Instructions to use jbern3812947/Test-Anime-Style-ZIT-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jbern3812947/Test-Anime-Style-ZIT-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("jbern3812947/Test-Anime-Style-ZIT-LoRA") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
help with training
#1
by TahirC - opened
I am training a ZIT style lora (drawing style) with 33 images with captions (generated using qwen3.5-4b) , at 1.2k steps i am not seeing any change is output sample
any advice ?
Sorry for a late response. If you are still having trouble with it, there's a few things I'd suggest you check. You should definitely be seeing differences at 1.2k steps.
- Trigger word. If you had a trigger word, check if the sample image prompts include it.
- Learning rate: You might have too low of a learning rate
- Captions: If your dataset captions are too detailed, it might be an issue. Also, check what your caption dropout rate is as well.
I was not getting expected output in training sample generated via ai-toolkit but when I ran it on comfy it was working fine
TahirC changed discussion status to closed