Download Dreambooth Models
Georgeann Raia
Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models lack the ability to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts. In this work, we present a new approach for "personalization" of text-to-image diffusion models (specializing them to users' needs). Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model (Imagen, although our method is not limited to a specific model) such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views, and lighting conditions that do not appear in the reference images. We apply our technique to several previously-unassailable tasks, including subject recontextualization, text-guided view synthesis, appearance modification, and artistic rendering (all while preserving the subject's key features).
Given a particular subject such as clock (shown in the real images on the left), it is very challenging to generate it in different contexts with state-of-the-art text-to-image models, while maintaining high fidelity to its key visual features. Even with dozens of iterations over a text prompt that contains a detailed description of the appearance of the clock ("retro style yellow alarm clock with a white clock face and a yellow number three on the right part of the clock face in the jungle"), the Imagen model [Saharia et al., 2022] can't reconstruct its key visual features (third column). Furthermore, even models whose text embedding lies in a shared language-vision space and can create semantic variations of the image, such as DALL-E2 [Ramesh et al., 2022], can neither reconstruct the appearance of the given subject nor modify the context (second column). In contrast, our approach (right) can synthesize the clock with high fidelity and in new contexts ("a [V] clock in the jungle").
This project aims to provide users with an effective tool for synthesizing personal subjects (animals, objects) in different contexts. While general text-to-image models might be biased towards specific attributes when synthesizing images from text, our approach enables the user to get a better reconstruction of their desirable subjects. On contrary, malicious parties might try to use such images to mislead viewers. This is a common issue, existing in other generative models approaches or content manipulation techniques. Future research in generative modeling, and specifically of personalized generative priors, must continue investigating and revalidating these concerns.
Acknowledgements: We thank Rinon Gal, Adi Zicher, Ron Mokady, Bill Freeman, Dilip Krishnan, Huiwen Chang and Daniel Cohen-Or for their valuable inputs that helped improve this work, and to Mohammad Norouzi, Chitwan Saharia and William Chan for providing us with their support and the pretrained models of Imagen. Finally, a special thank you to David Salesin for his feedback, advice and for his support for the project.
DreamBooth is a deep learning generation model used to personalize existing text-to-image models by fine-tuning. It was developed by researchers from Google Research and Boston University in 2022. Originally developed using Google's own Imagen text-to-image model, DreamBooth implementations can be applied to other text-to-image models, where it can allow the model to generate more fine-tuned and personalized outputs after training on three to five images of a subject.[1][2][3]
Pretrained text-to-image diffusion models, while often capable of offering a diverse range of different image output types, lack the specificity required to generate images of lesser-known subjects, and are limited in their ability to render known subjects in different situations and contexts.[1] The methodology used to run implementations of DreamBooth involves the fine-tuning the full UNet component of the diffusion model using a few images (usually 3--5) depicting a specific subject. Images are paired with text prompts that contain the name of the class the subject belongs to, plus a unique identifier. As an example, a photograph of a [Nissan R34 GTR] car, with car being the class); a class-specific prior preservation loss is applied to encourage the model to generate diverse instances of the subject based on what the model is already trained on for the original class.[1] Pairs of low-resolution and high-resolution images taken from the set of input images are used to fine-tune the super-resolution components, allowing the minute details of the subject to be maintained.[1]
DreamBooth can be used to fine-tune models such as Stable Diffusion, where it may alleviate a common shortcoming of Stable Diffusion not being able to adequately generate images of specific individual people.[4] Such a use case is quite VRAM intensive, however, and thus cost-prohibitive for hobbyist users.[4] The Stable Diffusion adaptation of DreamBooth in particular is released as a free and open-source project based on the technology outlined by the original paper published by Ruiz et. al. in 2022.[5] Concerns have been raised regarding the ability for bad actors to utilise DreamBooth to generate misleading images for malicious purposes, and that its open-source nature allows anyone to utilise or even make improvements to the technology.[6] In addition, artists have expressed their apprehension regarding the ethics of using DreamBooth to train model checkpoints that are specifically aimed at imitating specific art styles associated with human artists; one such critic is Hollie Mengert, an illustrator for Disney and Penguin Random House who has had her art style trained into a checkpoint model via DreamBooth and shared online, without her consent.[7][8]
2. Enter the MODEL_NAME. You can use the Stable Diffusion v1.5 model (HuggingFace page). You can find more models on HuggingFace here. The model name should be in the format user/model.
Unfortunately, the CivitAI models are not in a format that is ready for training (diffusers). You will need to find the same one or convert it yourself and put it on Hugging Face. e.g. -to-convert-ckpt-to-diffusers-format/35635
It all works perfectly for me, thank you so much! I have trained many models successfully. Since I started using Chrome on my M2 Mac Studio Ultra, that is, Safari was causing failures. Meanwhile Diffusionbee beta 2.3 supports SDXL, and I was wondering if you have a template to create such models and also what realistic initial model you would link to on Hugging face for this..
Yes, I just confirmed (again) the notebook is working. Note that it only works for v1 models. If you are using the same settings and images as in my post and still gets error, please post or email me the FULL error message.
Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models lack the ability to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts. In this work, we present a new approach for "personalization" of text-to-image diffusion models. Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can be used to synthesize novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views and lighting conditions that do not appear in the reference images. We apply our technique to several previously-unassailable tasks, including subject recontextualization, text-guided view synthesis, and artistic rendering, all while preserving the subject's key features. We also provide a new dataset and evaluation protocol for this new task of subject-driven generation. Project page:
Keep in mind that currently, the Stable Diffusion scene is moving at breakneck speed. Literally every time I run git pull there are new features, UI gets shifted around or something gets broken. Truly, move fast and break things. So the UI elements I mention here may not be the same, and some of the problems I have may have been fixed by the time you are reading. Speaking of which, to update the webui run git pull inside /git/stable-difffusion-webui, however, extensions are separate git repos. In order to update an extension, you need to go to one of the extension directories and git pull from there. eg. /git/stable-diffusion-webui/extensions/sd_dreambooth_extension/.
Notice we dropped the output into the /git/stable-diffusion-webui/models/Lora/ directory. (Upper case Lora.) This is where LoRAs will get picked up by the webui. Launch the webui again and switch the model to the base sd-v1-5-pruned.ckpt. Click on Show extra networks icon under Generate. Navigate to LoRA and Refresh. You should see your newly created LoRAs there. You can even set a thumbnail for each LORA.
Ever wanted to start generating your own unique creations using your own custom dreambooth model, but didn't have any idea where to start or what settings to choose? Well, one of our users decided to put together this epically comprehensive guide by running a ton of tests on RunPod! I'm hoping it will give you a good starting point, and maybe solve some of the common problems when getting started with dreambooth training. Enjoy!
df19127ead