Instructions to use July66/julijxtm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use July66/julijxtm2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("July66/julijxtm2", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from July66/julijxtm2: direct link, hf CLI and curl.
- Browser
- Download file 497 Bytes
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https://huggingface.co/July66/julijxtm2/resolve/main/README.md
- Command line
-
hf download hf://July66/julijxtm2/README.md
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curl -L -o README.md https://huggingface.co/July66/julijxtm2/resolve/main/README.md
497 Bytes
metadata
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
julijxtm2 Dreambooth model trained by July66 with TheLastBen's fast-DreamBooth notebook
Test the concept via A1111 Colab fast-Colab-A1111
Sample pictures of this concept: