Instructions to use fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2511", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Grid issues
You can lower the scale of the Lora , and also trying to use it without lighting if you can
You can lower the scale of the Lora , and also trying to use it without lighting if you can
Still I'm wondering how is Higgsfield is able to deliver generations using this lora so fast?
When generating an image via their Angles feature, image gets generated within 10 secs in sharp and high quality as if no lightning lora is used.
When using this endpoint https://fal.ai/models/fal-ai/qwen-image-edit-2511-multiple-angles
Setting acceleration to "Regular" does give fast results but they all contain this weird effect.
However turning it off results to 4 - 5x the generation time.
The grid/checker pattern artifact comes from the lightining lora.





