ControlNet training example

Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang and Maneesh Agrawala.

This example is based on the training example in the original ControlNet repository. It trains a ControlNet to fill circles using a small synthetic dataset.

Installing the dependencies

Before running the scripts, make sure to install the library's training dependencies:

Important

To make sure you can successfully run the latest versions of the example scripts, we highly recommend installing from source and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .

Then cd in the example folder and run

pip install -r requirements.txt

And initialize an 🤗Accelerate environment with:

accelerate config

Or for a default accelerate configuration without answering questions about your environment

accelerate config default

Or if your environment doesn't support an interactive shell e.g. a notebook

from accelerate.utils import write_basic_config
write_basic_config()

Circle filling dataset

The original dataset is hosted in the ControlNet repo. We re-uploaded it to be compatible with datasets here. Note that datasets handles dataloading within the training script.

Our training examples use Stable Diffusion 1.5 as the original set of ControlNet models were trained from it. However, ControlNet can be trained to augment any Stable Diffusion compatible model (such as CompVis/stable-diffusion-v1-4) or stabilityai/stable-diffusion-2-1.

Training

Our training examples use two test conditioning images. They can be downloaded by running

wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png

wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=4

This default configuration requires ~38GB VRAM.

By default, the training script logs outputs to tensorboard. Pass --report_to wandb to use weights and biases.

Gradient accumulation with a smaller batch size can be used to reduce training requirements to ~20 GB VRAM.

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4

Example results

After 300 steps with batch size 8


	red circle with blue background

	cyan circle with brown floral background

After 6000 steps with batch size 8:


	red circle with blue background

	cyan circle with brown floral background

Training on a 16 GB GPU

Optimizations:

Gradient checkpointing
bitsandbyte's 8-bit optimizer

bitandbytes install instructions.

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4 \
 --gradient_checkpointing \
 --use_8bit_adam

Training on a 12 GB GPU

Optimizations:

Gradient checkpointing
bitsandbyte's 8-bit optimizer
xformers
set grads to none

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4 \
 --gradient_checkpointing \
 --use_8bit_adam \
 --enable_xformers_memory_efficient_attention \
 --set_grads_to_none

When using enable_xformers_memory_efficient_attention, please make sure to install xformers by pip install xformers.

Training on an 8 GB GPU

We have not exhaustively tested DeepSpeed support for ControlNet. While the configuration does save memory, we have not confirmed the configuration to train successfully. You will very likely have to make changes to the config to have a successful training run.

Optimizations:

Gradient checkpointing
xformers
set grads to none
DeepSpeed stage 2 with parameter and optimizer offloading
fp16 mixed precision

DeepSpeed can offload tensors from VRAM to either CPU or NVME. This requires significantly more RAM (about 25 GB).

Use accelerate config to enable DeepSpeed stage 2.

The relevant parts of the resulting accelerate config file are

compute_environment: LOCAL_MACHINE
deepspeed_config:
  gradient_accumulation_steps: 4
  offload_optimizer_device: cpu
  offload_param_device: cpu
  zero3_init_flag: false
  zero_stage: 2
distributed_type: DEEPSPEED

See documentation for more DeepSpeed configuration options.

Changing the default Adam optimizer to DeepSpeed's Adam deepspeed.ops.adam.DeepSpeedCPUAdam gives a substantial speedup but it requires CUDA toolchain with the same version as pytorch. 8-bit optimizer does not seem to be compatible with DeepSpeed at the moment.

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="path to save model"

accelerate launch train_controlnet.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --train_batch_size=1 \
 --gradient_accumulation_steps=4 \
 --gradient_checkpointing \
 --enable_xformers_memory_efficient_attention \
 --set_grads_to_none \
 --mixed_precision fp16

Performing inference with the trained ControlNet

The trained model can be run the same as the original ControlNet pipeline with the newly trained ControlNet. Set base_model_path and controlnet_path to the values --pretrained_model_name_or_path and --output_dir were respectively set to in the training script.

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch

base_model_path = "path to model"
controlnet_path = "path to controlnet"

controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    base_model_path, controlnet=controlnet, torch_dtype=torch.float16
)

# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed
pipe.enable_xformers_memory_efficient_attention()

pipe.enable_model_cpu_offload()

control_image = load_image("./conditioning_image_1.png")
prompt = "pale golden rod circle with old lace background"

# generate image
generator = torch.manual_seed(0)
image = pipe(
     prompt, num_inference_steps=20, generator=generator, image=control_image
).images[0]

image.save("./output.png")

Training with Flax/JAX

For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.

Running on Google Cloud TPU

See below for commands to set up a TPU VM(--accelerator-type v4-8). For more details about how to set up and use TPUs, refer to Cloud docs for single VM setup.

First create a single TPUv4-8 VM and connect to it:

ZONE=us-central2-b
TPU_TYPE=v4-8
VM_NAME=hg_flax

gcloud alpha compute tpus tpu-vm create $VM_NAME \
    --zone $ZONE \
    --accelerator-type $TPU_TYPE \
    --version  tpu-vm-v4-base

gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \

When connected install JAX 0.4.5:

pip install "jax[tpu]==0.4.5" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

To verify that JAX was correctly installed, you can run the following command:

import jax
jax.device_count()

This should display the number of TPU cores, which should be 4 on a TPUv4-8 VM.

Then install Diffusers and the library's training dependencies:

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .

Then cd in the example folder and run

pip install -U -r requirements_flax.txt

Now let's downloading two conditioning images that we will use to run validation during the training in order to track our progress

wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png

We encourage you to store or share your model with the community. To use huggingface hub, please login to your Hugging Face account, or (create one if you don’t have one already):

huggingface-cli login

Make sure you have the MODEL_DIR,OUTPUT_DIR and HUB_MODEL_ID environment variables set. The OUTPUT_DIR and HUB_MODEL_ID variables specify where to save the model to on the Hub:

export MODEL_DIR="runwayml/stable-diffusion-v1-5"
export OUTPUT_DIR="control_out"
export HUB_MODEL_ID="fill-circle-controlnet"

And finally start the training

python3 train_controlnet_flax.py \
 --pretrained_model_name_or_path=$MODEL_DIR \
 --output_dir=$OUTPUT_DIR \
 --dataset_name=fusing/fill50k \
 --resolution=512 \
 --learning_rate=1e-5 \
 --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
 --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
 --validation_steps=1000 \
 --train_batch_size=2 \
 --revision="non-ema" \
 --from_pt \
 --report_to="wandb" \
 --max_train_steps=10000 \
 --push_to_hub \
 --hub_model_id=$HUB_MODEL_ID

Since we passed the --push_to_hub flag, it will automatically create a model repo under your huggingface account based on $HUB_MODEL_ID. By the end of training, the final checkpoint will be automatically stored on the hub. You can find an example model repo here.

Our training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set --max_train_samples. Here is an example command:

python3 train_controlnet_flax.py \
    --pretrained_model_name_or_path=$MODEL_DIR \
    --output_dir=$OUTPUT_DIR \
    --dataset_name=multimodalart/facesyntheticsspigacaptioned \
    --streaming \
    --conditioning_image_column=spiga_seg \
    --image_column=image \
    --caption_column=image_caption \
    --resolution=512 \
    --max_train_samples 50 \
    --max_train_steps 5 \
    --learning_rate=1e-5 \
    --validation_steps=2 \
    --train_batch_size=1 \
    --revision="flax" \
    --report_to="wandb"

Note, however, that the performance of the TPUs might get bottlenecked as streaming with datasets is not optimized for images. For ensuring maximum throughput, we encourage you to explore the following options: