Text-to-Image
Diffusers
TensorBoard
diffusers-training
dora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use YeBhoneLin10/bagan_lora1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use YeBhoneLin10/bagan_lora1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("YeBhoneLin10/bagan_lora1", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of bagan" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("YeBhoneLin10/bagan_lora1", dtype=torch.bfloat16, device_map="cuda")
prompt = "a photo of bagan"
image = pipe(prompt).images[0]SDXL LoRA DreamBooth - YeBhoneLin10/bagan_lora1
Model description
These are YeBhoneLin10/bagan_lora1 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Trigger words
You should use a photo of bagan 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
# 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 YeBhoneLin10/bagan_lora1
Base model
stabilityai/stable-diffusion-xl-base-1.0