Text-to-Image
Diffusers
TensorBoard
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
pip install -U diffusers transformers accelerate
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("ayush7/kalighat-lora-4")
prompt = "in the style of TOK"
image = pipe(prompt).images[0]SDXL LoRA DreamBooth - kalighat-lora-4
Model description
These are kalighat-lora-4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
Download model
Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- LoRA: download
kalighat-lora-4.safetensorshere 💾.- Place it on your
models/Lorafolder. - On AUTOMATIC1111, load the LoRA by adding
<lora:kalighat-lora-4:1>to your prompt. On ComfyUI just load it as a regular LoRA.
- Place it on your
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('kalighat-lora-4', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('in the style of TOK').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Trigger words
You should use in the style of TOK to trigger the image generation.
Details
All Files & versions.
The weights were trained using 🧨 diffusers Advanced Dreambooth Training Script.
LoRA for the text encoder was enabled. True.
Pivotal tuning was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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Base model
stabilityai/stable-diffusion-xl-base-1.0
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