(w), height=int(h)).images[0]
-    
-    if upscale and UPSCALE_OK:
-        progress(0.8, desc="Upscaling...")
-        up, _ = upsampler.enhance(cv2.cvtColor(np.array(result), cv2.COLOR_RGB2BGR), outscale=up_factor)
-        result = Image.fromarray(cv2.cvtColor(up, cv2.COLOR_BGR2RGB))
-    
-    return result
-
-# GradioのUIから呼び出されるラッパー関数
-def generate_ui(face_np, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress=gr.Progress(track_tqdm=True)):
-    if face_np is None: raise gr.Error("顔画像をアップロードしてください。")
-    # NumPy配列をPillow画像に変換
-    face_img = Image.fromarray(face_np)
-    return _generate_core(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress)
-
-
-# 5. Gradio UI Definition
-with gr.Blocks() as demo:
-    gr.Markdown("# InstantID – Beautiful Realistic Asians v7 (ZeroGPU)")
-    with gr.Row():
-        with gr.Column():
-            face_in = gr.Image(label="顔写真",type="numpy")
-            subj_in = gr.Textbox(label="被写体説明",placeholder="e.g. woman in black suit, smiling")
-            add_in = gr.Textbox(label="追加プロンプト")
-            addneg_in = gr.Textbox(label="追加ネガティブ")
-            with gr.Accordion("詳細設定", open=False):
-                ip_sld = gr.Slider(0,1.5,0.65,step=0.05,label="IP‑Adapter scale")
-                cfg_sld = gr.Slider(1,15,6,step=0.5,label="CFG")
-                step_sld = gr.Slider(10,50,20,step=1,label="Steps")
-                w_sld = gr.Slider(512,1024,512,step=64,label="幅")
-                h_sld = gr.Slider(512,1024,768,step=64,label="高さ")
-                up_ck = gr.Checkbox(label="アップスケール",value=True)
-                up_fac = gr.Slider(1,8,2,step=1,label="倍率")
-            btn = gr.Button("生成",variant="primary")
-        with gr.Column():
-            out_img = gr.Image(label="結果")
-            
-    # .queue() はGradioの通常機能として必要
-    demo.queue()
-    
-    btn.click(
-        fn=generate_ui, 
-        inputs=[face_in,subj_in,add_in,addneg_in,cfg_sld,ip_sld,step_sld,w_sld,h_sld,up_ck,up_fac], 
-        outputs=out_img
-    )
-
-# 6. FastAPI Mounting
-app = FastAPI()
-
-# FastAPIのエンドポイントを定義。こちらも内部で_generate_coreを呼ぶ
-@app.post("/api/predict")
-async def predict_endpoint(
-    face_image: UploadFile = File(...),
-    subject: str = Form("a woman"),
-    add_prompt: str = Form(""),
-    add_neg: str = Form(""),
-    cfg: float = Form(6.0),
-    ip_scale: float = Form(0.65),
-    steps: int = Form(20),
-    w: int = Form(512),
-    h: int = Form(768),
-    upscale: bool = Form(True),
-    up_factor: float = Form(2.0)
-):
-    try:
-        contents = await face_image.read()
-        pil_image = Image.open(io.BytesIO(contents))
-        
-        # FastAPI経由の呼び出しも同じコア関数を利用
-        result_pil_image = _generate_core(
-            pil_image, subject, add_prompt, add_neg, cfg, ip_scale, 
-            steps, w, h, upscale, up_factor
-        )
-
-        buffered = io.BytesIO()
-        result_pil_image.save(buffered, format="PNG")
-        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        
-        return {"image_base64": img_str}
-    except Exception as e:
-        traceback.print_exc()
-        raise HTTPException(status_code=500, detail=str(e))
-
-# GradioアプリをFastAPIアプリにマウント
-app = gr.mount_gradio_app(app, demo, path="/")
-
-print("Application startup script finished. Waiting for requests.")
