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Mistoline flux controlnet support.

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comfyanonymous
2024-09-05 00:04:52 -04:00
parent c7427375ee
commit 5cbaa9e07c
2 changed files with 85 additions and 30 deletions
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99
comfy/ldm/flux/controlnet.py
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@@ -1,4 +1,5 @@
#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
@@ -12,22 +13,65 @@ from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
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, image_model=None, dtype=None, device=None, operations=None, **kwargs):
def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, 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):
controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
self.controlnet_blocks.append(controlnet_block)
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(operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device))
self.controlnet_single_blocks.append(control_block())
self.num_union_modes = num_union_modes
self.controlnet_mode_embedder = None
@@ -38,23 +82,26 @@ class ControlNetFlux(Flux):
self.latent_input = latent_input
self.pos_embed_input = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
if not self.latent_input:
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)
)
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,
@@ -73,9 +120,6 @@ class ControlNetFlux(Flux):
# running on sequences img
img = self.img_in(img)
if not self.latent_input:
controlnet_cond = self.input_hint_block(controlnet_cond)
controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
controlnet_cond = self.pos_embed_input(controlnet_cond)
img = img + controlnet_cond
@@ -131,9 +175,14 @@ class ControlNetFlux(Flux):
patch_size = 2
if self.latent_input:
hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))
hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=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))