Image-to-Image
Diffusers
Safetensors
Sana
English
vae
autoencoder
image
stable-diffusion
sdxl
flux
qwen
Instructions to use BiliSakura/VAEs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/VAEs with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/VAEs", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Sana
How to use BiliSakura/VAEs with Sana:
# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://BiliSakura/VAEs") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, ) - Notebooks
- Google Colab
- Kaggle
Update all files for VAEs
Browse files- SD35-VAE/config.json +38 -0
SD35-VAE/config.json
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{
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"_class_name": "AutoencoderKL",
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"_diffusers_version": "0.31.0.dev0",
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"_name_or_path": "../sdxl-vae/",
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"act_fn": "silu",
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"block_out_channels": [
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128,
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256,
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512,
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512
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],
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"down_block_types": [
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D"
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],
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"force_upcast": true,
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"in_channels": 3,
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"latent_channels": 16,
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"latents_mean": null,
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"latents_std": null,
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"layers_per_block": 2,
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"mid_block_add_attention": true,
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"norm_num_groups": 32,
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"out_channels": 3,
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"sample_size": 1024,
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"scaling_factor": 1.5305,
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"shift_factor": 0.0609,
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"up_block_types": [
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"UpDecoderBlock2D"
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],
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"use_post_quant_conv": false,
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"use_quant_conv": false
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}
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