Environment:Huggingface Diffusers Training Environment

Knowledge Sources	Huggingface Diffusers Accelerate
Domains	Infrastructure, Training
Last Updated	2026-02-13 21:00 GMT

Overview

Training environment for Diffusers fine-tuning workflows: requires accelerate >= 0.31.0, peft >= 0.17.0, transformers >= 4.41.2, and a CUDA/XPU GPU.

Description

This environment extends the base PyTorch CUDA runtime with libraries needed for fine-tuning diffusion models. The core training stack includes HuggingFace Accelerate for distributed training and mixed precision, PEFT for parameter-efficient fine-tuning (LoRA adapters), and the Datasets library for data loading. The PEFT backend is auto-enabled when peft >= 0.6.0 and transformers >= 4.34.0 are both installed (controlled by the `USE_PEFT_BACKEND` flag in `constants.py`). Training requires a GPU — the MPS backend does not support training (`BACKEND_SUPPORTS_TRAINING["mps"] = False`).

Usage

Required for LoRA fine-tuning, DreamBooth personalization, and any custom training workflow using the `examples/` training scripts. This environment is the prerequisite for all training-related Implementation pages.

System Requirements

Category	Requirement	Notes
OS	Linux (recommended)	Full CUDA + NCCL support for distributed training
Hardware	NVIDIA GPU with >= 16GB VRAM	24GB+ recommended for SDXL fine-tuning; A100/H100 for large models
Disk	50GB+ SSD	For datasets, checkpoints, and model weights
RAM	32GB+	For dataset preprocessing and CPU offloading during training

Dependencies

System Packages

CUDA toolkit >= 11.8 (for PyTorch 2.x builds)
NCCL (for multi-GPU distributed training)

Python Packages

Core training stack:

`accelerate` >= 0.31.0
`peft` >= 0.17.0
`transformers` >= 4.41.2
`datasets`
`safetensors` >= 0.3.1

Optimizer and scheduling:

`torch` >= 2.0.0 (for fused AdamW and compiled training)
`prodigyopt` (optional — for Prodigy optimizer in DreamBooth)

Logging and monitoring:

`tensorboard`
`wandb` (optional — for Weights & Biases logging)

Data processing:

`Pillow`
`torchvision`
`Jinja2`

Credentials

The following environment variables may be needed:

`HF_TOKEN`: HuggingFace API token for gated model access and hub uploads
`WANDB_API_KEY`: Weights & Biases API key for experiment tracking (optional)

Quick Install

# Install diffusers with training support
pip install diffusers[training] transformers accelerate peft datasets tensorboard

# For LoRA fine-tuning example
pip install diffusers[torch] transformers accelerate peft datasets safetensors

Code Evidence

PEFT backend auto-detection from `constants.py:50-61`:

MIN_PEFT_VERSION = "0.6.0"
MIN_TRANSFORMERS_VERSION = "4.34.0"

_required_peft_version = is_peft_available() and version.parse(
    version.parse(importlib.metadata.version("peft")).base_version
) >= version.parse(MIN_PEFT_VERSION)
_required_transformers_version = is_transformers_available() and version.parse(
    version.parse(importlib.metadata.version("transformers")).base_version
) >= version.parse(MIN_TRANSFORMERS_VERSION)

USE_PEFT_BACKEND = _required_peft_version and _required_transformers_version

Accelerate version requirement for CPU offloading from `pipeline_utils.py:1202-1205`:

if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
    from accelerate import cpu_offload_with_hook
else:
    raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")

MPS training limitation from `torch_utils.py:31-40`:

BACKEND_SUPPORTS_TRAINING = {
    "cuda": True,
    "xpu": True,
    "cpu": True,
    "mps": False,
    "default": True,
}

Common Errors

Error Message	Cause	Solution
`enable_model_cpu_offload requires accelerate v0.17.0 or higher`	Old accelerate version	`pip install -U accelerate`
`Using bitsandbytes 4-bit quantization requires Accelerate >= 0.26.0`	Missing/old accelerate for quantized training	`pip install 'accelerate>=0.26.0'`
`BACKEND_SUPPORTS_TRAINING: mps = False`	Attempting training on Apple Silicon	Use CUDA GPU for training; MPS inference-only

Compatibility Notes

Multi-GPU: Requires NCCL backend for distributed training. Set up via `accelerate config`.
Mixed Precision: `fp16` and `bf16` supported via Accelerate. BF16 requires Ampere+ GPU (A100/H100).
Gradient Checkpointing: Supported for all UNet/Transformer models to reduce VRAM.
TF32: Examples enable TF32 for faster Ampere GPU training: `torch.backends.cuda.matmul.allow_tf32 = True`.

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