Instructions to use lightx2v/Qwen-Image-Lightning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Qwen-Image-Lightning with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lightx2v/Qwen-Image-Lightning") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Request: Lightning LoRA optimized for Qwen Image Edit 2509 FP8 non-scaled model
When editing any image using the Qwen Image Edit 2509 FP8 (non-scaled variant) model together with any Lightning LoRA, a grid-like pattern appears in the output. Using the scaled FP8 version provided in the repository resolves this issue, but inference becomes approximately twice as slow (20s/it) compared to the non-scaled variant (around 10s/it). What’s surprising is that it’s even slower than the full BF16 model with offloading (around 12 s/it). My hardware is an RTX 3060 and 64 GB of DDR4 RAM.
Would it be possible to release a dedicated set of Lightning LoRA weights optimized specifically for the non-scaled FP8 Image Edit 2509 base model, similar to what was done for the Qwen Image (non-edit) model?