
# DreamBooth model for the groot concept traine on the LinoyTsaban/dreambooth-hackathon-images-groot dataset.

This is a Stable Diffusion model fine-tuned on the groot concept with DreamBooth. It can be used by modifying the `instance_prompt`: groot figurine

This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!

params used:
scheduler- DDIMScheduler
resolution=512
learning_rate=2e-06
max_train_steps= 500
train_batch_size=1
gradient_accumulation_steps=2
max_grad_norm=1.0
gradient_checkpointing=True
use_8bit_adam=True
seed=14071995,
sample_batch_size=2


## Usage

```python
from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained('LinoyTsaban/dreambooth-groot')
name_of_concept = "groot"  
type_of_thing = "figurine"  

prompt = f"a photo of groot figurine in the Louvre museum" 

# Tune the guidance to control how closely the generations follow the prompt.
# Values between 7-11 usually work best
guidance_scale = 8 # 
image = pipeline(prompt, guidance_scale=guidance_scale).images[0]
image
```

{"tags": ["pytorch", "diffusers", "stable-diffusion", "text-to-image", "diffusion-models-class", "dreambooth-hackathon"]}
diffusers pytorch stable-diffusion text-to-image diffusion-models-class dreambooth-hackathon endpoints_compatible diffusers:StableDiffusionPipeline region:us
TAGS
#diffusers #pytorch #stable-diffusion #text-to-image #diffusion-models-class #dreambooth-hackathon #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us 


# DreamBooth model for the groot concept traine on the LinoyTsaban/dreambooth-hackathon-images-groot dataset.

This is a Stable Diffusion model fine-tuned on the groot concept with DreamBooth. It can be used by modifying the 'instance_prompt': groot figurine

This model was created as part of the DreamBooth Hackathon . Visit the organisation page for instructions on how to take part!

params used:
scheduler- DDIMScheduler
resolution=512
learning_rate=2e-06
max_train_steps= 500
train_batch_size=1
gradient_accumulation_steps=2
max_grad_norm=1.0
gradient_checkpointing=True
use_8bit_adam=True
seed=14071995,
sample_batch_size=2


## Usage



# DreamBooth model for the groot concept traine on the LinoyTsaban/dreambooth-hackathon-images-groot dataset.

This is a Stable Diffusion model fine-tuned on the groot concept with DreamBooth. It can be used by modifying the 'instance_prompt': groot figurine

This model was created as part of the DreamBooth Hackathon . Visit the organisation page for instructions on how to take part!

params used:
scheduler- DDIMScheduler
resolution=512
learning_rate=2e-06
max_train_steps= 500
train_batch_size=1
gradient_accumulation_steps=2
max_grad_norm=1.0
gradient_checkpointing=True
use_8bit_adam=True
seed=14071995,
sample_batch_size=2 ## Usage
TAGS
#diffusers #pytorch #stable-diffusion #text-to-image #diffusion-models-class #dreambooth-hackathon #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us 
 # DreamBooth model for the groot concept traine on the LinoyTsaban/dreambooth-hackathon-images-groot dataset.

This is a Stable Diffusion model fine-tuned on the groot concept with DreamBooth. It can be used by modifying the 'instance_prompt': groot figurine

This model was created as part of the DreamBooth Hackathon . Visit the organisation page for instructions on how to take part!

params used:
scheduler- DDIMScheduler
resolution=512
learning_rate=2e-06
max_train_steps= 500
train_batch_size=1
gradient_accumulation_steps=2
max_grad_norm=1.0
gradient_checkpointing=True
use_8bit_adam=True
seed=14071995,
sample_batch_size=2 ## Usage
TAGS
#diffusers #pytorch #stable-diffusion #text-to-image #diffusion-models-class #dreambooth-hackathon #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us 
# DreamBooth model for the groot concept traine on the LinoyTsaban/dreambooth-hackathon-images-groot dataset.

This is a Stable Diffusion model fine-tuned on the groot concept with DreamBooth. It can be used by modifying the 'instance_prompt': groot figurine

This model was created as part of the DreamBooth Hackathon . Visit the organisation page for instructions on how to take part!

params used:
scheduler- DDIMScheduler
resolution=512
learning_rate=2e-06
max_train_steps= 500
train_batch_size=1
gradient_accumulation_steps=2
max_grad_norm=1.0
gradient_checkpointing=True
use_8bit_adam=True
seed=14071995,
sample_batch_size=2## Usage