huggingface · pcuenca · Oct 28, 2022 · Oct 27, 2022 · Oct 27, 2022 · Oct 27, 2022
diff --git a/README.md b/README.md
@@ -182,9 +182,9 @@ image.save("astronaut_rides_horse.png")
 
 ### JAX/Flax
 
-To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax.
+Diffusers offers a JAX / Flax implementation of Stable Diffusion for very fast inference. JAX shines specially on TPU hardware because each TPU server has 8 accelerators working in parallel, but it runs great on GPUs too.
 
-Running the pipeline with default PNDMScheduler
+Running the pipeline with the default PNDMScheduler:
 
 ```python
 import jax
@@ -331,8 +331,25 @@ You can generate your own latents to reproduce results, or tweak your prompt on
 
 For more details, check out [the Stable Diffusion notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb)
 and have a look into the [release notes](https://github.com/huggingface/diffusers/releases/tag/v0.2.0).
-
-## Examples
+
+## Fine-Tuning Stable Diffusion
+
+Fine-tuning techniques make it possible to adapt Stable Diffusion to your own dataset, or add new subjects to it. These are some of the techniques supported in `diffusers`:
+
+Textual Inversion is a technique for capturing novel concepts from a small number of example images in a way that can later be used to control text-to-image pipelines. It does so by learning new 'words' in the embedding space of the pipeline's text encoder. These special words can then be used within text prompts to achieve very fine-grained control of the resulting images. 
+
+- Textual Inversion. Capture novel concepts from a small set of sample images, and associate them with new "words" in the embedding space of the text encoder. Please, refer to [our training examples](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) or [documentation](https://huggingface.co/docs/diffusers/training/text_inversion) to try for yourself.
+
+- Dreambooth. Another technique to capture new concepts in Stable Diffusion. This method fine-tunes the UNet (and, optionally, also the text encoder) of the pipeline to achieve impressive results. Please, refer to [our training examples](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) and [training report](https://wandb.ai/psuraj/dreambooth/reports/Dreambooth-Training-Analysis--VmlldzoyNzk0NDc3) for additional details and training recommendations.
+
+- Full Stable Diffusion fine-tuning. If you have a more sizable dataset with a specific look or style, you can fine-tune Stable Diffusion so that it outputs images following those examples. This was the approach taken to create [a Pokémon Stable Diffusion model](https://huggingface.co/justinpinkney/pokemon-stable-diffusion) (by Justing Pinkney / Lambda Labs), [a Japanese specific version of Stable Diffusion](https://huggingface.co/spaces/rinna/japanese-stable-diffusion) (by [Rinna Co.](https://github.com/rinnakk/japanese-stable-diffusion/) and others. You can start at [our text-to-image fine-tuning example](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) and go from there.
+
+
+## Stable Diffusion Community Pipelines
+
+The release of Stable Diffusion as an open source model has fostered a lot of interesting ideas and experimentation. Our [Community Examples folder](https://github.com/huggingface/diffusers/tree/main/examples/community) contains many ideas worth exploring, like interpolating to create animated videos, using CLIP Guidance for additional prompt fidelity, term weighting, and much more! Take a look and [contribute your own](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipelines).
+
+## Other Examples
 
 There are many ways to try running Diffusers! Here we outline code-focused tools (primarily using `DiffusionPipeline`s and Google Colab) and interactive web-tools.
 

diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
@@ -46,9 +46,11 @@
   - local: training/unconditional_training
     title: "Unconditional Image Generation"
   - local: training/text_inversion
-    title: "Text Inversion"
+    title: "Textual Inversion"
+  - local: training/dreambooth
+    title: "Dreambooth"
   - local: training/text2image
-    title: "Text-to-image"
+    title: "Text-to-image fine-tuning"
   title: "Training"
 - sections:
   - local: conceptual/stable_diffusion

diff --git a/docs/source/training/dreambooth.mdx b/docs/source/training/dreambooth.mdx
@@ -0,0 +1,240 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# DreamBooth fine-tuning example
+
+[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text-to-image models like stable diffusion given just a few (3~5) images of a subject.
