"""Anima 2B Image Generation (ZeroGPU) — sd.cpp runtime build""" import os, re, time, shutil, subprocess import spaces import gradio as gr from huggingface_hub import hf_hub_download # --------------------------------------------------------------------------- # sd.cpp を実行時にCUDA付きでコンパイル（初回のみ、アプリディレクトリにキャッシュ） # --------------------------------------------------------------------------- SD_CLI = "/home/user/app/sd_bin" SD_SRC_DIR = "/tmp/sd_src" SD_BLD_DIR = "/tmp/sd_build" def build_sd_cli() -> str: if os.path.exists(SD_CLI) and os.access(SD_CLI, os.X_OK): print("[init] sd binary already exists, skipping build.") return SD_CLI print("[init] Cloning stable-diffusion.cpp (with submodules) ...") subprocess.run( ["git", "clone", "--recurse-submodules", "--depth=1", "--shallow-submodules", "https://github.com/leejet/stable-diffusion.cpp", SD_SRC_DIR], check=True, ) print("[init] CMake configure (CUDA) ...") os.makedirs(SD_BLD_DIR, exist_ok=True) subprocess.run( ["cmake", "-B", SD_BLD_DIR, "-DSD_CUBLAS=ON", "-DSD_BUILD_EXAMPLES=ON", "-DCMAKE_BUILD_TYPE=Release", SD_SRC_DIR], check=True, ) print("[init] Building ...") subprocess.run( ["cmake", "--build", SD_BLD_DIR, "--config", "Release", "-j", str(os.cpu_count() or 4)], check=True, ) # ビルド後にバイナリを動的に探索 result = subprocess.run( ["find", SD_BLD_DIR, "-type", "f", "-executable"], capture_output=True, text=True, ) candidates = [p.strip() for p in result.stdout.splitlines() if p.strip()] print(f"[init] Built executables: {candidates}") sd_bin = None for priority in ["sd", "sd-server"]: for c in candidates: if os.path.basename(c) == priority: sd_bin = c break if sd_bin: break if not sd_bin: raise RuntimeError(f"sd binary not found.\nCandidates: {candidates}") shutil.copy2(sd_bin, SD_CLI) os.chmod(SD_CLI, 0o755) print(f"[init] Build complete: {SD_CLI} (source: {sd_bin})") return SD_CLI def detect_flags(sd_bin: str) -> dict: """--help を解析して実際のフラグ名を自動検出する。""" res = subprocess.run([sd_bin, "--help"], capture_output=True, text=True) help_text = res.stdout + res.stderr # 全文をログに出力（デバッグ用） print(f"[init] === sd --help ===\n{help_text}\n===================") def pick(candidates: list) -> str: """help テキストに現れる最初の候補を返す。""" for c in candidates: if re.search(r'(?:^|\s)' + re.escape(c) + r'(?:\s|,|\]|$)', help_text, re.MULTILINE): return c return candidates[0] # 見つからなければ先頭をデフォルトに flags = { "output": pick(["--output", "-o", "--out"]), "prompt": pick(["--prompt", "-p"]), "neg": pick(["--negative-prompt", "-n", "--neg-prompt"]), "width": pick(["--width", "-W"]), "height": pick(["--height", "-H"]), "seed": pick(["--seed", "-s"]), } print(f"[init] Detected flags: {flags}") return flags sd_cli = build_sd_cli() SD_FLAGS = detect_flags(sd_cli) # 起動のたびに --help を解析 # --------------------------------------------------------------------------- # モデルダウンロード # --------------------------------------------------------------------------- MODELS_DIR = "/tmp/anima_models" LORA_DIR = "/tmp/loras" os.makedirs(MODELS_DIR, exist_ok=True) os.makedirs(LORA_DIR, exist_ok=True) def ensure_model(repo_id: str, filename: str, subdir: str = "") -> str: dest = os.path.join(MODELS_DIR, filename) if os.path.exists(dest): return dest print(f"[init] Downloading {repo_id}/{filename} ...") src = hf_hub_download( repo_id=repo_id, filename=f"{subdir}/{filename}" if subdir else filename, ) shutil.copy2(src, dest) return dest print("[init] Ensuring model files ...") t0 = time.time() diffusion_path = ensure_model("JusteLeo/Anima2-GGUF", "anima-preview2_q4_K_M.gguf") llm_path = ensure_model("circlestone-labs/Anima", "qwen_3_06b_base.safetensors", "split_files/text_encoders") vae_path = ensure_model("circlestone-labs/Anima", "qwen_image_vae.safetensors", "split_files/vae") _lora_src = hf_hub_download("Einhorn/Anima-Preview2-Turbo-LoRA", "anima_preview2_turbo_8step.safetensors") lora_path = os.path.join(LORA_DIR, "anima_turbo_8step.safetensors") if not os.path.exists(lora_path): shutil.copy2(_lora_src, lora_path) print(f"[init] Models ready in {time.time()-t0:.1f}s") # --------------------------------------------------------------------------- # ZeroGPU 推論（subprocess + CUDA sd binary） # --------------------------------------------------------------------------- from PIL import Image import tempfile RESOLUTIONS = ["512x512", "768x768", "1024x1024", "1024x768", "768x1024"] @spaces.GPU(duration=60) def generate(prompt: str, negative_prompt: str, resolution: str, steps: int, cfg_scale: float, seed: int): if not prompt or not prompt.strip(): raise gr.Error("プロンプトを入力してください。") w, h = (int(x) for x in resolution.split("x")) seed_val = int(seed) if int(seed) >= 0 else -1 with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f: output_path = f.name f = SD_FLAGS cmd = [ sd_cli, "--diffusion-model", diffusion_path, "--llm", llm_path, "--vae", vae_path, "--lora-model-dir", LORA_DIR, f["prompt"], f" {prompt}", f["neg"], negative_prompt or "", f["width"], str(w), f["height"], str(h), "--steps", str(int(steps)), "--cfg-scale", str(float(cfg_scale)), "--sampling-method", "euler", f["output"], output_path, "--diffusion-fa", "--vae-tiling", "-v", ] if seed_val >= 0: cmd += [f["seed"], str(seed_val)] print(f"[gen] cmd: {' '.join(cmd)}") t0 = time.time() try: result = subprocess.run(cmd, capture_output=True, text=True, timeout=1800) elapsed = time.time() - t0 if result.returncode != 0: # デバッグ用に stdout/stderr を全出力 print(f"[gen] stdout:\n{result.stdout}") print(f"[gen] stderr:\n{result.stderr}") err = result.stderr[-800:] if result.stderr else "Unknown error" raise gr.Error(f"sd failed (code {result.returncode}): {err}") if not os.path.exists(output_path) or os.path.getsize(output_path) == 0: raise gr.Error("画像の生成に失敗しました。") img = Image.open(output_path) status = f"Generated in {elapsed:.1f}s ({w}×{h}, {steps} steps, cfg {cfg_scale})" print(f"[gen] {status}") return img, status except subprocess.TimeoutExpired: raise gr.Error("タイムアウト（30分制限）") except gr.Error: raise except Exception as e: raise gr.Error(f"エラー: {e}") # --------------------------------------------------------------------------- # Gradio UI # --------------------------------------------------------------------------- with gr.Blocks(title="Anima 2B (ZeroGPU)", theme="NoCrypt/miku") as demo: gr.Markdown( "# Anima 2B Image Generation (ZeroGPU)\n" "Generate anime images with [Anima 2B](https://huggingface.co/circlestone-labs/Anima) " "+ [Turbo LoRA](https://huggingface.co/Einhorn/Anima-Preview2-Turbo-LoRA) (8 steps). " "Running on **ZeroGPU (H200)**." ) with gr.Row(): with gr.Column(): prompt_input = gr.Textbox( label="Prompt", lines=3, placeholder="anime girl with silver hair, fantasy armor, dramatic lighting", ) neg_input = gr.Textbox( label="Negative Prompt", lines=2, value="lowres, bad anatomy, bad hands, text, error, worst quality, blurry, censored", ) res_input = gr.Dropdown(choices=RESOLUTIONS, value="512x512", label="Resolution") with gr.Row(): steps_input = gr.Slider(minimum=4, maximum=30, value=8, step=1, label="Steps") cfg_input = gr.Slider(minimum=1.0, maximum=10.0, value=1.0, step=0.5, label="CFG Scale") seed_input = gr.Number(value=-1, label="Seed", precision=0) gen_btn = gr.Button("Generate", variant="primary", size="lg") with gr.Column(): output_img = gr.Image(type="pil", label="Output") status_box = gr.Textbox(label="Status", interactive=False) gen_btn.click( fn=generate, inputs=[prompt_input, neg_input, res_input, steps_input, cfg_input, seed_input], outputs=[output_img, status_box], ) gr.Markdown( "---\n" "Anima 2B Q4_K_M + Turbo LoRA (8 steps) | " "[Model](https://huggingface.co/circlestone-labs/Anima) | " "[sd.cpp](https://github.com/leejet/stable-diffusion.cpp)" ) demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)