PixArt initial rewrite
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GNU AFFERO GENERAL PUBLIC LICENSE
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Version 3, 19 November 2007
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Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Remote Network Interaction; Use with the GNU General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, if you modify the
|
||||
Program, your modified version must prominently offer all users
|
||||
interacting with it remotely through a computer network (if your version
|
||||
supports such interaction) an opportunity to receive the Corresponding
|
||||
Source of your version by providing access to the Corresponding Source
|
||||
from a network server at no charge, through some standard or customary
|
||||
means of facilitating copying of software. This Corresponding Source
|
||||
shall include the Corresponding Source for any work covered by version 3
|
||||
of the GNU General Public License that is incorporated pursuant to the
|
||||
following paragraph.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the work with which it is combined will remain governed by version
|
||||
3 of the GNU General Public License.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU Affero General Public License from time to time. Such new versions
|
||||
will be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU Affero General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU Affero General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU Affero General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU Affero General Public License as published
|
||||
by the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU Affero General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU Affero General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If your software can interact with users remotely through a computer
|
||||
network, you should also make sure that it provides a way for users to
|
||||
get its source. For example, if your program is a web application, its
|
||||
interface could display a "Source" link that leads users to an archive
|
||||
of the code. There are many ways you could offer source, and different
|
||||
solutions will be better for different programs; see section 13 for the
|
||||
specific requirements.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU AGPL, see
|
||||
<https://www.gnu.org/licenses/>.
|
||||
-140
@@ -1,140 +0,0 @@
|
||||
"""
|
||||
List of all PixArt model types / settings
|
||||
"""
|
||||
|
||||
sampling_settings = {
|
||||
"beta_schedule" : "sqrt_linear",
|
||||
"linear_start" : 0.0001,
|
||||
"linear_end" : 0.02,
|
||||
"timesteps" : 1000,
|
||||
}
|
||||
|
||||
pixart_conf = {
|
||||
"PixArtMS_XL_2": { # models/PixArtMS
|
||||
"target": "PixArtMS",
|
||||
"unet_config": {
|
||||
"input_size" : 1024//8,
|
||||
"depth" : 28,
|
||||
"num_heads" : 16,
|
||||
"patch_size" : 2,
|
||||
"hidden_size" : 1152,
|
||||
"pe_interpolation": 2,
|
||||
},
|
||||
"sampling_settings" : sampling_settings,
|
||||
},
|
||||
"PixArtMS_Sigma_XL_2": {
|
||||
"target": "PixArtMSSigma",
|
||||
"unet_config": {
|
||||
"input_size" : 1024//8,
|
||||
"token_num" : 300,
|
||||
"depth" : 28,
|
||||
"num_heads" : 16,
|
||||
"patch_size" : 2,
|
||||
"hidden_size" : 1152,
|
||||
"micro_condition": False,
|
||||
"pe_interpolation": 2,
|
||||
"model_max_length": 300,
|
||||
},
|
||||
"sampling_settings" : sampling_settings,
|
||||
},
|
||||
"PixArtMS_Sigma_XL_2_900M": {
|
||||
"target": "PixArtMSSigma",
|
||||
"unet_config": {
|
||||
"input_size": 1024 // 8,
|
||||
"token_num": 300,
|
||||
"depth": 42,
|
||||
"num_heads": 16,
|
||||
"patch_size": 2,
|
||||
"hidden_size": 1152,
|
||||
"micro_condition": False,
|
||||
"pe_interpolation": 2,
|
||||
"model_max_length": 300,
|
||||
},
|
||||
"sampling_settings": sampling_settings,
|
||||
},
|
||||
"PixArtMS_Sigma_XL_2_2K": {
|
||||
"target": "PixArtMSSigma",
|
||||
"unet_config": {
|
||||
"input_size" : 2048//8,
|
||||
"token_num" : 300,
|
||||
"depth" : 28,
|
||||
"num_heads" : 16,
|
||||
"patch_size" : 2,
|
||||
"hidden_size" : 1152,
|
||||
"micro_condition": False,
|
||||
"pe_interpolation": 4,
|
||||
"model_max_length": 300,
|
||||
},
|
||||
"sampling_settings" : sampling_settings,
|
||||
},
|
||||
"PixArt_XL_2": { # models/PixArt
|
||||
"target": "PixArt",
|
||||
"unet_config": {
|
||||
"input_size" : 512//8,
|
||||
"token_num" : 120,
|
||||
"depth" : 28,
|
||||
"num_heads" : 16,
|
||||
"patch_size" : 2,
|
||||
"hidden_size" : 1152,
|
||||
"pe_interpolation": 1,
|
||||
},
|
||||
"sampling_settings" : sampling_settings,
|
||||
},
|
||||
}
|
||||
|
||||
pixart_conf.update({ # controlnet models
|
||||
"ControlPixArtHalf": {
|
||||
"target": "ControlPixArtHalf",
|
||||
"unet_config": pixart_conf["PixArt_XL_2"]["unet_config"],
|
||||
"sampling_settings": pixart_conf["PixArt_XL_2"]["sampling_settings"],
|
||||
},
|
||||
"ControlPixArtMSHalf": {
|
||||
"target": "ControlPixArtMSHalf",
|
||||
"unet_config": pixart_conf["PixArtMS_XL_2"]["unet_config"],
|
||||
"sampling_settings": pixart_conf["PixArtMS_XL_2"]["sampling_settings"],
|
||||
}
|
||||
})
|
||||
|
||||
pixart_res = {
|
||||
"PixArtMS_XL_2": { # models/PixArtMS 1024x1024
|
||||
'0.25': [512, 2048], '0.26': [512, 1984], '0.27': [512, 1920], '0.28': [512, 1856],
|
||||
'0.32': [576, 1792], '0.33': [576, 1728], '0.35': [576, 1664], '0.40': [640, 1600],
|
||||
'0.42': [640, 1536], '0.48': [704, 1472], '0.50': [704, 1408], '0.52': [704, 1344],
|
||||
'0.57': [768, 1344], '0.60': [768, 1280], '0.68': [832, 1216], '0.72': [832, 1152],
|
||||
'0.78': [896, 1152], '0.82': [896, 1088], '0.88': [960, 1088], '0.94': [960, 1024],
|
||||
'1.00': [1024,1024], '1.07': [1024, 960], '1.13': [1088, 960], '1.21': [1088, 896],
|
||||
'1.29': [1152, 896], '1.38': [1152, 832], '1.46': [1216, 832], '1.67': [1280, 768],
|
||||
'1.75': [1344, 768], '2.00': [1408, 704], '2.09': [1472, 704], '2.40': [1536, 640],
|
||||
'2.50': [1600, 640], '2.89': [1664, 576], '3.00': [1728, 576], '3.11': [1792, 576],
|
||||
'3.62': [1856, 512], '3.75': [1920, 512], '3.88': [1984, 512], '4.00': [2048, 512],
|
||||
},
|
||||
"PixArt_XL_2": { # models/PixArt 512x512
|
||||
'0.25': [256,1024], '0.26': [256, 992], '0.27': [256, 960], '0.28': [256, 928],
|
||||
'0.32': [288, 896], '0.33': [288, 864], '0.35': [288, 832], '0.40': [320, 800],
|
||||
'0.42': [320, 768], '0.48': [352, 736], '0.50': [352, 704], '0.52': [352, 672],
|
||||
'0.57': [384, 672], '0.60': [384, 640], '0.68': [416, 608], '0.72': [416, 576],
|
||||
'0.78': [448, 576], '0.82': [448, 544], '0.88': [480, 544], '0.94': [480, 512],
|
||||
'1.00': [512, 512], '1.07': [512, 480], '1.13': [544, 480], '1.21': [544, 448],
|
||||
'1.29': [576, 448], '1.38': [576, 416], '1.46': [608, 416], '1.67': [640, 384],
|
||||
'1.75': [672, 384], '2.00': [704, 352], '2.09': [736, 352], '2.40': [768, 320],
|
||||
'2.50': [800, 320], '2.89': [832, 288], '3.00': [864, 288], '3.11': [896, 288],
|
||||
'3.62': [928, 256], '3.75': [960, 256], '3.88': [992, 256], '4.00': [1024,256]
|
||||
},
|
||||
"PixArtMS_Sigma_XL_2_2K": {
|
||||
'0.25': [1024, 4096], '0.26': [1024, 3968], '0.27': [1024, 3840], '0.28': [1024, 3712],
|
||||
'0.32': [1152, 3584], '0.33': [1152, 3456], '0.35': [1152, 3328], '0.40': [1280, 3200],
|
||||
'0.42': [1280, 3072], '0.48': [1408, 2944], '0.50': [1408, 2816], '0.52': [1408, 2688],
|
||||
'0.57': [1536, 2688], '0.60': [1536, 2560], '0.68': [1664, 2432], '0.72': [1664, 2304],
|
||||
'0.78': [1792, 2304], '0.82': [1792, 2176], '0.88': [1920, 2176], '0.94': [1920, 2048],
|
||||
'1.00': [2048, 2048], '1.07': [2048, 1920], '1.13': [2176, 1920], '1.21': [2176, 1792],
|
||||
'1.29': [2304, 1792], '1.38': [2304, 1664], '1.46': [2432, 1664], '1.67': [2560, 1536],
|
||||
'1.75': [2688, 1536], '2.00': [2816, 1408], '2.09': [2944, 1408], '2.40': [3072, 1280],
|
||||
'2.50': [3200, 1280], '2.89': [3328, 1152], '3.00': [3456, 1152], '3.11': [3584, 1152],
|
||||
'3.62': [3712, 1024], '3.75': [3840, 1024], '3.88': [3968, 1024], '4.00': [4096, 1024]
|
||||
}
|
||||
}
|
||||
# These should be the same
|
||||
pixart_res.update({
|
||||
"PixArtMS_Sigma_XL_2": pixart_res["PixArtMS_XL_2"],
|
||||
"PixArtMS_Sigma_XL_2_512": pixart_res["PixArt_XL_2"],
|
||||
})
|
||||
@@ -0,0 +1,137 @@
|
||||
"""
|
||||
Model config and setting logic
|
||||
"""
|
||||
import math
|
||||
import logging
|
||||
|
||||
import comfy.supported_models_base
|
||||
import comfy.supported_models
|
||||
import comfy.latent_formats
|
||||
|
||||
from .model.pixart import PixArt
|
||||
from .model.pixartms import PixArtMS
|
||||
from ..text_encoders.pixart.tenc import PixArtTokenizer, PixArtT5XXL
|
||||
|
||||
class PixArtConfig(comfy.supported_models_base.BASE):
|
||||
unet_class = PixArtMS
|
||||
unet_config = {}
|
||||
unet_extra_config = {}
|
||||
|
||||
latent_format = comfy.latent_formats.SD15
|
||||
sampling_settings = {
|
||||
"beta_schedule" : "sqrt_linear",
|
||||
"linear_start" : 0.0001,
|
||||
"linear_end" : 0.02,
|
||||
"timesteps" : 1000,
|
||||
}
|
||||
|
||||
def model_type(self, state_dict, prefix=""):
|
||||
return comfy.model_base.ModelType.EPS
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
return PixArtModel(model_config=self, unet_model=self.unet_class, device=device)
|
||||
|
||||
def clip_target(self, state_dict={}):
|
||||
return comfy.supported_models_base.ClipTarget(PixArtTokenizer, PixArtT5XXL)
|
||||
|
||||
class PixArtModel(comfy.model_base.BaseModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
return out
|
||||
|
||||
def model_config_from_unet(sd):
|
||||
"""
|
||||
Guess config based on (converted) state dict.
|
||||
"""
|
||||
# Shared settings based on DiT_XL_2 - could be enumerated
|
||||
config = {
|
||||
"num_heads" : 16, # get from attention
|
||||
"patch_size" : 2, # final layer I guess?
|
||||
"hidden_size" : 1152, # pos_embed.shape[2]
|
||||
}
|
||||
config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28
|
||||
|
||||
try:
|
||||
# this is not present in the diffusers version for sigma?
|
||||
config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0]
|
||||
except KeyError:
|
||||
# need better logic to guess this
|
||||
config["model_max_length"] = 300
|
||||
|
||||
if "pos_embed" in sd:
|
||||
config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"]
|
||||
config["pe_interpolation"] = config["input_size"] // (512//8) # dumb guess
|
||||
|
||||
model_config = PixArtModel
|
||||
if config["model_max_length"] == 300:
|
||||
# Sigma
|
||||
model_class = PixArtMS
|
||||
model_config.latent_format = comfy.latent_formats.SDXL
|
||||
config["micro_condition"] = False
|
||||
if "input_size" not in config:
|
||||
# The diffusers weights for 1K/2K are exactly the same...?
|
||||
# replace patch embed logic with HyDiT?
|
||||
logging.warn(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!")
