Instructions to use vftfnn/sd35-medium-t5-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vftfnn/sd35-medium-t5-nf4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vftfnn/sd35-medium-t5-nf4") model = AutoModel.from_pretrained("vftfnn/sd35-medium-t5-nf4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
T5 v1.1 XXL encoder (the text_encoder_3 of Stable Diffusion 3.5 Medium) quantized to bitsandbytes NF4 (fp16 compute, wo kept in fp32). Load with T5EncoderModel.from_pretrained(repo, device_map={'': 'cuda:0'}) and pass it as text_encoder_3 to StableDiffusion3Pipeline. Weights are Google's T5 v1.1 XXL (Apache-2.0).
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 馃檵 Ask for provider support
Model tree for vftfnn/sd35-medium-t5-nf4
Base model
google/t5-v1_1-xxl