Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use Aedancodes/jj_text_encoder_trainedv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aedancodes/jj_text_encoder_trainedv2 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_trainedv2", dtype=torch.bfloat16, device_map="cuda") prompt = "An image in the style of zhr james jean" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-1000/optimizer.bin from Aedancodes/jj_text_encoder_trainedv2: direct link, hf CLI and curl.
- Browser
- Download file 1.97 GB
-
https://huggingface.co/Aedancodes/jj_text_encoder_trainedv2/resolve/main/checkpoint-1000/optimizer.bin
- Command line
-
hf download hf://Aedancodes/jj_text_encoder_trainedv2/checkpoint-1000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Aedancodes/jj_text_encoder_trainedv2/resolve/main/checkpoint-1000/optimizer.bin
1.97 GB
- Xet hash:
- 90da4527110334d60f8b7d4a1d2c57a7c12d5584aef800a964dc2f0bc4b22ce7
- Size of remote file:
- 1.97 GB
- SHA256:
- 06120dbda83cd1d19c502f8bd13cf593014b446f5ec3ce69a8140f8ef27cebb6
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