Instructions to use tancilon/pretrained_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tancilon/pretrained_vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tancilon/pretrained_vae", 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
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("tancilon/pretrained_vae", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重,可浏览“模型文件”页面获取。
您可以通过如下git clone命令,或者ModelScope SDK来下载模型
SDK下载
#安装ModelScope
pip install modelscope
#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('zzyzzzz699/recon_pretrained_vae')
Git下载
#Git模型下载
git clone https://www.modelscope.cn/zzyzzzz699/recon_pretrained_vae.git
如果您是本模型的贡献者,我们邀请您根据模型贡献文档,及时完善模型卡片内容。
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