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#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 (DoubleStreamBlock, EmbedND, LastLayer,
MLPEmbedder, SingleStreamBlock,
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", []))
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