Diffusers documentation
DreamBooth
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Inference
Using diffusion pipelines
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You are viewing main version, which requires installation from source. If you'd like
regular pip install, checkout the latest stable version (v0.40.0).
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.
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