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:

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.
```
defaults:
  - fw_inference: instruct_pix2pix/edit_cli
  ...
```
In the stages field of conf/config.yaml, make sure the fw_inference stage is included. For example,
```
stages:
  - fw_inference
  ...
```

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.

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

Execute launcher pipeline: python3 main.py

Remarks:

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.
We highly recommend users to use the same precision (i.e., trainer.precision) for inference as was used during training.
Tips for getting better quality results: https://github.com/timothybrooks/instruct-pix2pix#tips