feat(diffusers/models): add models like autoencoders, transformers, unets and etc.

[townwish4git]

What does this PR do?

Adds # (feature)

implements of models in mindone.diffusers.models, include:

Model

AutoEncoders

• [x] AutoencoderKL
• [x] AsymmetricAutoencoderKL
• [x] AutoencoderKLTemporalDecoder
• [x] ConsistencyDecoderVAE
• [x] AutoEncoderTiny
• [x] VQModel

UNets

• [ ] UNet1DModel
• [x] UNet2DModel
• [x] UNet2DConditionModel
• [x] UNet3DConditionModel
• [x] I2VGenXLUNet
• [x] Kandinsky3UNet
• [x] UNetSpatioTemporalConditionModel
• [x] UNetMotionModel
• [ ] StableCascadeUNet
• [x] UViT2DModel

Transformers

• [x] Transformer2DModel
• [x] TransformerTemporalModel
• [x] T5FilmDecoder
• [x] PriorTransformer
• [x] DualTransformer2DModel

supplement: https://gist.github.com/Cui-yshoho/7ff86a76323c37f1c197d7fef67702ec

Before submitting

• [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• [ ] Did you read the contributor guideline?
• [ ] Did you make sure to update the documentation with your changes? E.g. record bug fixes or new features in What's New. Here are the documentation guidelines
• [ ] Did you build and run the code without any errors?
• [ ] Did you report the running environment (NPU type/MS version) and performance in the doc? (better record it for data loading, model inference, or training tasks)
• [ ] Did you write any new necessary tests?

Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR.

@xxx

[townwish4git]

yushi，huiyu合入后，rebase， resolve conflict

done.

[townwish4git]

use magic number 0.797885 to replace math.sqrt(2.0 / math.pi), passed ut on cpu in {fp16, fp32} x {pynative, graph} with almost same results comparing to pytorch