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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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 so samples would be more in line with what the full pipeline would produce without needing to load the entire unconditional model.
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Model tree for ostris/ideogram_4_unconditional_lora
Base model
ideogram-ai/ideogram-4-fp8