Spaces:
Configuration error
Configuration error
File size: 6,303 Bytes
8866644 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
import gc
import json
import os
import random
import re
import torch
import folder_paths
import comfy.model_management as mm
from . import mz_kolors_core
def MZ_ChatGLM3TextEncode_call(args):
text = args.get("text")
chatglm3_model = args.get("chatglm3_model")
prompt_embeds, pooled_output = mz_kolors_core.chatglm3_text_encode(
chatglm3_model,
text,
)
from torch import nn
hid_proj: nn.Linear = args.get("hid_proj")
if hid_proj.weight.dtype != prompt_embeds.dtype:
with torch.cuda.amp.autocast(dtype=hid_proj.weight.dtype):
prompt_embeds = hid_proj(prompt_embeds)
else:
prompt_embeds = hid_proj(prompt_embeds)
return ([[
prompt_embeds,
{"pooled_output": pooled_output},
]], )
def load_unet_state_dict(sd): # load unet in diffusers or regular format
from comfy import model_management, model_detection
import comfy.utils
# Allow loading unets from checkpoint files
checkpoint = False
diffusion_model_prefix = 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
checkpoint = True
parameters = comfy.utils.calculate_parameters(sd)
unet_dtype = model_management.unet_dtype(model_params=parameters)
load_device = model_management.get_torch_device()
from torch import nn
hid_proj: nn.Linear = None
if True:
model_config = model_detection.model_config_from_diffusers_unet(sd)
if model_config is None:
return None
diffusers_keys = comfy.utils.unet_to_diffusers(
model_config.unet_config)
new_sd = {}
for k in diffusers_keys:
if k in sd:
new_sd[diffusers_keys[k]] = sd.pop(k)
else:
print("{} {}".format(diffusers_keys[k], k))
encoder_hid_proj_weight = sd.pop("encoder_hid_proj.weight")
encoder_hid_proj_bias = sd.pop("encoder_hid_proj.bias")
hid_proj = nn.Linear(
encoder_hid_proj_weight.shape[1], encoder_hid_proj_weight.shape[0])
hid_proj.weight.data = encoder_hid_proj_weight
hid_proj.bias.data = encoder_hid_proj_bias
hid_proj = hid_proj.to(load_device)
offload_device = model_management.unet_offload_device()
unet_dtype = model_management.unet_dtype(
model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
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 = model_config.get_model(new_sd, "")
model = model.to(offload_device)
model.load_model_weights(new_sd, "")
left_over = sd.keys()
if len(left_over) > 0:
print("left over keys in unet: {}".format(left_over))
return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device), hid_proj
def MZ_KolorsUNETLoader_call(kwargs):
from . import hook_comfyui_kolors_v1
with hook_comfyui_kolors_v1.apply_kolors():
unet_name = kwargs.get("unet_name")
unet_path = folder_paths.get_full_path("unet", unet_name)
import comfy.utils
sd = comfy.utils.load_torch_file(unet_path)
model, hid_proj = load_unet_state_dict(sd)
if model is None:
raise RuntimeError(
"ERROR: Could not detect model type of: {}".format(unet_path))
return (model, hid_proj)
def MZ_FakeCond_call(kwargs):
import torch
cond = torch.zeros(2, 256, 4096)
pool = torch.zeros(2, 4096)
dtype = kwargs.get("dtype")
if dtype == "fp16":
print("fp16")
cond = cond.half()
pool = pool.half()
elif dtype == "bf16":
print("bf16")
cond = cond.bfloat16()
pool = pool.bfloat16()
else:
print("fp32")
cond = cond.float()
pool = pool.float()
return ([[
cond,
{"pooled_output": pool},
]],)
NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
}
AUTHOR_NAME = "MinusZone"
CATEGORY_NAME = f"{AUTHOR_NAME} - Kolors"
class MZ_ChatGLM3TextEncode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"chatglm3_model": ("CHATGLM3MODEL", ),
"text": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"hid_proj": ("TorchLinear", ),
}
}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = CATEGORY_NAME + "/Legacy"
def encode(self, **kwargs):
return MZ_ChatGLM3TextEncode_call(kwargs)
NODE_CLASS_MAPPINGS["MZ_ChatGLM3"] = MZ_ChatGLM3TextEncode
NODE_DISPLAY_NAME_MAPPINGS[
"MZ_ChatGLM3"] = f"{AUTHOR_NAME} - ChatGLM3TextEncode"
class MZ_KolorsUNETLoader():
@classmethod
def INPUT_TYPES(s):
return {"required": {
"unet_name": (folder_paths.get_filename_list("unet"), ),
}}
RETURN_TYPES = ("MODEL", "TorchLinear")
RETURN_NAMES = ("model", "hid_proj")
FUNCTION = "load_unet"
CATEGORY = CATEGORY_NAME + "/Legacy"
def load_unet(self, **kwargs):
return MZ_KolorsUNETLoader_call(kwargs)
NODE_CLASS_MAPPINGS["MZ_KolorsUNETLoader"] = MZ_KolorsUNETLoader
NODE_DISPLAY_NAME_MAPPINGS[
"MZ_KolorsUNETLoader"] = f"{AUTHOR_NAME} - Kolors UNET Loader"
class MZ_FakeCond:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"seed": ("INT", {"default": 0}),
"dtype": ([
"fp32",
"fp16",
"bf16",
],),
}
}
RETURN_TYPES = ("CONDITIONING", )
RETURN_NAMES = ("prompt", )
FUNCTION = "encode"
CATEGORY = CATEGORY_NAME
def encode(self, **kwargs):
return MZ_FakeCond_call(kwargs)
try:
if os.environ.get("MZ_DEV", None) is not None:
NODE_CLASS_MAPPINGS["MZ_FakeCond"] = MZ_FakeCond
NODE_DISPLAY_NAME_MAPPINGS[
"MZ_FakeCond"] = f"{AUTHOR_NAME} - FakeCond"
except ImportError:
pass
|