Instructions to use Raelina/Raehoshi-illust-XL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Raelina/Raehoshi-illust-XL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Raelina/Raehoshi-illust-XL", 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
Overview
Introducing Raehoshi illust XL , an enhanced iteration built upon the Illustrious XL v0.1 model. It aims to elevate the visual style by addressing some of the limitations in the original, such as oversaturation and artifact noise. While these issues are not entirely eliminated, noticeable improvements have been made, and further refinements will continue. The goal is to deliver a more polished, balanced output while staying true to the strengths of the base model.
Model Details
- Developed by: Raelina
- Model type: Diffusion-based text-to-image generative model
- Model prompt style: Booru-tags
- License: Fair AI Public License 1.0-SD
- Finetuned from: Illustrious XL v0.1
Recommended settings
- Positive prompts:
masterpiece, best quality, good quality,
- Negative prompts:
lowres, (bad quality, worst quality:1.2), bad anatomy, sketch, jpeg artifacts, ugly, poorly drawn, signature, watermark,
- CFG: 7
- Sampling steps: 28
- Sampler: Euler a
- Supported Resolution:
1024 x 1024, 1152 x 896, 896 x 1152, 1216 x 832, 832 x 1216, 1344 x 768, 768 x 1344, 1536 x 640, 640 x 1536
Hires.fix Setting
- Upscaler: 4x_NMKD-YandereNeoXL
- Hires step: 10-15
- Denoising: 0.1-0.3 or 0.55 for latent upscaler
Training config
The model was developed using a two-stage fine-tuning process. In Stage 1, new series and characters were introduced into the model. Stage 2 focused on fixing issues and enhancing the overall style for improved output.
Stage 1
- Dataset : 31k
- Hardware : 2x A100 80gb
- Batch size : 32
- Gradient accumulation steps : 2
- Learning rate : 6e-6
- Text encoder : 3e-6
- Epoch : 15
Stage 2
- Dataset : 2.5k
- Hardware : 1x A100 80gb
- Batch size : 48
- Gradient accumulation steps : 1
- Learning rate : 3e-6
- Text encoder : disable
- Epoch : 15
License
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Model tree for Raelina/Raehoshi-illust-XL
Base model
KBlueLeaf/kohaku-xl-beta5