Text-to-Image
Diffusers
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use hbiny/text2img with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hbiny/text2img with Diffusers:
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("hbiny/text2img") prompt = "d0nar1dk, salute" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: d0nar1dk, blue cloth, red ribbon, yellow feet
widget:
- text: d0nar1dk, salute
output:
url: image_0.png
- text: d0nar1dk, salute
output:
url: image_1.png
- text: d0nar1dk, salute
output:
url: image_2.png
- text: d0nar1dk, salute
output:
url: image_3.png
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
SDXL LoRA DreamBooth - hbiny/text2img

- Prompt
- d0nar1dk, salute

- Prompt
- d0nar1dk, salute

- Prompt
- d0nar1dk, salute

- Prompt
- d0nar1dk, salute
Model description
These are hbiny/text2img 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 d0nar1dk, blue cloth, red ribbon, yellow feet 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]