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from tile_methods.abstractdiffusion import AbstractDiffusion
from tile_utils.utils import *
class MixtureOfDiffusers(AbstractDiffusion):
"""
Mixture-of-Diffusers Implementation
https://github.com/albarji/mixture-of-diffusers
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# weights for custom bboxes
self.custom_weights: List[Tensor] = []
self.get_weight = gaussian_weights
def hook(self):
if not hasattr(shared.sd_model, 'apply_model_original_md'):
shared.sd_model.apply_model_original_md = shared.sd_model.apply_model
shared.sd_model.apply_model = self.apply_model_hijack
@staticmethod
def unhook():
if hasattr(shared.sd_model, 'apply_model_original_md'):
shared.sd_model.apply_model = shared.sd_model.apply_model_original_md
del shared.sd_model.apply_model_original_md
def init_done(self):
super().init_done()
# The original gaussian weights can be extremely small, so we rescale them for numerical stability
self.rescale_factor = 1 / self.weights
# Meanwhile, we rescale the custom weights in advance to save time of slicing
for bbox_id, bbox in enumerate(self.custom_bboxes):
if bbox.blend_mode == BlendMode.BACKGROUND:
self.custom_weights[bbox_id] *= self.rescale_factor[bbox.slicer]
@grid_bbox
def get_tile_weights(self) -> Tensor:
# weights for grid bboxes
if not hasattr(self, 'tile_weights'):
self.tile_weights = self.get_weight(self.tile_w, self.tile_h)
return self.tile_weights
@custom_bbox
def init_custom_bbox(self, *args):
super().init_custom_bbox(*args)
for bbox in self.custom_bboxes:
if bbox.blend_mode == BlendMode.BACKGROUND:
custom_weights = self.get_weight(bbox.w, bbox.h)
self.weights[bbox.slicer] += custom_weights
self.custom_weights.append(custom_weights.unsqueeze(0).unsqueeze(0))
else:
self.custom_weights.append(None)
''' ↓↓↓ kernel hijacks ↓↓↓ '''
@torch.no_grad()
@keep_signature
def apply_model_hijack(self, x_in:Tensor, t_in:Tensor, cond:CondDict, noise_inverse_step:int=-1):
assert LatentDiffusion.apply_model
# KDiffusion Compatibility for naming
c_in: CondDict = cond
N, C, H, W = x_in.shape
if (H, W) != (self.h, self.w):
# We don't tile highres, let's just use the original apply_model
self.reset_controlnet_tensors()
return shared.sd_model.apply_model_original_md(x_in, t_in, c_in)
# clear buffer canvas
self.reset_buffer(x_in)
# Global sampling
if self.draw_background:
for batch_id, bboxes in enumerate(self.batched_bboxes): # batch_id is the `Latent tile batch size`
if state.interrupted: return x_in
# batching
x_tile_list = []
t_tile_list = []
tcond_tile_list = []
icond_tile_list = []
vcond_tile_list = []
for bbox in bboxes:
x_tile_list.append(x_in[bbox.slicer])
t_tile_list.append(t_in)
if isinstance(c_in, dict):
# tcond
tcond_tile = self.get_tcond(c_in) # cond, [1, 77, 768]
tcond_tile_list.append(tcond_tile)
# icond: might be dummy for txt2img, latent mask for img2img
icond = self.get_icond(c_in)
if icond.shape[2:] == (self.h, self.w):
icond = icond[bbox.slicer]
icond_tile_list.append(icond)
# vcond:
vcond = self.get_vcond(c_in)
vcond_tile_list.append(vcond)
else:
print('>> [WARN] not supported, make an issue on github!!')
