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-400/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-400/optimizer.bin
- Command line
-
hf download hf://Aedancodes/jj_text_encoder_trainedv3/checkpoint-400/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Aedancodes/jj_text_encoder_trainedv3/resolve/main/checkpoint-400/optimizer.bin
1.97 GB
- Xet hash:
- 4918cb9bbc53356af31ebb2d72a57ab4a7259b5fa89b3876a9d253b1398cbf31
- Size of remote file:
- 1.97 GB
- SHA256:
- 34b8219c0668e9346e746a7fa6fe188a38edf85152350f4bd22696ad1120274e
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