Instructions to use FireRedTeam/FireRed-Image-Edit-1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FireRedTeam/FireRed-Image-Edit-1.1 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("FireRedTeam/FireRed-Image-Edit-1.1", dtype=torch.bfloat16, device_map="cuda") 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] - Notebooks
- Google Colab
- Kaggle
Add VDEBench evaluation results
#18
by hongzhuyi - opened
Hi π this PR adds this model's VDEBench scores to .eval_results/, so they show up on the model page and the VDEBench leaderboard.
VDEBench evaluates instruction-guided editing of dense English/Chinese document pages (3,506 examples). Each score is the equally weighted mean of IoU, CDM, BLEU-4 and TEDS (0β1, higher is better):
| Task | Score β |
|---|---|
local (edited region, headline) |
0.638 |
global (full page) |
0.750 |
These results come from the VDEBench team's new full evaluation of FireRed-Image-Edit-1.1. Detailed results.
Thanks!