Instructions to use Silvelter/Sathariel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Silvelter/Sathariel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Silvelter/Sathariel", dtype=torch.bfloat16, device_map="cuda") prompt = "masterpiece, best quality, 1girl, fox ears, looking at viewer" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Silvelter/Sathariel", dtype=torch.bfloat16, device_map="cuda")
prompt = "masterpiece, best quality, 1girl, fox ears, looking at viewer"
image = pipe(prompt).images[0]Sathariel
Sathariel is a latent diffusion model tailored for anime enthusiasts. Similar to other anime-styled Stable Diffusion models, it supports danbooru tags to generate images.
To develop this model, I conducted two finetunes based on the merge of MidnightMixerV2 and MidnightMapleV1, then blended these finetunes together.
At present, the repository contains several versions of the model. These include SatharielV1 as well as alternative versions like SatharielV1A, SatharielV1B, or SatharielV1C. The differences among them are minimal, hence they were released as alternatives.
SatharielV1C_DX is a merge of SatharielV1C with a model by DutchaAlex, offering enhanced color rendering.
Although originally intended to emulate the style of artist Mito Nagishiro, the attempt fell short. For a better outcome, I recommend using NAIV3 instead, accompanied by the "by nagishiro mito" tag.
The "other" directory houses older mixes, models, experiments, etc.
Big Thanks to
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