|
||||
config["input_size"] = 1024//8
|
||||
else:
|
||||
# Alpha
|
||||
if "csize_embedder.mlp.0.weight" in sd:
|
||||
# MS (microconds)
|
||||
model_class = PixArtMS
|
||||
config["micro_condition"] = True
|
||||
if "input_size" not in config:
|
||||
config["input_size"] = 1024//8
|
||||
config["pe_interpolation"] = 2
|
||||
else:
|
||||
# PixArt
|
||||
model_class = PixArt
|
||||
if "input_size" not in config:
|
||||
config["input_size"] = 512//8
|
||||
config["pe_interpolation"] = 1
|
||||
model_config = PixArtConfig(config)
|
||||
model_config.unet_class = model_class
|
||||
logging.info(f"Detected PixArt model as [{model_class}]")
|
||||
logging.info(f"PixArt config:\n{config}")
|
||||
return model_config
|
||||
|
||||
resolutions = {
|
||||
"PixArt 512": {
|
||||
0.25: [256,1024], 0.26: [256, 992], 0.27: [256, 960], 0.28: [256, 928],
|
||||
0.32: [288, 896], 0.33: [288, 864], 0.35: [288, 832], 0.40: [320, 800],
|
||||
0.42: [320, 768], 0.48: [352, 736], 0.50: [352, 704], 0.52: [352, 672],
|
||||
0.57: [384, 672], 0.60: [384, 640], 0.68: [416, 608], 0.72: [416, 576],
|
||||
0.78: [448, 576], 0.82: [448, 544], 0.88: [480, 544], 0.94: [480, 512],
|
||||
1.00: [512, 512], 1.07: [512, 480], 1.13: [544, 480], 1.21: [544, 448],
|
||||
1.29: [576, 448], 1.38: [576, 416], 1.46: [608, 416], 1.67: [640, 384],
|
||||
1.75: [672, 384], 2.00: [704, 352], 2.09: [736, 352], 2.40: [768, 320],
|
||||
2.50: [800, 320], 2.89: [832, 288], 3.00: [864, 288], 3.11: [896, 288],
|
||||
3.62: [928, 256], 3.75: [960, 256], 3.88: [992, 256], 4.00: [1024,256]
|
||||
},
|
||||
"PixArt 1024": {
|
||||
0.25: [512, 2048], 0.26: [512, 1984], 0.27: [512, 1920], 0.28: [512, 1856],
|
||||
0.32: [576, 1792], 0.33: [576, 1728], 0.35: [576, 1664], 0.40: [640, 1600],
|
||||
0.42: [640, 1536], 0.48: [704, 1472], 0.50: [704, 1408], 0.52: [704, 1344],
|
||||
0.57: [768, 1344], 0.60: [768, 1280], 0.68: [832, 1216], 0.72: [832, 1152],
|
||||
0.78: [896, 1152], 0.82: [896, 1088], 0.88: [960, 1088], 0.94: [960, 1024],
|
||||
1.00: [1024,1024], 1.07: [1024, 960], 1.13: [1088, 960], 1.21: [1088, 896],
|
||||
1.29: [1152, 896], 1.38: [1152, 832], 1.46: [1216, 832], 1.67: [1280, 768],
|
||||
1.75: [1344, 768], 2.00: [1408, 704], 2.09: [1472, 704], 2.40: [1536, 640],
|
||||
2.50: [1600, 640], 2.89: [1664, 576], 3.00: [1728, 576], 3.11: [1792, 576],
|
||||
3.62: [1856, 512], 3.75: [1920, 512], 3.88: [1984, 512], 4.00: [2048, 512],
|
||||
},
|
||||
"PixArt 2K": {
|
||||
0.25: [1024, 4096], 0.26: [1024, 3968], 0.27: [1024, 3840], 0.28: [1024, 3712],
|
||||
0.32: [1152, 3584], 0.33: [1152, 3456], 0.35: [1152, 3328], 0.40: [1280, 3200],
|
||||
0.42: [1280, 3072], 0.48: [1408, 2944], 0.50: [1408, 2816], 0.52: [1408, 2688],
|
||||
0.57: [1536, 2688], 0.60: [1536, 2560], 0.68: [1664, 2432], 0.72: [1664, 2304],
|
||||
0.78: [1792, 2304], 0.82: [1792, 2176], 0.88: [1920, 2176], 0.94: [1920, 2048],
|
||||
1.00: [2048, 2048], 1.07: [2048, 1920], 1.13: [2176, 1920], 1.21: [2176, 1792],
|
||||
1.29: [2304, 1792], 1.38: [2304, 1664], 1.46: [2432, 1664], 1.67: [2560, 1536],
|
||||
1.75: [2688, 1536], 2.00: [2816, 1408], 2.09: [2944, 1408], 2.40: [3072, 1280],
|
||||
2.50: [3200, 1280], 2.89: [3328, 1152], 3.00: [3456, 1152], 3.11: [3584, 1152],
|
||||
3.62: [3712, 1024], 3.75: [3840, 1024], 3.88: [3968, 1024], 4.00: [4096, 1024]
|
||||
}
|
||||
}
|
||||
+96
-200
@@ -4,220 +4,116 @@
|
||||
import torch
|
||||
|
||||
conversion_map_ms = [ # for multi_scale_train (MS)
|
||||
# Resolution
|
||||
("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
|
||||
("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"),
|
||||
("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
|
||||
("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"),
|
||||
# Aspect ratio
|
||||
("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
|
||||
("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
|
||||
("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
|
||||
("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
|
||||
# Resolution
|
||||
("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
|
||||
("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"),
|
||||
("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
|
||||
("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"),
|
||||
# Aspect ratio
|
||||
("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
|
||||
("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
|
||||
("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
|
||||
("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
|
||||
]
|
||||
|
||||
def get_depth(state_dict):
|
||||
return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys())
|
||||
return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys())
|
||||
|
||||
def get_lora_depth(state_dict):
|
||||
cnt = max([
|
||||
sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys()),
|
||||
sum(key.endswith('_attn1_to_k.lora_A.weight') for key in state_dict.keys()),
|
||||
sum(key.endswith('.attn1.to_k.lora_up.weight') for key in state_dict.keys()),
|
||||
sum(key.endswith('_attn1_to_k.lora_up.weight') for key in state_dict.keys()),
|
||||
])
|
||||
assert cnt > 0, "Unable to detect model depth!"
|
||||
return cnt
|
||||
cnt = max([
|
||||
sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys()),
|
||||
sum(key.endswith('_attn1_to_k.lora_A.weight') for key in state_dict.keys()),
|
||||
sum(key.endswith('.attn1.to_k.lora_up.weight') for key in state_dict.keys()),
|
||||
sum(key.endswith('_attn1_to_k.lora_up.weight') for key in state_dict.keys()),
|
||||
])
|
||||
assert cnt > 0, "Unable to detect model depth!"
|
||||
return cnt
|
||||
|
||||
def get_conversion_map(state_dict):
|
||||
conversion_map = [ # main SD conversion map (PixArt reference, HF Diffusers)
|
||||
# Patch embeddings
|
||||
("x_embedder.proj.weight", "pos_embed.proj.weight"),
|
||||
("x_embedder.proj.bias", "pos_embed.proj.bias"),
|
||||
# Caption projection
|
||||
("y_embedder.y_embedding", "caption_projection.y_embedding"),
|
||||
("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
|
||||
("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
|
||||
("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
|
||||
("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
|
||||
# AdaLN-single LN
|
||||
("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
|
||||
("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
|
||||
("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
|
||||
("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
|
||||
# Shared norm
|
||||
("t_block.1.weight", "adaln_single.linear.weight"),
|
||||
("t_block.1.bias", "adaln_single.linear.bias"),
|
||||
# Final block
|
||||
("final_layer.linear.weight", "proj_out.weight"),
|
||||
("final_layer.linear.bias", "proj_out.bias"),
|
||||
("final_layer.scale_shift_table", "scale_shift_table"),
|
||||
]
|
||||
conversion_map = [ # main SD conversion map (PixArt reference, HF Diffusers)
|
||||
# Patch embeddings
|
||||
("x_embedder.proj.weight", "pos_embed.proj.weight"),
|
||||
("x_embedder.proj.bias", "pos_embed.proj.bias"),
|
||||
# Caption projection
|
||||
("y_embedder.y_embedding", "caption_projection.y_embedding"),
|
||||
("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
|
||||
("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
|
||||
("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
|
||||
("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
|
||||
# AdaLN-single LN
|
||||
("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
|
||||
("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
|
||||
("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
|
||||
("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
|
||||
# Shared norm
|
||||
("t_block.1.weight", "adaln_single.linear.weight"),
|
||||
("t_block.1.bias", "adaln_single.linear.bias"),
|
||||
# Final block
|
||||
("final_layer.linear.weight", "proj_out.weight"),
|
||||
("final_layer.linear.bias", "proj_out.bias"),
|
||||
("final_layer.scale_shift_table", "scale_shift_table"),
|
||||
]
|
||||
|
||||
# Add actual transformer blocks
|
||||
for depth in range(get_depth(state_dict)):
|
||||
# Transformer blocks
|
||||
conversion_map += [
|
||||
(f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"),
|
||||
# Projection
|
||||
(f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"),
|
||||
(f"blocks.{depth}.attn.proj.bias", f"transformer_blocks.{depth}.attn1.to_out.0.bias"),
|
||||
# Feed-forward
|
||||
(f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"),
|
||||
(f"blocks.{depth}.mlp.fc1.bias", f"transformer_blocks.{depth}.ff.net.0.proj.bias"),
|
||||
(f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"),
|
||||
(f"blocks.{depth}.mlp.fc2.bias", f"transformer_blocks.{depth}.ff.net.2.bias"),
|
||||
# Cross-attention (proj)
|
||||
(f"blocks.{depth}.cross_attn.proj.weight" ,f"transformer_blocks.{depth}.attn2.to_out.0.weight"),
|
||||
(f"blocks.{depth}.cross_attn.proj.bias" ,f"transformer_blocks.{depth}.attn2.to_out.0.bias"),
|
||||
]
|
||||
return conversion_map
|
||||
# Add actual transformer blocks
|
||||
for depth in range(get_depth(state_dict)):
|
||||
# Transformer blocks
|
||||
conversion_map += [
|
||||
(f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"),
|
||||
# Projection
|
||||
(f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"),
|
||||
(f"blocks.{depth}.attn.proj.bias", f"transformer_blocks.{depth}.attn1.to_out.0.bias"),
|
||||
# Feed-forward
|
||||
(f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"),
|
||||
(f"blocks.{depth}.mlp.fc1.bias", f"transformer_blocks.{depth}.ff.net.0.proj.bias"),
|
||||
(f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"),
|
||||
(f"blocks.{depth}.mlp.fc2.bias", f"transformer_blocks.{depth}.ff.net.2.bias"),
|
||||
# Cross-attention (proj)
|
||||
(f"blocks.{depth}.cross_attn.proj.weight" ,f"transformer_blocks.{depth}.attn2.to_out.0.weight"),
|
||||
(f"blocks.{depth}.cross_attn.proj.bias" ,f"transformer_blocks.{depth}.attn2.to_out.0.bias"),
|
||||
]
|
||||
return conversion_map
|
||||
|
||||
def find_prefix(state_dict, target_key):
|
||||
prefix = ""
|
||||
for k in state_dict.keys():
|
||||
if k.endswith(target_key):
|
||||
prefix = k.split(target_key)[0]
|
||||
break
|
||||
return prefix
|
||||
prefix = ""
|
||||
for k in state_dict.keys():
|
||||
if k.endswith(target_key):
|
||||
prefix = k.split(target_key)[0]
|
||||
break
|
||||
return prefix
|
||||
|
||||
def convert_state_dict(state_dict):
|
||||
if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys():
|
||||
cmap = get_conversion_map(state_dict) + conversion_map_ms
|
||||
else:
|
||||
cmap = get_conversion_map(state_dict)
|
||||
if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys():
|
||||
cmap = get_conversion_map(state_dict) + conversion_map_ms
|
||||
else:
|
||||
cmap = get_conversion_map(state_dict)
|
||||
|
||||
missing = [k for k,v in cmap if v not in state_dict]
|
||||
new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing}
|
||||
matched = list(v for k,v in cmap if v in state_dict.keys())
|
||||
|
||||
for depth in range(get_depth(state_dict)):
|
||||
for wb in ["weight", "bias"]:
|
||||
# Self Attention
|
||||
key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}"
|
||||
new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat((
|
||||
state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
|
||||
), dim=0)
|
||||
matched += [key('q'), key('k'), key('v')]
|
||||
missing = [k for k,v in cmap if v not in state_dict]
|
||||
new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing}
|
||||
matched = list(v for k,v in cmap if v in state_dict.keys())
|
||||
|
||||
for depth in range(get_depth(state_dict)):
|
||||
for wb in ["weight", "bias"]:
|
||||
# Self Attention
|
||||
key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}"
|
||||
new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat((
|
||||
state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
|
||||
), dim=0)
|
||||
matched += [key('q'), key('k'), key('v')]
|
||||
|
||||
# Cross-attention (linear)
|
||||
key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}"
|
||||
new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')]
|
||||
new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat((
|
||||
state_dict[key('k')], state_dict[key('v')]
|
||||
), dim=0)
|
||||
matched += [key('q'), key('k'), key('v')]
|
||||
# Cross-attention (linear)
|
||||
key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}"
|
||||
new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')]
|
||||
new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat((
|
||||
state_dict[key('k')], state_dict[key('v')]
|
||||
), dim=0)
|
||||
matched += [key('q'), key('k'), key('v')]
|
||||
|
||||
if len(matched) < len(state_dict):
|
||||
print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
|
||||
print(list( set(state_dict.keys()) - set(matched) ))
|
||||
if len(matched) < len(state_dict):
|
||||
print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
|
||||
print(list( set(state_dict.keys()) - set(matched) ))
|
||||
|
||||
if len(missing) > 0:
|
||||
print(f"PixArt: UNET conversion has missing keys!")
|
||||
print(missing)
|
||||
if len(missing) > 0:
|
||||
print(f"PixArt: UNET conversion has missing keys!")
