Instructions to use codyreading/custom_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codyreading/custom_diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("codyreading/custom_diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "None" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("codyreading/custom_diffusion", dtype=torch.bfloat16, device_map="cuda")
prompt = "None"
image = pipe(prompt).images[0]Custom Diffusion - codyreading/custom_diffusion
These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. 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.
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Model tree for codyreading/custom_diffusion
Base model
CompVis/stable-diffusion-v1-4