489 lines
20 KiB
Python
489 lines
20 KiB
Python
import torch
|
|
import math
|
|
import os
|
|
import comfy.utils
|
|
import comfy.model_management
|
|
import comfy.model_detection
|
|
import comfy.model_patcher
|
|
|
|
import comfy.cldm.cldm
|
|
import comfy.t2i_adapter.adapter
|
|
|
|
|
|
def broadcast_image_to(tensor, target_batch_size, batched_number):
|
|
current_batch_size = tensor.shape[0]
|
|
#print(current_batch_size, target_batch_size)
|
|
if current_batch_size == 1:
|
|
return tensor
|
|
|
|
per_batch = target_batch_size // batched_number
|
|
tensor = tensor[:per_batch]
|
|
|
|
if per_batch > tensor.shape[0]:
|
|
tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
|
|
|
|
current_batch_size = tensor.shape[0]
|
|
if current_batch_size == target_batch_size:
|
|
return tensor
|
|
else:
|
|
return torch.cat([tensor] * batched_number, dim=0)
|
|
|
|
class ControlBase:
|
|
def __init__(self, device=None):
|
|
self.cond_hint_original = None
|
|
self.cond_hint = None
|
|
self.strength = 1.0
|
|
self.timestep_percent_range = (1.0, 0.0)
|
|
self.timestep_range = None
|
|
|
|
if device is None:
|
|
device = comfy.model_management.get_torch_device()
|
|
self.device = device
|
|
self.previous_controlnet = None
|
|
self.global_average_pooling = False
|
|
|
|
def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)):
|
|
self.cond_hint_original = cond_hint
|
|
self.strength = strength
|
|
self.timestep_percent_range = timestep_percent_range
|
|
return self
|
|
|
|
def pre_run(self, model, percent_to_timestep_function):
|
|
self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))
|
|
if self.previous_controlnet is not None:
|
|
self.previous_controlnet.pre_run(model, percent_to_timestep_function)
|
|
|
|
def set_previous_controlnet(self, controlnet):
|
|
self.previous_controlnet = controlnet
|
|
return self
|
|
|
|
def cleanup(self):
|
|
if self.previous_controlnet is not None:
|
|
self.previous_controlnet.cleanup()
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
self.timestep_range = None
|
|
|
|
def get_models(self):
|
|
out = []
|
|
if self.previous_controlnet is not None:
|
|
out += self.previous_controlnet.get_models()
|
|
return out
|
|
|
|
def copy_to(self, c):
|
|
c.cond_hint_original = self.cond_hint_original
|
|
c.strength = self.strength
|
|
c.timestep_percent_range = self.timestep_percent_range
|
|
|
|
def inference_memory_requirements(self, dtype):
|
|
if self.previous_controlnet is not None:
|
|
return self.previous_controlnet.inference_memory_requirements(dtype)
|
|
return 0
|
|
|
|
def control_merge(self, control_input, control_output, control_prev, output_dtype):
|
|
out = {'input':[], 'middle':[], 'output': []}
|
|
|
|
if control_input is not None:
|
|
for i in range(len(control_input)):
|
|
key = 'input'
|
|
x = control_input[i]
|
|
if x is not None:
|
|
x *= self.strength
|
|
if x.dtype != output_dtype:
|
|
x = x.to(output_dtype)
|
|
out[key].insert(0, x)
|
|
|
|
if control_output is not None:
|
|
for i in range(len(control_output)):
|
|
if i == (len(control_output) - 1):
|
|
key = 'middle'
|
|
index = 0
|
|
else:
|
|
key = 'output'
|
|
index = i
|
|
x = control_output[i]
|
|
if x is not None:
|
|
if self.global_average_pooling:
|
|
x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
|
|
|
|
x *= self.strength
|
|
if x.dtype != output_dtype:
|
|
x = x.to(output_dtype)
|
|
|
|
out[key].append(x)
|
|
if control_prev is not None:
|
|
for x in ['input', 'middle', 'output']:
|
|
o = out[x]
|
|
for i in range(len(control_prev[x])):
|
|
prev_val = control_prev[x][i]
|
|
if i >= len(o):
|
|
o.append(prev_val)
|
|
elif prev_val is not None:
|
|
if o[i] is None:
|
|
o[i] = prev_val
