Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
diffusers-training
Instructions to use JJSLL/Cat_object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JJSLL/Cat_object 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("JJSLL/Cat_object") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| base_model: runwayml/stable-diffusion-v1-5 | |
| library_name: diffusers | |
| license: creativeml-openrail-m | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - textual_inversion | |
| - diffusers-training | |
| inference: true | |
| <!-- This model card has been generated automatically according to the information the training script had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Textual inversion text2image fine-tuning - JJSLL/Cat_object | |
| 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 | |
| ```python | |
| # 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] |