Buckets:
DreamBooth
DreamBooth personalizes a pretrained model to a specific subject from a few images (for example, your cat) by fine-tuning the full weights and binding that subject to a unique identifier in the prompt (sks cat). You can then generate the subject in new settings, lighting, poses, and styles.
DreamBooth checkpoints are typically a few GBs because they contain the full model weights. Load them with from_pretrained() and include the unique identifier in the prompt to trigger generation.
import torch
from diffusers import AutoPipelineForText2Image
pipeline = AutoPipelineForText2Image.from_pretrained(
"sd-dreambooth-library/herge-style",
dtype=torch.float16
).to("cuda") # or "mps", "xpu", "cpu"
prompt = "A cute sks herge_style brown bear eating a slice of pizza, stunning color scheme, masterpiece, illustration"
pipeline(prompt).images[0]
To train your own checkpoint, see Train DreamBooth.
Xet Storage Details
- Size:
- 1.11 kB
- Xet hash:
- 9e94b02441b4413b420d5b632f5f73fdc2aad26007c2125b03a5718c3ec5556b
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.