ComfyUI/comfy/ldm/common_dit.py

47 lines
1.9 KiB
Python

import torch
from einops import rearrange, repeat
import comfy.rmsnorm
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)
rms_norm = comfy.rmsnorm.rms_norm
def process_img(x, index=0, h_offset=0, w_offset=0, patch_size=(2, 2), transformer_options={}):
bs, c, h, w = x.shape
x = pad_to_patch_size(x, (patch_size, patch_size))
img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
h_len = ((h + (patch_size // 2)) // patch_size)
w_len = ((w + (patch_size // 2)) // patch_size)
h_offset = ((h_offset + (patch_size // 2)) // patch_size)
w_offset = ((w_offset + (patch_size // 2)) // patch_size)
steps_h = h_len
steps_w = w_len
rope_options = transformer_options.get("rope_options", None)
if rope_options is not None:
h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
index += rope_options.get("shift_t", 0.0)
h_offset += rope_options.get("shift_y", 0.0)
w_offset += rope_options.get("shift_x", 0.0)
img_ids = torch.zeros((steps_h, steps_w, 3), device=x.device, dtype=x.dtype)
img_ids[:, :, 0] = img_ids[:, :, 1] + index
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=x.dtype).unsqueeze(1)
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=x.dtype).unsqueeze(0)
return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)