chore: vendor selva_core from jnwnlee/selva@d7d40a9

Pure PyTorch SelVA source for SelvaModelLoader/FeatureExtractor/Sampler nodes. Imports rewritten from selva.* to selva_core.*. mel_converter.py: replaced librosa.filters.mel with pure-numpy implementation to avoid librosa→numba→NumPy version incompatibility in some ComfyUI environments. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-04 15:18:09 +02:00
parent 762b19fd3a
commit 6bc3fd6443
106 changed files with 11323 additions and 0 deletions
@@ -0,0 +1,35 @@
+from typing import Union
+
+import torch
+from einops import rearrange
+from torch import Tensor
+
+# Ref: https://github.com/black-forest-labs/flux/blob/main/src/flux/math.py
+# Ref: https://github.com/lucidrains/rotary-embedding-torch
+
+
+def compute_rope_rotations(length: int,
+                           dim: int,
+                           theta: int,
+                           *,
+                           freq_scaling: float = 1.0,
+                           device: Union[torch.device, str] = 'cpu') -> Tensor:
+    assert dim % 2 == 0
+
+    with torch.amp.autocast(device_type='cuda', enabled=False):
+        pos = torch.arange(length, dtype=torch.float32, device=device)
+        freqs = 1.0 / (theta**(torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+        freqs *= freq_scaling
+
+        rot = torch.einsum('..., f -> ... f', pos, freqs)
+        rot = torch.stack([torch.cos(rot), -torch.sin(rot), torch.sin(rot), torch.cos(rot)], dim=-1)
+        rot = rearrange(rot, 'n d (i j) -> 1 n d i j', i=2, j=2)
+        return rot
+
+
+def apply_rope(x: Tensor, rot: Tensor) -> tuple[Tensor, Tensor]:
+    with torch.amp.autocast(device_type='cuda', enabled=False):
+        _x = x.float()
+        _x = _x.view(*_x.shape[:-1], -1, 1, 2)
+        x_out = rot[..., 0] * _x[..., 0] + rot[..., 1] * _x[..., 1]
+        return x_out.reshape(*x.shape).to(dtype=x.dtype)