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# Credits: <https://github.com/mycodeiscat/ControlNet-LLLite-diffusers>
# <https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI/blob/main/node_control_net_lllite.py>
import re
import torch
from safetensors.torch import load_file
all_hack = {}
class LLLiteModule(torch.nn.Module):
def __init__(
self,
name: str,
is_conv2d: bool,
in_dim: int,
depth: int,
cond_emb_dim: int,
mlp_dim: int,
):
super().__init__()
self.name = name
self.is_conv2d = is_conv2d
self.is_first = False
modules = []
modules.append(torch.nn.Conv2d(3, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0)) # to latent (from VAE) size*2
if depth == 1:
modules.append(torch.nn.ReLU(inplace=True))
modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=2, stride=2, padding=0))
elif depth == 2:
modules.append(torch.nn.ReLU(inplace=True))
modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=4, stride=4, padding=0))
elif depth == 3:
# kernel size 8ใฏๅคงใใใใใฎใงใ4ใซใใ / kernel size 8 is too large, so set it to 4
modules.append(torch.nn.ReLU(inplace=True))
modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0))
modules.append(torch.nn.ReLU(inplace=True))
modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=2, stride=2, padding=0))
self.conditioning1 = torch.nn.Sequential(*modules)
if self.is_conv2d:
self.down = torch.nn.Sequential(
torch.nn.Conv2d(in_dim, mlp_dim, kernel_size=1, stride=1, padding=0),
torch.nn.ReLU(inplace=True),
)
self.mid = torch.nn.Sequential(
torch.nn.Conv2d(mlp_dim + cond_emb_dim, mlp_dim, kernel_size=1, stride=1, padding=0),
torch.nn.ReLU(inplace=True),
)
self.up = torch.nn.Sequential(
torch.nn.Conv2d(mlp_dim, in_dim, kernel_size=1, stride=1, padding=0),
)
else:
self.down = torch.nn.Sequential(
torch.nn.Linear(in_dim, mlp_dim),
torch.nn.ReLU(inplace=True),
)
self.mid = torch.nn.Sequential(
torch.nn.Linear(mlp_dim + cond_emb_dim, mlp_dim),
torch.nn.ReLU(inplace=True),
)
self.up = torch.nn.Sequential(
torch.nn.Linear(mlp_dim, in_dim),
)
self.depth = depth
self.cond_image = None
self.cond_emb = None
def set_cond_image(self, cond_image):
self.cond_image = cond_image
self.cond_emb = None
def forward(self, x):
if self.cond_emb is None:
cx = self.conditioning1(self.cond_image.to(x.device, dtype=x.dtype))
# if blk_shape is not None:
# b, c, h, w = blk_shape
# cx = torch.nn.functional.interpolate(cx, (h, w), mode="nearest-exact")
if not self.is_conv2d:
# reshape / b,c,h,w -> b,h*w,c
n, c, h, w = cx.shape
cx = cx.view(n, c, h * w).permute(0, 2, 1)
self.cond_emb = cx
cx = self.cond_emb
# uncond/condใงxใฏใใใใตใคใบใ2ๅ
if x.shape[0] != cx.shape[0]:
if self.is_conv2d:
cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1, 1)
else:
cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1)
cx = torch.cat([cx, self.down(x)], dim=1 if self.is_conv2d else 2)
cx = self.mid(cx)
cx = self.up(cx)
return cx
def clear_all_lllite():
global all_hack # pylint: disable=global-statement
for k, v in all_hack.items():
k.forward = v
k.lllite_list = []
all_hack = {}
return
class ControlNetLLLite(torch.nn.Module): # pylint: disable=abstract-method
def __init__(self, path: str):
super().__init__()
module_weights = {}
try:
state_dict = load_file(path)
except Exception as e:
raise RuntimeError(f"Failed to load {path}") from e
for key, value in state_dict.items():
fragments = key.split(".")
module_name = fragments[0]
weight_name = ".".join(fragments[1:])
if module_name not in module_weights:
module_weights[module_name] = {}
module_weights[module_name][weight_name] = value
modules = {}
for module_name, weights in module_weights.items():
if "conditioning1.4.weight" in weights:
depth = 3
elif weights["conditioning1.2.weight"].shape[-1] == 4:
depth = 2
else:
depth = 1
module = LLLiteModule(
name=module_name,
is_conv2d=weights["down.0.weight"].ndim == 4,
in_dim=weights["down.0.weight"].shape[1],
depth=depth,
cond_emb_dim=weights["conditioning1.0.weight"].shape[0] * 2,
mlp_dim=weights["down.0.weight"].shape[0],
)
# info = module.load_state_dict(weights)
modules[module_name] = module
setattr(self, module_name, module)
if len(modules) == 1:
module.is_first = True
self.modules = modules
return
@torch.no_grad()
def apply(self, pipe, cond, weight): # pylint: disable=arguments-differ
map_down_lllite_to_unet = {4: (1, 0), 5: (1, 1), 7: (2, 0), 8: (2, 1)}
model = pipe.unet
if type(cond) != torch.Tensor:
cond = torch.tensor(cond)
cond = cond/255 # 0-255 -> 0-1
cond_image = cond.unsqueeze(dim=0).permute(0, 3, 1, 2) # h,w,c -> b,c,h,w
cond_image = cond_image * 2.0 - 1.0 # 0-1 -> -1-1
for module in self.modules.values():
module.set_cond_image(cond_image)
for k, v in self.modules.items():
k = k.replace('middle_block', 'middle_blocks_0')
match = re.match("lllite_unet_(.*)_blocks_(.*)_1_transformer_blocks_(.*)_(.*)_to_(.*)", k, re.M | re.I)
assert match, 'Failed to load ControlLLLite!'
root = match.group(1)
block = match.group(2)
block_number = match.group(3)
attn_name = match.group(4)
proj_name = match.group(5)
if root == 'input':
mapped_block, mapped_number = map_down_lllite_to_unet[int(block)]
b = model.down_blocks[mapped_block].attentions[int(mapped_number)].transformer_blocks[int(block_number)]
elif root == 'output':
pass # not implemented
else:
b = model.mid_block.attentions[0].transformer_blocks[int(block_number)]
b = getattr(b, attn_name, None)
assert b is not None, 'Failed to load ControlLLLite!'
b = getattr(b, 'to_' + proj_name, None)
assert b is not None, 'Failed to load ControlLLLite!'
if not hasattr(b, 'lllite_list'):
b.lllite_list = []
if len(b.lllite_list) == 0:
all_hack[b] = b.forward
b.forward = self.get_hacked_forward(original_forward=b.forward, model=model, blk=b)
b.lllite_list.append((weight, v))
return
def get_hacked_forward(self, original_forward, model, blk):
@torch.no_grad()
def forward(x, **kwargs):
hack = 0
for weight, module in blk.lllite_list:
module.to(x.device)
module.to(x.dtype)
hack = hack + module(x) * weight
x = x + hack
return original_forward(x, **kwargs)
return forward
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