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Merge branch 'master' into dr-support-pip-cm

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Dr.Lt.Data 2025-08-05 06:21:27 +09:00
commit 02317a1f71
4 changed files with 29 additions and 19 deletions
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21
comfy/conds.py
View File

@ -1,6 +1,7 @@
import torch import torch
import math import math
import comfy.utils import comfy.utils
import logging
class CONDRegular: class CONDRegular:
@ -10,12 +11,15 @@ class CONDRegular:
def _copy_with(self, cond): def _copy_with(self, cond):
return self.__class__(cond) return self.__class__(cond)
def process_cond(self, batch_size, device, **kwargs): def process_cond(self, batch_size, **kwargs):
return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size))
def can_concat(self, other): def can_concat(self, other):
if self.cond.shape != other.cond.shape: if self.cond.shape != other.cond.shape:
return False return False
if self.cond.device != other.cond.device:
logging.warning("WARNING: conds not on same device, skipping concat.")
return False
return True return True
def concat(self, others): def concat(self, others):
@ -29,14 +33,14 @@ class CONDRegular:
class CONDNoiseShape(CONDRegular): class CONDNoiseShape(CONDRegular):
def process_cond(self, batch_size, device, area, **kwargs): def process_cond(self, batch_size, area, **kwargs):
data = self.cond data = self.cond
if area is not None: if area is not None:
dims = len(area) // 2 dims = len(area) // 2
for i in range(dims): for i in range(dims):
data = data.narrow(i + 2, area[i + dims], area[i]) data = data.narrow(i + 2, area[i + dims], area[i])
return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device)) return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size))
class CONDCrossAttn(CONDRegular): class CONDCrossAttn(CONDRegular):
@ -51,6 +55,9 @@ class CONDCrossAttn(CONDRegular):
diff = mult_min // min(s1[1], s2[1]) diff = mult_min // min(s1[1], s2[1])
if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
return False return False
if self.cond.device != other.cond.device:
logging.warning("WARNING: conds not on same device: skipping concat.")
return False
return True return True
def concat(self, others): def concat(self, others):
@ -73,7 +80,7 @@ class CONDConstant(CONDRegular):
def __init__(self, cond): def __init__(self, cond):
self.cond = cond self.cond = cond
def process_cond(self, batch_size, device, **kwargs): def process_cond(self, batch_size, **kwargs):
return self._copy_with(self.cond) return self._copy_with(self.cond)
def can_concat(self, other): def can_concat(self, other):
@ -92,10 +99,10 @@ class CONDList(CONDRegular):
def __init__(self, cond): def __init__(self, cond):
self.cond = cond self.cond = cond
def process_cond(self, batch_size, device, **kwargs): def process_cond(self, batch_size, **kwargs):
out = [] out = []
for c in self.cond: for c in self.cond:
out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device)) out.append(comfy.utils.repeat_to_batch_size(c, batch_size))
return self._copy_with(out) return self._copy_with(out)

5
comfy/controlnet.py
View File

@ -28,6 +28,7 @@ import comfy.model_detection
import comfy.model_patcher import comfy.model_patcher
import comfy.ops import comfy.ops
import comfy.latent_formats import comfy.latent_formats
import comfy.model_base
import comfy.cldm.cldm import comfy.cldm.cldm
import comfy.t2i_adapter.adapter import comfy.t2i_adapter.adapter
@ -264,12 +265,12 @@ class ControlNet(ControlBase):
for c in self.extra_conds: for c in self.extra_conds:
temp = cond.get(c, None) temp = cond.get(c, None)
if temp is not None: if temp is not None:
extra[c] = temp.to(dtype) extra[c] = comfy.model_base.convert_tensor(temp, dtype, x_noisy.device)
timestep = self.model_sampling_current.timestep(t) timestep = self.model_sampling_current.timestep(t)
x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra) control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=comfy.model_management.cast_to_device(context, x_noisy.device, dtype), **extra)
return self.control_merge(control, control_prev, output_dtype=None) return self.control_merge(control, control_prev, output_dtype=None)
def copy(self): def copy(self):

