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import torch
import torch.nn as nn
from modules import devices, lowvram, shared, scripts
cond_cast_unet = getattr(devices, 'cond_cast_unet', lambda x: x)
from ldm.modules.diffusionmodules.util import timestep_embedding
from ldm.modules.diffusionmodules.openaimodel import UNetModel
class TorchHijackForUnet:
"""
This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
"""
def __getattr__(self, item):
if item == 'cat':
return self.cat
if hasattr(torch, item):
return getattr(torch, item)
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
def cat(self, tensors, *args, **kwargs):
if len(tensors) == 2:
a, b = tensors
if a.shape[-2:] != b.shape[-2:]:
a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
tensors = (a, b)
return torch.cat(tensors, *args, **kwargs)
th = TorchHijackForUnet()
class ControlParams:
def __init__(
self,
control_model,
hint_cond,
guess_mode,
weight,
guidance_stopped,
start_guidance_percent,
stop_guidance_percent,
advanced_weighting,
is_adapter,
is_extra_cond
):
self.control_model = control_model
self.hint_cond = hint_cond
self.guess_mode = guess_mode
self.weight = weight
self.guidance_stopped = guidance_stopped
self.start_guidance_percent = start_guidance_percent
self.stop_guidance_percent = stop_guidance_percent
self.advanced_weighting = advanced_weighting
self.is_adapter = is_adapter
self.is_extra_cond = is_extra_cond
class UnetHook(nn.Module):
def __init__(self, lowvram=False) -> None:
super().__init__()
self.lowvram = lowvram
self.batch_cond_available = True
self.only_mid_control = shared.opts.data.get("control_net_only_mid_control", False)
def hook(self, model):
outer = self
def guidance_schedule_handler(x):
for param in self.control_params:
current_sampling_percent = (x.sampling_step / x.total_sampling_steps)
param.guidance_stopped = current_sampling_percent < param.start_guidance_percent or current_sampling_percent > param.stop_guidance_percent
def cfg_based_adder(base, x, require_autocast, is_adapter=False):
if isinstance(x, float):
return base + x
if require_autocast:
zeros = torch.zeros_like(base)
zeros[:, :x.shape[1], ...] = x
x = zeros
# assume the input format is [cond, uncond] and they have same shape
# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/0cc0ee1bcb4c24a8c9715f66cede06601bfc00c8/modules/sd_samplers_kdiffusion.py#L114
if base.shape[0] % 2 == 0 and (self.guess_mode or shared.opts.data.get("control_net_cfg_based_guidance", False)):
if self.is_vanilla_samplers:
uncond, cond = base.chunk(2)
if x.shape[0] % 2 == 0:
_, x_cond = x.chunk(2)
return torch.cat([uncond, cond + x_cond], dim=0)
if is_adapter:
return torch.cat([uncond, cond + x], dim=0)
else:
cond, uncond = base.chunk(2)
if x.shape[0] % 2 == 0:
x_cond, _ = x.chunk(2)
return torch.cat([cond + x_cond, uncond], dim=0)
if is_adapter:
return torch.cat([cond + x, uncond], dim=0)
return base + x
def forward(self, x, timesteps=None, context=None, **kwargs):
total_control = [0.0] * 13
total_adapter = [0.0] * 4
total_extra_cond = torch.zeros([0, context.shape[-1]]).to(devices.get_device_for("controlnet"))
only_mid_control = outer.only_mid_control
require_inpaint_hijack = False
# handle external cond first
for param in outer.control_params:
if param.guidance_stopped or not param.is_extra_cond:
continue
if outer.lowvram:
param.control_model.to(devices.get_device_for("controlnet"))
control = param.control_model(x=x, hint=param.hint_cond, timesteps=timesteps, context=context)
total_extra_cond = torch.cat([total_extra_cond, control.clone().squeeze(0) * param.weight])
# check if it's non-batch-cond mode (lowvram, edit model etc)
if context.shape[0] % 2 != 0 and outer.batch_cond_available:
outer.batch_cond_available = False
if len(total_extra_cond) > 0 or outer.guess_mode or shared.opts.data.get("control_net_cfg_based_guidance", False):
print("Warning: StyleAdapter and cfg/guess mode may not works due to non-batch-cond inference")
# concat styleadapter to cond, pad uncond to same length
if len(total_extra_cond) > 0 and outer.batch_cond_available:
total_extra_cond = torch.repeat_interleave(total_extra_cond.unsqueeze(0), context.shape[0] // 2, dim=0)
if outer.is_vanilla_samplers:
uncond, cond = context.chunk(2)
cond = torch.cat([cond, total_extra_cond], dim=1)
uncond = torch.cat([uncond, uncond[:, -total_extra_cond.shape[1]:, :]], dim=1)
context = torch.cat([uncond, cond], dim=0)
else:
cond, uncond = context.chunk(2)
cond = torch.cat([cond, total_extra_cond], dim=1)
uncond = torch.cat([uncond, uncond[:, -total_extra_cond.shape[1]:, :]], dim=1)
context = torch.cat([cond, uncond], dim=0)
# handle unet injection stuff
for param in outer.control_params:
if param.guidance_stopped or param.is_extra_cond:
continue
if outer.lowvram:
param.control_model.to(devices.get_device_for("controlnet"))
# hires stuffs
# note that this method may not works if hr_scale < 1.1
if abs(x.shape[-1] - param.hint_cond.shape[-1] // 8) > 8:
only_mid_control = shared.opts.data.get("control_net_only_midctrl_hires", True)
