Image-Text-to-Image
Diffusers
Safetensors
QwenImageEditPlusPipeline
qwen-image;
text-to-image
image-edit
causal-image-edit
Instructions to use lightx2v/Qwen-Image-Edit-Causal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Qwen-Image-Edit-Causal with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Qwen-Image-Edit-Causal", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Expected performance?
#1
by willhsmit - opened
In terms of the inference time performance, is it expected that this will run 4 steps faster than the lightning loras run 4 steps? Or is it intended to reduce the number of steps needed to get a certain quality/fidelity?
@willhsmit
Hi, it is expected to be faster than the Lightning LoRAs, as tested when both are run for 4 steps.
Please see the diagram in our repo