| import torch |
| import comfy.ops |
|
|
| def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"): |
| if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()): |
| padding_mode = "reflect" |
|
|
| pad = () |
| for i in range(img.ndim - 2): |
| pad = (0, (patch_size[i] - img.shape[i + 2] % patch_size[i]) % patch_size[i]) + pad |
|
|
| return torch.nn.functional.pad(img, pad, mode=padding_mode) |
|
|
| try: |
| rms_norm_torch = torch.nn.functional.rms_norm |
| except: |
| rms_norm_torch = None |
|
|
| def rms_norm(x, weight=None, eps=1e-6): |
| if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()): |
| if weight is None: |
| return rms_norm_torch(x, (x.shape[-1],), eps=eps) |
| else: |
| return rms_norm_torch(x, weight.shape, weight=comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps) |
| else: |
| r = x * torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps) |
| if weight is None: |
| return r |
| else: |
| return r * comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device) |
|
|
