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---
license: other
datasets:
- Mitsua/vroid-image-dataset-lite
pipeline_tag: text-to-image
---
# Model Card for VRoid Diffusion
<!-- Provide a quick summary of what the model is/does. -->
This is a latent text-to-image diffusion model to demonstrate how U-Net training affects the generated images.
- Text Encoder is from [OpenCLIP ViT-H/14](https://github.com/mlfoundations/open_clip), MIT License, Training Data : LAION-2B
- VAE is from [Mitsua Diffusion One](https://huggingface.co/Mitsua/mitsua-diffusion-one), Mitsua Open RAIL-M License, Training Data: Public Domain/CC0 + Licensed
- 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.
- VRoid is a trademark or registered trademark of Pixiv inc. in Japan and other regions.
## Model Details
- `vroid_diffusion_test.safetensors`
- base variant.
- `vroid_diffusion_test_invert_red_blue.safetensors`
- `red` and `blue` in the caption is swapped.
- `pink` and `skyblue` in the caption is swapped.
- `vroid_diffusion_test_monochrome.safetensors`
- all training images are converted to grayscale.
## Model Variant
- [VRoid Diffusion Unconditional](https://huggingface.co/Mitsua/vroid-diffusion-test-unconditional)
- This is unconditional image generator without CLIP.
### Model Description
- **Developed by:** Abstract Engine.
- **License:** Mitsua Open RAIL-M License.
## Uses
### Direct Use
Text-to-Image generation for research and educational purposes.
### Out-of-Scope Use
Any deployed use case of the model.
## Training Details
- Trained resolution : 256x256
- Batch Size : 48
- Steps : 45k
- LR : 1e-5 with warmup 1000 steps
### Training Data
We use full version of [VRoid Image Dataset Lite](https://huggingface.co/datasets/Mitsua/vroid-image-dataset-lite) with some modifications.
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