Instructions to use poctexttoimage/checking-model-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use poctexttoimage/checking-model-1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("poctexttoimage/checking-model-1", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder_2/model.onnx_data from poctexttoimage/checking-model-1: direct link, hf CLI and curl.
- Browser
- Download file 2.78 GB
-
https://huggingface.co/poctexttoimage/checking-model-1/resolve/main/text_encoder_2/model.onnx_data
- Command line
-
hf download hf://poctexttoimage/checking-model-1/text_encoder_2/model.onnx_data
-
curl -L -o model.onnx_data https://huggingface.co/poctexttoimage/checking-model-1/resolve/main/text_encoder_2/model.onnx_data
2.78 GB
- Xet hash:
- 8372d427a34c4769da7c029247d5d79eaf162562debe3484f76f44b9d20424f5
- Size of remote file:
- 2.78 GB
- SHA256:
- 3da7ac65349fbd092e836e3eeca2c22811317bc804fd70af157b4550f2d4bcb5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.