# Stable Diffusion text-to-image fine-tuning The `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset. ___Note___: ___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___ ## Running locally with PyTorch ### 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: ```bash git clone https://github.com/huggingface/diffusers cd diffusers pip install . ``` Then cd in the example folder and run ```bash pip install -r requirements.txt ``` And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with: ```bash accelerate config ``` ### Pokemon example You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens). Run the following command to authenticate your token ```bash huggingface-cli login ``` If you have already cloned the repo, then you won't need to go through these steps.
#### Hardware With `gradient_checkpointing` and `mixed_precision` it should be possible to fine tune the model on a single 24GB GPU. For higher `batch_size` and faster training it's better to use GPUs with >30GB memory. **___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___** ```bash export MODEL_NAME="CompVis/stable-diffusion-v1-4" export DATASET_NAME="lambdalabs/pokemon-blip-captions" accelerate launch --mixed_precision="fp16" train_text_to_image.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --dataset_name=$DATASET_NAME \ --use_ema \ --resolution=512 --center_crop --random_flip \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --gradient_checkpointing \ --max_train_steps=15000 \ --learning_rate=1e-05 \ --max_grad_norm=1 \ --lr_scheduler="constant" --lr_warmup_steps=0 \ --output_dir="sd-pokemon-model" ``` To run on your own training files prepare the dataset according to the format required by `datasets`, you can find the instructions for how to do that in this [document](https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata). If you wish to use custom loading logic, you should modify the script, we have left pointers for that in the training script. ```bash export MODEL_NAME="CompVis/stable-diffusion-v1-4" export TRAIN_DIR="path_to_your_dataset" accelerate launch --mixed_precision="fp16" train_text_to_image.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --train_data_dir=$TRAIN_DIR \ --use_ema \ --resolution=512 --center_crop --random_flip \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --gradient_checkpointing \ --max_train_steps=15000 \ --learning_rate=1e-05 \ --max_grad_norm=1 \ --lr_scheduler="constant" --lr_warmup_steps=0 \ --output_dir="sd-pokemon-model" ``` Once the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `sd-pokemon-model`. To load the fine-tuned model for inference just pass that path to `StableDiffusionPipeline` ```python from diffusers import StableDiffusionPipeline model_path = "path_to_saved_model" pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) pipe.to("cuda") image = pipe(prompt="yoda").images[0] image.save("yoda-pokemon.png") ``` Checkpoints only save the unet, so to run inference from a checkpoint, just load the unet ```python from diffusers import StableDiffusionPipeline, UNet2DConditionModel model_path = "path_to_saved_model" unet = UNet2DConditionModel.from_pretrained(model_path + "/checkpoint-/unet") pipe = StableDiffusionPipeline.from_pretrained("", unet=unet, torch_dtype=torch.float16) pipe.to("cuda") image = pipe(prompt="yoda").images[0] image.save("yoda-pokemon.png") ``` #### Training with multiple GPUs `accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch) for running distributed training with `accelerate`. Here is an example command: ```bash export MODEL_NAME="CompVis/stable-diffusion-v1-4" export DATASET_NAME="lambdalabs/pokemon-blip-captions" accelerate launch --mixed_precision="fp16" --multi_gpu train_text_to_image.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --dataset_name=$DATASET_NAME \ --use_ema \ --resolution=512 --center_crop --random_flip \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --gradient_checkpointing \ --max_train_steps=15000 \ --learning_rate=1e-05 \ --max_grad_norm=1 \ --lr_scheduler="constant" --lr_warmup_steps=0 \ --output_dir="sd-pokemon-model" ``` #### Training with Min-SNR weighting We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence by rebalancing the loss. In order to use it, one needs to set the `--snr_gamma` argument. The recommended value when using it is 5.0. You can find [this project on Weights and Biases](https://wandb.ai/sayakpaul/text2image-finetune-minsnr) that compares the loss surfaces of the following setups: * Training without the Min-SNR weighting strategy * Training with the Min-SNR weighting strategy (`snr_gamma` set to 5.0) * Training with the Min-SNR weighting strategy (`snr_gamma` set to 1.0) For our small Pokemons dataset, the effects of Min-SNR weighting strategy might not appear to be pronounced, but for larger datasets, we believe the effects will be more pronounced. Also, note that in this example, we either predict `epsilon` (i.e., the noise) or the `v_prediction`. For both of these cases, the formulation of the Min-SNR weighting strategy that we have used holds. ## Training with LoRA Low-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) 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](https://www.pnas.org/doi/10.1073/pnas.1611835114). - 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](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/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](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions). **___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/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](https://docs.wandb.ai/quickstart) 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.___** ```bash 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. ```bash huggingface-cli login ``` Now we can start training! ```bash 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" ``` 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](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4). **___Note: [The final weights](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/pytorch_lora_weights.bin) 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](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/q4lc0xsw). ### 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`. ```python 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") ``` If you are loading the LoRA parameters from the Hub and if the Hub repository has a `base_model` tag (such as [this](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/README.md?code=true#L4)), then you can do: ```py from huggingface_hub.repocard import RepoCard lora_model_id = "sayakpaul/sd-model-finetuned-lora-t4" card = RepoCard.load(lora_model_id) base_model_id = card.data.to_dict()["base_model"] pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16) ... ``` ## 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. **___Note: The flax example doesn't yet support features like gradient checkpoint, gradient accumulation etc, so to use flax for faster training we will need >30GB cards or TPU v3.___** Before running the scripts, make sure to install the library's training dependencies: ```bash pip install -U -r requirements_flax.txt ``` ```bash export MODEL_NAME="duongna/stable-diffusion-v1-4-flax" export DATASET_NAME="lambdalabs/pokemon-blip-captions" python train_text_to_image_flax.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --dataset_name=$DATASET_NAME \ --resolution=512 --center_crop --random_flip \ --train_batch_size=1 \ --mixed_precision="fp16" \ --max_train_steps=15000 \ --learning_rate=1e-05 \ --max_grad_norm=1 \ --output_dir="sd-pokemon-model" ``` To run on your own training files prepare the dataset according to the format required by `datasets`, you can find the instructions for how to do that in this [document](https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata). If you wish to use custom loading logic, you should modify the script, we have left pointers for that in the training script. ```bash export MODEL_NAME="duongna/stable-diffusion-v1-4-flax" export TRAIN_DIR="path_to_your_dataset" python train_text_to_image_flax.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --train_data_dir=$TRAIN_DIR \ --resolution=512 --center_crop --random_flip \ --train_batch_size=1 \ --mixed_precision="fp16" \ --max_train_steps=15000 \ --learning_rate=1e-05 \ --max_grad_norm=1 \ --output_dir="sd-pokemon-model" ``` ### Training with xFormers: You can enable memory efficient attention by [installing xFormers](https://huggingface.co/docs/diffusers/main/en/optimization/xformers) and passing the `--enable_xformers_memory_efficient_attention` argument to the script. xFormers training is not available for Flax/JAX. **Note**: According to [this issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training in some GPUs. If you observe that problem, please install a development version as indicated in that comment. ## Stable Diffusion XL * We support fine-tuning the UNet shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) via the `train_text_to_image_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md). * We also support fine-tuning of the UNet and Text Encoder shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) with LoRA via the `train_text_to_image_lora_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md).