Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
Instructions to use PricedAsh/tomato-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PricedAsh/tomato-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("PricedAsh/tomato-model", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of a 00_21 tomato plant" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/text_encoder/pytorch_model.bin from PricedAsh/tomato-model: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/PricedAsh/tomato-model/resolve/main/checkpoint-2000/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://PricedAsh/tomato-model/checkpoint-2000/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PricedAsh/tomato-model/resolve/main/checkpoint-2000/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- 36f4ec759e4c6710e149e17561bff33818996579ae82673ab5077631b477223c
- Size of remote file:
- 1.36 GB
- SHA256:
- 238dfe8acf4204706556c2623e02f6e8e24f09068b091c41dca6e5bda86974c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.