x_tile = torch.cat(x_tile_list, dim=0) # differs each
t_tile = torch.cat(t_tile_list, dim=0) # just repeat
tcond_tile = torch.cat(tcond_tile_list, dim=0) # just repeat
icond_tile = torch.cat(icond_tile_list, dim=0) # differs each
vcond_tile = torch.cat(vcond_tile_list, dim=0) if None not in vcond_tile_list else None # just repeat
c_tile = self.make_cond_dict(c_in, tcond_tile, icond_tile, vcond_tile)
# controlnet
self.switch_controlnet_tensors(batch_id, N, len(bboxes), is_denoise=True)
# stablesr
self.switch_stablesr_tensors(batch_id)
# denoising: here the x is the noise
x_tile_out = shared.sd_model.apply_model_original_md(x_tile, t_tile, c_tile)
# de-batching
for i, bbox in enumerate(bboxes):
# This weights can be calcluated in advance, but will cost a lot of vram
# when you have many tiles. So we calculate it here.
w = self.tile_weights * self.rescale_factor[bbox.slicer]
self.x_buffer[bbox.slicer] += x_tile_out[i*N:(i+1)*N, :, :, :] * w
self.update_pbar()
# Custom region sampling
x_feather_buffer = None
x_feather_mask = None
x_feather_count = None
if len(self.custom_bboxes) > 0:
for bbox_id, bbox in enumerate(self.custom_bboxes):
if not self.p.disable_extra_networks:
with devices.autocast():
extra_networks.activate(self.p, bbox.extra_network_data)
x_tile = x_in[bbox.slicer]
if noise_inverse_step < 0:
x_tile_out = self.custom_apply_model(x_tile, t_in, c_in, bbox_id, bbox)
else:
tcond = Condition.reconstruct_cond(bbox.cond, noise_inverse_step)
icond = self.get_icond(c_in)
if icond.shape[2:] == (self.h, self.w):
icond = icond[bbox.slicer]
vcond = self.get_vcond(c_in)
c_out = self.make_cond_dict(c_in, tcond, icond, vcond)
x_tile_out = shared.sd_model.apply_model(x_tile, t_in, cond=c_out)
if bbox.blend_mode == BlendMode.BACKGROUND:
self.x_buffer[bbox.slicer] += x_tile_out * self.custom_weights[bbox_id]
elif bbox.blend_mode == BlendMode.FOREGROUND:
if x_feather_buffer is None:
x_feather_buffer = torch.zeros_like(self.x_buffer)
x_feather_mask = torch.zeros((1, 1, H, W), device=self.x_buffer.device)
x_feather_count = torch.zeros((1, 1, H, W), device=self.x_buffer.device)
x_feather_buffer[bbox.slicer] += x_tile_out
x_feather_mask [bbox.slicer] += bbox.feather_mask
x_feather_count [bbox.slicer] += 1
self.update_pbar()
if not self.p.disable_extra_networks:
with devices.autocast():
extra_networks.deactivate(self.p, bbox.extra_network_data)
x_out = self.x_buffer
if x_feather_buffer is not None:
# Average overlapping feathered regions
x_feather_buffer = torch.where(x_feather_count > 1, x_feather_buffer / x_feather_count, x_feather_buffer)
x_feather_mask = torch.where(x_feather_count > 1, x_feather_mask / x_feather_count, x_feather_mask)
# Weighted average with original x_buffer
x_out = torch.where(x_feather_count > 0, x_out * (1 - x_feather_mask) + x_feather_buffer * x_feather_mask, x_out)
# For mixture of diffusers, we cannot fill the not denoised area.
# So we just leave it as it is.
return x_out
def custom_apply_model(self, x_in, t_in, c_in, bbox_id, bbox) -> Tensor:
if self.is_kdiff:
return self.kdiff_custom_forward(x_in, t_in, c_in, bbox_id, bbox, forward_func=shared.sd_model.apply_model_original_md)
else:
def forward_func(x, c, ts, unconditional_conditioning, *args, **kwargs) -> Tensor:
# copy from p_sample_ddim in ddim.py
c_in: CondDict = dict()
for k in c:
if isinstance(c[k], list):
c_in[k] = [torch.cat([unconditional_conditioning[k][i], c[k][i]]) for i in range(len(c[k]))]
else:
c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
self.set_custom_controlnet_tensors(bbox_id, x.shape[0])
self.set_custom_stablesr_tensors(bbox_id)
return shared.sd_model.apply_model_original_md(x, ts, c_in)
return self.ddim_custom_forward(x_in, c_in, bbox, ts=t_in, forward_func=forward_func)
@torch.no_grad()
def get_noise(self, x_in:Tensor, sigma_in:Tensor, cond_in:Dict[str, Tensor], step:int) -> Tensor:
return self.apply_model_hijack(x_in, sigma_in, cond=cond_in, noise_inverse_step=step)
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