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

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This commit is contained in:
Dr.Lt.Data 2025-11-26 21:44:25 +09:00
commit a5e0674474
9 changed files with 85 additions and 14 deletions
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40
comfy/latent_formats.py
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@ -6,6 +6,7 @@ class LatentFormat:
latent_dimensions = 2
latent_rgb_factors = None
latent_rgb_factors_bias = None
latent_rgb_factors_reshape = None
taesd_decoder_name = None
def process_in(self, latent):
@ -181,6 +182,45 @@ class Flux(SD3):
class Flux2(LatentFormat):
latent_channels = 128
def __init__(self):
self.latent_rgb_factors =[
[0.0058, 0.0113, 0.0073],
[0.0495, 0.0443, 0.0836],
[-0.0099, 0.0096, 0.0644],
[0.2144, 0.3009, 0.3652],
[0.0166, -0.0039, -0.0054],
[0.0157, 0.0103, -0.0160],
[-0.0398, 0.0902, -0.0235],
[-0.0052, 0.0095, 0.0109],
[-0.3527, -0.2712, -0.1666],
[-0.0301, -0.0356, -0.0180],
[-0.0107, 0.0078, 0.0013],
[0.0746, 0.0090, -0.0941],
[0.0156, 0.0169, 0.0070],
[-0.0034, -0.0040, -0.0114],
[0.0032, 0.0181, 0.0080],
[-0.0939, -0.0008, 0.0186],
[0.0018, 0.0043, 0.0104],
[0.0284, 0.0056, -0.0127],
[-0.0024, -0.0022, -0.0030],
[0.1207, -0.0026, 0.0065],
[0.0128, 0.0101, 0.0142],
[0.0137, -0.0072, -0.0007],
[0.0095, 0.0092, -0.0059],
[0.0000, -0.0077, -0.0049],
[-0.0465, -0.0204, -0.0312],
[0.0095, 0.0012, -0.0066],
[0.0290, -0.0034, 0.0025],
[0.0220, 0.0169, -0.0048],
[-0.0332, -0.0457, -0.0468],
[-0.0085, 0.0389, 0.0609],
[-0.0076, 0.0003, -0.0043],
[-0.0111, -0.0460, -0.0614],
]
self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2)
def process_in(self, latent):
return latent

2
comfy/model_base.py
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@ -926,7 +926,7 @@ class Flux(BaseModel):
out = {}
ref_latents = kwargs.get("reference_latents", None)
if ref_latents is not None:
out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
return out
class Flux2(Flux):

2
comfy/model_patcher.py
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@ -132,7 +132,7 @@ class LowVramPatch:
def __call__(self, weight):
intermediate_dtype = weight.dtype
if self.convert_func is not None:
weight = self.convert_func(weight.to(dtype=torch.float32, copy=True), inplace=True)
weight = self.convert_func(weight, inplace=False)
if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
intermediate_dtype = torch.float32

22
comfy/ops.py
View File

@ -117,6 +117,8 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
if weight_has_function or weight.dtype != dtype:
with wf_context:
weight = weight.to(dtype=dtype)
if isinstance(weight, QuantizedTensor):
weight = weight.dequantize()
for f in s.weight_function:
weight = f(weight)
@ -502,7 +504,7 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None
weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
return weight
else:
return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype)
return weight.to(dtype=torch.float32) * self.scale_weight.to(device=weight.device, dtype=torch.float32)
def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
@ -643,6 +645,24 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
not isinstance(input, QuantizedTensor)):
input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, dtype=self.weight.dtype)
return self._forward(input, self.weight, self.bias)
def convert_weight(self, weight, inplace=False, **kwargs):
if isinstance(weight, QuantizedTensor):
return weight.dequantize()
else:
return weight
def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
if getattr(self, 'layout_type', None) is not None:
weight = QuantizedTensor.from_float(weight, self.layout_type, scale=None, dtype=self.weight.dtype, stochastic_rounding=seed, inplace_ops=True)
else:
weight = weight.to(self.weight.dtype)
if return_weight:
return weight
assert inplace_update is False # TODO: eventually remove the inplace_update stuff
self.weight = torch.nn.Parameter(weight, requires_grad=False)
return MixedPrecisionOps
def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None, model_config=None):

21
comfy/quant_ops.py
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@ -1,6 +1,7 @@
import torch
import logging
from typing import Tuple, Dict
import comfy.float
_LAYOUT_REGISTRY = {}
_GENERIC_UTILS = {}
@ -393,7 +394,7 @@ class TensorCoreFP8Layout(QuantizedLayout):
- orig_dtype: Original dtype before quantization (for casting back)
"""
@classmethod
def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn):
def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn, stochastic_rounding=0, inplace_ops=False):
orig_dtype = tensor.dtype
if scale is None:
@ -403,17 +404,23 @@ class TensorCoreFP8Layout(QuantizedLayout):
scale = torch.tensor(scale)
scale = scale.to(device=tensor.device, dtype=torch.float32)
tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype)
# TODO: uncomment this if it's actually needed because the clamp has a small performance penality'
lp_amax = torch.finfo(dtype).max
torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format)
if inplace_ops:
tensor *= (1.0 / scale).to(tensor.dtype)
else:
tensor = tensor * (1.0 / scale).to(tensor.dtype)
if stochastic_rounding > 0:
tensor = comfy.float.stochastic_rounding(tensor, dtype=dtype, seed=stochastic_rounding)
else:
lp_amax = torch.finfo(dtype).max
torch.clamp(tensor, min=-lp_amax, max=lp_amax, out=tensor)
tensor = tensor.to(dtype, memory_format=torch.contiguous_format)
layout_params = {
'scale': scale,
'orig_dtype': orig_dtype
}
return qdata, layout_params
return tensor, layout_params
@staticmethod
def dequantize(qdata, scale, orig_dtype, **kwargs):

1
comfy/weight_adapter/lora.py
View File

@ -194,6 +194,7 @@ class LoRAAdapter(WeightAdapterBase):
lora_diff = torch.mm(
mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)
).reshape(weight.shape)
del mat1, mat2
if dora_scale is not None:
weight = weight_decompose(
dora_scale,

2
comfyui_version.py
View File

@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.3.73"
__version__ = "0.3.75"

7
latent_preview.py
View File

@ -37,13 +37,16 @@ class TAESDPreviewerImpl(LatentPreviewer):
class Latent2RGBPreviewer(LatentPreviewer):
def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None):
def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None, latent_rgb_factors_reshape=None):
self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1)
self.latent_rgb_factors_bias = None
if latent_rgb_factors_bias is not None:
self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu")
self.latent_rgb_factors_reshape = latent_rgb_factors_reshape
def decode_latent_to_preview(self, x0):
if self.latent_rgb_factors_reshape is not None:
x0 = self.latent_rgb_factors_reshape(x0)
self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
if self.latent_rgb_factors_bias is not None:
self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
@ -85,7 +88,7 @@ def get_previewer(device, latent_format):
if previewer is None:
if latent_format.latent_rgb_factors is not None:
previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias)
previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias, latent_format.latent_rgb_factors_reshape)
return previewer
def prepare_callback(model, steps, x0_output_dict=None):

2
pyproject.toml
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@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.3.73"
version = "0.3.75"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.9"