Instructions to use stepfun-ai/NextStep-1-Large-Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stepfun-ai/NextStep-1-Large-Edit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="stepfun-ai/NextStep-1-Large-Edit", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("stepfun-ai/NextStep-1-Large-Edit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vae/config.json from stepfun-ai/NextStep-1-Large-Edit: direct link, hf CLI and curl.
- Browser
- Download file 302 Bytes
-
https://huggingface.co/stepfun-ai/NextStep-1-Large-Edit/resolve/bac0d9ff590906759a41292124c3b7fe6979b689/vae/config.json
- Command line
-
hf download hf://stepfun-ai/NextStep-1-Large-Edit@bac0d9ff590906759a41292124c3b7fe6979b689/vae/config.json
-
curl -L -o config.json https://huggingface.co/stepfun-ai/NextStep-1-Large-Edit/resolve/bac0d9ff590906759a41292124c3b7fe6979b689/vae/config.json
302 Bytes
| { | |
| "resolution": 256, | |
| "in_channels": 3, | |
| "ch": 128, | |
| "out_ch": 3, | |
| "ch_mult": [1, 2, 4, 4], | |
| "num_res_blocks": 2, | |
| "z_channels": 16, | |
| "shift_factor": 0, | |
| "scaling_factor": 1, | |
| "deterministic": true, | |
| "norm_fn": "layer_norm", | |
| "norm_level": "channel", | |
| "psz": 1 | |
| } |