What does this PR do?

Add from single file mixin to StableDiffusionUpscalePipeline and StableDiffusionLatentUpscalePipeline

Fixes # (issue)
#5150

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@patrickvonplaten Added the mixins to the upscale pipelines. Little confused on how to test this.

Is the expectation that the user is supposed to provide the appropriate yaml config when using from_single_file? Is this the case even if a single file version of the model is present on the hub? Or are we expected to fetch the config somehow if it is present on the hub?

e.g
https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/blob/main/x4-upscaler-ema.safetensors

The following should work:

wget https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/main/x4-upscaler-ema.safetensors

And then doing:

from diffusers import StableDiffusionUpscalePipeline

pipeline = StableDiffusionUpscalePipeline.from_single_file("./x4-upscaler-ema.safetensors")

without giving any config.

No need to test the latent upscaler, I don't think there is even an official single file for this

The following should work:

wget https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/main/x4-upscaler-ema.safetensors

And then doing:

from diffusers import StableDiffusionUpscalePipeline

pipeline = StableDiffusionUpscalePipeline.from_single_file("./x4-upscaler-ema.safetensors")

without giving any config.

No need to test the latent upscaler, I don't think there is even an official single file for this

So that snippet will fail since there is a mismatch in the layer shapes in the UNet.

ValueError: Trying to set a tensor of shape torch.Size([1024, 256]) in "weight" (which has shape torch.Size([1280, 320])), this look incorrect.

Without a config from_single_file creates a default Unet config that doesn't match the one in the checkpoint. I think it would apply to other components too.

The following should work:

wget https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/main/x4-upscaler-ema.safetensors

And then doing:

from diffusers import StableDiffusionUpscalePipeline

pipeline = StableDiffusionUpscalePipeline.from_single_file("./x4-upscaler-ema.safetensors")

without giving any config.
No need to test the latent upscaler, I don't think there is even an official single file for this

So that snippet will fail since there is a mismatch in the layer shapes in the UNet.

ValueError: Trying to set a tensor of shape torch.Size([1024, 256]) in "weight" (which has shape torch.Size([1280, 320])), this look incorrect.

Without a config from_single_file creates a default Unet config that doesn't match the one in the checkpoint. I think it would apply to other components too.

Then we should add a new if statement that automatically detects when the input model is a upsampling model just from the checkpoint and then set the correct configs

@patrickvonplaten this is ready for another review. There's a failing ShapE test that's unrelated. Rest of the CI is green.

Very cool! Nice job

ldm unet model can not load after this pr, exception.
File "/root/miniconda3/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py", line 450, in convert_ldm_unet_checkpoint
new_checkpoint["class_embedding.weight"] = unet_state_dict["label_emb.weight"]
KeyError: 'label_emb.weight'

(in 0.21.0, ldm model can be loaded correctly)

@xiaoyong-z could you open a new PR? cc @DN6 here as well for viz

#5917 @patrickvonplaten