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
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- checkpoint-1000/optimizer.bin
- checkpoint-1000/random_states_0.pkl
- checkpoint-1000/scaler.pt
- checkpoint-1000/scheduler.bin
- checkpoint-1000/text_encoder/pytorch_model.bin
- checkpoint-1000/unet/diffusion_pytorch_model.safetensors
- checkpoint-1500/optimizer.bin
- checkpoint-1500/random_states_0.pkl
- checkpoint-1500/scaler.pt
- checkpoint-1500/text_encoder/pytorch_model.bin
- checkpoint-1500/unet/diffusion_pytorch_model.safetensors
- checkpoint-2000/optimizer.bin
- checkpoint-2000/random_states_0.pkl
- checkpoint-2000/scaler.pt
- checkpoint-2000/text_encoder/pytorch_model.bin
- checkpoint-2000/unet/diffusion_pytorch_model.safetensors
- checkpoint-500/optimizer.bin
- checkpoint-500/random_states_0.pkl
- checkpoint-500/scaler.pt
- checkpoint-500/scheduler.bin
- checkpoint-500/text_encoder/pytorch_model.bin
- checkpoint-500/unet/diffusion_pytorch_model.safetensors
- text_encoder/model.safetensors
- unet/diffusion_pytorch_model.safetensors