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import gradio as gr
import numpy as np
from huggingface_hub import hf_hub_url, cached_download
import PIL
import onnx
import onnxruntime
config_file_url = hf_hub_url("Jacopo/ToonClip", filename="model.onnx")
model_file = cached_download(config_file_url)
onnx_model = onnx.load(model_file)
onnx.checker.check_model(onnx_model)
opts = onnxruntime.SessionOptions()
opts.intra_op_num_threads = 16
ort_session = onnxruntime.InferenceSession(model_file, sess_options=opts)
input_name = ort_session.get_inputs()[0].name
output_name = ort_session.get_outputs()[0].name
def normalize(x, mean=(0., 0., 0.), std=(1.0, 1.0, 1.0)):
# x = (x - mean) / std
x = np.asarray(x, dtype=np.float32)
if len(x.shape) == 4:
for dim in range(3):
x[:, dim, :, :] = (x[:, dim, :, :] - mean[dim]) / std[dim]
if len(x.shape) == 3:
for dim in range(3):
x[dim, :, :] = (x[dim, :, :] - mean[dim]) / std[dim]
return x
def denormalize(x, mean=(0., 0., 0.), std=(1.0, 1.0, 1.0)):
# x = (x * std) + mean
x = np.asarray(x, dtype=np.float32)
if len(x.shape) == 4:
for dim in range(3):
x[:, dim, :, :] = (x[:, dim, :, :] * std[dim]) + mean[dim]
if len(x.shape) == 3:
for dim in range(3):
x[dim, :, :] = (x[dim, :, :] * std[dim]) + mean[dim]
return x
def nogan(input_img):
i = np.asarray(input_img)
i = i.astype("float32")
i = np.transpose(i, (2, 0, 1))
i = np.expand_dims(i, 0)
i = i / 255.0
i = normalize(i, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
ort_outs = ort_session.run([output_name], {input_name: i})
output = ort_outs
output = output[0][0]
output = denormalize(output, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
output = output * 255.0
output = output.astype('uint8')
output = np.transpose(output, (1, 2, 0))
output_image = PIL.Image.fromarray(output, 'RGB')
return output_image
title = "Zoom, Clip, Toon"
description = """Image to Toon Using AI"""
article = """
<p style='text-align: center'>The \"ToonClip\" model was trained by <a href='https://twitter.com/JacopoMangia' target='_blank'>Jacopo Mangiavacchi</a> and available at <a href='https://github.com/jacopomangiavacchi/ComicsHeroMobileUNet' target='_blank'>Github Repo ComicsHeroMobileUNet</a></p>
<br>
"""
examples=[['1m_hires.jpeg'],['2m_hires.jpeg'],['3m_hires.jpeg'],['1f_hires.jpeg'],['2f_hires.jpeg'],['3f_hires.jpeg']]
iface = gr.Interface(
nogan,
gr.inputs.Image(type="pil", shape=(1024, 1024)),
gr.outputs.Image(type="pil"),
title=title,
description=description,
article=article,
examples=examples)
iface.launch() |