|
||||
print(missing)
|
||||
|
||||
return new_state_dict
|
||||
|
||||
# Same as above but for LoRA weights:
|
||||
def convert_lora_state_dict(state_dict, peft=True):
|
||||
# koyha
|
||||
rep_ak = lambda x: x.replace(".weight", ".lora_down.weight")
|
||||
rep_bk = lambda x: x.replace(".weight", ".lora_up.weight")
|
||||
rep_pk = lambda x: x.replace(".weight", ".alpha")
|
||||
if peft: # peft
|
||||
rep_ap = lambda x: x.replace(".weight", ".lora_A.weight")
|
||||
rep_bp = lambda x: x.replace(".weight", ".lora_B.weight")
|
||||
rep_pp = lambda x: x.replace(".weight", ".alpha")
|
||||
|
||||
prefix = find_prefix(state_dict, "adaln_single.linear.lora_A.weight")
|
||||
state_dict = {k[len(prefix):]:v for k,v in state_dict.items()}
|
||||
else: # OneTrainer
|
||||
rep_ap = lambda x: x.replace(".", "_")[:-7] + ".lora_down.weight"
|
||||
rep_bp = lambda x: x.replace(".", "_")[:-7] + ".lora_up.weight"
|
||||
rep_pp = lambda x: x.replace(".", "_")[:-7] + ".alpha"
|
||||
|
||||
prefix = "lora_transformer_"
|
||||
t5_marker = "lora_te_encoder"
|
||||
t5_keys = []
|
||||
for key in list(state_dict.keys()):
|
||||
if key.startswith(prefix):
|
||||
state_dict[key[len(prefix):]] = state_dict.pop(key)
|
||||
elif t5_marker in key:
|
||||
t5_keys.append(state_dict.pop(key))
|
||||
if len(t5_keys) > 0:
|
||||
print(f"Text Encoder not supported for PixArt LoRA, ignoring {len(t5_keys)} keys")
|
||||
|
||||
cmap = []
|
||||
cmap_unet = get_conversion_map(state_dict) + conversion_map_ms # todo: 512 model
|
||||
for k, v in cmap_unet:
|
||||
if v.endswith(".weight"):
|
||||
cmap.append((rep_ak(k), rep_ap(v)))
|
||||
cmap.append((rep_bk(k), rep_bp(v)))
|
||||
if not peft:
|
||||
cmap.append((rep_pk(k), rep_pp(v)))
|
||||
|
||||
missing = [k for k,v in cmap if v not in state_dict]
|
||||
new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing}
|
||||
matched = list(v for k,v in cmap if v in state_dict.keys())
|
||||
|
||||
lora_depth = get_lora_depth(state_dict)
|
||||
for fp, fk in ((rep_ap, rep_ak),(rep_bp, rep_bk)):
|
||||
for depth in range(lora_depth):
|
||||
# Self Attention
|
||||
key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
|
||||
new_state_dict[fk(f"blocks.{depth}.attn.qkv.weight")] = torch.cat((
|
||||
state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
|
||||
), dim=0)
|
||||
|
||||
matched += [key('q'), key('k'), key('v')]
|
||||
if not peft:
|
||||
akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
|
||||
new_state_dict[rep_pk((f"blocks.{depth}.attn.qkv.weight"))] = state_dict[akey("q")]
|
||||
matched += [akey('q'), akey('k'), akey('v')]
|
||||
|
||||
# Self Attention projection?
|
||||
key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
|
||||
new_state_dict[fk(f"blocks.{depth}.attn.proj.weight")] = state_dict[key('out.0')]
|
||||
matched += [key('out.0')]
|
||||
|
||||
# Cross-attention (linear)
|
||||
key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
|
||||
new_state_dict[fk(f"blocks.{depth}.cross_attn.q_linear.weight")] = state_dict[key('q')]
|
||||
new_state_dict[fk(f"blocks.{depth}.cross_attn.kv_linear.weight")] = torch.cat((
|
||||
state_dict[key('k')], state_dict[key('v')]
|
||||
), dim=0)
|
||||
matched += [key('q'), key('k'), key('v')]
|
||||
if not peft:
|
||||
akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
|
||||
new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.q_linear.weight"))] = state_dict[akey("q")]
|
||||
new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.kv_linear.weight"))] = state_dict[akey("k")]
|
||||
matched += [akey('q'), akey('k'), akey('v')]
|
||||
|
||||
# Cross Attention projection?
|
||||
key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
|
||||
new_state_dict[fk(f"blocks.{depth}.cross_attn.proj.weight")] = state_dict[key('out.0')]
|
||||
matched += [key('out.0')]
|
||||
|
||||
try:
|
||||
key = fp(f"transformer_blocks.{depth}.ff.net.0.proj.weight")
|
||||
new_state_dict[fk(f"blocks.{depth}.mlp.fc1.weight")] = state_dict[key]
|
||||
matched += [key]
|
||||
except KeyError:
|
||||
pass
|
||||
|
||||
try:
|
||||
key = fp(f"transformer_blocks.{depth}.ff.net.2.weight")
|
||||
new_state_dict[fk(f"blocks.{depth}.mlp.fc2.weight")] = state_dict[key]
|
||||
matched += [key]
|
||||
except KeyError:
|
||||
pass
|
||||
|
||||
if len(matched) < len(state_dict):
|
||||
print(f"PixArt: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
|
||||
print(list( set(state_dict.keys()) - set(matched) ))
|
||||
|
||||
if len(missing) > 0:
|
||||
print(f"PixArt: LoRA conversion has missing keys! (probably)")
|
||||
print(missing)
|
||||
|
||||
return new_state_dict
|
||||
return new_state_dict
|
||||
|
||||
+54
-158
@@ -1,180 +1,76 @@
|
||||
import comfy.supported_models_base
|
||||
import comfy.latent_formats
|
||||
import comfy.model_detection
|
||||
import comfy.model_patcher
|
||||
import comfy.model_base
|
||||
import comfy.utils
|
||||
import comfy.conds
|
||||
import logging
|
||||
import torch
|
||||
import math
|
||||
|
||||
from comfy import model_management
|
||||
from .diffusers_convert import convert_state_dict
|
||||
from .config import model_config_from_unet
|
||||
|
||||
class EXM_PixArt(comfy.supported_models_base.BASE):
|
||||
unet_config = {}
|
||||
unet_extra_config = {}
|
||||
latent_format = comfy.latent_formats.SD15
|
||||
class PixArtModel(comfy.model_base.BaseModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
|
||||
def __init__(self, model_conf):
|
||||
self.model_target = model_conf.get("target")
|
||||
self.unet_config = model_conf.get("unet_config", {})
|
||||
self.sampling_settings = model_conf.get("sampling_settings", {})
|
||||
self.latent_format = self.latent_format()
|
||||
# UNET is handled by extension
|
||||
self.unet_config["disable_unet_model_creation"] = True
|
||||
img_hw = kwargs.get("img_hw", None)
|
||||
if img_hw is not None:
|
||||
out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw))
|
||||
|
||||
def model_type(self, state_dict, prefix=""):
|
||||
return comfy.model_base.ModelType.EPS
|
||||
aspect_ratio = kwargs.get("aspect_ratio", None)
|
||||
if aspect_ratio is not None:
|
||||
out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio))
|
||||
|
||||
class EXM_PixArt_Model(comfy.model_base.BaseModel):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
return out
|
||||
|
||||
img_hw = kwargs.get("img_hw", None)
|
||||
if img_hw is not None:
|
||||
out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw))
|
||||
|
||||
aspect_ratio = kwargs.get("aspect_ratio", None)
|
||||
if aspect_ratio is not None:
|
||||
out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio))
|
||||
def load_pixart_state_dict(sd, model_options={}):
|
||||
# prefix / format
|
||||
sd = sd.get("model", sd) # ref ckpt
|
||||
diffusion_model_prefix = comfy.model_detection.unet_prefix_from_state_dict(sd)
|
||||
temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
|
||||
if len(temp_sd) > 0:
|
||||
sd = temp_sd
|
||||
|
||||
cn_hint = kwargs.get("cn_hint", None)
|
||||
if cn_hint is not None:
|
||||
out["cn_hint"] = comfy.conds.CONDRegular(cn_hint)
|
||||
# diffusers convert
|
||||
if "adaln_single.linear.weight" in sd:
|
||||
sd = convert_state_dict(sd)
|
||||
|
||||
return out
|
||||
# model config
|
||||
model_config = model_config_from_unet(sd)
|
||||
|
||||
def load_pixart(model_path, model_conf=None):
|
||||
state_dict = comfy.utils.load_torch_file(model_path)
|
||||
state_dict = state_dict.get("model", state_dict)
|
||||
# TODO: move lines below to utils
|
||||
parameters = comfy.utils.calculate_parameters(sd)
|
||||
load_device = model_management.get_torch_device()
|
||||
offload_device = comfy.model_management.unet_offload_device()
|
||||
|
||||
# prefix
|
||||
for prefix in ["model.diffusion_model.",]:
|
||||
if any(True for x in state_dict if x.startswith(prefix)):
|
||||
state_dict = {k[len(prefix):]:v for k,v in state_dict.items()}
|
||||
dtype = model_options.get("dtype", torch.float16) # TODO: fix this
|
||||
weight_dtype = comfy.utils.weight_dtype(sd)
|
||||
unet_weight_dtype = list(model_config.supported_inference_dtypes)
|
||||
|
||||
# diffusers
|
||||
if "adaln_single.linear.weight" in state_dict:
|
||||
state_dict = convert_state_dict(state_dict) # Diffusers
|
||||
if weight_dtype is not None and model_config.scaled_fp8 is None:
|
||||
unet_weight_dtype.append(weight_dtype)
|
||||
|
||||
# guess auto config
|
||||
if model_conf is None:
|
||||
model_conf = guess_pixart_config(state_dict)
|
||||
if dtype is None:
|
||||
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
|
||||
else:
|
||||
unet_dtype = dtype
|
||||
|
||||
parameters = comfy.utils.calculate_parameters(state_dict)
|
||||
unet_dtype = model_management.unet_dtype(model_params=parameters)
|
||||
load_device = comfy.model_management.get_torch_device()
|
||||
offload_device = comfy.model_management.unet_offload_device()
|
||||
manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
|
||||
model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
|
||||
model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
|
||||
if model_options.get("fp8_optimizations", False):
|
||||
model_config.optimizations["fp8"] = True
|
||||
|
||||
# ignore fp8/etc and use directly for now
|
||||
manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
|
||||
if manual_cast_dtype:
|
||||
print(f"PixArt: falling back to {manual_cast_dtype}")
|
||||
unet_dtype = manual_cast_dtype
|
||||
|
||||
model_conf = EXM_PixArt(model_conf) # convert to object
|
||||
model = EXM_PixArt_Model( # same as comfy.model_base.BaseModel
|
||||
model_conf,
|
||||
model_type=comfy.model_base.ModelType.EPS,
|
||||
device=model_management.get_torch_device()
|
||||
)
|
||||
|
||||
if model_conf.model_target == "PixArtMS":
|
||||
from .models.PixArtMS import PixArtMS
|
||||
model.diffusion_model = PixArtMS(**model_conf.unet_config)
|
||||
elif model_conf.model_target == "PixArt":
|
||||
from .models.PixArt import PixArt
|
||||
model.diffusion_model = PixArt(**model_conf.unet_config)
|
||||
elif model_conf.model_target == "PixArtMSSigma":
|
||||
from .models.PixArtMS import PixArtMS
|
||||
model.diffusion_model = PixArtMS(**model_conf.unet_config)
|
||||
model.latent_format = comfy.latent_formats.SDXL()
|
||||
elif model_conf.model_target == "ControlPixArtMSHalf":
|
||||
from .models.PixArtMS import PixArtMS
|
||||
from .models.pixart_controlnet import ControlPixArtMSHalf
|
||||
model.diffusion_model = PixArtMS(**model_conf.unet_config)
|
||||
model.diffusion_model = ControlPixArtMSHalf(model.diffusion_model)
|
||||
elif model_conf.model_target == "ControlPixArtHalf":
|
||||
from .models.PixArt import PixArt
|
||||
from .models.pixart_controlnet import ControlPixArtHalf
|
||||
model.diffusion_model = PixArt(**model_conf.unet_config)
|
||||
model.diffusion_model = ControlPixArtHalf(model.diffusion_model)
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown model target '{model_conf.model_target}'")
|
||||
|
||||
m, u = model.diffusion_model.load_state_dict(state_dict, strict=False)
|
||||
if len(m) > 0: print("Missing UNET keys", m)
|
||||
if len(u) > 0: print("Leftover UNET keys", u)
|
||||
model.diffusion_model.dtype = unet_dtype
|
||||
model.diffusion_model.eval()
|
||||
model.diffusion_model.to(unet_dtype)
|
||||
|
||||
model_patcher = comfy.model_patcher.ModelPatcher(
|
||||
model,
|
||||
load_device = load_device,
|
||||
offload_device = offload_device,
|
||||
)
|
||||
return model_patcher
|
||||
|
||||
def guess_pixart_config(sd):
|
||||
"""
|
||||
Guess config based on converted state dict.
|
||||
"""
|
||||
# Shared settings based on DiT_XL_2 - could be enumerated
|
||||
config = {
|
||||
"num_heads" : 16, # get from attention
|
||||
"patch_size" : 2, # final layer I guess?
|
||||
"hidden_size" : 1152, # pos_embed.shape[2]
|
||||
}
|
||||
config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28
|
||||
|
||||
try:
|
||||
# this is not present in the diffusers version for sigma?
|
||||
config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0]
|
||||
except KeyError:
|
||||
# need better logic to guess this
|
||||
config["model_max_length"] = 300
|
||||
|
||||
if "pos_embed" in sd:
|
||||
config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"]
|
||||
config["pe_interpolation"] = config["input_size"] // (512//8) # dumb guess
|
||||
|
||||
target_arch = "PixArtMS"
|
||||
if config["model_max_length"] == 300:
|
||||
# Sigma
|
||||
target_arch = "PixArtMSSigma"
|
||||
config["micro_condition"] = False
|
||||
if "input_size" not in config:
|
||||
# The diffusers weights for 1K/2K are exactly the same...?
|
||||
# replace patch embed logic with HyDiT?
|
||||
print(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!")
|
||||
config["input_size"] = 1024//8
|
||||
else:
|
||||
# Alpha
|
||||
if "csize_embedder.mlp.0.weight" in sd:
|
||||
# MS (microconds)
|
||||
target_arch = "PixArtMS"
|
||||
config["micro_condition"] = True
|
||||
if "input_size" not in config:
|
||||
config["input_size"] = 1024//8
|
||||
config["pe_interpolation"] = 2
|
||||
else:
|
||||
# PixArt
|
||||
target_arch = "PixArt"
|
||||
if "input_size" not in config:
|
||||
config["input_size"] = 512//8
|
||||
config["pe_interpolation"] = 1
|
||||
|
||||
print("PixArt guessed config:", target_arch, config)
|
||||
return {
|
||||
"target": target_arch,
|
||||
"unet_config": config,
|
||||
"sampling_settings": {
|
||||
"beta_schedule" : "sqrt_linear",
|
||||
"linear_start" : 0.0001,
|
||||
"linear_end" : 0.02,
|
||||
"timesteps" : 1000,
|
||||
}
|
||||
}
|
||||
model = model_config.get_model(sd, "")
|
||||
model = model.to(offload_device).eval()
|
||||
model.load_model_weights(sd, "")
|
||||
left_over = sd.keys()
|
||||
if len(left_over) > 0:
|
||||
logging.info("left over keys in unet: {}".format(left_over))
|
||||
return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
|
||||
|
||||
-146
@@ -1,146 +0,0 @@
|
||||
import os
|
||||
import copy
|
||||
import json
|
||||
import torch
|
||||
import comfy.lora
|
||||
import comfy.model_management
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
from .diffusers_convert import convert_lora_state_dict
|
||||
|
||||
class EXM_PixArt_ModelPatcher(ModelPatcher):
|
||||
def calculate_weight(self, patches, weight, key):
|
||||
"""
|
||||
This is almost the same as the comfy function, but stripped down to just the LoRA patch code.
