add Block component creation callback

Add FP32 fallback support on sd_vae_approx

fix not able to exit metadata popup when pop up is too big

Add support for SD 2.1 Turbo

infotext updates: add option to disregard certain infotext fields, add option to not include VAE in infotext, add explanation to infotext settings page, move some options to infotext settings page

Fix bug where is_using_v_parameterization_for_sd2 fails because the sd_hijack is only partially undone

prevent the pop-up from being too big as to making exiting the pop-up impossible

This tries to execute interpolate with FP32 if it failed.

Background is that
on some environment such as Mx chip MacOS devices, we get error as follows:

```
"torch/nn/functional.py", line 3931, in interpolate
        return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    RuntimeError: "upsample_nearest2d_channels_last" not implemented for 'Half'
```

In this case, ```--no-half``` doesn't help to solve. Therefore this commits add the FP32 fallback execution to solve it.

Note that the ```upsample_nearest2d``` is called from ```torch.nn.functional.interpolate```.
And the fallback for torch.nn.functional.interpolate is necessary at
```modules/sd_vae_approx.py``` 's ```VAEApprox.forward```
```repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py``` 's ```Upsample.forward```

This reverts commit 58c19545.
Since the modification is expected to move to mac_specific.py
(https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046#issuecomment-1826731532)

json.dump(ensure_ascii=False)

improve json readability

Compatibility

Update Ruff to 0.1.6

Move exception_records related methods to errors.py

support HyperTile optimization

This tries to execute interpolate with FP32 if it failed.

Background is that
on some environment such as Mx chip MacOS devices, we get error as follows:

```
"torch/nn/functional.py", line 3931, in interpolate
        return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    RuntimeError: "upsample_nearest2d_channels_last" not implemented for 'Half'
```

In this case, ```--no-half``` doesn't help to solve. Therefore this commits add the FP32 fallback execution to solve it.

Note that the submodule may require additional modifications. The following is the example modification on the other submodule.

```repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py

class Upsample(nn.Module):
..snip..
    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
            )
        else:
            try:
                x = F.interpolate(x, scale_factor=2, mode="nearest")
            except:
                x = F.interpolate(x.to(th.float32), scale_factor=2, mode="nearest").to(x.dtype)
        if self.use_conv:
            x = self.conv(x)
        return x
..snip..
```

You can see the FP32 fallback execution as same as sd_vae_approx.py.

Option to show batch img2img results in UI

implementing script metadata and DAG sorting mechanism

rework extensions metadata: use custom sorter that doesn't mess the order as much and ignores cyclic errors, use classes with named fields instead of dictionaries, eliminate some duplicated code

save sysinfo as .json

GitHub now allows uploading of .json files in issues

shared.opts.img2img_batch_show_results_limit
limit the number of images return to the UI for batch img2img
default limit 32
0 no images are shown
-1 unlimited, all images are shown