# =====================================================================
# requirements.txt — FLUX.1-dev 3-stage LoRA pipeline
# (1_build_dataset.py · 2_train_lora.py · 3_generate.py)
# =====================================================================
#
# INSTALL ORDER MATTERS for PyTorch + CUDA. Do this first, separately,
# matching your CUDA version (example shows CUDA 12.4):
#
#   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
#
# Then install the rest:
#
#   pip install -r requirements.txt
#
# FLUX.1-dev is gated — accept the license on its Hugging Face page, then:
#
#   huggingface-cli login
#
# ---------------------------------------------------------------------

# --- core diffusion / model stack ---
diffusers>=0.32.0          # FluxPipeline, LoRA save/load, flow-matching helpers
transformers>=4.44.0       # BLIP captioner + FLUX's CLIP/T5 text encoders
peft>=0.13.0               # LoRA adapters (add_adapter, get_peft_model_state_dict)
accelerate>=0.34.0         # device placement / training utilities
safetensors>=0.4.3         # .safetensors LoRA serialization
sentencepiece>=0.2.0       # required by the T5 tokenizer FLUX uses
protobuf>=4.25.0           # required by some HF tokenizers/configs

# --- memory savings (quantization / 8-bit optimizers) for 16–24GB GPUs ---
bitsandbytes>=0.43.0       # float8 / 8-bit; CUDA only (skip on macOS/CPU)

# --- data handling & images ---
pandas>=2.0.0              # read/write dataset.csv (Stage 1 & 2)
numpy>=1.24.0              # image <-> latent array conversion (Stage 2)
Pillow>=10.0.0             # open/resize images
tqdm>=4.66.0               # progress bars

# --- hub access for gated FLUX.1-dev + BLIP download ---
huggingface_hub>=0.24.0

# NOTE: torch / torchvision are intentionally NOT pinned here — install them
# from the PyTorch CUDA index first (see header). Pinning them in this file
# would pull the CPU-only build and break GPU training.