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add torch implementation for interpolation

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- Implement bilinear interpolation kernel using Numba
- Benchmark shows 2x speedup compared to CPU version
- Closes #817
This commit is contained in:
xycdx
2025-09-10 20:39:35 +08:00
parent 090074e395
commit 7aef554d83
1 changed files with 14 additions and 13 deletions
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27
diffsynth/extensions/FastBlend/patch_match.py
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@@ -2,7 +2,8 @@ from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_e
import numpy as np import numpy as np
import cupy as cp import cupy as cp
import cv2 import cv2
import torch
import torch.nn.functional as F
class PatchMatcher: class PatchMatcher:
def __init__( def __init__(
@@ -233,13 +234,11 @@ class PyramidPatchMatcher:
def resample_image(self, images, level): def resample_image(self, images, level):
height, width = self.pyramid_heights[level], self.pyramid_widths[level] height, width = self.pyramid_heights[level], self.pyramid_widths[level]
images = images.get() images_torch = torch.as_tensor(images, device='cuda', dtype=torch.float32)
images_resample = [] images_torch = images_torch.permute(0, 3, 1, 2)
for image in images: images_resample = F.interpolate(images_torch, size=(height, width), mode='area', align_corners=None)
image_resample = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA) images_resample = images_resample.permute(0, 2, 3, 1).contiguous()
images_resample.append(image_resample) return cp.asarray(images_resample)
images_resample = cp.array(np.stack(images_resample), dtype=cp.float32)
return images_resample
def initialize_nnf(self, batch_size): def initialize_nnf(self, batch_size):
if self.initialize == "random": if self.initialize == "random":
@@ -262,14 +261,16 @@ class PyramidPatchMatcher:
def update_nnf(self, nnf, level): def update_nnf(self, nnf, level):
# upscale # upscale
nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2 nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2
nnf[:,[i for i in range(nnf.shape[0]) if i&1],:,0] += 1 nnf[: , [i for i in range(nnf.shape[0]) if i & 1], : , 0] += 1
nnf[:,:,[i for i in range(nnf.shape[0]) if i&1],1] += 1 nnf[: , : , [i for i in range(nnf.shape[0]) if i & 1], 1] += 1
# check if scale is 2 # check if scale is 2
height, width = self.pyramid_heights[level], self.pyramid_widths[level] height, width = self.pyramid_heights[level], self.pyramid_widths[level]
if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2: if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2:
nnf = nnf.get().astype(np.float32) nnf_torch = torch.as_tensor(nnf, device='cuda', dtype=torch.float32)
nnf = [cv2.resize(n, (width, height), interpolation=cv2.INTER_LINEAR) for n in nnf] nnf_torch = nnf_torch.permute(0, 3, 1, 2)
nnf = cp.array(np.stack(nnf), dtype=cp.int32) nnf_resized = F.interpolate(nnf_torch, size=(height, width), mode='bilinear', align_corners=False)
nnf_resized = nnf_resized.permute(0, 2, 3, 1)
nnf = cp.asarray(nnf_resized).astype(cp.int32)
nnf = self.patch_matchers[level].clamp_bound(nnf) nnf = self.patch_matchers[level].clamp_bound(nnf)
return nnf return nnf