Framework Inference

For InstructPix2Pix models, our inference script processes an original image based on a provided edit prompt, modifies the image accordingly, and saves the edited image as a new file.

To enable the inference stage with a InstructPix2Pix model, configure the configuration files:

  1. In the defaults section of conf/config.yaml, update the fw_inference field to point to the desired Instruct Pix2Pix configuration file. For example, if you want to use the instruct_pix2pix/edit_cli configuration, change the fw_inference field to instruct_pix2pix/edit_cli.

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    defaults: - fw_inference: instruct_pix2pix/edit_cli ...

  2. In the stages field of conf/config.yaml, make sure the fw_inference stage is included. For example,

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    stages: - fw_inference ...

  3. Configure the edit section in conf/fw_inference/instruct_pix2pix/edit_cli.yaml. Most importantly, set the input field to the path of the original image for inference, and provide an edit prompt in the prompt field. The script will generate num_images_per_prompt images at once based on the provided prompt.

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    edit: resolution: 512 steps: 100 input: ??? # path/to/input/picture outpath: ${fw_inference.run.results_dir} prompt: "" cfg_text: 7.5 cfg_image: 1.2 num_images_per_prompt: 8 combine_images: [2, 4] # [row, column], set to null if don't want to combine seed: 1234

  4. Execute launcher pipeline: python3 main.py

Remarks:

  1. To load a pretrained checkpoint for inference, set the restore_from_path field in the model section to the path of the pretrained checkpoint in .nemo format in conf/fw_inference/vit/imagenet1k.yaml. By default, this field links to the .nemo format checkpoint located in the ImageNet 1K fine-tuning checkpoints folder.

  2. We highly recommend users to use the same precision (i.e., trainer.precision) for inference as was used during training.

  3. Tips for getting better quality results: https://github.com/timothybrooks/instruct-pix2pix#tips

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