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InstantX canny controlnet.

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comfyanonymous
2024-08-28 18:56:33 -04:00
parent 34eda0f853
commit b33cd61070
3 changed files with 63 additions and 27 deletions
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67
comfy/ldm/flux/controlnet_xlabs.py → comfy/ldm/flux/controlnet.py
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@@ -1,6 +1,7 @@
#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
import torch
import math
from torch import Tensor, nn
from einops import rearrange, repeat
@@ -13,34 +14,38 @@ import comfy.ldm.common_dit
class ControlNetFlux(Flux):
def __init__(self, image_model=None, dtype=None, device=None, operations=None, **kwargs):
def __init__(self, latent_input=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
# add ControlNet blocks
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)
# controlnet_block = zero_module(controlnet_block)
self.controlnet_blocks.append(controlnet_block)
self.pos_embed_input = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
self.gradient_checkpointing = False
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)
)
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)
)
def forward_orig(
self,
@@ -58,8 +63,10 @@ class ControlNetFlux(Flux):
# running on sequences img
img = self.img_in(img)
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)
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
vec = self.time_in(timestep_embedding(timesteps, 256))
@@ -82,13 +89,25 @@ class ControlNetFlux(Flux):
block_res_sample = controlnet_block(block_res_sample)
controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
return {"input": (controlnet_block_res_samples * 10)[:19]}
repeat = math.ceil(self.main_model_double / len(controlnet_block_res_samples))
if self.latent_input:
out_input = ()
for x in controlnet_block_res_samples:
out_input += (x,) * repeat
else:
out_input = (controlnet_block_res_samples * repeat)
return {"input": out_input[:self.main_model_double]}
def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
hint = hint * 2.0 - 1.0
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)
else:
hint = hint * 2.0 - 1.0
bs, c, h, w = x.shape
patch_size = 2
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)