Text-to-Image
Diffusers
TensorBoard
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
dora
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("basakozsoy/maya_LoRA")
prompt = "a photo of TOK cat"
image = pipe(prompt).images[0]SDXL LoRA DreamBooth - basakozsoy/maya_LoRA
Model description
These are basakozsoy/maya_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Trigger words
You should use a photo of TOK cat to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Intended uses & limitations
How to use
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
repo_id = 'basakozsoy/maya_LoRA'
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae,
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
pipe.load_lora_weights(repo_id)
_ = pipe.to("cuda")
pipe.load_lora_weights(repo_id)
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 basakozsoy/maya_LoRA
Base model
stabilityai/stable-diffusion-xl-base-1.0