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

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Dr.Lt.Data
2025-07-08 12:34:29 +09:00
parent 2ce64b131c 059cd38aa2
commit 552fe9df02
7 changed files with 78 additions and 76 deletions
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47
comfy/gligen.py
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@@ -1,55 +1,10 @@
import math
import torch
from torch import nn
from .ldm.modules.attention import CrossAttention
from inspect import isfunction
from .ldm.modules.attention import CrossAttention, FeedForward
import comfy.ops
ops = comfy.ops.manual_cast
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = ops.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * torch.nn.functional.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
ops.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
ops.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
class GatedCrossAttentionDense(nn.Module):
def __init__(self, query_dim, context_dim, n_heads, d_head):

36
comfy/k_diffusion/sampling.py
View File

@@ -412,9 +412,13 @@ def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, o
ds.pop(0)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
cur_order = min(i + 1, order)
coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
if sigmas[i + 1] == 0:
# Denoising step
x = denoised
else:
cur_order = min(i + 1, order)
coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
return x
@@ -1067,7 +1071,9 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None,
d_cur = (x_cur - denoised) / t_cur
order = min(max_order, i+1)
if order == 1: # First Euler step.
if t_next == 0: # Denoising step
x_next = denoised
elif order == 1: # First Euler step.
x_next = x_cur + (t_next - t_cur) * d_cur
elif order == 2: # Use one history point.
x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
@@ -1085,6 +1091,7 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None,
return x_next
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
#under Apache 2 license
def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
@@ -1108,7 +1115,9 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
d_cur = (x_cur - denoised) / t_cur
order = min(max_order, i+1)
if order == 1: # First Euler step.
if t_next == 0: # Denoising step
x_next = denoised
elif order == 1: # First Euler step.
x_next = x_cur + (t_next - t_cur) * d_cur
elif order == 2: # Use one history point.
h_n = (t_next - t_cur)
@@ -1148,6 +1157,7 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
return x_next
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
#under Apache 2 license
@torch.no_grad()
@@ -1198,6 +1208,7 @@ def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None,
return x_next
@torch.no_grad()
def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
extra_args = {} if extra_args is None else extra_args
@@ -1404,6 +1415,7 @@ def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=N
def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
@torch.no_grad()
def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False):
"""Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
@@ -1430,19 +1442,19 @@ def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None,
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
dt = sigmas[i + 1] - sigmas[i]
if i == 0:
if sigmas[i + 1] == 0:
# Denoising step
x = denoised
else:
# Euler method
if cfg_pp:
x = denoised + d * sigmas[i + 1]
else:
x = x + d * dt
else:
# Gradient estimation
if cfg_pp:
if i >= 1:
# Gradient estimation
d_bar = (ge_gamma - 1) * (d - old_d)
x = denoised + d * sigmas[i + 1] + d_bar * dt
else:
d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
x = x + d_bar * dt
old_d = d
return x

1
comfy/utils.py
View File

@@ -77,6 +77,7 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
if safe_load or ALWAYS_SAFE_LOAD:
pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args)
else:
logging.warning("WARNING: loading {} unsafely, upgrade your pytorch to 2.4 or newer to load this file safely.".format(ckpt))
pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
if "state_dict" in pl_sd:
sd = pl_sd["state_dict"]