Qwen-Image-Lightx2v
Collection
4 items β’ Updated β’ 9
How to use lightx2v/Qwen-Image-2512-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-2512", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("lightx2v/Qwen-Image-2512-Lightning")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]How to use lightx2v/Qwen-Image-2512-Lightning with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
This model suite supports two mainstream usage frameworks, with detailed guides provided below:
Qwen-Image-Lightning Framework For full documentation on model usage within the Qwen-Image-Lightning ecosystem (including environment setup, inference pipelines, and customization), please refer to: Qwen-Image-Lightning GitHub Repository
LightX2V Framework The models are fully compatible with the LightX2V lightweight video/image generation inference framework. For step-by-step usage examples, configuration templates, and performance optimization tips, see: LightX2V Qwen Image Documentation
Base model
Qwen/Qwen-Image-2512