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xlabs Flux controlnet implementation. (#4260)

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* xlabs Flux controlnet.

* Fix not working on old python.

* Remove comment.
This commit is contained in:
comfyanonymous
2024-08-12 21:22:22 -04:00
committed by GitHub
parent b8ffb2937f
commit c032b11e07
3 changed files with 135 additions and 7 deletions
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104
comfy/ldm/flux/controlnet_xlabs.py Normal file
View File

@@ -0,0 +1,104 @@
#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
import torch
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 ControlNetFlux(Flux):
def __init__(self, image_model=None, dtype=None, device=None, operations=None, **kwargs):
super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
# 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)
)
def forward_orig(
self,
img: Tensor,
img_ids: Tensor,
controlnet_cond: Tensor,
txt: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
y: Tensor,
guidance: 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.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))
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)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
block_res_samples = ()
for block in self.double_blocks:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
block_res_samples = block_res_samples + (img,)
controlnet_block_res_samples = ()
for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks):
block_res_sample = controlnet_block(block_res_sample)
controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
return {"output": (controlnet_block_res_samples * 10)[:19]}
def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
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)
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)

21
comfy/ldm/flux/model.py
View File

@@ -38,7 +38,7 @@ class Flux(nn.Module):
Transformer model for flow matching on sequences.
"""
def __init__(self, image_model=None, dtype=None, device=None, operations=None, **kwargs):
def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
super().__init__()
self.dtype = dtype
params = FluxParams(**kwargs)
@@ -83,7 +83,8 @@ class Flux(nn.Module):
]
)
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
if final_layer:
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
def forward_orig(
self,
@@ -94,6 +95,7 @@ class Flux(nn.Module):
timesteps: Tensor,
y: Tensor,
guidance: Tensor = None,
control=None,
) -> Tensor:
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -112,8 +114,15 @@ class Flux(nn.Module):
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
for block in self.double_blocks:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
for i in range(len(self.double_blocks)):
img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe)
if control is not None: #Controlnet
control_o = control.get("output")
if i < len(control_o):
add = control_o[i]
if add is not None:
img += add
img = torch.cat((txt, img), 1)
for block in self.single_blocks:
@@ -123,7 +132,7 @@ class Flux(nn.Module):
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img
def forward(self, x, timestep, context, y, guidance, **kwargs):
def forward(self, x, timestep, context, y, guidance, control=None, **kwargs):
bs, c, h, w = x.shape
patch_size = 2
x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
@@ -138,5 +147,5 @@ class Flux(nn.Module):
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)
out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance)
out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control)
return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]