|
|
else:
|
|
o[i] += prev_val
|
|
return out
|
|
|
|
class ControlNet(ControlBase):
|
|
def __init__(self, control_model, global_average_pooling=False, device=None):
|
|
super().__init__(device)
|
|
self.control_model = control_model
|
|
self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
|
|
self.global_average_pooling = global_average_pooling
|
|
|
|
def get_control(self, x_noisy, t, cond, batched_number):
|
|
control_prev = None
|
|
if self.previous_controlnet is not None:
|
|
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
|
|
|
|
if self.timestep_range is not None:
|
|
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
|
|
if control_prev is not None:
|
|
return control_prev
|
|
else:
|
|
return None
|
|
|
|
output_dtype = x_noisy.dtype
|
|
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
|
|
if x_noisy.shape[0] != self.cond_hint.shape[0]:
|
|
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
|
|
|
|
|
|
context = cond['c_crossattn']
|
|
y = cond.get('c_adm', None)
|
|
if y is not None:
|
|
y = y.to(self.control_model.dtype)
|
|
control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y)
|
|
return self.control_merge(None, control, control_prev, output_dtype)
|
|
|
|
def copy(self):
|
|
c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling)
|
|
self.copy_to(c)
|
|
return c
|
|
|
|
def get_models(self):
|
|
out = super().get_models()
|
|
out.append(self.control_model_wrapped)
|
|
return out
|
|
|
|
class ControlLoraOps:
|
|
class Linear(torch.nn.Module):
|
|
def __init__(self, in_features: int, out_features: int, bias: bool = True,
|
|
device=None, dtype=None) -> None:
|
|
factory_kwargs = {'device': device, 'dtype': dtype}
|
|
super().__init__()
|
|
self.in_features = in_features
|
|
self.out_features = out_features
|
|
self.weight = None
|
|
self.up = None
|
|
self.down = None
|
|
self.bias = None
|
|
|
|
def forward(self, input):
|
|
if self.up is not None:
|
|
return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
|
|
else:
|
|
return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias)
|
|
|
|
class Conv2d(torch.nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
groups=1,
|
|
bias=True,
|
|
padding_mode='zeros',
|
|
device=None,
|
|
dtype=None
|
|
):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.kernel_size = kernel_size
|
|
self.stride = stride
|
|
self.padding = padding
|
|
self.dilation = dilation
|
|
self.transposed = False
|
|
self.output_padding = 0
|
|
self.groups = groups
|
|
self.padding_mode = padding_mode
|
|
|
|
self.weight = None
|
|
self.bias = None
|
|
self.up = None
|
|
self.down = None
|
|
|
|
|
|
def forward(self, input):
|
|
if self.up is not None:
|
|
return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
|
|
else:
|
|
return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups)
|
|
|
|
def conv_nd(self, dims, *args, **kwargs):
|
|
if dims == 2:
|
|
return self.Conv2d(*args, **kwargs)
|
|
else:
|
|
raise ValueError(f"unsupported dimensions: {dims}")
|
|
|
|
|
|
class ControlLora(ControlNet):
|
|
def __init__(self, control_weights, global_average_pooling=False, device=None):
|
|
ControlBase.__init__(self, device)
|
|
self.control_weights = control_weights
|
|
self.global_average_pooling = global_average_pooling
|
|
|
|
def pre_run(self, model, percent_to_timestep_function):
|
|
super().pre_run(model, percent_to_timestep_function)
|
|
controlnet_config = model.model_config.unet_config.copy()
|
|
controlnet_config.pop("out_channels")
|
|
controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
|
|
controlnet_config["operations"] = ControlLoraOps()
|
|
self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
