# SVS Dreambooth Checkpoint

This is a dreambooth checkpoint for the SVS project. I used [ReV Animated v1.2.2](https://civitai.com/models/7371/rev-animated) as my base model.

> Sample pictures of this concept (along with prompts) are present in `SAMPLES.md`

> Note: I haven't yet upload the diffuers compatible weights. The provided safetensors weights are directly compatible with the AUTOMATIC1111/stable-diffusion-webui

## Model Overview:

- Trigger: `<svs-person>`
- Base Model: [ReV Animated v1.2.2](https://civitai.com/models/7371/rev-animated)
- VAE: [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse)
- This model likes: ((best quality)), ((masterpiece)), (detailed) in beginning of prompt if you want anime-2.5D type
- Model works best on these resolutions:
  - 512x512
  - 768x512
  - 768x512

## Training

For training, I used a modified version of the hugginface diffuers [dreambooth training script](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth). Training script is present in 'train_dreambooth.py'.
For launching the training process I used the following command:

```bash
export INSTANCE_DIR="svs_20_no_full_body/"
export CLASS_DIR="regularization/REGULARIZATION-IMAGES-revAnimated/person/"
export OUTPUT_DIR="models/$(date +%Y%m%d-%H%M%S)"

accelerate launch train_dreambooth.py \
    --pretrained_model_name_or_path="models/hub/revAnimated_v122" \
    --pretrained_vae_name_or_path="stabilityai/sd-vae-ft-mse" \
    --instance_data_dir=$INSTANCE_DIR \
    --class_data_dir=$CLASS_DIR \
    --output_dir=$OUTPUT_DIR \
    --with_prior_preservation --prior_loss_weight=1.0 \
    --seed=145391 \
    --instance_prompt="a photo of <svs-person> person" \
    --class_prompt="a photo of a person" \
    --resolution=512 \
    --train_batch_size=1 \
    --train_text_encoder \
    --mixed_precision="fp16" \
    --use_8bit_adam \
    --enable_xformers_memory_efficient_attention \
    --gradient_accumulation_steps=1 \
    --learning_rate=1e-6 \
    --lr_scheduler="constant" \
    --lr_warmup_steps=0 \
    --num_class_images=300 \
    --max_train_steps=300 \
    --checkpoints_total_limit=1 \
    --checkpointing_steps=100000 \
    --validation_steps 25 \
    --report_to wandb \
    --validation_prompt "((best quality)), ((masterpiece)), (detailed), <svs-person> person with red hair, doing a bicep curl in the gym"
```

## Note about Dataset

I starting by training on the full dataset and the default parameters of the original dreambooth training script but I found that the models overfitted very easily to the same pose fully body images. So I decided to train on a 20 image subset of the original dataset. These dataset mostly contained the close up face images and the upper body images of svs. I did not use any of the whole body images, the models tend to overfit on those very easily. I tried to include closeups and half body shots of svs. I did not use full body shots as they loose too much resolution and SD can actually make pretty good full body poses without them.

## Training logs

Some of the training runs were logged into wandb and can be found here: [logs](https://wandb.ai/ayushman/dreambooth)

{}
diffusers endpoints_compatible diffusers:StableDiffusionPipeline region:us
TAGS
#diffusers #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us 

# SVS Dreambooth Checkpoint

This is a dreambooth checkpoint for the SVS project. I used ReV Animated v1.2.2 as my base model.

> Sample pictures of this concept (along with prompts) are present in 'URL'

> Note: I haven't yet upload the diffuers compatible weights. The provided safetensors weights are directly compatible with the AUTOMATIC1111/stable-diffusion-webui

## Model Overview:

- Trigger: '<svs-person>'
- Base Model: ReV Animated v1.2.2
- VAE: stabilityai/sd-vae-ft-mse
- This model likes: ((best quality)), ((masterpiece)), (detailed) in beginning of prompt if you want anime-2.5D type
- Model works best on these resolutions:
  - 512x512
  - 768x512
  - 768x512

## Training

For training, I used a modified version of the hugginface diffuers dreambooth training script. Training script is present in 'train_dreambooth.py'.
For launching the training process I used the following command:



## Note about Dataset

I starting by training on the full dataset and the default parameters of the original dreambooth training script but I found that the models overfitted very easily to the same pose fully body images. So I decided to train on a 20 image subset of the original dataset. These dataset mostly contained the close up face images and the upper body images of svs. I did not use any of the whole body images, the models tend to overfit on those very easily. I tried to include closeups and half body shots of svs. I did not use full body shots as they loose too much resolution and SD can actually make pretty good full body poses without them.

