Instructions to use UCSC-VLAA/gpt-image-edit-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/gpt-image-edit-training 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="UCSC-VLAA/gpt-image-edit-training")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/gpt-image-edit-training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download final_checkpoint/preprocessor_config.json from UCSC-VLAA/gpt-image-edit-training: direct link, hf CLI and curl.
- Browser
- Download file 575 Bytes
-
https://huggingface.co/UCSC-VLAA/gpt-image-edit-training/resolve/main/final_checkpoint/preprocessor_config.json
- Command line
-
hf download hf://UCSC-VLAA/gpt-image-edit-training/final_checkpoint/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/UCSC-VLAA/gpt-image-edit-training/resolve/main/final_checkpoint/preprocessor_config.json
575 Bytes
| { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "max_pixels": 12845056, | |
| "merge_size": 2, | |
| "min_pixels": 3136, | |
| "patch_size": 14, | |
| "processor_class": "Qwen2_5_VLProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 12845056, | |
| "shortest_edge": 3136 | |
| }, | |
| "temporal_patch_size": 2 | |
| } | |