![[[tensor([[[ 2.0239e+00, 2.4920e+01, 3.4078e-02, ..., 6.2714e-01, -1.2514e+00, -8.1803e-01],
[ 3.1599e+00, -1.2740e+01, -1.3952e+00, ..., -1.9791e-01, 1.1577e+00, -9.2629e-01],
[ 4.7585e-01, -9.9672e+00, -1.2308e+00, ..., -2.2492e-02, 1.7354e+00, -6.1503e-01],
...,
[ 2.3481e+00, -1.9933e+00, -1.4882e+00, ..., -1.8223e+00, -1.2274e+00, -1.6702e-01],
[-7.0952e-01, -9.6455e+00, -1.3491e+00, ..., -1.4164e+00, 2.5065e+00, -5.8218e-01],
[-1.1106e+00, -2.6446e+01, -1.3372e+00, ..., -2.6410e+00, 1.6824e+00, -1.1048e+00]]]), {'pooled_output': None, 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 't5xxl_ids': tensor([20975, 6, 200, 463, 6, 2604, 834, 1298, 6, 2604, 834, 11864, 2604, 834, 940, 6, 1], dtype=torch.int32), 't5xxl_weights': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])}], [tensor([[[ 2.4446e+00, 2.6514e+01, 2.2589e-01, ..., -7.9117e-01, -2.3427e+00, -2.3382e+00],
[ 8.3039e-01, 3.6959e+00, -1.4458e+00, ..., -1.0280e-01, 1.5833e-01, -9.1742e-01],
[-1.1269e+00, -5.9010e+00, -1.4158e+00, ..., 4.2985e-01, 1.1156e+00, -5.8133e-01],
...,
[ 3.3389e+00, -2.0507e+00, -1.2302e+00, ..., 5.7437e-02, 2.9621e-01, -1.2738e-02],
[ 1.1232e-01, -9.6350e+00, -1.3226e+00, ..., -1.0761e-02, 9.6381e-01, -3.2890e-01],
[-1.6113e-01, -2.1555e+01, -1.4302e+00, ..., -6.7249e-01, 1.6835e+00, -8.7834e-01]]]), {'pooled_output': None, 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 't5xxl_ids': tensor([ 209, 7531, 6, 954, 107, 7512, 15, 51, 23, 834, 13513, 6, 6995, 6468, 6, 5571, 8677, 6, 14118, 6, 14602, 6026, 6, 2261, 3893, 6, 3595, 2458, 6, 1659, 1057, 6, 1], dtype=torch.int32), 't5xxl_weights': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])}]]](https://c.genur.art/2f3ba58a-7020-42f1-9b8e-eca35cd83410_large.webp)
[[tensor([[[ 2.0239e+00, 2.4920e+01, 3.4078e-02, ..., 6.2714e-01, -1.2514e+00, -8.1803e-01], [ 3.1599e+00, -1.2740e+01, -1.3952e+00, ..., -1.9791e-01, 1.1577e+00, -9.2629e-01], [ 4.7585e-01, -9.9672e+00, -1.2308e+00, ..., -2.2492e-02, 1.7354e+00, -6.1503e-01], ..., [ 2.3481e+00, -1.9933e+00, -1.4882e+00, ..., -1.8223e+00, -1.2274e+00, -1.6702e-01], [-7.0952e-01, -9.6455e+00, -1.3491e+00, ..., -1.4164e+00, 2.5065e+00, -5.8218e-01], [-1.1106e+00, -2.6446e+01, -1.3372e+00, ..., -2.6410e+00, 1.6824e+00, -1.1048e+00]]]), {'pooled_output': None, 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 't5xxl_ids': tensor([20975, 6, 200, 463, 6, 2604, 834, 1298, 6, 2604, 834, 11864, 2604, 834, 940, 6, 1], dtype=torch.int32), 't5xxl_weights': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])}], [tensor([[[ 2.4446e+00, 2.6514e+01, 2.2589e-01, ..., -7.9117e-01, -2.3427e+00, -2.3382e+00], [ 8.3039e-01, 3.6959e+00, -1.4458e+00, ..., -1.0280e-01, 1.5833e-01, -9.1742e-01], [-1.1269e+00, -5.9010e+00, -1.4158e+00, ..., 4.2985e-01, 1.1156e+00, -5.8133e-01], ..., [ 3.3389e+00, -2.0507e+00, -1.2302e+00, ..., 5.7437e-02, 2.9621e-01, -1.2738e-02], [ 1.1232e-01, -9.6350e+00, -1.3226e+00, ..., -1.0761e-02, 9.6381e-01, -3.2890e-01], [-1.6113e-01, -2.1555e+01, -1.4302e+00, ..., -6.7249e-01, 1.6835e+00, -8.7834e-01]]]), {'pooled_output': None, 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 't5xxl_ids': tensor([ 209, 7531, 6, 954, 107, 7512, 15, 51, 23, 834, 13513, 6, 6995, 6468, 6, 5571, 8677, 6, 14118, 6, 14602, 6026, 6, 2261, 3893, 6, 3595, 2458, 6, 1659, 1057, 6, 1], dtype=torch.int32), 't5xxl_weights': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])}]]
Parameters used to generate this content
[[tensor([[[-8.7145e-01, 3.9894e+01, 1.6217e-01, ..., 8.2393e-01, 1.1331e+00, -1.6909e+00], [-1.7550e+00, -2.0871e+00, -1.2725e+00, ..., -1.1681e-01, 7.3614e-01, 7.2888e-02], [ 1.4858e+00, -1.7115e+01, -1.2974e+00, ..., -4.5827e-01, 1.2693e+00, -7.8627e-01], ..., [ 4.9852e-01, -1.8048e+00, -1.3479e+00, ..., -1.0725e+00, -1.6752e-02, -1.1539e+00], [-1.5014e-01, -1.7266e+01, -1.3332e+00, ..., -1.6477e+00, 9.5836e-01, -3.1116e+00], [ 2.4641e-01, 7.3293e+00, -1.1961e+00, ..., -1.4506e+00, -7.3863e-01, -1.1902e-01]]]), {'pooled_output': None, 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 't5xxl_ids': tensor([ 6025, 463, 6, 731, 463, 6, 2604, 834, 4347, 2604, 834, 4482, 2604, 834, 6355, 2377, 564, 6, 16810, 651, 6, 3, 354, 855, 122, 768, 23, 8717, 7, 6, 731, 60, 7, 6, 3, 25486, 1], dtype=torch.int32), 't5xxl_weights': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])}], [tensor([[[ 2.2252, 16.8812, -0.0797, ..., -0.7956, -1.1600, -1.1189]]]), {'pooled_output': None, 'attention_mask': tensor([[0]]), 't5xxl_ids': tensor([1], dtype=torch.int32), 't5xxl_weights': tensor([1.])}]]
Copies the original metadata text embedded by AUTOMATIC1111.
AI models used to generate this content