|
||||
The problem with the original code is the q/k/v keys being combined into one for the attention.
|
||||
In the diffusers code, they're treated as separate keys, but in the reference code they're recombined (q+kv|qkv).
|
||||
This means, for example, that the [1152,1152] weights become [3456,1152] in the state dict.
|
||||
The issue with this is that the LoRA weights are [128,1152],[1152,128] and become [384,1162],[3456,128] instead.
|
||||
|
||||
This is the best thing I could think of that would fix that, but it's very fragile.
|
||||
- Check key shape to determine if it needs the fallback logic
|
||||
- Cut the input into parts based on the shape (undoing the torch.cat)
|
||||
- Do the matrix multiplication logic
|
||||
- Recombine them to match the expected shape
|
||||
"""
|
||||
for p in patches:
|
||||
alpha = p[0]
|
||||
v = p[1]
|
||||
strength_model = p[2]
|
||||
if strength_model != 1.0:
|
||||
weight *= strength_model
|
||||
|
||||
if isinstance(v, list):
|
||||
v = (self.calculate_weight(v[1:], v[0].clone(), key), )
|
||||
|
||||
if len(v) == 2:
|
||||
patch_type = v[0]
|
||||
v = v[1]
|
||||
|
||||
if patch_type == "lora":
|
||||
mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
|
||||
mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
|
||||
if v[2] is not None:
|
||||
alpha *= v[2] / mat2.shape[0]
|
||||
try:
|
||||
mat1 = mat1.flatten(start_dim=1)
|
||||
mat2 = mat2.flatten(start_dim=1)
|
||||
|
||||
ch1 = mat1.shape[0] // mat2.shape[1]
|
||||
ch2 = mat2.shape[0] // mat1.shape[1]
|
||||
### Fallback logic for shape mismatch ###
|
||||
if mat1.shape[0] != mat2.shape[1] and ch1 == ch2 and (mat1.shape[0]/mat2.shape[1])%1 == 0:
|
||||
mat1 = mat1.chunk(ch1, dim=0)
|
||||
mat2 = mat2.chunk(ch1, dim=0)
|
||||
weight += torch.cat(
|
||||
[alpha * torch.mm(mat1[x], mat2[x]) for x in range(ch1)],
|
||||
dim=0,
|
||||
).reshape(weight.shape).type(weight.dtype)
|
||||
else:
|
||||
weight += (alpha * torch.mm(mat1, mat2)).reshape(weight.shape).type(weight.dtype)
|
||||
except Exception as e:
|
||||
print("ERROR", key, e)
|
||||
return weight
|
||||
|
||||
def clone(self):
|
||||
n = EXM_PixArt_ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
|
||||
n.patches = {}
|
||||
for k in self.patches:
|
||||
n.patches[k] = self.patches[k][:]
|
||||
|
||||
n.object_patches = self.object_patches.copy()
|
||||
n.model_options = copy.deepcopy(self.model_options)
|
||||
n.model_keys = self.model_keys
|
||||
return n
|
||||
|
||||
def replace_model_patcher(model):
|
||||
n = EXM_PixArt_ModelPatcher(
|
||||
model = model.model,
|
||||
size = model.size,
|
||||
load_device = model.load_device,
|
||||
offload_device = model.offload_device,
|
||||
weight_inplace_update = model.weight_inplace_update,
|
||||
)
|
||||
n.patches = {}
|
||||
for k in model.patches:
|
||||
n.patches[k] = model.patches[k][:]
|
||||
|
||||
n.object_patches = model.object_patches.copy()
|
||||
n.model_options = copy.deepcopy(model.model_options)
|
||||
return n
|
||||
|
||||
def find_peft_alpha(path):
|
||||
def load_json(json_path):
|
||||
with open(json_path) as f:
|
||||
data = json.load(f)
|
||||
alpha = data.get("lora_alpha")
|
||||
alpha = alpha or data.get("alpha")
|
||||
if not alpha:
|
||||
print(" Found config but `lora_alpha` is missing!")
|
||||
else:
|
||||
print(f" Found config at {json_path} [alpha:{alpha}]")
|
||||
return alpha
|
||||
|
||||
# For some weird reason peft doesn't include the alpha in the actual model
|
||||
print("PixArt: Warning! This is a PEFT LoRA. Trying to find config...")
|
||||
files = [
|
||||
f"{os.path.splitext(path)[0]}.json",
|
||||
f"{os.path.splitext(path)[0]}.config.json",
|
||||
os.path.join(os.path.dirname(path),"adapter_config.json"),
|
||||
]
|
||||
for file in files:
|
||||
if os.path.isfile(file):
|
||||
return load_json(file)
|
||||
|
||||
print(" Missing config/alpha! assuming alpha of 8. Consider converting it/adding a config json to it.")
|
||||
return 8.0
|
||||
|
||||
def load_pixart_lora(model, lora, lora_path, strength):
|
||||
k_back = lambda x: x.replace(".lora_up.weight", "")
|
||||
# need to convert the actual weights for this to work.
|
||||
if any(True for x in lora.keys() if x.endswith("adaln_single.linear.lora_A.weight")):
|
||||
lora = convert_lora_state_dict(lora, peft=True)
|
||||
alpha = find_peft_alpha(lora_path)
|
||||
lora.update({f"{k_back(x)}.alpha":torch.tensor(alpha) for x in lora.keys() if "lora_up" in x})
|
||||
else: # OneTrainer
|
||||
lora = convert_lora_state_dict(lora, peft=False)
|
||||
|
||||
key_map = {k_back(x):f"diffusion_model.{k_back(x)}.weight" for x in lora.keys() if "lora_up" in x} # fake
|
||||
|
||||
loaded = comfy.lora.load_lora(lora, key_map)
|
||||
if model is not None:
|
||||
# switch to custom model patcher when using LoRAs
|
||||
if isinstance(model, EXM_PixArt_ModelPatcher):
|
||||
new_modelpatcher = model.clone()
|
||||
else:
|
||||
new_modelpatcher = replace_model_patcher(model)
|
||||
k = new_modelpatcher.add_patches(loaded, strength)
|
||||
else:
|
||||
k = ()
|
||||
new_modelpatcher = None
|
||||
|
||||
k = set(k)
|
||||
for x in loaded:
|
||||
if (x not in k):
|
||||
print("NOT LOADED", x)
|
||||
|
||||
return new_modelpatcher
|
||||
@@ -186,7 +186,7 @@
|
||||
same "printed page" as the copyright notice for easier
|
||||
identification within third-party archives.
|
||||
|
||||
Copyright [yyyy] [name of copyright owner]
|
||||
Copyright 2024 Junsong Chen, Jincheng Yu, Enze Xie
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
@@ -18,7 +18,7 @@ from timm.models.vision_transformer import PatchEmbed, Mlp
|
||||
|
||||
|
||||
from .utils import auto_grad_checkpoint, to_2tuple
|
||||
from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, LabelEmbedder, FinalLayer
|
||||
from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, LabelEmbedder, FinalLayer
|
||||
|
||||
|
||||
class PixArtBlock(nn.Module):
|
||||
@@ -15,8 +15,8 @@ from timm.models.layers import DropPath
|
||||
from timm.models.vision_transformer import Mlp
|
||||
|
||||
from .utils import auto_grad_checkpoint, to_2tuple
|
||||
from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
|
||||
from .PixArt import PixArt, get_2d_sincos_pos_embed
|
||||
from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
|
||||
from .pixart import PixArt, get_2d_sincos_pos_embed
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
@@ -1,312 +0,0 @@
|
||||
import re
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from copy import deepcopy
|
||||
from torch import Tensor
|
||||
from torch.nn import Module, Linear, init
|
||||
from typing import Any, Mapping
|
||||
|
||||
from .PixArt import PixArt, get_2d_sincos_pos_embed
|
||||
from .PixArtMS import PixArtMSBlock, PixArtMS
|
||||
from .utils import auto_grad_checkpoint
|
||||
|
||||
# The implementation of ControlNet-Half architrecture
|
||||
# https://github.com/lllyasviel/ControlNet/discussions/188
|
||||
class ControlT2IDitBlockHalf(Module):
|
||||
def __init__(self, base_block: PixArtMSBlock, block_index: 0) -> None:
|
||||
super().__init__()
|
||||
self.copied_block = deepcopy(base_block)
|
||||
self.block_index = block_index
|
||||
|
||||
for p in self.copied_block.parameters():
|
||||
p.requires_grad_(True)
|
||||
|
||||
self.copied_block.load_state_dict(base_block.state_dict())
|
||||
self.copied_block.train()
|
||||
|
||||
self.hidden_size = hidden_size = base_block.hidden_size
|
||||
if self.block_index == 0:
|
||||
self.before_proj = Linear(hidden_size, hidden_size)
|
||||
init.zeros_(self.before_proj.weight)
|
||||
init.zeros_(self.before_proj.bias)
|
||||
self.after_proj = Linear(hidden_size, hidden_size)
|
||||
init.zeros_(self.after_proj.weight)
|
||||
init.zeros_(self.after_proj.bias)
|
||||
|
||||
def forward(self, x, y, t, mask=None, c=None):
|
||||
|
||||
if self.block_index == 0:
|
||||
# the first block
|
||||
c = self.before_proj(c)
|
||||
c = self.copied_block(x + c, y, t, mask)
|
||||
c_skip = self.after_proj(c)
|
||||
else:
|
||||
# load from previous c and produce the c for skip connection
|
||||
c = self.copied_block(c, y, t, mask)
|
||||
c_skip = self.after_proj(c)
|
||||
|
||||
return c, c_skip
|
||||
|
||||
|
||||
# The implementation of ControlPixArtHalf net
|
||||
class ControlPixArtHalf(Module):
|
||||
# only support single res model
|
||||
def __init__(self, base_model: PixArt, copy_blocks_num: int = 13) -> None:
|
||||
super().__init__()
|
||||
self.dtype = torch.get_default_dtype()
|
||||
self.base_model = base_model.eval()
|
||||
self.controlnet = []
|
||||
self.copy_blocks_num = copy_blocks_num
|
||||
self.total_blocks_num = len(base_model.blocks)
|
||||
for p in self.base_model.parameters():
|
||||
p.requires_grad_(False)
|
||||
|
||||
# Copy first copy_blocks_num block
|
||||
for i in range(copy_blocks_num):
|
||||
self.controlnet.append(ControlT2IDitBlockHalf(base_model.blocks[i], i))
|
||||
self.controlnet = nn.ModuleList(self.controlnet)
|
||||
|
||||
def __getattr__(self, name: str) -> Tensor or Module:
|
||||
if name in ['forward', 'forward_with_dpmsolver', 'forward_with_cfg', 'forward_c', 'load_state_dict']:
|
||||
return self.__dict__[name]
|
||||
elif name in ['base_model', 'controlnet']:
|
||||
return super().__getattr__(name)
|
||||
else:
|
||||
return getattr(self.base_model, name)
|
||||
|
||||
def forward_c(self, c):
|
||||
self.h, self.w = c.shape[-2]//self.patch_size, c.shape[-1]//self.patch_size
|
||||
pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(c.device).to(self.dtype)
|
||||
return self.x_embedder(c) + pos_embed if c is not None else c
|
||||
|
||||
# def forward(self, x, t, c, **kwargs):
|
||||
# return self.base_model(x, t, c=self.forward_c(c), **kwargs)
|
||||
def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs):
|
||||
# modify the original PixArtMS forward function
|
||||
if c is not None:
|
||||
c = c.to(self.dtype)
|
||||
c = self.forward_c(c)
|
||||
"""
|
||||
Forward pass of PixArt.
