Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use Aedancodes/jj_text_encoder_trainedv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aedancodes/jj_text_encoder_trainedv3 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_trainedv3", 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-600/optimizer.bin from Aedancodes/jj_text_encoder_trainedv3: direct link, hf CLI and curl.
- Browser
- Download file 1.97 GB
-
https://huggingface.co/Aedancodes/jj_text_encoder_trainedv3/resolve/main/checkpoint-600/optimizer.bin
- Command line
-
hf download hf://Aedancodes/jj_text_encoder_trainedv3/checkpoint-600/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Aedancodes/jj_text_encoder_trainedv3/resolve/main/checkpoint-600/optimizer.bin
1.97 GB
- Xet hash:
- 3aba720554eb1a0901b56c08cdd581b354c05c4c602e449b9dbc1d83446285cf
- Size of remote file:
- 1.97 GB
- SHA256:
- 6e22912c06cac004d1d17ee8a4ceb0bfa0d5c46997cca003d44e5493be253e76
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