# =============================================================================
# GPU / Local Diffusion dependencies
# Install alongside requirements.txt when running with AI_PROVIDER=local_gpu
#
# Usage:
#   pip install -r requirements.txt -r requirements.gpu.txt
#
# These are pre-installed in Dockerfile.gpu; optional in the standard image.
# =============================================================================

# HuggingFace Diffusers ecosystem
# Pinned <0.29.0: diffusers 0.29.0 added torch.xpu (Intel GPU) which fails on
# PyTorch 2.1.x with "AttributeError: module 'torch' has no attribute 'xpu'".
# Upgrade the base image in Dockerfile.gpu to pytorch 2.4+ before lifting this pin.
# (FLUX support requires diffusers>=0.29 + PyTorch>=2.4; SDXL/SD works fine here.)
diffusers>=0.28.0,<0.29.0
transformers>=4.36.0,<4.40.0
accelerate>=0.27.0
huggingface-hub>=0.23.0
safetensors>=0.4.0

# Required by SDXL pipelines
invisible-watermark>=0.2.0
omegaconf>=2.3.0

# Required by FLUX (T5 text encoder tokenizer)
sentencepiece>=0.2.0

# xformers — reduces attention VRAM ~20-30%, often unlocks the next model tier
# Must match your PyTorch+CUDA version; leave out if unsure.
# Install post-container-start if needed:
#   pip install xformers --index-url https://download.pytorch.org/whl/cu121
# xformers

# Background removal — BEN2 (default, clean cutouts/hair) + BiRefNet-HR
# (high-res/print alternate). Both MIT-licensed. Verified against upstream
# source: neither requires torch>=2.5 despite the BiRefNet repo's own
# requirements.txt floor — that pin is for its training/eval scripts, not
# the inference path used here. Weights download from HuggingFace on first
# use (cached via the hf_cache bind mount, same as the diffusion models).
ben2 @ git+https://github.com/PramaLLC/BEN2.git
timm>=1.0.10
einops>=0.6.0
kornia>=0.7.0