|
|
dtype = model.get_dtype()
|
|
self.control_model.to(dtype)
|
|
self.control_model.to(comfy.model_management.get_torch_device())
|
|
diffusion_model = model.diffusion_model
|
|
sd = diffusion_model.state_dict()
|
|
cm = self.control_model.state_dict()
|
|
|
|
for k in sd:
|
|
weight = comfy.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k)
|
|
try:
|
|
comfy.utils.set_attr(self.control_model, k, weight)
|
|
except:
|
|
pass
|
|
|
|
for k in self.control_weights:
|
|
if k not in {"lora_controlnet"}:
|
|
comfy.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
|
|
|
|
def copy(self):
|
|
c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
|
|
self.copy_to(c)
|
|
return c
|
|
|
|
def cleanup(self):
|
|
del self.control_model
|
|
self.control_model = None
|
|
super().cleanup()
|
|
|
|
def get_models(self):
|
|
out = ControlBase.get_models(self)
|
|
return out
|
|
|
|
def inference_memory_requirements(self, dtype):
|
|
return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
|
|
|
|
def load_controlnet(ckpt_path, model=None):
|
|
controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
|
|
if "lora_controlnet" in controlnet_data:
|
|
return ControlLora(controlnet_data)
|
|
|
|
controlnet_config = None
|
|
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
|
|
use_fp16 = comfy.model_management.should_use_fp16()
|
|
controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16)
|
|
diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
|
|
diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
|
|
diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
|
|
|
|
count = 0
|
|
loop = True
|
|
while loop:
|
|
suffix = [".weight", ".bias"]
|
|
for s in suffix:
|
|
k_in = "controlnet_down_blocks.{}{}".format(count, s)
|
|
k_out = "zero_convs.{}.0{}".format(count, s)
|
|
if k_in not in controlnet_data:
|
|
loop = False
|
|
break
|
|
diffusers_keys[k_in] = k_out
|
|
count += 1
|
|
|
|
count = 0
|
|
loop = True
|
|
while loop:
|
|
suffix = [".weight", ".bias"]
|
|
for s in suffix:
|
|
if count == 0:
|
|
k_in = "controlnet_cond_embedding.conv_in{}".format(s)
|
|
else:
|
|
k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
|
|
k_out = "input_hint_block.{}{}".format(count * 2, s)
|
|
if k_in not in controlnet_data:
|
|
k_in = "controlnet_cond_embedding.conv_out{}".format(s)
|
|
loop = False
|
|
diffusers_keys[k_in] = k_out
|
|
count += 1
|
|
|
|
new_sd = {}
|
|
for k in diffusers_keys:
|
|
if k in controlnet_data:
|
|
new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
|
|
|
|
leftover_keys = controlnet_data.keys()
|
|
if len(leftover_keys) > 0:
|
|
print("leftover keys:", leftover_keys)
|
|
controlnet_data = new_sd
|
|
|
|
pth_key = 'control_model.zero_convs.0.0.weight'
|
|
pth = False
|
|
key = 'zero_convs.0.0.weight'
|
|
if pth_key in controlnet_data:
|
|
pth = True
|
|
key = pth_key
|
|
prefix = "control_model."
|
|
elif key in controlnet_data:
|
|
prefix = ""
|
|
else:
|
|
net = load_t2i_adapter(controlnet_data)
|
|
if net is None:
|
|
print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
|
|
return net
|
|
|
|
if controlnet_config is None:
|
|
use_fp16 = comfy.model_management.should_use_fp16()
|
|
controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16, True).unet_config
|
|
controlnet_config.pop("out_channels")
|
|
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
|
|
control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
|
|
|
|
if pth:
|
|
if 'difference' in controlnet_data:
|
|
if model is not None:
|
|
comfy.model_management.load_models_gpu([model])
|
|
model_sd = model.model_state_dict()
|
|
for x in controlnet_data:
|
|
c_m = "control_model."
