Stable Diffusion text-to-image fine-tuning

This extended LoRA training script was authored by haofanwang. This is an experimental LoRA extension of this example. We further support add LoRA layers for text encoder.

Training with LoRA

Low-Rank Adaption of Large Language Models was first introduced by Microsoft in LoRA: Low-Rank Adaptation of Large Language Models by Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen.

In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and only training those newly added weights. This has a couple of advantages:

Previous pretrained weights are kept frozen so that model is not prone to catastrophic forgetting.
Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.
LoRA attention layers allow to control to which extent the model is adapted toward new training images via a scale parameter.

cloneofsimo was the first to try out LoRA training for Stable Diffusion in the popular lora GitHub repository.

With LoRA, it's possible to fine-tune Stable Diffusion on a custom image-caption pair dataset on consumer GPUs like Tesla T4, Tesla V100.

Training

First, you need to set up your development environment as is explained in the installation section. Make sure to set the MODEL_NAME and DATASET_NAME environment variables. Here, we will use Stable Diffusion v1-4 and the Pokemons dataset.

Note: Change the resolution to 768 if you are using the stable-diffusion-2 768x768 model.

Note: It is quite useful to monitor the training progress by regularly generating sample images during training. Weights and Biases is a nice solution to easily see generating images during training. All you need to do is to run pip install wandb before training to automatically log images.

export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATASET_NAME="lambdalabs/pokemon-blip-captions"

For this example we want to directly store the trained LoRA embeddings on the Hub, so we need to be logged in and add the --push_to_hub flag.

huggingface-cli login

Now we can start training!

accelerate launch --mixed_precision="fp16" train_text_to_image_lora.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$DATASET_NAME --caption_column="text" \
  --resolution=512 --random_flip \
  --train_batch_size=1 \
  --num_train_epochs=100 --checkpointing_steps=5000 \
  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
  --seed=42 \
  --output_dir="sd-pokemon-model-lora" \
  --validation_prompt="cute dragon creature" --report_to="wandb"
  --use_peft \
  --lora_r=4 --lora_alpha=32 \
  --lora_text_encoder_r=4 --lora_text_encoder_alpha=32

The above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.

Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Here we use 1e-4 instead of the usual 1e-5. Also, by using LoRA, it's possible to run train_text_to_image_lora.py in consumer GPUs like T4 or V100.

The final LoRA embedding weights have been uploaded to sayakpaul/sd-model-finetuned-lora-t4. Note: The final weights are only 3 MB in size, which is orders of magnitudes smaller than the original model.

You can check some inference samples that were logged during the course of the fine-tuning process here.

Inference

Once you have trained a model using above command, the inference can be done simply using the StableDiffusionPipeline after loading the trained LoRA weights. You need to pass the output_dir for loading the LoRA weights which, in this case, is sd-pokemon-model-lora.

from diffusers import StableDiffusionPipeline
import torch

model_path = "sayakpaul/sd-model-finetuned-lora-t4"
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.unet.load_attn_procs(model_path)
pipe.to("cuda")

prompt = "A pokemon with green eyes and red legs."
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
image.save("pokemon.png")