Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
Instructions to use DexHeim/textual_inversion_face with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DexHeim/textual_inversion_face with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_textual_inversion("DexHeim/textual_inversion_face") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from DexHeim/textual_inversion_face: direct link, hf CLI and curl.
- Browser
- Download file 940 Bytes
-
https://huggingface.co/DexHeim/textual_inversion_face/resolve/main/README.md
- Command line
-
hf download hf://DexHeim/textual_inversion_face/README.md
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curl -L -o README.md https://huggingface.co/DexHeim/textual_inversion_face/resolve/main/README.md
940 Bytes
metadata
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
base_model: runwayml/stable-diffusion-v1-5
Textual inversion text2image fine-tuning - SammyTime/textual_inversion_face
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
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]