Instructions to use stepfun-ai/Step1X-Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stepfun-ai/Step1X-Edit with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-image" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-image", model="stepfun-ai/Step1X-Edit")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("stepfun-ai/Step1X-Edit", device_map="auto") - 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) |
0.482 |
global (full page) |
0.580 |
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!