Hugging Face's logo

Spaces:
marlonbarrios
/
Sound-AI-SFX
Running on Zero

App Files Community
Sound-AI-SFX
File size: 2,989 Bytes 
ffead1e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
 
<!--Copyright 2023 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

# Models

Diffusers contains pretrained models for popular algorithms and modules for creating the next set of diffusion models.
The primary function of these models is to denoise an input sample, by modeling the distribution $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$.
The models are built on the base class ['ModelMixin'] that is a `torch.nn.module` with basic functionality for saving and loading models both locally and from the HuggingFace hub.

## ModelMixin
[[autodoc]] ModelMixin

## UNet2DOutput
[[autodoc]] models.unet_2d.UNet2DOutput

## UNet2DModel
[[autodoc]] UNet2DModel

## UNet1DOutput
[[autodoc]] models.unet_1d.UNet1DOutput

## UNet1DModel
[[autodoc]] UNet1DModel

## UNet2DConditionOutput
[[autodoc]] models.unet_2d_condition.UNet2DConditionOutput

## UNet2DConditionModel
[[autodoc]] UNet2DConditionModel

## UNet3DConditionOutput
[[autodoc]] models.unet_3d_condition.UNet3DConditionOutput

## UNet3DConditionModel
[[autodoc]] UNet3DConditionModel

## DecoderOutput
[[autodoc]] models.vae.DecoderOutput

## VQEncoderOutput
[[autodoc]] models.vq_model.VQEncoderOutput

## VQModel
[[autodoc]] VQModel

## AutoencoderKLOutput
[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput

## AutoencoderKL
[[autodoc]] AutoencoderKL

## Transformer2DModel
[[autodoc]] Transformer2DModel

## Transformer2DModelOutput
[[autodoc]] models.transformer_2d.Transformer2DModelOutput

## TransformerTemporalModel
[[autodoc]] models.transformer_temporal.TransformerTemporalModel

## Transformer2DModelOutput
[[autodoc]] models.transformer_temporal.TransformerTemporalModelOutput

## PriorTransformer
[[autodoc]] models.prior_transformer.PriorTransformer

## PriorTransformerOutput
[[autodoc]] models.prior_transformer.PriorTransformerOutput

## ControlNetOutput
[[autodoc]] models.controlnet.ControlNetOutput

## ControlNetModel
[[autodoc]] ControlNetModel

## FlaxModelMixin
[[autodoc]] FlaxModelMixin

## FlaxUNet2DConditionOutput
[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionOutput

## FlaxUNet2DConditionModel
[[autodoc]] FlaxUNet2DConditionModel

## FlaxDecoderOutput
[[autodoc]] models.vae_flax.FlaxDecoderOutput

## FlaxAutoencoderKLOutput
[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput

## FlaxAutoencoderKL
[[autodoc]] FlaxAutoencoderKL

## FlaxControlNetOutput
[[autodoc]] models.controlnet_flax.FlaxControlNetOutput

## FlaxControlNetModel
[[autodoc]] FlaxControlNetModel