20
comfy/model_base.py
View File

@ -109,9 +109,9 @@ def model_sampling(model_config, model_type):
def convert_tensor(extra, dtype, device): def convert_tensor(extra, dtype, device):
if hasattr(extra, "dtype"): if hasattr(extra, "dtype"):
if extra.dtype != torch.int and extra.dtype != torch.long: if extra.dtype != torch.int and extra.dtype != torch.long:
extra = extra.to(dtype=dtype, device=device) extra = comfy.model_management.cast_to_device(extra, device, dtype)
else: else:
extra = extra.to(device=device) extra = comfy.model_management.cast_to_device(extra, device, None)
return extra return extra
@ -162,7 +162,7 @@ class BaseModel(torch.nn.Module):
xc = self.model_sampling.calculate_input(sigma, x) xc = self.model_sampling.calculate_input(sigma, x)
if c_concat is not None: if c_concat is not None:
xc = torch.cat([xc] + [c_concat], dim=1) xc = torch.cat([xc] + [comfy.model_management.cast_to_device(c_concat, xc.device, xc.dtype)], dim=1)
context = c_crossattn context = c_crossattn
dtype = self.get_dtype() dtype = self.get_dtype()
@ -174,7 +174,7 @@ class BaseModel(torch.nn.Module):
device = xc.device device = xc.device
t = self.model_sampling.timestep(t).float() t = self.model_sampling.timestep(t).float()
if context is not None: if context is not None:
context = context.to(dtype=dtype, device=device) context = comfy.model_management.cast_to_device(context, device, dtype)
extra_conds = {} extra_conds = {}
for o in kwargs: for o in kwargs:
@ -401,7 +401,7 @@ class SD21UNCLIP(BaseModel):
unclip_conditioning = kwargs.get("unclip_conditioning", None) unclip_conditioning = kwargs.get("unclip_conditioning", None)
device = kwargs["device"] device = kwargs["device"]
if unclip_conditioning is None: if unclip_conditioning is None:
return torch.zeros((1, self.adm_channels)) return torch.zeros((1, self.adm_channels), device=device)
else: else:
return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10)
@ -615,9 +615,11 @@ class IP2P:
if image is None: if image is None:
image = torch.zeros_like(noise) image = torch.zeros_like(noise)
else:
image = image.to(device=device)
if image.shape[1:] != noise.shape[1:]: if image.shape[1:] != noise.shape[1:]:
image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") image = utils.common_upscale(image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
image = utils.resize_to_batch_size(image, noise.shape[0]) image = utils.resize_to_batch_size(image, noise.shape[0])
return self.process_ip2p_image_in(image) return self.process_ip2p_image_in(image)
@ -696,7 +698,7 @@ class StableCascade_B(BaseModel):
#size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched
prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device)) prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))
out["effnet"] = comfy.conds.CONDRegular(prior) out["effnet"] = comfy.conds.CONDRegular(prior.to(device=noise.device))
out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
return out return out
@ -1161,10 +1163,10 @@ class WAN21_Vace(WAN21):
vace_frames_out = [] vace_frames_out = []
for j in range(len(vace_frames)): for j in range(len(vace_frames)):
vf = vace_frames[j].clone() vf = vace_frames[j].to(device=noise.device, dtype=noise.dtype, copy=True)
for i in range(0, vf.shape[1], 16): for i in range(0, vf.shape[1], 16):
vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16]) vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16])
vf = torch.cat([vf, mask[j]], dim=1) vf = torch.cat([vf, mask[j].to(device=noise.device, dtype=noise.dtype)], dim=1)
vace_frames_out.append(vf) vace_frames_out.append(vf)
vace_frames = torch.stack(vace_frames_out, dim=1) vace_frames = torch.stack(vace_frames_out, dim=1)

2
comfy/samplers.py
View File

@ -89,7 +89,7 @@ def get_area_and_mult(conds, x_in, timestep_in):
conditioning = {} conditioning = {}
model_conds = conds["model_conds"] model_conds = conds["model_conds"]
for c in model_conds: for c in model_conds:
conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], area=area)
hooks = conds.get('hooks', None) hooks = conds.get('hooks', None)
control = conds.get('control', None) control = conds.get('control', None)