mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-11 05:52:33 +08:00
80848f3c54
- Add .runpod/hub.json with serverless configuration - Add .runpod/tests.json with comprehensive test cases - Add storage access test to Dockerfile build process - Add RunPod badge to README.md - Include model download script for build-time optimization - Test storage accessibility during Docker build phase
90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
#!/usr/bin/env python3
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"""
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Docker build sırasında temel modelleri indir
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"""
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import os
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import sys
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from pathlib import Path
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from huggingface_hub import hf_hub_download
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def download_model(repo_id, filename, target_dir):
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"""Model indir ve hedef dizine kopyala"""
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try:
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print(f"📥 İndiriliyor: {repo_id}/{filename}")
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# Model'i indir
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model_path = hf_hub_download(
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repo_id=repo_id,
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filename=filename,
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cache_dir="/tmp/hf_cache"
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)
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# Hedef dizini oluştur
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os.makedirs(target_dir, exist_ok=True)
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# Dosyayı kopyala
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target_path = os.path.join(target_dir, filename)
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os.system(f"cp '{model_path}' '{target_path}'")
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print(f"✅ Kaydedildi: {target_path}")
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return True
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except Exception as e:
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print(f"❌ Hata: {repo_id}/{filename} - {e}")
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return False
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def main():
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"""Temel modelleri indir"""
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print("🚀 Docker build - Model indirme başlatılıyor...")
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models_base = "/app/models"
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# İndirilecek modeller
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models_to_download = [
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# SDXL Base Model
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{
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"repo_id": "stabilityai/stable-diffusion-xl-base-1.0",
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"filename": "sd_xl_base_1.0.safetensors",
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"target_dir": f"{models_base}/checkpoints"
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},
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# SDXL VAE
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{
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"repo_id": "stabilityai/sdxl-vae",
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"filename": "sdxl_vae.safetensors",
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"target_dir": f"{models_base}/vae"
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},
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# CLIP Text Encoder
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{
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"repo_id": "openai/clip-vit-large-patch14",
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"filename": "pytorch_model.bin",
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"target_dir": f"{models_base}/clip"
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}
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]
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success_count = 0
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for model in models_to_download:
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if download_model(
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model["repo_id"],
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model["filename"],
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model["target_dir"]
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):
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success_count += 1
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print(f"\n🎉 Model indirme tamamlandı: {success_count}/{len(models_to_download)}")
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# Model klasörlerini listele
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print("\n📁 Model klasörleri:")
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for root, dirs, files in os.walk(models_base):
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level = root.replace(models_base, '').count(os.sep)
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indent = ' ' * 2 * level
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print(f"{indent}{os.path.basename(root)}/")
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subindent = ' ' * 2 * (level + 1)
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for file in files:
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file_size = os.path.getsize(os.path.join(root, file))
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size_mb = file_size / (1024 * 1024)
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print(f"{subindent}{file} ({size_mb:.1f} MB)")
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if __name__ == "__main__":
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main() |