|
|
if x.startswith(c_m):
|
|
sd_key = "diffusion_model.{}".format(x[len(c_m):])
|
|
if sd_key in model_sd:
|
|
cd = controlnet_data[x]
|
|
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
|
|
else:
|
|
print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
|
|
|
|
class WeightsLoader(torch.nn.Module):
|
|
pass
|
|
w = WeightsLoader()
|
|
w.control_model = control_model
|
|
missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
|
|
else:
|
|
missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
|
|
print(missing, unexpected)
|
|
|
|
if use_fp16:
|
|
control_model = control_model.half()
|
|
|
|
global_average_pooling = False
|
|
filename = os.path.splitext(ckpt_path)[0]
|
|
if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
|
|
global_average_pooling = True
|
|
|
|
control = ControlNet(control_model, global_average_pooling=global_average_pooling)
|
|
return control
|
|
|
|
class T2IAdapter(ControlBase):
|
|
def __init__(self, t2i_model, channels_in, device=None):
|
|
super().__init__(device)
|
|
self.t2i_model = t2i_model
|
|
self.channels_in = channels_in
|
|
self.control_input = None
|
|
|
|
def scale_image_to(self, width, height):
|
|
unshuffle_amount = self.t2i_model.unshuffle_amount
|
|
width = math.ceil(width / unshuffle_amount) * unshuffle_amount
|
|
height = math.ceil(height / unshuffle_amount) * unshuffle_amount
|
|
return width, height
|
|
|
|
def get_control(self, x_noisy, t, cond, batched_number):
|
|
control_prev = None
|
|
if self.previous_controlnet is not None:
|
|
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
|
|
|
|
if self.timestep_range is not None:
|
|
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
|
|
if control_prev is not None:
|
|
return control_prev
|
|
else:
|
|
return {}
|
|
|
|
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.control_input = None
|
|
self.cond_hint = None
|
|
width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
|
|
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device)
|
|
if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
|
|
self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
|
|
if x_noisy.shape[0] != self.cond_hint.shape[0]:
|
|
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
|
|
if self.control_input is None:
|
|
self.t2i_model.to(x_noisy.dtype)
|
|
self.t2i_model.to(self.device)
|
|
self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
|
|
self.t2i_model.cpu()
|
|
|
|
control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
|
|
mid = None
|
|
if self.t2i_model.xl == True:
|
|
mid = control_input[-1:]
|
|
control_input = control_input[:-1]
|
|
return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
|
|
|
|
def copy(self):
|
|
c = T2IAdapter(self.t2i_model, self.channels_in)
|
|
self.copy_to(c)
|
|
return c
|
|
|
|
def load_t2i_adapter(t2i_data):
|
|
if 'adapter' in t2i_data:
|
|
t2i_data = t2i_data['adapter']
|
|
if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format
|
|
prefix_replace = {}
|
|
for i in range(4):
|
|
for j in range(2):
|
|
prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j)
|
|
prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2)
|
|
prefix_replace["adapter."] = ""
|
|
t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace)
|
|
keys = t2i_data.keys()
|
|
|
|
if "body.0.in_conv.weight" in keys:
|
|
cin = t2i_data['body.0.in_conv.weight'].shape[1]
|
|
model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
|
|
elif 'conv_in.weight' in keys:
|
|
cin = t2i_data['conv_in.weight'].shape[1]
|
|
channel = t2i_data['conv_in.weight'].shape[0]
|
|
ksize = t2i_data['body.0.block2.weight'].shape[2]
|
|
use_conv = False
|
|
down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys))
|
|
if len(down_opts) > 0:
|
|
use_conv = True
|
|
xl = False
|
|
if cin == 256 or cin == 768:
|
|
xl = True
|
|
model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
|
|
else:
|
|
return None
|
|
missing, unexpected = model_ad.load_state_dict(t2i_data)
|
|
if len(missing) > 0:
|
|
print("t2i missing", missing)
|
|
|
|
if len(unexpected) > 0:
|
|
print("t2i unexpected", unexpected)
|
|
|
|
return T2IAdapter(model_ad, model_ad.input_channels)
|