Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
diffusers-training
Instructions to use mnpham/van_gogh_inversion_v1-4_20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mnpham/van_gogh_inversion_v1-4_20 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_textual_inversion("mnpham/van_gogh_inversion_v1-4_20") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Textual inversion text2image fine-tuning - mnpham/van_gogh_inversion_v1-4_20
These are textual inversion adaption weights for CompVis/stable-diffusion-v1-4. 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]
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Model tree for mnpham/van_gogh_inversion_v1-4_20
Base model
CompVis/stable-diffusion-v1-4