May be I'm missing some vital piece of information, but it feels like
```c++
  const auto neg_inf = at::scalar_tensor(-std::numeric_limits<float>::infinity(), at::TensorOptions().dtype(out.dtype()).device(out.device()));
  const auto masked = self.eq(neg_inf);
```
should be equivalent to [`torch.isneginf`](https://pytorch.org/docs/stable/generated/torch.isneginf.html) call
Pull Request resolved: #139763
Approved by: https://github.com/Skylion007
ghstack dependencies: #139788, #139784

As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.

Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time

vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
                                    subfolder='vae',
                                    torch_dtype=torch.bfloat16,
                                    force_upcast=False).to('mps')

pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
                                                 torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
             num_inference_steps=10,
             guidance_scale=8,
             generator=torch.Generator("mps").manual_seed(42),
             ).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```

Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro, though generated image remains the same:
![image](https://github.com/user-attachments/assets/1a35efef-9f80-4cd0-ac9c-30203eab6bb1)

Fixes #139389
Pull Request resolved: #139791
Approved by: https://github.com/drisspg, https://github.com/Skylion007
ghstack dependencies: #139788, #139784, #139763

The only difference between `convert_boolean_attn_mask_cudnn` and `convert_boolean_attn_mask` is the value we initialize boolean tensor to
Reduce duplication by introducing `convert_boolean_attn_mask_` that takes `neg_inf` value and make abovementioned implementations are trivial oneline call
Also, as suggested by @Skylion007, replace `at::where(foo->logical_not, -inf, 0)` with `at::where(*foo, 0, -inf)`
Pull Request resolved: pytorch#139784
Approved by: https://github.com/Skylion007, https://github.com/drisspg
ghstack dependencies: pytorch#139788

May be I'm missing some vital piece of information, but it feels like
```c++
  const auto neg_inf = at::scalar_tensor(-std::numeric_limits<float>::infinity(), at::TensorOptions().dtype(out.dtype()).device(out.device()));
  const auto masked = self.eq(neg_inf);
```
should be equivalent to [`torch.isneginf`](https://pytorch.org/docs/stable/generated/torch.isneginf.html) call
Pull Request resolved: pytorch#139763
Approved by: https://github.com/Skylion007
ghstack dependencies: pytorch#139788, pytorch#139784

…139791)

As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.

Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time

vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
                                    subfolder='vae',
                                    torch_dtype=torch.bfloat16,
                                    force_upcast=False).to('mps')

pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
                                                 torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
             num_inference_steps=10,
             guidance_scale=8,
             generator=torch.Generator("mps").manual_seed(42),
             ).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```

Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro, though generated image remains the same:
![image](https://github.com/user-attachments/assets/1a35efef-9f80-4cd0-ac9c-30203eab6bb1)

Fixes pytorch#139389
Pull Request resolved: pytorch#139791
Approved by: https://github.com/drisspg, https://github.com/Skylion007
ghstack dependencies: pytorch#139788, pytorch#139784, pytorch#139763