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#!/usr/bin/env python
# coding: utf-8
from typing import List
from rknn.api import RKNN
from math import exp
from sys import exit
import argparse
def convert_pipeline_component(onnx_path: str, resolution_list: List[List[int]], target_platform: str = 'rk3588'):
print(f'Converting {onnx_path} to RKNN model')
print(f'with target platform {target_platform}')
print(f'with resolutions:')
for res in resolution_list:
print(f'- {res[0]}x{res[1]}')
use_dynamic_shape = False
if(len(resolution_list) > 1):
print("Warning: RKNN dynamic shape support is probably broken, may throw errors")
use_dynamic_shape = True
batch_size = 1
LATENT_RESIZE_FACTOR = 8
# build shape list
if "text_encoder" in onnx_path:
input_size_list = [[[1,77]]]
inputs=['input_ids']
use_dynamic_shape = False
elif "unet" in onnx_path:
# batch_size = 2 # for classifier free guidance # broken for rknn python api
input_size_list = []
for res in resolution_list:
input_size_list.append(
[[1,4, res[0]//LATENT_RESIZE_FACTOR, res[1]//LATENT_RESIZE_FACTOR],
[1],
[1, 77, 768],
[1, 256]]
)
inputs=['sample','timestep','encoder_hidden_states','timestep_cond']
elif "vae_decoder" in onnx_path:
input_size_list = []
for res in resolution_list:
input_size_list.append(
[[1,4, res[0]//LATENT_RESIZE_FACTOR, res[1]//LATENT_RESIZE_FACTOR]]
)
inputs=['latent_sample']
else:
print("Unknown component: ", onnx_path)
exit(1)
rknn = RKNN(verbose=True)
# pre-process config
print('--> Config model')
rknn.config(target_platform='rk3588', optimization_level=3, single_core_mode=True,
dynamic_input= input_size_list if use_dynamic_shape else None)
print('done')
# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model=onnx_path,
inputs=None if use_dynamic_shape else inputs,
input_size_list= None if use_dynamic_shape else input_size_list[0])
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=False, rknn_batch_size=batch_size)
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
#export
print('--> Export RKNN model')
ret = rknn.export_rknn(onnx_path.replace('.onnx', '.rknn'))
if ret != 0:
print('Export RKNN model failed!')
exit(ret)
print('done')
rknn.release()
print('RKNN model is converted successfully!')
def parse_resolution_list(resolution: str) -> List[List[int]]:
resolution_pairs = resolution.split(',')
parsed_resolutions = []
for pair in resolution_pairs:
width, height = map(int, pair.split('x'))
parsed_resolutions.append([width, height])
return parsed_resolutions
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Convert Stable Diffusion ONNX models to RKNN models')
parser.add_argument('-m','--model-dir', type=str, help='Directory containing the Stable Diffusion ONNX models', required=True)
parser.add_argument('-c','--components', type=str, help='Name of the components to convert, e.g. "text_encoder,unet,vae_decoder"', default='text_encoder, unet, vae_decoder')
parser.add_argument('-r','--resolutions', type=str, help='Comma-separated list of resolutions for the model, e.g. "256x256,512x512"', default='256x256')
parser.add_argument('--target_platform', type=str, help='Target platform for the RKNN model, default is "rk3588"', default='rk3588')
args = parser.parse_args()
components = args.components.split(',')
for component in components:
onnx_path = f'{args.model_dir}/{component.strip()}/model.onnx'
resolution_list = parse_resolution_list(args.resolutions)
if(len(resolution_list) == 0):
print("Error: No resolutions specified")
exit(1)
convert_pipeline_component(onnx_path, resolution_list, args.target_platform)
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