|
||||
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
|
||||
t: (N,) tensor of diffusion timesteps
|
||||
y: (N, 1, 120, C) tensor of class labels
|
||||
"""
|
||||
x = x.to(self.dtype)
|
||||
timestep = timestep.to(self.dtype)
|
||||
y = y.to(self.dtype)
|
||||
pos_embed = self.pos_embed.to(self.dtype)
|
||||
self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
|
||||
x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
|
||||
t = self.t_embedder(timestep.to(x.dtype)) # (N, D)
|
||||
t0 = self.t_block(t)
|
||||
y = self.y_embedder(y, self.training) # (N, 1, L, D)
|
||||
if mask is not None:
|
||||
if mask.shape[0] != y.shape[0]:
|
||||
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
|
||||
mask = mask.squeeze(1).squeeze(1)
|
||||
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
|
||||
y_lens = mask.sum(dim=1).tolist()
|
||||
else:
|
||||
y_lens = [y.shape[2]] * y.shape[0]
|
||||
y = y.squeeze(1).view(1, -1, x.shape[-1])
|
||||
|
||||
# define the first layer
|
||||
x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs) # (N, T, D) #support grad checkpoint
|
||||
|
||||
if c is not None:
|
||||
# update c
|
||||
for index in range(1, self.copy_blocks_num + 1):
|
||||
c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs)
|
||||
x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs)
|
||||
|
||||
# update x
|
||||
for index in range(self.copy_blocks_num + 1, self.total_blocks_num):
|
||||
x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
|
||||
else:
|
||||
for index in range(1, self.total_blocks_num):
|
||||
x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
|
||||
|
||||
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
|
||||
x = self.unpatchify(x) # (N, out_channels, H, W)
|
||||
return x
|
||||
|
||||
def forward(self, x, timesteps, context, cn_hint=None, **kwargs):
|
||||
"""
|
||||
Forward pass that adapts comfy input to original forward function
|
||||
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
|
||||
timesteps: (N,) tensor of diffusion timesteps
|
||||
context: (N, 1, 120, C) conditioning
|
||||
cn_hint: controlnet hint
|
||||
"""
|
||||
## Still accepts the input w/o that dim but returns garbage
|
||||
if len(context.shape) == 3:
|
||||
context = context.unsqueeze(1)
|
||||
|
||||
## run original forward pass
|
||||
out = self.forward_raw(
|
||||
x = x.to(self.dtype),
|
||||
timestep = timesteps.to(self.dtype),
|
||||
y = context.to(self.dtype),
|
||||
c = cn_hint,
|
||||
)
|
||||
|
||||
## only return EPS
|
||||
out = out.to(torch.float)
|
||||
eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
|
||||
return eps
|
||||
|
||||
def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs):
|
||||
model_out = self.forward_raw(x, t, y, data_info=data_info, c=c, **kwargs)
|
||||
return model_out.chunk(2, dim=1)[0]
|
||||
|
||||
# def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs):
|
||||
# return self.base_model.forward_with_dpmsolver(x, t, y, data_info=data_info, c=self.forward_c(c), **kwargs)
|
||||
|
||||
def forward_with_cfg(self, x, t, y, cfg_scale, data_info, c, **kwargs):
|
||||
return self.base_model.forward_with_cfg(x, t, y, cfg_scale, data_info, c=self.forward_c(c), **kwargs)
|
||||
|
||||
def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
|
||||
if all((k.startswith('base_model') or k.startswith('controlnet')) for k in state_dict.keys()):
|
||||
return super().load_state_dict(state_dict, strict)
|
||||
else:
|
||||
new_key = {}
|
||||
for k in state_dict.keys():
|
||||
new_key[k] = re.sub(r"(blocks\.\d+)(.*)", r"\1.base_block\2", k)
|
||||
for k, v in new_key.items():
|
||||
if k != v:
|
||||
print(f"replace {k} to {v}")
|
||||
state_dict[v] = state_dict.pop(k)
|
||||
|
||||
return self.base_model.load_state_dict(state_dict, strict)
|
||||
|
||||
def unpatchify(self, x):
|
||||
"""
|
||||
x: (N, T, patch_size**2 * C)
|
||||
imgs: (N, H, W, C)
|
||||
"""
|
||||
c = self.out_channels
|
||||
p = self.x_embedder.patch_size[0]
|
||||
assert self.h * self.w == x.shape[1]
|
||||
|
||||
x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
|
||||
x = torch.einsum('nhwpqc->nchpwq', x)
|
||||
imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
|
||||
return imgs
|
||||
|
||||
# @property
|
||||
# def dtype(self):
|
||||
## 返回模型参数的数据类型
|
||||
# return next(self.parameters()).dtype
|
||||
|
||||
|
||||
# The implementation for PixArtMS_Half + 1024 resolution
|
||||
class ControlPixArtMSHalf(ControlPixArtHalf):
|
||||
# support multi-scale res model (multi-scale model can also be applied to single reso training & inference)
|
||||
def __init__(self, base_model: PixArtMS, copy_blocks_num: int = 13) -> None:
|
||||
super().__init__(base_model=base_model, copy_blocks_num=copy_blocks_num)
|
||||
|
||||
def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs):
|
||||
# modify the original PixArtMS forward function
|
||||
"""
|
||||
Forward pass of PixArt.
|
||||
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
|
||||
t: (N,) tensor of diffusion timesteps
|
||||
y: (N, 1, 120, C) tensor of class labels
|
||||
"""
|
||||
if c is not None:
|
||||
c = c.to(self.dtype)
|
||||
c = self.forward_c(c)
|
||||
bs = x.shape[0]
|
||||
x = x.to(self.dtype)
|
||||
timestep = timestep.to(self.dtype)
|
||||
y = y.to(self.dtype)
|
||||
c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
|
||||
self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
|
||||
|
||||
pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(x.device).to(self.dtype)
|
||||
x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
|
||||
t = self.t_embedder(timestep) # (N, D)
|
||||
csize = self.csize_embedder(c_size, bs) # (N, D)
|
||||
ar = self.ar_embedder(ar, bs) # (N, D)
|
||||
t = t + torch.cat([csize, ar], dim=1)
|
||||
t0 = self.t_block(t)
|
||||
y = self.y_embedder(y, self.training) # (N, D)
|
||||
if mask is not None:
|
||||
if mask.shape[0] != y.shape[0]:
|
||||
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
|
||||
mask = mask.squeeze(1).squeeze(1)
|
||||
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
|
||||
y_lens = mask.sum(dim=1).tolist()
|
||||
else:
|
||||
y_lens = [y.shape[2]] * y.shape[0]
|
||||
y = y.squeeze(1).view(1, -1, x.shape[-1])
|
||||
|
||||
# define the first layer
|
||||
x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs) # (N, T, D) #support grad checkpoint
|
||||
|
||||
if c is not None:
|
||||
# update c
|
||||
for index in range(1, self.copy_blocks_num + 1):
|
||||
c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs)
|
||||
x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs)
|
||||
|
||||
# update x
|
||||
for index in range(self.copy_blocks_num + 1, self.total_blocks_num):
|
||||
x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
|
||||
else:
|
||||
for index in range(1, self.total_blocks_num):
|
||||
x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
|
||||
|
||||
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
|
||||
x = self.unpatchify(x) # (N, out_channels, H, W)
|
||||
return x
|
||||
|
||||
def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, cn_hint=None, **kwargs):
|
||||
"""
|
||||
Forward pass that adapts comfy input to original forward function
|
||||
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
|
||||
timesteps: (N,) tensor of diffusion timesteps
|
||||
context: (N, 1, 120, C) conditioning
|
||||
img_hw: height|width conditioning
|
||||
aspect_ratio: aspect ratio conditioning
|
||||
cn_hint: controlnet hint
|
||||
"""
|
||||
## size/ar from cond with fallback based on the latent image shape.
|
||||
bs = x.shape[0]
|
||||
data_info = {}
|
||||
if img_hw is None:
|
||||
data_info["img_hw"] = torch.tensor(
|
||||
[[x.shape[2]*8, x.shape[3]*8]],
|
||||
dtype=self.dtype,
|
||||
device=x.device
|
||||
).repeat(bs, 1)
|
||||
else:
|
||||
data_info["img_hw"] = img_hw.to(x.dtype)
|
||||
if aspect_ratio is None or True:
|
||||
data_info["aspect_ratio"] = torch.tensor(
|
||||
[[x.shape[2]/x.shape[3]]],
|
||||
dtype=self.dtype,
|
||||
device=x.device
|
||||
).repeat(bs, 1)
|
||||
else:
|
||||
data_info["aspect_ratio"] = aspect_ratio.to(x.dtype)
|
||||
|
||||
## Still accepts the input w/o that dim but returns garbage
|
||||
if len(context.shape) == 3:
|
||||
context = context.unsqueeze(1)
|
||||
|
||||
## run original forward pass
|
||||
out = self.forward_raw(
|
||||
x = x.to(self.dtype),
|
||||
timestep = timesteps.to(self.dtype),
|
||||
y = context.to(self.dtype),
|
||||
c = cn_hint,
|
||||
data_info=data_info,
|
||||
)
|
||||
|
||||
## only return EPS
|
||||
out = out.to(torch.float)
|
||||
eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
|
||||
return eps
|
||||
+73
-264
@@ -1,278 +1,87 @@
|
||||
import os
|
||||
import json
|
||||
import torch
|
||||
import folder_paths
|
||||
|
||||
from comfy import utils
|
||||
from .conf import pixart_conf, pixart_res
|
||||
from .lora import load_pixart_lora
|
||||
from .loader import load_pixart
|
||||
|
||||
class PixArtCheckpointLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
|
||||
"model": (list(pixart_conf.keys()),),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
RETURN_NAMES = ("model",)
|
||||
FUNCTION = "load_checkpoint"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt Checkpoint Loader"
|
||||
|
||||
def load_checkpoint(self, ckpt_name, model):
|
||||
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
|
||||
model_conf = pixart_conf[model]
|
||||
model = load_pixart(
|
||||
model_path = ckpt_path,
|
||||
model_conf = model_conf,
|
||||
)
|
||||
return (model,)
|
||||
|
||||
class PixArtCheckpointLoaderSimple(PixArtCheckpointLoader):
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
|
||||
}
|
||||
}
|
||||
TITLE = "PixArt Checkpoint Loader (auto)"
|
||||
|
||||
def load_checkpoint(self, ckpt_name):
|
||||
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
|
||||
model = load_pixart(model_path=ckpt_path)
|
||||
return (model,)
|
||||
|
||||
class PixArtResolutionSelect():
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": (list(pixart_res.keys()),),
|
||||
# keys are the same for both
|
||||
"ratio": (list(pixart_res["PixArtMS_XL_2"].keys()),{"default":"1.00"}),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("INT","INT")
|
||||
RETURN_NAMES = ("width","height")
|
||||
FUNCTION = "get_res"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt Resolution Select"
|
||||
|
||||
def get_res(self, model, ratio):
|
||||
width, height = pixart_res[model][ratio]
|
||||
return (width,height)
|
||||
|
||||
class PixArtLoraLoader:
|
||||
def __init__(self):
|
||||
self.loaded_lora = None
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"lora_name": (folder_paths.get_filename_list("loras"), ),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "load_lora"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt Load LoRA"
|
||||
|
||||
def load_lora(self, model, lora_name, strength,):
|
||||
if strength == 0:
|
||||
return (model)
|
||||
|
||||
lora_path = folder_paths.get_full_path("loras", lora_name)
|
||||
lora = None
|
||||
if self.loaded_lora is not None:
|
||||
if self.loaded_lora[0] == lora_path:
|
||||
lora = self.loaded_lora[1]
|
||||
else:
|
||||
temp = self.loaded_lora
|
||||
self.loaded_lora = None
|
||||
del temp
|
||||
|
||||
if lora is None:
|
||||
lora = utils.load_torch_file(lora_path, safe_load=True)
|
||||
self.loaded_lora = (lora_path, lora)
|
||||
|
||||
model_lora = load_pixart_lora(model, lora, lora_path, strength,)
|
||||
return (model_lora,)
|
||||
|
||||
class PixArtResolutionCond:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"cond": ("CONDITIONING", ),
|
||||
"width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
|
||||
"height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
|
||||
}
|
||||
}
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"cond": ("CONDITIONING", ),
|
||||
"width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
|
||||
"height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
RETURN_NAMES = ("cond",)
|
||||
FUNCTION = "add_cond"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt Resolution Conditioning"
|
||||
|
||||
def add_cond(self, cond, width, height):
|
||||
for c in range(len(cond)):
|
||||
cond[c][1].update({
|
||||
"img_hw": [[height, width]],
|
||||
"aspect_ratio": [[height/width]],
|
||||
})
|
||||
return (cond,)
|
||||
|
||||
class PixArtControlNetCond:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"cond": ("CONDITIONING",),
|
||||
"latent": ("LATENT",),
|
||||
# "image": ("IMAGE",),
|
||||
# "vae": ("VAE",),
|
||||
# "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
RETURN_NAMES = ("cond",)
|
||||
FUNCTION = "add_cond"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt ControlNet Conditioning"
|
||||
|
||||
def add_cond(self, cond, latent):
|
||||
for c in range(len(cond)):
|
||||
cond[c][1]["cn_hint"] = latent["samples"] * 0.18215
|
||||
return (cond,)
|
||||
|
||||
class PixArtT5TextEncode:
|
||||
"""
|
||||
Reference code, mostly to verify compatibility.
|
||||
Once everything works, this should instead inherit from the
|
||||
T5 text encode node and simply add the extra conds (res/ar).
|
||||
"""
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"text": ("STRING", {"multiline": True}),
|
||||
"T5": ("T5",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "encode"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt T5 Text Encode [Reference]"
|
||||
|
||||
def mask_feature(self, emb, mask):
|
||||
if emb.shape[0] == 1:
|
||||
keep_index = mask.sum().item()
|
||||
return emb[:, :, :keep_index, :], keep_index
|
||||
else:
|
||||
masked_feature = emb * mask[:, None, :, None]
|
||||
return masked_feature, emb.shape[2]
|
||||
|
||||
def encode(self, text, T5):
|
||||
text = text.lower().strip()
|
||||
tokenizer_out = T5.tokenizer.tokenizer(
|
||||
text,
|
||||
max_length = 120,
|
||||
padding = 'max_length',
|
||||
truncation = True,
|
||||
return_attention_mask = True,
|
||||
add_special_tokens = True,
|
||||
return_tensors = 'pt'
|
||||
)
|
||||
tokens = tokenizer_out["input_ids"]
|
||||
mask = tokenizer_out["attention_mask"]
|
||||
embs = T5.cond_stage_model.transformer(
|
||||
input_ids = tokens.to(T5.load_device),
|
||||
attention_mask = mask.to(T5.load_device),
|
||||
)['last_hidden_state'].float()[:, None]
|
||||
masked_embs, keep_index = self.mask_feature(
|
||||
embs.detach().to("cpu"),
|
||||
mask.detach().to("cpu")
|
||||
)
|
||||
masked_embs = masked_embs.squeeze(0) # match CLIP/internal
|
||||
print("Encoded T5:", masked_embs.shape)
|
||||
return ([[masked_embs, {}]], )
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
RETURN_NAMES = ("cond",)
|
||||
FUNCTION = "add_cond"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt Resolution Conditioning"
|
||||
|
||||
def add_cond(self, cond, width, height):
|
||||
for c in range(len(cond)):
|
||||
cond[c][1].update({
|
||||
"img_hw": [[height, width]],
|
||||
"aspect_ratio": [[height/width]],
|
||||
})
|
||||
return (cond,)
|
||||
|
||||
class PixArtT5FromSD3CLIP:
|
||||
"""
|
||||
Split the T5 text encoder away from SD3
|
||||
"""
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"sd3_clip": ("CLIP",),
|
||||
"padding": ("INT", {"default": 1, "min": 1, "max": 300}),
|
||||
}
|
||||
}
|
||||
"""
|
||||
Split the T5 text encoder away from SD3
|
||||
"""
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"sd3_clip": ("CLIP",),
|
||||
"padding": ("INT", {"default": 1, "min": 1, "max": 300}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
RETURN_NAMES = ("t5",)
|
||||
FUNCTION = "split"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt T5 from SD3 CLIP"
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
RETURN_NAMES = ("t5",)
|
||||
FUNCTION = "split"
|
||||
CATEGORY = "ExtraModels/PixArt"
|
||||
TITLE = "PixArt T5 from SD3 CLIP"
|
||||
|
||||
def split(self, sd3_clip, padding):
|
||||
try:
|
||||
from comfy.text_encoders.sd3_clip import SD3Tokenizer, SD3ClipModel
|
||||
except ImportError:
|
||||
# fallback for older ComfyUI versions
|
||||
from comfy.sd3_clip import SD3Tokenizer, SD3ClipModel
|
||||
import copy
|
||||
|
||||
clip = sd3_clip.clone()
|
||||
assert clip.cond_stage_model.t5xxl is not None, "CLIP must have T5 loaded!"
