Instructions to use Evel/YoShin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Evel/YoShin with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Evel/YoShin", 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
Commit History
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Correct `sample_size` of Stable Diffusion 1's unet to have correct width and height default
Since `diffusers==0.9.0` the width and height is automatically inferred from the `sample_size` attribute of your unet's config. It seems like your diffusion model has the same architecture as Stable Diffusion 1 which means that when using this model, by default an image size of 512x512 should be generated. This in turn means the unet's sample size should be **64**. In order to suppress to update your configuration on the fly and to suppress the deprecation warning added in this PR: https://github.com/huggingface/diffusers/pull/1406/files#r1035703505 it is strongly recommended to merge this PR.c5b1384
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Add Diffusers weights (#2)
- Add Diffusers weights (86438a89aaf7db5836a8ba7d3e2d9d6b51421c08) Co-authored-by: Ahsen Khaliq <akhaliq@users.noreply.huggingface.co>102cbe9
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enable inference widget (#1)
- enable inference widget (0b64d29c3f97513b69350da84b724e224de13721) Co-authored-by: Ahsen Khaliq <akhaliq@users.noreply.huggingface.co>dc55b19 -
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add stable diffusion weights
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In my experience, it works better in img2img, txt2img gives snakes most of the time. This was made with dreambooth extension for Automatic 111 using 47 images and 4000 steps. License: This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: You can't use the model to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license herefa90d55 -
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