Instructions to use ostris/ideogram_4_unconditional_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ostris/ideogram_4_unconditional_lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ideogram-ai/ideogram-4-fp8", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ostris/ideogram_4_unconditional_lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: other | |
| license_name: ideogram-4-non-commercial | |
| license_link: https://huggingface.co/ideogram-ai/ideogram-4-fp8/blob/main/LICENSE.md | |
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| base_model: ideogram-ai/ideogram-4-fp8 | |
| # Ideogram 4 Unconditional LoRA | |
| This is a LoRA that was initialized by extracting the difference of the Ideogram 4 conditional and unconditional model weights. | |
| It was further tuned using student teacher training on real data and a loss was performed on a per layer basis to more closely | |
| match the unconditional model. This can be used on the conditional Ideogram 4 model during the unconditional pass | |
| as a replacement to the full 9B paramiter unconditional model. | |
| Using the full unconditional model will likely yield better results, but this will work as a light weight alternative. It was | |
| originally trained to be used in [Ostris AI Toolkit](https://github.com/ostris/ai-toolkit) so samples would be more in line | |
| with what the full pipeline would produce without needing to load the entire unconditional model. |