|
||||
def split(self, sd3_clip, padding):
|
||||
try:
|
||||
from comfy.text_encoders.sd3_clip import SD3Tokenizer, SD3ClipModel
|
||||
except ImportError:
|
||||
# fallback for older ComfyUI versions
|
||||
from comfy.sd3_clip import SD3Tokenizer, SD3ClipModel # type: ignore
|
||||
import copy
|
||||
|
||||
clip = sd3_clip.clone()
|
||||
assert clip.cond_stage_model.t5xxl is not None, "CLIP must have T5 loaded!"
|
||||
|
||||
# remove transformer
|
||||
transformer = clip.cond_stage_model.t5xxl.transformer
|
||||
clip.cond_stage_model.t5xxl.transformer = None
|
||||
# remove transformer
|
||||
transformer = clip.cond_stage_model.t5xxl.transformer
|
||||
clip.cond_stage_model.t5xxl.transformer = None
|
||||
|
||||
# clone object
|
||||
tmp = SD3ClipModel(clip_l=False, clip_g=False, t5=False)
|
||||
tmp.t5xxl = copy.deepcopy(clip.cond_stage_model.t5xxl)
|
||||
# put transformer back
|
||||
clip.cond_stage_model.t5xxl.transformer = transformer
|
||||
tmp.t5xxl.transformer = transformer
|
||||
# clone object
|
||||
tmp = SD3ClipModel(clip_l=False, clip_g=False, t5=False)
|
||||
tmp.t5xxl = copy.deepcopy(clip.cond_stage_model.t5xxl)
|
||||
# put transformer back
|
||||
clip.cond_stage_model.t5xxl.transformer = transformer
|
||||
tmp.t5xxl.transformer = transformer
|
||||
|
||||
# override special tokens
|
||||
tmp.t5xxl.special_tokens = copy.deepcopy(clip.cond_stage_model.t5xxl.special_tokens)
|
||||
tmp.t5xxl.special_tokens.pop("end") # make sure empty tokens match
|
||||
|
||||
# add attn mask opt if present in original
|
||||
if hasattr(sd3_clip.cond_stage_model, "t5_attention_mask"):
|
||||
tmp.t5_attention_mask = False
|
||||
# override special tokens
|
||||
tmp.t5xxl.special_tokens = copy.deepcopy(clip.cond_stage_model.t5xxl.special_tokens)
|
||||
tmp.t5xxl.special_tokens.pop("end") # make sure empty tokens match
|
||||
|
||||
# add attn mask opt if present in original
|
||||
if hasattr(sd3_clip.cond_stage_model, "t5_attention_mask"):
|
||||
tmp.t5_attention_mask = False
|
||||
|
||||
# tokenizer
|
||||
tok = SD3Tokenizer()
|
||||
tok.t5xxl.min_length = padding
|
||||
|
||||
clip.cond_stage_model = tmp
|
||||
clip.tokenizer = tok
|
||||
# tokenizer
|
||||
tok = SD3Tokenizer()
|
||||
tok.t5xxl.min_length = padding
|
||||
|
||||
clip.cond_stage_model = tmp
|
||||
clip.tokenizer = tok
|
||||
|
||||
return (clip, )
|
||||
return (clip, )
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"PixArtCheckpointLoader" : PixArtCheckpointLoader,
|
||||
"PixArtCheckpointLoaderSimple" : PixArtCheckpointLoaderSimple,
|
||||
"PixArtResolutionSelect" : PixArtResolutionSelect,
|
||||
"PixArtLoraLoader" : PixArtLoraLoader,
|
||||
"PixArtT5TextEncode" : PixArtT5TextEncode,
|
||||
"PixArtResolutionCond" : PixArtResolutionCond,
|
||||
"PixArtControlNetCond" : PixArtControlNetCond,
|
||||
"PixArtT5FromSD3CLIP": PixArtT5FromSD3CLIP,
|
||||
"PixArtResolutionCond" : PixArtResolutionCond,
|
||||
"PixArtT5FromSD3CLIP": PixArtT5FromSD3CLIP,
|
||||
}
|
||||
|
||||
@@ -1,674 +0,0 @@
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 3, 29 June 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU General Public License is a free, copyleft license for
|
||||
software and other kinds of works.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
the GNU General Public License is intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users. We, the Free Software Foundation, use the
|
||||
GNU General Public License for most of our software; it applies also to
|
||||
any other work released this way by its authors. You can apply it to
|
||||
your programs, too.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
To protect your rights, we need to prevent others from denying you
|
||||
these rights or asking you to surrender the rights. Therefore, you have
|
||||
certain responsibilities if you distribute copies of the software, or if
|
||||
you modify it: responsibilities to respect the freedom of others.
|
||||
|
||||
For example, if you distribute copies of such a program, whether
|
||||
gratis or for a fee, you must pass on to the recipients the same
|
||||
freedoms that you received. You must make sure that they, too, receive
|
||||
or can get the source code. And you must show them these terms so they
|
||||
know their rights.
|
||||
|
||||
Developers that use the GNU GPL protect your rights with two steps:
|
||||
(1) assert copyright on the software, and (2) offer you this License
|
||||
giving you legal permission to copy, distribute and/or modify it.
|
||||
|
||||
For the developers' and authors' protection, the GPL clearly explains
|
||||
that there is no warranty for this free software. For both users' and
|
||||
authors' sake, the GPL requires that modified versions be marked as
|
||||
changed, so that their problems will not be attributed erroneously to
|
||||
authors of previous versions.
|
||||
|
||||
Some devices are designed to deny users access to install or run
|
||||
modified versions of the software inside them, although the manufacturer
|
||||
can do so. This is fundamentally incompatible with the aim of
|
||||
protecting users' freedom to change the software. The systematic
|
||||
pattern of such abuse occurs in the area of products for individuals to
|
||||
use, which is precisely where it is most unacceptable. Therefore, we
|
||||
have designed this version of the GPL to prohibit the practice for those
|
||||
products. If such problems arise substantially in other domains, we
|
||||
stand ready to extend this provision to those domains in future versions
|
||||
of the GPL, as needed to protect the freedom of users.
|
||||
|
||||
Finally, every program is threatened constantly by software patents.
|
||||
States should not allow patents to restrict development and use of
|
||||
software on general-purpose computers, but in those that do, we wish to
|
||||
avoid the special danger that patents applied to a free program could
|
||||
make it effectively proprietary. To prevent this, the GPL assures that
|
||||
patents cannot be used to render the program non-free.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU General Public License.
|
||||
|
||||
"Copyright" also means copyright-like laws that apply to other kinds of
|
||||
works, such as semiconductor masks.
|
||||
|
||||
"The Program" refers to any copyrightable work licensed under this
|
||||
License. Each licensee is addressed as "you". "Licensees" and
|
||||
"recipients" may be individuals or organizations.
|
||||
|
||||
To "modify" a work means to copy from or adapt all or part of the work
|
||||
in a fashion requiring copyright permission, other than the making of an
|
||||
exact copy. The resulting work is called a "modified version" of the
|
||||
earlier work or a work "based on" the earlier work.
|
||||
|
||||
A "covered work" means either the unmodified Program or a work based
|
||||
on the Program.
|
||||
|
||||
To "propagate" a work means to do anything with it that, without
|
||||
permission, would make you directly or secondarily liable for
|
||||
infringement under applicable copyright law, except executing it on a
|
||||
computer or modifying a private copy. Propagation includes copying,
|
||||
distribution (with or without modification), making available to the
|
||||
public, and in some countries other activities as well.
|
||||
|
||||
To "convey" a work means any kind of propagation that enables other
|
||||
parties to make or receive copies. Mere interaction with a user through
|
||||
a computer network, with no transfer of a copy, is not conveying.
|
||||
|
||||
An interactive user interface displays "Appropriate Legal Notices"
|
||||
to the extent that it includes a convenient and prominently visible
|
||||
feature that (1) displays an appropriate copyright notice, and (2)
|
||||
tells the user that there is no warranty for the work (except to the
|
||||
extent that warranties are provided), that licensees may convey the
|
||||
work under this License, and how to view a copy of this License. If
|
||||
the interface presents a list of user commands or options, such as a
|
||||
menu, a prominent item in the list meets this criterion.
|
||||
|
||||
1. Source Code.
|
||||
|
||||
The "source code" for a work means the preferred form of the work
|
||||
for making modifications to it. "Object code" means any non-source
|
||||
form of a work.
|
||||
|
||||
A "Standard Interface" means an interface that either is an official
|
||||
standard defined by a recognized standards body, or, in the case of
|
||||
interfaces specified for a particular programming language, one that
|
||||
is widely used among developers working in that language.
|
||||
|
||||
The "System Libraries" of an executable work include anything, other
|
||||
than the work as a whole, that (a) is included in the normal form of
|
||||
packaging a Major Component, but which is not part of that Major
|
||||
Component, and (b) serves only to enable use of the work with that
|
||||
Major Component, or to implement a Standard Interface for which an
|
||||
implementation is available to the public in source code form. A
|
||||
"Major Component", in this context, means a major essential component
|
||||
(kernel, window system, and so on) of the specific operating system
|
||||
(if any) on which the executable work runs, or a compiler used to
|
||||
produce the work, or an object code interpreter used to run it.
|
||||
|
||||
The "Corresponding Source" for a work in object code form means all
|
||||
the source code needed to generate, install, and (for an executable
|
||||
work) run the object code and to modify the work, including scripts to
|
||||
control those activities. However, it does not include the work's
|
||||
System Libraries, or general-purpose tools or generally available free
|
||||
programs which are used unmodified in performing those activities but
|
||||
which are not part of the work. For example, Corresponding Source
|
||||
includes interface definition files associated with source files for
|
||||
the work, and the source code for shared libraries and dynamically
|
||||
linked subprograms that the work is specifically designed to require,
|
||||
such as by intimate data communication or control flow between those
|
||||
subprograms and other parts of the work.
|
||||
|
||||
The Corresponding Source need not include anything that users
|
||||
can regenerate automatically from other parts of the Corresponding
|
||||
Source.
|
||||
|
||||
The Corresponding Source for a work in source code form is that
|
||||
same work.
|
||||
|
||||
2. Basic Permissions.
|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
copyright on the Program, and are irrevocable provided the stated
|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
||||
in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
|
||||
|
||||
Conveying under any other circumstances is permitted solely under
|
||||
the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||
|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
is effected by exercising rights under this License with respect to
|
||||
the covered work, and you disclaim any intention to limit operation or
|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
technological measures.
|
||||
|
||||
4. Conveying Verbatim Copies.
|
||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
||||
|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
Corresponding Source. Regardless of what server hosts the
|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
a modified version of its Corresponding Source. The information must
|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
User Product is transferred to the recipient in perpetuity or for a
|
||||
fixed term (regardless of how the transaction is characterized), the
|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
modified object code on the User Product (for example, the work has
|
||||
been installed in ROM).
|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
requirement to continue to provide support service, warranty, or updates
|
||||
for a work that has been modified or installed by the recipient, or for
|
||||
the User Product in which it has been modified or installed. Access to a
|
||||
network may be denied when the modification itself materially and
|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
unpacking, reading or copying.
|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
be treated as though they were included in this License, to the extent
|
||||
that they are valid under applicable law. If additional permissions
|
||||
apply only to part of the Program, that part may be used separately
|
||||
under those permissions, but the entire Program remains governed by
|
||||
this License without regard to the additional permissions.
|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
remove any additional permissions from that copy, or from any part of
|
||||
it. (Additional permissions may be written to require their own
|
||||
removal in certain cases when you modify the work.) You may place
|
||||
additional permissions on material, added by you to a covered work,
|
||||
for which you have or can give appropriate copyright permission.
|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
add to a covered work, you may (if authorized by the copyright holders of
|
||||
that material) supplement the terms of this License with terms:
|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
terms of sections 15 and 16 of this License; or
|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
author attributions in that material or in the Appropriate Legal
|
||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
requiring that modified versions of such material be marked in
|
||||
reasonable ways as different from the original version; or
|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
authors of the material; or
|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
trade names, trademarks, or service marks; or
|
||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
material by anyone who conveys the material (or modified versions of
|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
restrictions" within the meaning of section 10. If the Program as you
|
||||
received it, or any part of it, contains a notice stating that it is
|
||||
governed by this License along with a term that is a further
|
||||
restriction, you may remove that term. If a license document contains
|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
this License (including any patent licenses granted under the third
|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
license from a particular copyright holder is reinstated (a)
|
||||
provisionally, unless and until the copyright holder explicitly and
|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
holder fails to notify you of the violation by some reasonable means
|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
violation by some reasonable means, this is the first time you have
|
||||
received notice of violation of this License (for any work) from that
|
||||
copyright holder, and you cure the violation prior to 30 days after
|
||||
your receipt of the notice.
|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
licenses of parties who have received copies or rights from you under
|
||||
this License. If your rights have been terminated and not permanently
|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
material under section 10.
|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Use with the GNU Affero General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU Affero General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the special requirements of the GNU Affero General Public License,
|
||||
section 13, concerning interaction through a network will apply to the
|
||||
combination as such.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program does terminal interaction, make it output a short
|
||||
notice like this when it starts in an interactive mode:
|
||||
|
||||
<program> Copyright (C) <year> <name of author>
|
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it
|
||||
under certain conditions; type `show c' for details.
|
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||
parts of the General Public License. Of course, your program's commands
|
||||
might be different; for a GUI interface, you would use an "about box".
