Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use Aedancodes/jj_text_encoder_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aedancodes/jj_text_encoder_trained with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Aedancodes/jj_text_encoder_trained", dtype=torch.bfloat16, device_map="cuda") prompt = "in the style of james jean" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from Aedancodes/jj_text_encoder_trained: direct link, hf CLI and curl.
- Browser
- Download file 1.97 GB
-
https://huggingface.co/Aedancodes/jj_text_encoder_trained/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://Aedancodes/jj_text_encoder_trained/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Aedancodes/jj_text_encoder_trained/resolve/main/checkpoint-500/optimizer.bin
1.97 GB
- Xet hash:
- 99d1c6df19481f88bc9a5070f53226641f252db24192a45b1a9873f81cad7469
- Size of remote file:
- 1.97 GB
- SHA256:
- 9b6f891b7f4a009a09e32d2852a4a746736784b4287900cfda1069e47612c615
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