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-600/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-600/optimizer.bin
- Command line
-
hf download hf://Aedancodes/jj_text_encoder_trainedv2/checkpoint-600/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Aedancodes/jj_text_encoder_trainedv2/resolve/main/checkpoint-600/optimizer.bin
1.97 GB
- Xet hash:
- 9493bc89c6dda613f88555e55b2d478ea29197919a9106eeecaa81cecdf74455
- Size of remote file:
- 1.97 GB
- SHA256:
- 6cd7d6f539e8aba86d9dd259e346664409d7ad2b3a89aa61b6091d200874e46d
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