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU GPL, see
|
||||
<https://www.gnu.org/licenses/>.
|
||||
|
||||
The GNU General Public License does not permit incorporating your program
|
||||
into proprietary programs. If your program is a subroutine library, you
|
||||
may consider it more useful to permit linking proprietary applications with
|
||||
the library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License. But first, please read
|
||||
<https://www.gnu.org/licenses/why-not-lgpl.html>.
|
||||
-118
@@ -1,118 +0,0 @@
|
||||
import os
|
||||
import torch
|
||||
import comfy.utils
|
||||
import comfy.model_patcher
|
||||
from comfy import model_management
|
||||
import folder_paths
|
||||
|
||||
from .t5v11 import T5v11Model, T5v11Tokenizer
|
||||
|
||||
class EXM_T5v11:
|
||||
def __init__(self, textmodel_ver="xxl", embedding_directory=None, textmodel_path=None, no_init=False, device="cpu", dtype=None):
|
||||
if no_init:
|
||||
return
|
||||
|
||||
if device == "auto":
|
||||
size = 0
|
||||
self.load_device = model_management.text_encoder_device()
|
||||
self.offload_device = model_management.text_encoder_offload_device()
|
||||
self.init_device = "cpu"
|
||||
elif dtype == "bnb8bit":
|
||||
# BNB doesn't support size enum
|
||||
size = 12.4 * (1024**3)
|
||||
# Or moving between devices
|
||||
self.load_device = model_management.get_torch_device()
|
||||
self.offload_device = self.load_device
|
||||
self.init_device = self.load_device
|
||||
elif dtype == "bnb4bit":
|
||||
# This seems to use the same VRAM as 8bit on Pascal?
|
||||
size = 6.2 * (1024**3)
|
||||
self.load_device = model_management.get_torch_device()
|
||||
self.offload_device = self.load_device
|
||||
self.init_device = self.load_device
|
||||
elif device == "cpu":
|
||||
size = 0
|
||||
self.load_device = "cpu"
|
||||
self.offload_device = "cpu"
|
||||
self.init_device="cpu"
|
||||
elif device.startswith("cuda"):
|
||||
print("Direct CUDA device override!\nVRAM will not be freed by default.")
|
||||
size = 0
|
||||
self.load_device = device
|
||||
self.offload_device = device
|
||||
self.init_device = device
|
||||
else:
|
||||
size = 0
|
||||
self.load_device = model_management.get_torch_device()
|
||||
self.offload_device = "cpu"
|
||||
self.init_device="cpu"
|
||||
|
||||
self.cond_stage_model = T5v11Model(
|
||||
textmodel_ver = textmodel_ver,
|
||||
textmodel_path = textmodel_path,
|
||||
device = device,
|
||||
dtype = dtype,
|
||||
)
|
||||
self.tokenizer = T5v11Tokenizer(embedding_directory=embedding_directory)
|
||||
self.patcher = comfy.model_patcher.ModelPatcher(
|
||||
self.cond_stage_model,
|
||||
load_device = self.load_device,
|
||||
offload_device = self.offload_device,
|
||||
size = size,
|
||||
)
|
||||
|
||||
def clone(self):
|
||||
n = T5(no_init=True)
|
||||
n.patcher = self.patcher.clone()
|
||||
n.cond_stage_model = self.cond_stage_model
|
||||
n.tokenizer = self.tokenizer
|
||||
return n
|
||||
|
||||
def tokenize(self, text, return_word_ids=False):
|
||||
return self.tokenizer.tokenize_with_weights(text, return_word_ids)
|
||||
|
||||
def encode_from_tokens(self, tokens):
|
||||
self.load_model()
|
||||
return self.cond_stage_model.encode_token_weights(tokens)
|
||||
|
||||
def encode(self, text):
|
||||
tokens = self.tokenize(text)
|
||||
return self.encode_from_tokens(tokens)
|
||||
|
||||
def load_sd(self, sd):
|
||||
return self.cond_stage_model.load_sd(sd)
|
||||
|
||||
def get_sd(self):
|
||||
return self.cond_stage_model.state_dict()
|
||||
|
||||
def load_model(self):
|
||||
if self.load_device != "cpu":
|
||||
model_management.load_model_gpu(self.patcher)
|
||||
return self.patcher
|
||||
|
||||
def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
|
||||
return self.patcher.add_patches(patches, strength_patch, strength_model)
|
||||
|
||||
def get_key_patches(self):
|
||||
return self.patcher.get_key_patches()
|
||||
|
||||
|
||||
def load_t5(model_type, model_ver, model_path, path_type="file", device="cpu", dtype=None):
|
||||
assert model_type in ["t5v11"] # Only supported model for now
|
||||
model_args = {
|
||||
"textmodel_ver" : model_ver,
|
||||
"device" : device,
|
||||
"dtype" : dtype,
|
||||
}
|
||||
|
||||
if path_type == "folder":
|
||||
# pass directly to transformers and initialize there
|
||||
# this is to avoid having to handle multi-file state dict loading for now.
|
||||
model_args["textmodel_path"] = os.path.dirname(model_path)
|
||||
return EXM_T5v11(**model_args)
|
||||
else:
|
||||
# for some reason this returns garbage with torch.int8 weights, or just OOMs
|
||||
model = EXM_T5v11(**model_args)
|
||||
sd = comfy.utils.load_torch_file(model_path)
|
||||
model.load_sd(sd)
|
||||
return model
|
||||
-95
@@ -1,95 +0,0 @@
|
||||
import os
|
||||
import json
|
||||
import torch
|
||||
import folder_paths
|
||||
|
||||
from .loader import load_t5
|
||||
from ..utils.dtype import string_to_dtype
|
||||
|
||||
# initialize custom folder path
|
||||
os.makedirs(
|
||||
os.path.join(folder_paths.models_dir,"t5"),
|
||||
exist_ok = True,
|
||||
)
|
||||
folder_paths.folder_names_and_paths["t5"] = (
|
||||
[
|
||||
os.path.join(folder_paths.models_dir,"t5"),
|
||||
*folder_paths.folder_names_and_paths.get("t5", [[],set()])[0]
|
||||
],
|
||||
folder_paths.supported_pt_extensions
|
||||
)
|
||||
|
||||
dtypes = [
|
||||
"default",
|
||||
"auto (comfy)",
|
||||
"FP32",
|
||||
"FP16",
|
||||
# Note: remove these at some point
|
||||
"bnb8bit",
|
||||
"bnb4bit",
|
||||
]
|
||||
try: torch.float8_e5m2
|
||||
except AttributeError: print("Torch version too old for FP8")
|
||||
else: dtypes += ["FP8 E4M3", "FP8 E5M2"]
|
||||
|
||||
class T5v11Loader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
devices = ["auto", "cpu", "gpu"]
|
||||
# hack for using second GPU as offload
|
||||
for k in range(1, torch.cuda.device_count()):
|
||||
devices.append(f"cuda:{k}")
|
||||
return {
|
||||
"required": {
|
||||
"t5v11_name": (folder_paths.get_filename_list("t5"),),
|
||||
"t5v11_ver": (["xxl"],),
|
||||
"path_type": (["folder", "file"],),
|
||||
"device": (devices, {"default":"cpu"}),
|
||||
"dtype": (dtypes,),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("T5",)
|
||||
FUNCTION = "load_model"
|
||||
CATEGORY = "ExtraModels/T5"
|
||||
TITLE = "T5v1.1 Loader"
|
||||
|
||||
def load_model(self, t5v11_name, t5v11_ver, path_type, device, dtype):
|
||||
if "bnb" in dtype:
|
||||
assert device == "gpu" or device.startswith("cuda"), "BitsAndBytes only works on CUDA! Set device to 'gpu'."
|
||||
dtype = string_to_dtype(dtype, "text_encoder")
|
||||
if device == "cpu":
|
||||
assert dtype in [None, torch.float32], f"Can't use dtype '{dtype}' with CPU! Set dtype to 'default'."
|
||||
|
||||
return (load_t5(
|
||||
model_type = "t5v11",
|
||||
model_ver = t5v11_ver,
|
||||
model_path = folder_paths.get_full_path("t5", t5v11_name),
|
||||
path_type = path_type,
|
||||
device = device,
|
||||
dtype = dtype,
|
||||
),)
|
||||
|
||||
class T5TextEncode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"text": ("STRING", {"multiline": True}),
|
||||
"T5": ("T5",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "encode"
|
||||
CATEGORY = "ExtraModels/T5"
|
||||
TITLE = "T5 Text Encode"
|
||||
|
||||
def encode(self, text, T5=None):
|
||||
tokens = T5.tokenize(text)
|
||||
cond = T5.encode_from_tokens(tokens)
|
||||
return ([[cond, {}]], )
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"T5v11Loader" : T5v11Loader,
|
||||
"T5TextEncode" : T5TextEncode,
|
||||
}
|
||||
@@ -1,31 +0,0 @@
|
||||
{
|
||||
"_name_or_path": "google/t5-v1_1-xxl",
|
||||
"architectures": [
|
||||
"T5EncoderModel"
|
||||
],
|
||||
"d_ff": 10240,
|
||||
"d_kv": 64,
|
||||
"d_model": 4096,
|
||||
"decoder_start_token_id": 0,
|
||||
"dense_act_fn": "gelu_new",
|
||||
"dropout_rate": 0.1,
|
||||
"eos_token_id": 1,
|
||||
"feed_forward_proj": "gated-gelu",
|
||||
"initializer_factor": 1.0,
|
||||
"is_encoder_decoder": true,
|
||||
"is_gated_act": true,
|
||||
"layer_norm_epsilon": 1e-06,
|
||||
"model_type": "t5",
|
||||
"num_decoder_layers": 24,
|
||||
"num_heads": 64,
|
||||
"num_layers": 24,
|
||||
"output_past": true,
|
||||
"pad_token_id": 0,
|
||||
"relative_attention_max_distance": 128,
|
||||
"relative_attention_num_buckets": 32,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "float32",
|
||||
"transformers_version": "4.21.1",
|
||||
"use_cache": true,
|
||||
"vocab_size": 32128
|
||||
}
|
||||
-227
@@ -1,227 +0,0 @@
|
||||
"""
|
||||
Adapted from comfyui CLIP code.
|
||||
https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/sd1_clip.py
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from transformers import T5Tokenizer, T5EncoderModel, T5Config, modeling_utils
|
||||
import torch
|
||||
import traceback
|
||||
import zipfile
|
||||
from comfy import model_management
|
||||
|
||||
from comfy.sd1_clip import parse_parentheses, token_weights, escape_important, unescape_important, safe_load_embed_zip, expand_directory_list, load_embed
|
||||
|
||||
class T5v11Model(torch.nn.Module):
|
||||
def __init__(self, textmodel_ver="xxl", textmodel_json_config=None, textmodel_path=None, device="cpu", max_length=120, freeze=True, dtype=None):
|
||||
super().__init__()
|
||||
|
||||
self.num_layers = 24
|
||||
self.max_length = max_length
|
||||
self.bnb = False
|
||||
|
||||
if textmodel_path is not None:
|
||||
model_args = {}
|
||||
model_args["low_cpu_mem_usage"] = True # Don't take 2x system ram on cpu
|
||||
if dtype == "bnb8bit":
|
||||
self.bnb = True
|
||||
model_args["load_in_8bit"] = True
|
||||
elif dtype == "bnb4bit":
|
||||
self.bnb = True
|
||||
model_args["load_in_4bit"] = True
|
||||
else:
|
||||
if dtype: model_args["torch_dtype"] = dtype
|
||||
self.bnb = False
|
||||
# second GPU offload hack part 2
|
||||
if device.startswith("cuda"):
|
||||
model_args["device_map"] = device
|
||||
print(f"Loading T5 from '{textmodel_path}'")
|
||||
self.transformer = T5EncoderModel.from_pretrained(textmodel_path, **model_args)
|
||||
else:
|
||||
if textmodel_json_config is None:
|
||||
textmodel_json_config = os.path.join(
|
||||
os.path.dirname(os.path.realpath(__file__)),
|
||||
f"t5v11-{textmodel_ver}_config.json"
|
||||
)
|
||||
config = T5Config.from_json_file(textmodel_json_config)
|
||||
self.num_layers = config.num_hidden_layers
|
||||
with modeling_utils.no_init_weights():
|
||||
self.transformer = T5EncoderModel(config)
|
||||
|
||||
if freeze:
|
||||
self.freeze()
|
||||
self.empty_tokens = [[0] * self.max_length] # <pad> token
|
||||
|
||||
def freeze(self):
|
||||
self.transformer = self.transformer.eval()
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, tokens):
|
||||
device = self.transformer.get_input_embeddings().weight.device
|
||||
tokens = torch.LongTensor(tokens).to(device)
|
||||
attention_mask = torch.zeros_like(tokens)
|
||||
max_token = 1 # </s> token
|
||||
for x in range(attention_mask.shape[0]):
|
||||
for y in range(attention_mask.shape[1]):
|
||||
attention_mask[x, y] = 1
|
||||
if tokens[x, y] == max_token:
|
||||
break
|
||||
|
||||
outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask)
|
||||
|
||||
z = outputs['last_hidden_state']
|
||||
z.detach().cpu().float()
|
||||
return z
|
||||
|
||||
def encode(self, tokens):
|
||||
return self(tokens)
|
||||
|
||||
def load_sd(self, sd):
|
||||
return self.transformer.load_state_dict(sd, strict=False)
|
||||
|
||||
def to(self, *args, **kwargs):
|
||||
"""BNB complains if you try to change the device or dtype"""
|
||||
if self.bnb:
|
||||
print("Thanks to BitsAndBytes, T5 becomes an immovable rock.", args, kwargs)
|
||||
else:
|
||||
self.transformer.to(*args, **kwargs)
|
||||
|
||||
def encode_token_weights(self, token_weight_pairs, return_padded=False):
|
||||
to_encode = list(self.empty_tokens)
|
||||
for x in token_weight_pairs:
|
||||
tokens = list(map(lambda a: a[0], x))
|
||||
to_encode.append(tokens)
|
||||
|
||||
out = self.encode(to_encode)
|
||||
z_empty = out[0:1]
|
||||
|
||||
output = []
|
||||
for k in range(1, out.shape[0]):
|
||||
z = out[k:k+1]
|
||||
for i in range(len(z)):
|
||||
for j in range(len(z[i])):
|
||||
weight = token_weight_pairs[k - 1][j][1]
|
||||
z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
|
||||
output.append(z)
|
||||
|
||||
if (len(output) == 0):
|
||||
return z_empty.cpu()
|
||||
|
||||
out = torch.cat(output, dim=-2)
|
||||
if not return_padded:
|
||||
# Count number of tokens that aren't <pad>, then use that number as an index.
|
||||
keep_index = sum([sum([1 for y in x if y[0] != 0]) for x in token_weight_pairs])
|
||||
out = out[:, :keep_index, :]
|
||||
return out
|
||||
|
||||
|
||||
class T5v11Tokenizer:
|
||||
"""
|
||||
This is largely just based on the ComfyUI CLIP code.