## Training logs

Some of the training runs were logged into wandb and can be found here: logs

# SVS Dreambooth Checkpoint

This is a dreambooth checkpoint for the SVS project. I used ReV Animated v1.2.2 as my base model.

> Sample pictures of this concept (along with prompts) are present in 'URL'

> Note: I haven't yet upload the diffuers compatible weights. The provided safetensors weights are directly compatible with the AUTOMATIC1111/stable-diffusion-webui ## Model Overview:

- Trigger: '<svs-person>'
- Base Model: ReV Animated v1.2.2
- VAE: stabilityai/sd-vae-ft-mse
- This model likes: ((best quality)), ((masterpiece)), (detailed) in beginning of prompt if you want anime-2.5D type
- Model works best on these resolutions:
  - 512x512
  - 768x512
  - 768x512 ## Training

For training, I used a modified version of the hugginface diffuers dreambooth training script. Training script is present in 'train_dreambooth.py'.
For launching the training process I used the following command: ## Note about Dataset

I starting by training on the full dataset and the default parameters of the original dreambooth training script but I found that the models overfitted very easily to the same pose fully body images. So I decided to train on a 20 image subset of the original dataset. These dataset mostly contained the close up face images and the upper body images of svs. I did not use any of the whole body images, the models tend to overfit on those very easily. I tried to include closeups and half body shots of svs. I did not use full body shots as they loose too much resolution and SD can actually make pretty good full body poses without them. ## Training logs

Some of the training runs were logged into wandb and can be found here: logs
TAGS
#diffusers #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us 
 # SVS Dreambooth Checkpoint

This is a dreambooth checkpoint for the SVS project. I used ReV Animated v1.2.2 as my base model.

> Sample pictures of this concept (along with prompts) are present in 'URL'

> Note: I haven't yet upload the diffuers compatible weights. The provided safetensors weights are directly compatible with the AUTOMATIC1111/stable-diffusion-webui ## Model Overview:

- Trigger: '<svs-person>'
- Base Model: ReV Animated v1.2.2
- VAE: stabilityai/sd-vae-ft-mse
- This model likes: ((best quality)), ((masterpiece)), (detailed) in beginning of prompt if you want anime-2.5D type
- Model works best on these resolutions:
  - 512x512
  - 768x512
  - 768x512 ## Training

For training, I used a modified version of the hugginface diffuers dreambooth training script. Training script is present in 'train_dreambooth.py'.
For launching the training process I used the following command: ## Note about Dataset

I starting by training on the full dataset and the default parameters of the original dreambooth training script but I found that the models overfitted very easily to the same pose fully body images. So I decided to train on a 20 image subset of the original dataset. These dataset mostly contained the close up face images and the upper body images of svs. I did not use any of the whole body images, the models tend to overfit on those very easily. I tried to include closeups and half body shots of svs. I did not use full body shots as they loose too much resolution and SD can actually make pretty good full body poses without them. ## Training logs

Some of the training runs were logged into wandb and can be found here: logs
TAGS
#diffusers #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us 
# SVS Dreambooth Checkpoint

This is a dreambooth checkpoint for the SVS project. I used ReV Animated v1.2.2 as my base model.

> Sample pictures of this concept (along with prompts) are present in 'URL'

> Note: I haven't yet upload the diffuers compatible weights. The provided safetensors weights are directly compatible with the AUTOMATIC1111/stable-diffusion-webui## Model Overview:

- Trigger: '<svs-person>'
- Base Model: ReV Animated v1.2.2
- VAE: stabilityai/sd-vae-ft-mse
- This model likes: ((best quality)), ((masterpiece)), (detailed) in beginning of prompt if you want anime-2.5D type
- Model works best on these resolutions:
  - 512x512
  - 768x512
  - 768x512## Training

For training, I used a modified version of the hugginface diffuers dreambooth training script. Training script is present in 'train_dreambooth.py'.
For launching the training process I used the following command:## Note about Dataset

I starting by training on the full dataset and the default parameters of the original dreambooth training script but I found that the models overfitted very easily to the same pose fully body images. So I decided to train on a 20 image subset of the original dataset. These dataset mostly contained the close up face images and the upper body images of svs. I did not use any of the whole body images, the models tend to overfit on those very easily. I tried to include closeups and half body shots of svs. I did not use full body shots as they loose too much resolution and SD can actually make pretty good full body poses without them.## Training logs

Some of the training runs were logged into wandb and can be found here: logs