Instructions to use Mitsua/vroid-diffusion-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Mitsua/vroid-diffusion-test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Mitsua/vroid-diffusion-test", 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
- Draw Things
- DiffusionBee
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This is a latent text-to-image diffusion model to demonstrate how U-Net training affects the generated images.
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Text Encoder is from [OpenCLIP ViT-H/14](https://github.com/mlfoundations/open_clip), MIT License, Training Data : LAION-2B
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VAE is from [Mitsua Diffusion One](https://huggingface.co/Mitsua/mitsua-diffusion-one), Mitsua Open RAIL-M License, Training Data: Public Domain/CC0 + Licensed
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U-Net is trained from scratch using full version of [VRoid Image Dataset Lite](https://huggingface.co/datasets/Mitsua/vroid-image-dataset-lite) with some modifications.
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- VRoid is a trademark or registered trademark of Pixiv inc. in Japan and other regions.
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## Model Details
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This is a latent text-to-image diffusion model to demonstrate how U-Net training affects the generated images.
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- Text Encoder is from [OpenCLIP ViT-H/14](https://github.com/mlfoundations/open_clip), MIT License, Training Data : LAION-2B
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- VAE is from [Mitsua Diffusion One](https://huggingface.co/Mitsua/mitsua-diffusion-one), Mitsua Open RAIL-M License, Training Data: Public Domain/CC0 + Licensed
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- U-Net is trained from scratch using full version of [VRoid Image Dataset Lite](https://huggingface.co/datasets/Mitsua/vroid-image-dataset-lite) with some modifications.
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- VRoid is a trademark or registered trademark of Pixiv inc. in Japan and other regions.
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## Model Details
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