|
||||
"""
|
||||
def __init__(self, tokenizer_path=None, max_length=120, embedding_directory=None, embedding_size=4096, embedding_key='t5'):
|
||||
if tokenizer_path is None:
|
||||
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
|
||||
self.tokenizer = T5Tokenizer.from_pretrained(tokenizer_path)
|
||||
self.max_length = max_length
|
||||
self.max_tokens_per_section = self.max_length - 1 # </s> but no <BOS>
|
||||
|
||||
self.pad_token = self.tokenizer("<pad>", add_special_tokens=False)["input_ids"][0]
|
||||
self.end_token = self.tokenizer("</s>", add_special_tokens=False)["input_ids"][0]
|
||||
vocab = self.tokenizer.get_vocab()
|
||||
self.inv_vocab = {v: k for k, v in vocab.items()}
|
||||
self.embedding_directory = embedding_directory
|
||||
self.max_word_length = 8 # haven't verified this
|
||||
self.embedding_identifier = "embedding:"
|
||||
self.embedding_size = embedding_size
|
||||
self.embedding_key = embedding_key
|
||||
|
||||
def _try_get_embedding(self, embedding_name:str):
|
||||
'''
|
||||
Takes a potential embedding name and tries to retrieve it.
|
||||
Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
|
||||
'''
|
||||
embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
|
||||
if embed is None:
|
||||
stripped = embedding_name.strip(',')
|
||||
if len(stripped) < len(embedding_name):
|
||||
embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
|
||||
return (embed, embedding_name[len(stripped):])
|
||||
return (embed, "")
|
||||
|
||||
def tokenize_with_weights(self, text:str, return_word_ids=False):
|
||||
'''
|
||||
Takes a prompt and converts it to a list of (token, weight, word id) elements.
|
||||
Tokens can both be integer tokens and pre computed T5 tensors.
|
||||
Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
|
||||
Returned list has the dimensions NxM where M is the input size of T5
|
||||
'''
|
||||
pad_token = self.pad_token
|
||||
text = escape_important(text)
|
||||
parsed_weights = token_weights(text, 1.0)
|
||||
|
||||
#tokenize words
|
||||
tokens = []
|
||||
for weighted_segment, weight in parsed_weights:
|
||||
to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ')
|
||||
to_tokenize = [x for x in to_tokenize if x != ""]
|
||||
for word in to_tokenize:
|
||||
#if we find an embedding, deal with the embedding
|
||||
if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
|
||||
embedding_name = word[len(self.embedding_identifier):].strip('\n')
|
||||
embed, leftover = self._try_get_embedding(embedding_name)
|
||||
if embed is None:
|
||||
print(f"warning, embedding:{embedding_name} does not exist, ignoring")
|
||||
else:
|
||||
if len(embed.shape) == 1:
|
||||
tokens.append([(embed, weight)])
|
||||
else:
|
||||
tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
|
||||
#if we accidentally have leftover text, continue parsing using leftover, else move on to next word
|
||||
if leftover != "":
|
||||
word = leftover
|
||||
else:
|
||||
continue
|
||||
#parse word
|
||||
tokens.append([(t, weight) for t in self.tokenizer(word, add_special_tokens=False)["input_ids"]])
|
||||
|
||||
#reshape token array to T5 input size
|
||||
batched_tokens = []
|
||||
batch = []
|
||||
batched_tokens.append(batch)
|
||||
for i, t_group in enumerate(tokens):
|
||||
#determine if we're going to try and keep the tokens in a single batch
|
||||
is_large = len(t_group) >= self.max_word_length
|
||||
|
||||
while len(t_group) > 0:
|
||||
if len(t_group) + len(batch) > self.max_length - 1:
|
||||
remaining_length = self.max_length - len(batch) - 1
|
||||
#break word in two and add end token
|
||||
if is_large:
|
||||
batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
|
||||
batch.append((self.end_token, 1.0, 0))
|
||||
t_group = t_group[remaining_length:]
|
||||
#add end token and pad
|
||||
else:
|
||||
batch.append((self.end_token, 1.0, 0))
|
||||
batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
|
||||
#start new batch
|
||||
batch = []
|
||||
batched_tokens.append(batch)
|
||||
else:
|
||||
batch.extend([(t,w,i+1) for t,w in t_group])
|
||||
t_group = []
|
||||
|
||||
# fill last batch
|
||||
batch.extend([(self.end_token, 1.0, 0)] + [(self.pad_token, 1.0, 0)] * (self.max_length - len(batch) - 1))
|
||||
# instead of filling, just add EOS (DEBUG)
|
||||
# batch.extend([(self.end_token, 1.0, 0)])
|
||||
|
||||
if not return_word_ids:
|
||||
batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
|
||||
return batched_tokens
|
||||
|
||||
def untokenize(self, token_weight_pair):
|
||||
return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
|
||||
+35
-36
@@ -1,51 +1,50 @@
|
||||
# only import if running as a custom node
|
||||
try:
|
||||
import comfy.utils
|
||||
import comfy.utils
|
||||
except ImportError:
|
||||
pass
|
||||
pass
|
||||
else:
|
||||
NODE_CLASS_MAPPINGS = {}
|
||||
NODE_CLASS_MAPPINGS = {}
|
||||
|
||||
# Deci Diffusion
|
||||
# from .DeciDiffusion.nodes import NODE_CLASS_MAPPINGS as DeciDiffusion_Nodes
|
||||
# NODE_CLASS_MAPPINGS.update(DeciDiffusion_Nodes)
|
||||
# All text encoders
|
||||
from .text_encoders.nodes import NODE_CLASS_MAPPINGS as Tenc_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(Tenc_Nodes)
|
||||
|
||||
# DiT
|
||||
from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(DiT_Nodes)
|
||||
# Generic nodes
|
||||
from .nodes import NODE_CLASS_MAPPINGS as Base_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(Base_Nodes)
|
||||
|
||||
# PixArt
|
||||
from .PixArt.nodes import NODE_CLASS_MAPPINGS as PixArt_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(PixArt_Nodes)
|
||||
# DiT
|
||||
from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(DiT_Nodes)
|
||||
|
||||
# T5
|
||||
from .T5.nodes import NODE_CLASS_MAPPINGS as T5_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(T5_Nodes)
|
||||
# PixArt
|
||||
from .PixArt.nodes import NODE_CLASS_MAPPINGS as PixArt_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(PixArt_Nodes)
|
||||
|
||||
# HYDiT
|
||||
from .HunYuanDiT.nodes import NODE_CLASS_MAPPINGS as HunYuanDiT_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(HunYuanDiT_Nodes)
|
||||
# HYDiT
|
||||
from .HunYuanDiT.nodes import NODE_CLASS_MAPPINGS as HunYuanDiT_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(HunYuanDiT_Nodes)
|
||||
|
||||
# VAE
|
||||
from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(VAE_Nodes)
|
||||
# VAE
|
||||
from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(VAE_Nodes)
|
||||
|
||||
# MiaoBi
|
||||
from .MiaoBi.nodes import NODE_CLASS_MAPPINGS as MiaoBi_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(MiaoBi_Nodes)
|
||||
# MiaoBi
|
||||
from .MiaoBi.nodes import NODE_CLASS_MAPPINGS as MiaoBi_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(MiaoBi_Nodes)
|
||||
|
||||
# Extra
|
||||
from .utils.nodes import NODE_CLASS_MAPPINGS as Extra_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(Extra_Nodes)
|
||||
# Extra
|
||||
from .utils.nodes import NODE_CLASS_MAPPINGS as Extra_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(Extra_Nodes)
|
||||
|
||||
# Sana
|
||||
from .Sana.nodes import NODE_CLASS_MAPPINGS as Sana_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(Sana_Nodes)
|
||||
# Sana
|
||||
from .Sana.nodes import NODE_CLASS_MAPPINGS as Sana_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(Sana_Nodes)
|
||||
|
||||
# Gemma
|
||||
from .Gemma.nodes import NODE_CLASS_MAPPINGS as Gemma_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(Gemma_Nodes)
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()}
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
|
||||
# Gemma
|
||||
from .Gemma.nodes import NODE_CLASS_MAPPINGS as Gemma_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(Gemma_Nodes)
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()}
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
|
||||
|
||||
@@ -0,0 +1,34 @@
|
||||
import folder_paths
|
||||
import comfy.utils
|
||||
|
||||
from .PixArt.loader import load_pixart_state_dict
|
||||
|
||||
loaders = {
|
||||
"PixArt": load_pixart_state_dict,
|
||||
}
|
||||
|
||||
class EXMUnetLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"unet_name": (folder_paths.get_filename_list("unet"),),
|
||||
"model_type": (list(loaders.keys()),)
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "load_unet"
|
||||
CATEGORY = "ExtraModels"
|
||||
TITLE = "Load Diffusion Model (ExtraModels)"
|
||||
|
||||
def load_unet(self, unet_name, model_type):
|
||||
model_options = {}
|
||||
unet_path = folder_paths.get_full_path("diffusion_models", unet_name)
|
||||
loader_fn = loaders[model_type]
|
||||
sd = comfy.utils.load_torch_file(unet_path)
|
||||
return (loader_fn(sd),)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"EXMUnetLoader": EXMUnetLoader,
|
||||
}
|
||||
@@ -0,0 +1,37 @@
|
||||
import folder_paths
|
||||
|
||||
from .tenc import load_text_encoder, tenc_names
|
||||
|
||||
class EXMCLIPLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
files = []
|
||||
files += folder_paths.get_filename_list("clip")
|
||||
# if "clip_gguf" in folder_paths.folder_names_and_paths:
|
||||
# files += folder_paths.get_filename_list("clip_gguf")
|
||||
return {
|
||||
"required": {
|
||||
"clip_name": (files, ),
|
||||
"type": (["PixArt", "MiaoBi", "Sana"],),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "load_clip"
|
||||
CATEGORY = "ExtraModels"
|
||||
TITLE = "CLIPLoader (ExtraModels)"
|
||||
|
||||
def load_clip(self, clip_name, type):
|
||||
clip_path = folder_paths.get_full_path("clip", clip_name)
|
||||
clip_type = tenc_names.get(type, None)
|
||||
|
||||
clip = load_text_encoder(
|
||||
ckpt_paths =[clip_path],
|
||||
embedding_directory = folder_paths.get_folder_paths("embeddings"),
|
||||
clip_type = clip_type
|
||||
)
|
||||
return (clip,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"EXMCLIPLoader": EXMCLIPLoader,
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
from comfy import sd1_clip
|
||||
import comfy.text_encoders.t5
|
||||
import comfy.text_encoders.sd3_clip
|
||||
import comfy.model_management
|
||||
from transformers import T5TokenizerFast
|
||||
import torch
|
||||
import os
|
||||
|
||||
class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
# make sure empty tokens match
|
||||
self.special_tokens.pop("end")
|
||||
|
||||
class PixArtT5XXL(sd1_clip.SD1ClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
|
||||
|
||||
class T5XXLTokenizer(sd1_clip.SDTokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
tokenizer_path = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
|
||||
"tokenizers", "t5_tokenizer",
|
||||
)
|
||||
super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1)
|
||||
|
||||
class PixArtTokenizer(sd1_clip.SD1Tokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
|
||||
|
||||
# TODO: don't duplicate this?
|
||||
def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
|
||||
class PixArtTEModel_(PixArtT5XXL):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
|
||||
model_options = model_options.copy()
|
||||
model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
|
||||
if dtype is None:
|
||||
dtype = dtype_t5
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options)
|
||||
return PixArtTEModel_
|
||||
@@ -0,0 +1,72 @@
|
||||
import logging
|
||||
from enum import Enum
|
||||
|
||||
import comfy.sd
|
||||
import comfy.utils
|
||||
import comfy.text_encoders
|
||||
|
||||
from .pixart.tenc import pixart_te, PixArtTokenizer
|
||||
|
||||
class TencType(Enum):
|
||||
# offset in case we ever integrate w/ original
|
||||
PixArt = 1001
|
||||
MiaoBi = 1002
|
||||
# HunYuan = 1003 # deprecated
|
||||
Sana = 1004
|
||||
|
||||
tenc_names = {
|
||||
# for node readout
|
||||
"PixArt": TencType.PixArt,
|
||||
"MiaoBi": TencType.MiaoBi,
|
||||
# "HunYuan": TencType.HunYuan,
|
||||
"Sana": TencType.Sana,
|
||||
}
|
||||
|
||||
|
||||
def load_text_encoder(ckpt_paths, embedding_directory=None, clip_type=TencType.PixArt, model_options={}):
|
||||
# Partial duplicate of ComfyUI/comfy/sd:load_clip
|
||||
clip_data = []
|
||||
for p in ckpt_paths:
|
||||
if p.lower().endswith(".gguf"):
|
||||
# TODO: cross-node call w/o code duplication
|
||||
raise NotImplementedError("Planned!")
|
||||
else:
|
||||
clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
|
||||
return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
|
||||
|
||||
def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=TencType.PixArt, model_options={}):
|
||||
# Partial duplicate of ComfyUI/comfy/sd:load_text_encoder_state_dicts
|
||||
clip_data = state_dicts
|
||||
|
||||
class EmptyClass:
|
||||
pass
|
||||
|
||||
for i in range(len(clip_data)):
|
||||
if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
|
||||
clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
|
||||
else:
|
||||
if "text_projection" in clip_data[i]:
|
||||
clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
|
||||
|
||||
clip_target = EmptyClass()
|
||||
clip_target.params = {}
|
||||
|
||||
if clip_type == TencType.PixArt:
|
||||
clip_target.clip = pixart_te(**comfy.sd.t5xxl_detect(clip_data))
|
||||
clip_target.tokenizer = PixArtTokenizer
|
||||
|
||||
parameters = 0
|
||||
tokenizer_data = {}
|
||||
for c in clip_data:
|
||||
parameters += comfy.utils.calculate_parameters(c)
|
||||
tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
|
||||
|
||||
clip = comfy.sd.CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options)
|
||||
for c in clip_data:
|
||||
m, u = clip.load_sd(c)
|
||||
if len(m) > 0:
|
||||
logging.warning("clip missing: {}".format(m))
|
||||
|
||||
if len(u) > 0:
|
||||
logging.debug("clip unexpected: {}".format(u))
|
||||
return clip
|
||||
Reference in New Issue
Block a user