import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("dixisouls/anime-diffusion", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Anime-Diffusion UNet
A UNet2DConditionModel fine-tuned for anime-style image generation, based on Stable Diffusion v1.4.
Model Details
- Architecture: UNet2DConditionModel from CompVis/stable-diffusion-v1-4
- EMA Decay: 0.9995
- Output Resolution: 512×512
- Prediction Type: epsilon
Companion Models (required for inference)
| Component | Model ID |
|---|---|
| VAE | stabilityai/sd-vae-ft-mse |
| Text Encoder | openai/clip-vit-large-patch14 |
| Tokenizer | openai/clip-vit-large-patch14 |
Training Details
- Dataset: none-yet/anime-captions (~337k image-caption pairs)
- Steps: 10,000
- Batch Size: 128 (32 per GPU × 4 GPUs)
- Learning Rate: 1e-4 with cosine schedule (500 warmup steps)
- Optimizer: AdamW (weight decay 0.01)
- Mixed Precision: fp16
- Noise Schedule: DDPM, 1000 linear timesteps
- Gradient Clipping: 1.0
Usage
import torch
from diffusers import AutoencoderKL, DDIMScheduler, UNet2DConditionModel
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import CLIPTextModel, CLIPTokenizer
# Load models
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet")
# Load fine-tuned EMA weights
weights_path = hf_hub_download(repo_id="dixisouls/anime-diffusion", filename="model.safetensors")
unet.load_state_dict(load_file(weights_path))
# Use DDIMScheduler for inference
scheduler = DDIMScheduler(
num_train_timesteps=1000,
beta_schedule="linear",
clip_sample=False,
prediction_type="epsilon",
)
See the companion HuggingFace Space for a full interactive demo.
Limitations
- Trained exclusively on anime-style images; not suitable for photorealistic generation
- Fixed output resolution of 512×512
- Single-subject prompts work best; complex multi-character scenes may be inconsistent
- Downloads last month
- 10
Model tree for dixisouls/anime-diffusion
Base model
CompVis/stable-diffusion-v1-4