+
+![Dreambooth examples from the project's blog](https://dreambooth.github.io/DreamBooth_files/teaser_static.jpg)
+_Dreambooth examples from the [project's blog](https://dreambooth.github.io)._
+
+The [Dreambooth training script](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) shows how to implement this training procedure on a pre-trained Stable Diffusion model.
+
+<Tip warning={true}>
+
+<!-- TODO: replace with our blog when it's done -->
+
+Dreambooth fine-tuning is very sensitive to hyperparameters and easy to overfit. We recommend you take a look at our [in-depth analysis](https://wandb.ai/psuraj/dreambooth/reports/Dreambooth-Training-Analysis--VmlldzoyNzk0NDc3) with recommended settings for different subjects, and go from there.
+
+</Tip>
+
+## Training locally 
+
+### Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies. We also recommend to install `diffusers` from the `main` github branch.
+
+```bash
+pip install git+https://github.com/huggingface/diffusers
+pip install -U -r diffusers/examples/dreambooth/requirements.txt
+```
+
+Then initialize and configure a [🤗 Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+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. Instead, you can pass the path to your local checkout to the training script and it will be loaded from there.
+
+### Dog toy example
+
+In this example we'll use [these images](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ) to add a new concept to Stable Diffusion using the Dreambooth process. They will be our training data. Please, download them and place them somewhere in your system.
+
+Then you can launch the training script using:
+
+```bash
+export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export INSTANCE_DIR="path_to_training_images"
+export OUTPUT_DIR="path_to_saved_model"
+
+accelerate launch train_dreambooth.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --instance_prompt="a photo of sks dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=1 \
+  --learning_rate=5e-6 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=400
+```
+
+### Training with a prior-preserving loss
+
+Prior preservation is used to avoid overfitting and language-drift. Please, refer to the paper to learn more about it if you are interested. For prior preservation, we use other images of the same class as part of the training process. The nice thing is that we can generate those images using the Stable Diffusion model itself! The training script will save the generated images to a local path we specify.
+
+According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior preservation. 200-300 works well for most cases.
+
+```bash
+export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export INSTANCE_DIR="path_to_training_images"
+export CLASS_DIR="path_to_class_images"
+export OUTPUT_DIR="path_to_saved_model"
+
+accelerate launch train_dreambooth.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --class_data_dir=$CLASS_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --with_prior_preservation --prior_loss_weight=1.0 \
+  --instance_prompt="a photo of sks dog" \
+  --class_prompt="a photo of dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=1 \
+  --learning_rate=5e-6 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --num_class_images=200 \
+  --max_train_steps=800
+```
+
+### Training on a 16GB GPU
+
+With the help of gradient checkpointing and the 8-bit optimizer from [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), it's possible to train dreambooth on a 16GB GPU.
+
+```bash
+pip install bitsandbytes
+```
+
+Then pass the `--use_8bit_adam` option to the training script.
+
+```bash
+export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export INSTANCE_DIR="path_to_training_images"
+export CLASS_DIR="path_to_class_images"
+export OUTPUT_DIR="path_to_saved_model"
+
+accelerate launch train_dreambooth.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --class_data_dir=$CLASS_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --with_prior_preservation --prior_loss_weight=1.0 \
+  --instance_prompt="a photo of sks dog" \
+  --class_prompt="a photo of dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=2 --gradient_checkpointing \
+  --use_8bit_adam \
+  --learning_rate=5e-6 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --num_class_images=200 \
+  --max_train_steps=800
+```
+
+### Fine-tune the text encoder in addition to the UNet
+
+The script also allows to fine-tune the `text_encoder` along with the `unet`. It has been observed experimentally that this gives much better results, especially on faces. Please, refer to [our report](https://wandb.ai/psuraj/dreambooth/reports/Dreambooth-Training-Analysis--VmlldzoyNzk0NDc3) for more details.
+
+To enable this option, pass the `--train_text_encoder` argument to the training script.
+
+<Tip>
+Training the text encoder requires additional memory, so training won't fit on a 16GB GPU. You'll need at least 24GB VRAM to use this option.
+</Tip>
+
+```bash
+export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export INSTANCE_DIR="path_to_training_images"
+export CLASS_DIR="path_to_class_images"
+export OUTPUT_DIR="path_to_saved_model"
+
+accelerate launch train_dreambooth.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --train_text_encoder \
+  --instance_data_dir=$INSTANCE_DIR \
+  --class_data_dir=$CLASS_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --with_prior_preservation --prior_loss_weight=1.0 \
+  --instance_prompt="a photo of sks dog" \
+  --class_prompt="a photo of dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --use_8bit_adam
+  --gradient_checkpointing \
+  --learning_rate=2e-6 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --num_class_images=200 \
+  --max_train_steps=800
+```
+
+### Training on a 8 GB GPU:
+
+Using [DeepSpeed](https://www.deepspeed.ai/) it's even possible to offload some
+tensors from VRAM to either CPU or NVME, allowing training to proceed with less GPU memory.
+
+DeepSpeed needs to be enabled with `accelerate config`. During configuration,
+answer yes to "Do you want to use DeepSpeed?". Combining DeepSpeed stage 2, fp16
+mixed precision, and offloading both the model parameters and the optimizer state to CPU, it's
+possible to train on under 8 GB VRAM. The drawback is that this requires more system RAM (about 25 GB). See [the DeepSpeed documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more configuration options.
+
+Changing the default Adam optimizer to DeepSpeed's special version of Adam
+`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup, but enabling
+it requires the system's CUDA toolchain version to be the same as the one installed with PyTorch. 8-bit optimizers don't seem to be compatible with DeepSpeed at the moment.
+
+```bash
+export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export INSTANCE_DIR="path_to_training_images"
+export CLASS_DIR="path_to_class_images"
+export OUTPUT_DIR="path_to_saved_model"
+
+accelerate launch train_dreambooth.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --instance_data_dir=$INSTANCE_DIR \
+  --class_data_dir=$CLASS_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --with_prior_preservation --prior_loss_weight=1.0 \
+  --instance_prompt="a photo of sks dog" \
+  --class_prompt="a photo of dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --sample_batch_size=1 \
+  --gradient_accumulation_steps=1 --gradient_checkpointing \
+  --learning_rate=5e-6 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --num_class_images=200 \
+  --max_train_steps=800 \
+  --mixed_precision=fp16
+```
+
+## Inference
+
+Once you have trained a model, inference can be done using the `StableDiffusionPipeline`, by simply indicating the path where the model was saved. Make sure that your prompts include the special `identifier` used during training (`sks` in the previous examples).
+
+```python
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = "path_to_saved_model"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
+
+prompt = "A photo of sks dog in a bucket"
+image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
+
+image.save("dog-bucket.png")
+```
diff --git a/docs/source/training/overview.mdx b/docs/source/training/overview.mdx
@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # 🧨 Diffusers Training Examples
 
-Diffusers examples are a collection of scripts to demonstrate how to effectively use the `diffusers` library
+Diffusers training examples are a collection of scripts to demonstrate how to effectively use the `diffusers` library
 for a variety of use cases.
 
 **Note**: If you are looking for **official** examples on how to use `diffusers` for inference, 
@@ -36,13 +36,15 @@ Training examples show how to pretrain or fine-tune diffusion models for a varie
 - [Unconditional Training](./unconditional_training)
 - [Text-to-Image Training](./text2image)
 - [Text Inversion](./text_inversion)
+- [Dreambooth](./dreambooth)
 
 
 | Task | 🤗 Accelerate | 🤗 Datasets | Colab
 |---|---|:---:|:---:|
 | [**Unconditional Image Generation**](./unconditional_training) | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [**Text-to-Image**](./text2image) | - | - | 
-| [**Text-Inversion**](./text_inversion) | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
+| [**Text-to-Image fine-tuning**](./text2image) | ✅ | ✅ | 
+| [**Textual Inversion**](./text_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
+| [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)
 
 ## Community