Text-to-Image
Diffusers
TensorBoard
Safetensors
stable-diffusion
stable-diffusion-diffusers
custom-diffusion
diffusers-training
Instructions to use Sudanl/stable-diffusion-2-1-base-custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Sudanl/stable-diffusion-2-1-base-custom with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Sudanl/stable-diffusion-2-1-base-custom", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Custom Diffusion - Sudanl/stable-diffusion-2-1-base-custom
These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on None using Custom Diffusion. You can find some example images in the following.
For more details on the training, please follow this link.
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 Sudanl/stable-diffusion-2-1-base-custom
Base model
stabilityai/stable-diffusion-2-1-base