CodeCow/stable-diffusion-webui-forge
1
0
Fork 0
mirror of https://github.com/lllyasviel/stable-diffusion-webui-forge.git synced 2026-07-21 21:01:24 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

resize

Browse Source
This commit is contained in:
Haoming 2026-04-29 10:39:21 +08:00
parent c184c04be6
commit f70169fc43
1 changed files with 25 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

36
backend/misc/image_resize.py
View File

@ -3,9 +3,9 @@ import torch
from PIL import Image
def bislerp(samples, width, height):
def bislerp(samples: torch.Tensor, width: int, height: int) -> torch.Tensor:
def slerp(b1, b2, r):
"""slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC"""
"""slerps batches b1, b2 according to ratio r, batches should be flat (e.g. NxC)"""
c = b1.shape[-1]
@ -80,18 +80,26 @@ def bislerp(samples, width, height):
return result.to(orig_dtype)
def lanczos(samples, width, height):
images = [Image.fromarray(np.clip(255.0 * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
def lanczos(samples: torch.Tensor, width: int, height: int) -> torch.Tensor:
samples = samples.squeeze(1) if samples.shape[1] == 1 else samples.movedim(1, -1)
images = [Image.fromarray(np.clip(255.0 * image.cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]
result = torch.stack(images)
return result.to(samples.device, samples.dtype)
def adaptive_resize(samples, width, height, upscale_method, crop):
def adaptive_resize(samples: torch.Tensor, width: int, height: int, upscale_method: str, crop: str) -> torch.Tensor:
# https://github.com/Comfy-Org/ComfyUI/blob/v0.20.1/comfy/utils.py#L1030
orig_shape = tuple(samples.shape)
if len(orig_shape) > 4:
samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1])
samples = samples.movedim(2, 1)
samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1])
if crop == "center":
old_width = samples.shape[3]
old_height = samples.shape[2]
old_width = samples.shape[-1]
old_height = samples.shape[-2]
old_aspect = old_width / old_height
new_aspect = width / height
x = 0
@ -100,13 +108,19 @@ def adaptive_resize(samples, width, height, upscale_method, crop):
x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
elif old_aspect < new_aspect:
y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
s = samples[:, :, y : old_height - y, x : old_width - x]
s = samples.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2)
else:
s = samples
if upscale_method == "bislerp":
return bislerp(s, width, height)
out = bislerp(s, width, height)
elif upscale_method == "lanczos":
return lanczos(s, width, height)
out = lanczos(s, width, height)
else:
return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
if len(orig_shape) == 4:
return out
out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width))
return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width))