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
#13
by SaylorTwift HF Staff - opened
- .eval_results/vdebench.yaml +22 -0
.eval_results/vdebench.yaml
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- dataset:
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id: hongzhuyi/VDEBench
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task_id: local
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value: 0.69
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date: '2026-10-15'
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source:
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url: https://arxiv.org/abs/2602.00122v4
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name: VDEBench paper (v4), Table 2
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user: hongzhuyi
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notes: local setting, n=3,506, PaddleOCR-VL; IoU 0.731, CDM 0.810, BLEU-4 0.476,
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TEDS 0.744
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- dataset:
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id: hongzhuyi/VDEBench
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task_id: global
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value: 0.748
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date: '2026-10-15'
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source:
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url: https://arxiv.org/abs/2602.00122v4
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name: VDEBench paper (v4), Table 2
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user: hongzhuyi
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notes: global setting, n=3,506, PaddleOCR-VL; IoU 0.856, CDM 0.862, BLEU-4 0.485,
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TEDS 0.789
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