Text-to-Image
Diffusers
lora
diffusers-training
stable-diffusion
stable-diffusion-diffusers
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("kfkas/test_lora_dreambooth_v2")
prompt = "a photo of Korea sks Sungnyemun Gate"
image = pipe(prompt).images[0]LoRA DreamBooth - kfkas/test_lora_dreambooth_v2
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of Korea sks Sungnyemun Gate using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for kfkas/test_lora_dreambooth_v2
Base model
CompVis/stable-diffusion-v1-4