#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
#modified to support different types of flux controlnets
import torch
import math
from torch import Tensor, nn
from einops import rearrange, repeat
from .layers import (timestep_embedding)
from .model import Flux
import comfy.ldm.common_dit
class MistolineCondDownsamplBlock(nn.Module):
def __init__(self, dtype=None, device=None, operations=None):
super().__init__()
self.encoder = nn.Sequential(
operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
)
def forward(self, x):
return self.encoder(x)
class MistolineControlnetBlock(nn.Module):
def __init__(self, hidden_size, dtype=None, device=None, operations=None):
super().__init__()
self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device)
self.act = nn.SiLU()
def forward(self, x):
return self.act(self.linear(x))
class ControlNetFlux(Flux):
def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs):
super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
self.main_model_double = 19
self.main_model_single = 38
self.mistoline = mistoline
# add ControlNet blocks
if self.mistoline:
control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations)
else:
control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
self.controlnet_blocks = nn.ModuleList([])
for _ in range(self.params.depth):
self.controlnet_blocks.append(control_block())
self.controlnet_single_blocks = nn.ModuleList([])
for _ in range(self.params.depth_single_blocks):
self.controlnet_single_blocks.append(control_block())
self.num_union_modes = num_union_modes
self.controlnet_mode_embedder = None
if self.num_union_modes > 0:
self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device)
self.gradient_checkpointing = False
self.latent_input = latent_input
if control_latent_channels is None:
control_latent_channels = self.in_channels
else:
control_latent_channels *= 2 * 2 #patch size
self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
if not self.latent_input:
if self.mistoline:
self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations)
else:
self.input_hint_block = nn.Sequential(
operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
nn.SiLU(),
operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
)
def forward_orig(
self,
img: Tensor,
img_ids: Tensor,
controlnet_cond: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
y: Tensor,
guidance: Tensor = None,
control_type: Tensor = None,
) -> Tensor:
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
# running on sequences img
img = self.img_in(img)
controlnet_cond = self.pos_embed_input(controlnet_cond)
img = img + controlnet_cond
vec = self.time_in(timestep_embedding(timesteps, 256))
if self.params.guidance_embed:
vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
vec = vec + self.vector_in(y)
txt = self.txt_in(txt)
if self.controlnet_mode_embedder is not None and len(control_type) > 0:
control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1))
txt = torch.cat([control_cond, txt], dim=1)
txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
controlnet_double = ()
for i in range(len(self.double_blocks)):
img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe)
controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),)
img = torch.cat((txt, img), 1)
controlnet_single = ()
for i in range(len(self.single_blocks)):
img = self.single_blocks[i](img, vec=vec, pe=pe)
controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),)
repeat = math.ceil(self.main_model_double / len(controlnet_double))
if self.latent_input:
out_input = ()
for x in controlnet_double:
out_input += (x,) * repeat
else:
out_input = (controlnet_double * repeat)
out = {"input": out_input[:self.main_model_double]}
if len(controlnet_single) > 0:
repeat = math.ceil(self.main_model_single / len(controlnet_single))
out_output = ()
if self.latent_input:
for x in controlnet_single:
out_output += (x,) * repeat
else:
out_output = (controlnet_single * repeat)
out["output"] = out_output[:self.main_model_single]
return out
def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
patch_size = 2
if self.latent_input:
hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))
elif self.mistoline:
hint = hint * 2.0 - 1.0
hint = self.input_cond_block(hint)
else:
hint = hint * 2.0 - 1.0
hint = self.input_hint_block(hint)
hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
bs, c, h, w = x.shape
x = comfy.ldm.common_dit.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)
img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", []))