update readme

Signed-off-by: Pengfei Guo <[email protected]>

models/maisi_ct_generative/docs/README.md

@@ -22,6 +22,30 @@ The inference requires:
 - GPU: at least 58GB GPU memory for 512 x 512 x 512
 - Disk Memory: at least 21GB disk memory
 
+#### Inference parameters:
+The information for the inference input, like body region and anatomy to generate, is stored in [./configs/inference.json](../configs/inference.json). Please feel free to play with it. Here are the details of the parameters.
+
+- `"num_output_samples"`: int, the number of output image/mask pairs it will generate.
+- `"spacing"`: voxel size of generated images. E.g., if set to `[1.5, 1.5, 2.0]`, it will generate images with a resolution of 1.5x1.5x2.0 mm.
+- `"output_size"`: volume size of generated images. E.g., if set to `[512, 512, 256]`, it will generate images with size of 512x512x256. They need to be divisible by 16. If you have a small GPU memory size, you should adjust it to small numbers. Note that `"spacing"` and `"output_size"` together decide the output field of view (FOV). For eample, if set them to `[1.5, 1.5, 2.0]`mm and `[512, 512, 256]`, the FOV is 768x768x512 mm. We recommend the FOV in x and y axis are same and to be at least 256mm for head, and at least 384mm for other body regions like abdomen. There is no such restriction for z-axis.
+- `"controllable_anatomy_size"`: a list of controllable anatomy and its size scale (0--1). E.g., if set to `[["liver", 0.5],["hepatic tumor", 0.3]]`, the generated image will contain liver that have a median size, with size around 50% percentile, and hepatic tumor that is relatively small, with around 30% percentile. In addition, if the size scale is set to -1, it indicates that the organ does not exist or should be removed. The output will contain paired image and segmentation mask for the controllable anatomy. 
+The following organs support generation with a controllable size: ``["liver", "gallbladder", "stomach", "pancreas", "colon", "lung tumor", "bone lesion", "hepatic tumor", "colon cancer primaries", "pancreatic tumor"]``.
+The raw output of the current mask generation model has a fixed size of $256^3$ voxels with a spacing of $1.5^3$ mm. If the "output_size" differs from this default, the generated masks will be resampled to the desired `"output_size"` and `"spacing"`. Note that resampling may degrade the quality of the generated masks and could trigger multiple inference attempts if the images fail to pass the [image quality check](../scripts/quality_check.py).
+- `"body_region"`: If "controllable_anatomy_size" is not specified, "body_region" will be used to constrain the region of generated images. It needs to be chosen from "head", "chest", "thorax", "abdomen", "pelvis", "lower".
+- `"anatomy_list"`: If "controllable_anatomy_size" is not specified, the output will contain paired image and segmentation mask for the anatomy in "./configs/label_dict.json". 
+- `"autoencoder_sliding_window_infer_size"`: in order to save GPU memory, we use sliding window inference when decoding latents to image when `"output_size"` is large. This is the patch size of the sliding window. Small value will reduce GPU memory but increase time cost. They need to be divisible by 16.
+- `"autoencoder_sliding_window_infer_overlap"`: float between 0 and 1. Large value will reduce the stitching artifacts when stitching patches during sliding window inference, but increase time cost. If you do not observe seam lines in the generated image result, you can use a smaller value to save inference time.
+
+To generate images with substantial dimensions, such as 512 × 512 × 512 or larger, using GPUs with 80GB of memory, it is advisable to configure the `"num_splits"` parameter in [the auto-encoder configuration](./configs/config_maisi.json#L11-L37) to 16. This adjustment is crucial to avoid out-of-memory issues during inference.
+
+#### Recommended spacing for different output sizes:
+
+|`"output_size"`| Recommended `"spacing"`|
+|:-----:|:-----:|
+[256, 256, 256]  | [1.5, 1.5, 1.5] |
+[512, 512, 128]  | [0.8, 0.8, 2.5] |
+[512, 512, 512]  | [1.0, 1.0, 1.0] |
+
 ### Execute inference
 The following code generates a synthetic image from a random sampled noise.
 ```