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
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
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