# If you want to completely disable control net, uncomment this.
# return self._original_forward(x, timesteps=timesteps, context=context, **kwargs)
# inpaint model workaround
x_in = x
control_model = param.control_model.control_model
if not param.is_adapter and x.shape[1] != control_model.input_blocks[0][0].in_channels and x.shape[1] == 9:
# inpaint_model: 4 data + 4 downscaled image + 1 mask
x_in = x[:, :4, ...]
require_inpaint_hijack = True
assert param.hint_cond is not None, f"Controlnet is enabled but no input image is given"
control = param.control_model(x=x_in, hint=param.hint_cond, timesteps=timesteps, context=context)
control_scales = ([param.weight] * 13)
if outer.lowvram:
param.control_model.to("cpu")
if param.guess_mode:
if param.is_adapter:
# see https://github.com/Mikubill/sd-webui-controlnet/issues/269
control_scales = param.weight * [0.25, 0.62, 0.825, 1.0]
else:
control_scales = [param.weight * (0.825 ** float(12 - i)) for i in range(13)]
if param.advanced_weighting is not None:
control_scales = param.advanced_weighting
control = [c * scale for c, scale in zip(control, control_scales)]
for idx, item in enumerate(control):
target = total_adapter if param.is_adapter else total_control
target[idx] += item
control = total_control
assert timesteps is not None, ValueError(f"insufficient timestep: {timesteps}")
hs = []
with th.no_grad():
t_emb = cond_cast_unet(timestep_embedding(timesteps, self.model_channels, repeat_only=False))
emb = self.time_embed(t_emb)
h = x.type(self.dtype)
for i, module in enumerate(self.input_blocks):
h = module(h, emb, context)
# t2i-adatper, same as openaimodel.py:744
if ((i+1)%3 == 0) and len(total_adapter):
h = cfg_based_adder(h, total_adapter.pop(0), require_inpaint_hijack, is_adapter=True)
hs.append(h)
h = self.middle_block(h, emb, context)
control_in = control.pop()
h = cfg_based_adder(h, control_in, require_inpaint_hijack)
for i, module in enumerate(self.output_blocks):
if only_mid_control:
hs_input = hs.pop()
h = th.cat([h, hs_input], dim=1)
else:
hs_input, control_input = hs.pop(), control.pop()
h = th.cat([h, cfg_based_adder(hs_input, control_input, require_inpaint_hijack)], dim=1)
h = module(h, emb, context)
h = h.type(x.dtype)
return self.out(h)
def forward2(*args, **kwargs):
# webui will handle other compoments
try:
if shared.cmd_opts.lowvram:
lowvram.send_everything_to_cpu()
return forward(*args, **kwargs)
finally:
if self.lowvram:
[param.control_model.to("cpu") for param in self.control_params]
model._original_forward = model.forward
model.forward = forward2.__get__(model, UNetModel)
scripts.script_callbacks.on_cfg_denoiser(guidance_schedule_handler)
def notify(self, params, is_vanilla_samplers): # lint: list[ControlParams]
self.is_vanilla_samplers = is_vanilla_samplers
self.control_params = params
self.guess_mode = any([param.guess_mode for param in params])
def restore(self, model):
scripts.script_callbacks.remove_current_script_callbacks()
if hasattr(self, "control_params"):
del self.control_params
if not hasattr(model, "_original_forward"):
# no such handle, ignore
return
model.forward = model._original_forward
del model._original_forward
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