Claude
b2d064573f
Matches the existing vendor/easy-asterisk convention (used by services/asterisk.sh) instead of two one-off top-level directories that cluttered the repo root and didn't look like anything else next to setup.sh, lib/, services/, extras/. Only the two services' own SRC_DIR path resolution and header comments needed updating — nothing else in the repo referenced the old ./ai-stack / ./paintplus paths. Also documents vendor/ in README.md's Layout section.
45 lines
1.8 KiB
Plaintext
45 lines
1.8 KiB
Plaintext
# =============================================================================
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# GPU / Local Diffusion dependencies
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# Install alongside requirements.txt when running with AI_PROVIDER=local_gpu
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#
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# Usage:
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# pip install -r requirements.txt -r requirements.gpu.txt
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#
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# These are pre-installed in Dockerfile.gpu; optional in the standard image.
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# =============================================================================
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# HuggingFace Diffusers ecosystem
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# Pinned <0.29.0: diffusers 0.29.0 added torch.xpu (Intel GPU) which fails on
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# PyTorch 2.1.x with "AttributeError: module 'torch' has no attribute 'xpu'".
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# Upgrade the base image in Dockerfile.gpu to pytorch 2.4+ before lifting this pin.
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# (FLUX support requires diffusers>=0.29 + PyTorch>=2.4; SDXL/SD works fine here.)
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diffusers>=0.28.0,<0.29.0
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transformers>=4.36.0,<4.40.0
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accelerate>=0.27.0
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huggingface-hub>=0.23.0
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safetensors>=0.4.0
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# Required by SDXL pipelines
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invisible-watermark>=0.2.0
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omegaconf>=2.3.0
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# Required by FLUX (T5 text encoder tokenizer)
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sentencepiece>=0.2.0
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# xformers — reduces attention VRAM ~20-30%, often unlocks the next model tier
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# Must match your PyTorch+CUDA version; leave out if unsure.
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# Install post-container-start if needed:
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# pip install xformers --index-url https://download.pytorch.org/whl/cu121
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# xformers
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# Background removal — BEN2 (default, clean cutouts/hair) + BiRefNet-HR
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# (high-res/print alternate). Both MIT-licensed. Verified against upstream
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# source: neither requires torch>=2.5 despite the BiRefNet repo's own
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# requirements.txt floor — that pin is for its training/eval scripts, not
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# the inference path used here. Weights download from HuggingFace on first
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# use (cached via the hf_cache bind mount, same as the diffusion models).
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ben2 @ git+https://github.com/PramaLLC/BEN2.git
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timm>=1.0.10
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einops>=0.6.0
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kornia>=0.7.0
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