Instructions to use Quorlen/Z_Image_Turbo_Colour_Grading_Tests_LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Quorlen/Z_Image_Turbo_Colour_Grading_Tests_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("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Quorlen/Z_Image_Turbo_Colour_Grading_Tests_LoRA") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Colour Grading Test LoRA

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Model description
LoRA trained on procedurally generated images using user defined colour palettes in an experiment to see if the results would be usable. https://github.com/QuorlenVerse/Cheat_Sheets_and_Simple_Scripts/tree/main/300_random_colour_pallete_images_with_prompts Results are good, The RGB version removing the washed out look of the base model, hover at the cost of consistency in text and certain styles. Cover images are from the RGB model.
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Model tree for Quorlen/Z_Image_Turbo_Colour_Grading_Tests_LoRA
Base model
Tongyi-MAI/Z-Image-Turbo