+# app.py — ZeroGPU対応版
+import gradio as gr
+import spaces
+import torch
+import numpy as np
+from PIL import Image
+import os
+import subprocess
+import traceback
+import base64
+import io
+from pathlib import Path
+
+# FastAPI関連（ハイブリッド構成のため維持）
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+
+# グローバル変数としてパイプラインを定義（初期値はNone）
+pipe = None
+face_app = None
+upsampler = None
+UPSCALE_OK = False
+
+# 0. Cache dir & helpers (起動時に実行)
+PERSIST_BASE = Path("/data")
+CACHE_ROOT = (PERSIST_BASE / "instantid_cache" if PERSIST_BASE.exists() and os.access(PERSIST_BASE, os.W_OK)
+              else Path.home() / ".cache" / "instantid_cache")
+MODELS_DIR, LORA_DIR, EMB_DIR, UPSCALE_DIR = CACHE_ROOT/"models", CACHE_ROOT/"models"/"Lora", CACHE_ROOT/"embeddings", CACHE_ROOT/"realesrgan"
+
+for p in (MODELS_DIR, LORA_DIR, EMB_DIR, UPSCALE_DIR):
+    p.mkdir(parents=True, exist_ok=True)
+
+def dl(url: str, dst: Path, attempts: int = 2):
+    if dst.exists(): return
+    for i in range(1, attempts + 1):
+        print(f"⬇ Downloading {dst.name} (try {i}/{attempts})")
+        if subprocess.call(["wget", "-q", "-O", str(dst), url]) == 0: return
+    raise RuntimeError(f"download failed → {url}")
+
+# 1. Asset download (起動時に実行)
+print("— Starting asset download check —")
+BASE_CKPT = MODELS_DIR / "beautiful_realistic_asians_v7_fp16.safetensors"
+dl("https://civitai.com/api/download/models/177164?type=Model&format=SafeTensor&size=pruned&fp=fp16", BASE_CKPT)
+IP_BIN_FILE = LORA_DIR / "ip-adapter-plus-face_sd15.bin"
+dl("https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.bin", IP_BIN_FILE)
+LORA_FILE = LORA_DIR / "ip-adapter-faceid-plusv2_sd15_lora.safetensors"
+dl("https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors", LORA_FILE)
+print("— Asset download check finished —")
+
+
+# 2. パイプライン初期化関数 (GPU確保後に呼び出される)
+def initialize_pipelines():
+    global pipe, face_app, upsampler, UPSCALE_OK
+    
+    # torch/diffusers/onnxruntimeなどのインポートを関数内に移動
+    from diffusers import StableDiffusionPipeline, ControlNetModel, DPMSolverMultistepScheduler, AutoencoderKL
+    from insightface.app import FaceAnalysis
+
+    print("--- Initializing Pipelines (GPU is now available) ---")
+    
+    device = torch.device("cuda") # ZeroGPUではGPUが保証されている
+    dtype = torch.float16
+
+    # FaceAnalysis
+    if face_app is None:
+        print("Initializing FaceAnalysis...")
+        providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+        face_app = FaceAnalysis(name="buffalo_l", root=str(CACHE_ROOT), providers=providers)
+        face_app.prepare(ctx_id=0, det_size=(640, 640))
+        print("FaceAnalysis initialized.")
+
+    # Main Pipeline
+    if pipe is None:
+        print("Loading ControlNet...")
+        controlnet = ControlNetModel.from_pretrained("InstantX/InstantID", subfolder="ControlNetModel", torch_dtype=dtype)
+        
+        print("Loading StableDiffusionPipeline...")
+        pipe = StableDiffusionPipeline.from_single_file(BASE_CKPT, torch_dtype=dtype, safety_checker=None, use_safetensors=True, clip_skip=2)
+        
+        print("Moving pipeline to GPU...")
+        pipe.to(device) # .to(device)をここで呼ぶ
+
+        print("Loading VAE...")
+        pipe.vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=dtype).to(device)
+        pipe.controlnet = controlnet
+        
+        print("Configuring Scheduler...")
+        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
+        
+        print("Loading IP-Adapter and LoRA...")
+        pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name=IP_BIN_FILE.name)
+        pipe.load_lora_weights(str(LORA_DIR), weight_name=LORA_FILE.name)
+        
+        pipe.set_ip_adapter_scale(0.65)
+        print("Main pipeline initialized.")
+
+    # Upscaler
+    if upsampler is None and not UPSCALE_OK: # 一度失敗したら再試行しない
+        print("Checking for Upscaler...")
+        try:
+            from basicsr.archs.rrdb_arch import RRDBNet
+            from realesrgan import RealESRGAN
+            rrdb = RRDBNet(3, 3, 64, 23, 32, scale=8)
+            upsampler = RealESRGAN(device, rrdb, scale=8)
+            upsampler.load_weights(str(UPSCALE_DIR / "RealESRGAN_x8plus.pth"))
+            UPSCALE_OK = True
+            print("Upscaler initialized successfully.")
+        except Exception as e:
+            UPSCALE_OK = False # 失敗を記録
+            print(f"Real-ESRGAN disabled → {e}")
+    
+    print("--- All pipelines ready ---")
+
+
+# 4. Core generation logic
+BASE_PROMPT = ("(masterpiece:1.2), best quality, ultra-realistic, RAW photo, 8k,\n""photo of {subject},\n""cinematic lighting, golden hour, rim light, shallow depth of field,\n""textured skin, high detail, shot on Canon EOS R5, 85 mm f/1.4, ISO 200,\n""<lora:ip-adapter-faceid-plusv2_sd15_lora:0.65>, (face),\n""(aesthetic:1.1), (cinematic:0.8)")
+NEG_PROMPT = ("ng_deepnegative_v1_75t, CyberRealistic_Negative-neg, UnrealisticDream, ""(worst quality:2), (low quality:1.8), lowres, (jpeg artifacts:1.2), ""painting, sketch, illustration, drawing, cartoon, anime, cgi, render, 3d, ""monochrome, grayscale, text, logo, watermark, signature, username, ""(MajicNegative_V2:0.8), bad hands, extra digits, fused fingers, malformed limbs, ""missing arms, missing legs, (badhandv4:0.7), BadNegAnatomyV1-neg, skin blemishes, acnes, age spot, glans")
+
+
+# ZeroGPUで実行される本体。durationを60秒に設定。
+@spaces.GPU(duration=60)
+def _generate_core(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress=gr.Progress(track_tqdm=True)):
+    # 初回呼び出し時にパイプラインを初期化
+    initialize_pipelines()
+
+    progress(0, desc="Generating image...")
+    prompt = BASE_PROMPT.format(subject=(subject.strip() or "a beautiful 20yo woman"))
+    if add_prompt: prompt += ", " + add_prompt
+    neg = NEG_PROMPT + (", " + add_neg if add_neg else "")
+    pipe.set_ip_adapter_scale(ip_scale)
+
+    result = pipe(prompt=prompt, negative_prompt=neg, ip_adapter_image=face_img, image=face_img, controlnet_conditioning_scale=0.9, num_inference_steps=int(steps) + 5, guidance_scale=cfg, width=int(w), height=int(h)).images[0]
+    
+    if upscale and UPSCALE_OK:
+        progress(0.8, desc="Upscaling...")
+        up, _ = upsampler.enhance(cv2.cvtColor(np.array(result), cv2.COLOR_RGB2BGR), outscale=up_factor)
+        result = Image.fromarray(cv2.cvtColor(up, cv2.COLOR_BGR2RGB))
+    
+    return result
+
+# GradioのUIから呼び出されるラッパー関数
+def generate_ui(face_np, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress=gr.Progress(track_tqdm=True)):
+    if face_np is None: raise gr.Error("顔画像をアップロードしてください。")
+    # NumPy配列をPillow画像に変換
+    face_img = Image.fromarray(face_np)
+    return _generate_core(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress)
+
+
+# 5. Gradio UI Definition
+with gr.Blocks() as demo:
+    gr.Markdown("# InstantID – Beautiful Realistic Asians v7 (ZeroGPU)")
+    with gr.Row():
+        with gr.Column():
+            face_in = gr.Image(label="顔写真",type="numpy")
+            subj_in = gr.Textbox(label="被写体説明",placeholder="e.g. woman in black suit, smiling")
+            add_in = gr.Textbox(label="追加プロンプト")
+            addneg_in = gr.Textbox(label="追加ネガティブ")
+            with gr.Accordion("詳細設定", open=False):
+                ip_sld = gr.Slider(0,1.5,0.65,step=0.05,label="IP‑Adapter scale")
+                cfg_sld = gr.Slider(1,15,6,step=0.5,label="CFG")
+                step_sld = gr.Slider(10,50,20,step=1,label="Steps")
+                w_sld = gr.Slider(512,1024,512,step=64,label="幅")
+                h_sld = gr.Slider(512,1024,768,step=64,label="高さ")
+                up_ck = gr.Checkbox(label="アップスケール",value=True)
+                up_fac = gr.Slider(1,8,2,step=1,label="倍率")
+            btn = gr.Button("生成",variant="primary")
+        with gr.Column():
+            out_img = gr.Image(label="結果")
+            
+    # .queue() はGradioの通常機能として必要
+    demo.queue()
+    
+    btn.click(
+        fn=generate_ui, 
+        inputs=[face_in,subj_in,add_in,addneg_in,cfg_sld,ip_sld,step_sld,w_sld,h_sld,up_ck,up_fac], 
+        outputs=out_img
+    )
+
+# 6. FastAPI Mounting
+app = FastAPI()
+
+# FastAPIのエンドポイントを定義。こちらも内部で_generate_coreを呼ぶ
+@app.post("/api/predict")
+async def predict_endpoint(
+    face_image: UploadFile = File(...),
+    subject: str = Form("a woman"),
+    add_prompt: str = Form(""),
+    add_neg: str = Form(""),
+    cfg: float = Form(6.0),
+    ip_scale: float = Form(0.65),
+    steps: int = Form(20),
+    w: int = Form(512),
+    h: int = Form(768),
+    upscale: bool = Form(True),
+    up_factor: float = Form(2.0)
+):
+    try:
+        contents = await face_image.read()
+        pil_image = Image.open(io.BytesIO(contents))
+        
+        # FastAPI経由の呼び出しも同じコア関数を利用
+        result_pil_image = _generate_core(
+            pil_image, subject, add_prompt, add_neg, cfg, ip_scale, 
+            steps, w, h, upscale, up_factor
+        )
+
+        buffered = io.BytesIO()
+        result_pil_image.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        
+        return {"image_base64": img_str}
+    except Exception as e:
+        traceback.print_exc()
+        raise HTTPException(status_code=500, detail=str(e))
+
+# GradioアプリをFastAPIアプリにマウント
+app = gr.mount_gradio_app(app, demo, path="/")
+
+print("Application startup script finished. Waiting for requests.")
+# app.py の末尾に追加
+
+if __name__ == "__main__":
+    import uvicorn
+    # SpacesでGradioアプリを動かす際の標準ポートは7860です
+    uvicorn.run(app, host="0.0.0.0", port=7860)