Instructions to use FireRedTeam/FireRed-Image-Edit-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FireRedTeam/FireRed-Image-Edit-1.0 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-1.0", 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Add VDEBench evaluation results
Hi π this PR adds this model's VDEBench scores to .eval_results/, so they show up on the model page and on 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 |
|---|---|
local (edited region, headline) |
0.690 |
global (full page) |
0.748 |
Scores come from the VDEBench paper (arXiv:2602.00122v4, Table 2), and the VDEBench authors confirmed both the values and the evaluated checkpoint (discussion).
While this PR is open the scores appear as community-provided; merging it makes them official, and closing it removes them. Docs: https://huggingface.co/docs/hub/eval-results Β· https://huggingface.co/docs/hub/leaderboard-data-guide
Thanks!