---
license: openrail++
language:
- en
thumbnail: "https://huggingface.co/Linaqruf/hermitage-xl/resolve/main/sample_images/thumbnail.png"
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
inference: true
widget:
- text: >-
masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn,
cumulonimbus clouds, lighting, blue sky, falling leaves, garden
example_title: example 1girl
- text: >-
masterpiece, best quality, 1boy, medium hair, blonde hair, blue eyes,
bishounen, colorful, autumn, cumulonimbus clouds, lighting, blue sky,
falling leaves, garden
example_title: example 1boy
library_name: diffusers
---
Hermitage XL
## Overview
Hermitage XL is a high-resolution, latent text-to-image diffusion model. The model has been fine-tuned using a learning rate of 4e-7 over 5000 steps with a batch size of 16 on a curated dataset of superior-quality anime-style images. This model is derived from Stable Diffusion XL 1.0.
e.g. **_1girl, white hair, golden eyes, beautiful eyes, detail, flower meadow, cumulonimbus clouds, lighting, detailed sky, garden_**
- Use it with the [`Stable Diffusion Webui`](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
- Use it with 🧨 [`diffusers`](https://huggingface.co/docs/diffusers/index)
- Use it with the [`ComfyUI`](https://github.com/comfyanonymous/ComfyUI)
## Features
1. High-Resolution Images: The model trained with 1024x1024 resolution. The model is trained using [NovelAI Aspect Ratio Bucketing Tool](https://github.com/NovelAI/novelai-aspect-ratio-bucketing) so that it can be trained at non-square resolutions.
2. Anime-styled Generation: Based on given text prompts, the model can create high quality anime-styled images.
3. Fine-Tuned Diffusion Process: The model utilizes a fine-tuned diffusion process to ensure high quality and unique image output.
## Model Details
- **Developed by:** [Linaqruf](https://github.com/Linaqruf)
- **Model type:** Diffusion-based text-to-image generative model
- **Model Description:** This is a model that can be used to generate and modify anime-themed images based on text prompts.
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **Finetuned from model:** [Stable Diffusion XL 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
## How to Use:
- Download `Hermitage XL` [here](https://huggingface.co/Linaqruf/hermitage-xl/resolve/main/hermitage-xl.safetensors), the model is in `.safetensors` format.
- You need to use Danbooru-style tag as prompt instead of natural language, otherwise you will get realistic result instead of anime
- You can use any generic negative prompt or use the following suggested negative prompt to guide the model towards high aesthetic generationse:
```
lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
```
- And, the following should also be prepended to prompts to get high aesthetic results:
```
masterpiece, best quality, illustration, beautiful detailed, finely detailed, dramatic light, intricate details
```
## 🧨 Diffusers
Make sure to upgrade diffusers to >= 0.18.2:
```
pip install diffusers --upgrade
```
In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark:
```
pip install invisible_watermark transformers accelerate safetensors
```
Running the pipeline (if you don't swap the scheduler it will run with the default **EulerDiscreteScheduler** in this example we are swapping it to **EulerAncestralDiscreteScheduler**:
```py
import torch
from torch import autocast
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
model = "Linaqruf/hermitage-xl"
vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
pipe = StableDiffusionXLPipeline.from_pretrained(
model,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
vae=vae
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')
prompt = "masterpiece, best quality, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck"
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
image = pipe(
prompt,
negative_prompt=negative_prompt,
width=1024,
height=1024,
guidance_scale=12,
target_size=(1024,1024),
original_size=(4096,4096),
num_inference_steps=50
).images[0]
image.save("anime_girl.png")
```
## Limitation
1. This model inherit Stable Diffusion XL 1.0 [limitation](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0#limitations)
2. This model is overfitted and cannot follow prompts well, because it's fine-tuned for 5000 steps with small scale datasets.
3. It's only a preview model to find good hyperparameter and training config for Stable Diffusion XL 1.0
## Example
Here is some cherrypicked samples and comparison between available models: