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 Settings
- Draw Things
- DiffusionBee
| 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. | |