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import json
import os
import torch
import subprocess
import sys
import comfy.supported_models
import comfy.model_patcher
import comfy.model_management
import comfy.model_detection as model_detection
import comfy.model_base as model_base
from comfy.model_base import sdxl_pooled, CLIPEmbeddingNoiseAugmentation, Timestep, ModelType
from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel
from comfy.clip_vision import ClipVisionModel, Output
from comfy.utils import load_torch_file
from .chatglm.modeling_chatglm import ChatGLMModel, ChatGLMConfig
from .chatglm.tokenization_chatglm import ChatGLMTokenizer
class KolorsUNetModel(UNetModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.encoder_hid_proj = torch.nn.Linear(4096, 2048, bias=True)
def forward(self, *args, **kwargs):
with torch.cuda.amp.autocast(enabled=True):
if "context" in kwargs:
kwargs["context"] = self.encoder_hid_proj(kwargs["context"])
result = super().forward(*args, **kwargs)
return result
class KolorsSDXL(model_base.SDXL):
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
model_base.BaseModel.__init__(self, model_config, model_type, device=device, unet_model=KolorsUNetModel)
self.embedder = Timestep(256)
self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
def encode_adm(self, **kwargs):
clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
width = kwargs.get("width", 768)
height = kwargs.get("height", 768)
crop_w = kwargs.get("crop_w", 0)
crop_h = kwargs.get("crop_h", 0)
target_width = kwargs.get("target_width", width)
target_height = kwargs.get("target_height", height)
out = []
out.append(self.embedder(torch.Tensor([height])))
out.append(self.embedder(torch.Tensor([width])))
out.append(self.embedder(torch.Tensor([crop_h])))
out.append(self.embedder(torch.Tensor([crop_w])))
out.append(self.embedder(torch.Tensor([target_height])))
out.append(self.embedder(torch.Tensor([target_width])))
flat = torch.flatten(torch.cat(out)).unsqueeze(
dim=0).repeat(clip_pooled.shape[0], 1)
return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
class Kolors(comfy.supported_models.SDXL):
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 2, 2, 10, 10],
"context_dim": 2048,
"adm_in_channels": 5632,
"use_temporal_attention": False,
}
def get_model(self, state_dict, prefix="", device=None):
out = KolorsSDXL(self, model_type=self.model_type(state_dict, prefix), device=device, )
out.__class__ = model_base.SDXL
if self.inpaint_model():
out.set_inpaint()
return out
def kolors_unet_config_from_diffusers_unet(state_dict, dtype=None):
match = {}
transformer_depth = []
attn_res = 1
count_blocks = model_detection.count_blocks
down_blocks = count_blocks(state_dict, "down_blocks.{}")
for i in range(down_blocks):
attn_blocks = count_blocks(
state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
res_blocks = count_blocks(
state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
for ab in range(attn_blocks):
transformer_count = count_blocks(
state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
transformer_depth.append(transformer_count)
if transformer_count > 0:
match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(
i, ab)].shape[1]
attn_res *= 2
if attn_blocks == 0:
for i in range(res_blocks):
transformer_depth.append(0)
match["transformer_depth"] = transformer_depth
match["model_channels"] = state_dict["conv_in.weight"].shape[0]
match["in_channels"] = state_dict["conv_in.weight"].shape[1]
match["adm_in_channels"] = None
if "class_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
elif "add_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
Kolors = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
Kolors_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 9,
'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
Kolors_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 8,
'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4,
'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4,
'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True,
'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4,
'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 0,
'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1,
'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
supported_models = [Kolors, Kolors_inpaint,
Kolors_ip2p, SDXL, SDXL_mid_cnet, SDXL_small_cnet]
for unet_config in supported_models:
matches = True
for k in match:
if match[k] != unet_config[k]:
# print("key {} does not match".format(k), match[k], "||", unet_config[k])
matches = False
break
if matches:
return model_detection.convert_config(unet_config)
return None
# chatglm3 model
class chatGLM3Model(torch.nn.Module):
def __init__(self, textmodel_json_config=None, device='cpu', offload_device='cpu', model_path=None):
super().__init__()
if model_path is None:
raise ValueError("model_path is required")
self.device = device
if textmodel_json_config is None:
textmodel_json_config = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"chatglm",
"config_chatglm.json"
)
with open(textmodel_json_config, 'r') as file:
config = json.load(file)
textmodel_json_config = ChatGLMConfig(**config)
is_accelerate_available = False
try:
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
is_accelerate_available = True
except:
pass
from contextlib import nullcontext
with (init_empty_weights() if is_accelerate_available else nullcontext()):
with torch.no_grad():
print('torch version:', torch.__version__)
self.text_encoder = ChatGLMModel(textmodel_json_config).eval()
if '4bit' in model_path:
try:
import cpm_kernels
except ImportError:
print("Installing cpm_kernels...")
subprocess.run([sys.executable, "-m", "pip", "install", "cpm_kernels"], check=True)
pass
self.text_encoder.quantize(4)
elif '8bit' in model_path:
self.text_encoder.quantize(8)
sd = load_torch_file(model_path)
if is_accelerate_available:
for key in sd:
set_module_tensor_to_device(self.text_encoder, key, device=offload_device, value=sd[key])
else:
print("WARNING: Accelerate not available, use load_state_dict load model")
self.text_encoder.load_state_dict()
def load_chatglm3(model_path=None):
if model_path is None:
return
load_device = comfy.model_management.text_encoder_device()
offload_device = comfy.model_management.text_encoder_offload_device()
glm3model = chatGLM3Model(
device=load_device,
offload_device=offload_device,
model_path=model_path
)
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'chatglm', "tokenizer")
tokenizer = ChatGLMTokenizer.from_pretrained(tokenizer_path)
text_encoder = glm3model.text_encoder
return {"text_encoder":text_encoder, "tokenizer":tokenizer}
# clipvision model
def load_clipvision_vitl_336(path):
sd = load_torch_file(path)
if "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
else:
raise Exception("Unsupported clip vision model")
clip = ClipVisionModel(json_config)
m, u = clip.load_sd(sd)
if len(m) > 0:
print("missing clip vision: {}".format(m))
u = set(u)
keys = list(sd.keys())
for k in keys:
if k not in u:
t = sd.pop(k)
del t
return clip
class applyKolorsUnet:
def __enter__(self):
import comfy.ldm.modules.diffusionmodules.openaimodel
import comfy.utils
import comfy.clip_vision
self.original_UNET_MAP_BASIC = comfy.utils.UNET_MAP_BASIC.copy()
comfy.utils.UNET_MAP_BASIC.add(("encoder_hid_proj.weight", "encoder_hid_proj.weight"),)
comfy.utils.UNET_MAP_BASIC.add(("encoder_hid_proj.bias", "encoder_hid_proj.bias"),)
self.original_unet_config_from_diffusers_unet = model_detection.unet_config_from_diffusers_unet
model_detection.unet_config_from_diffusers_unet = kolors_unet_config_from_diffusers_unet
import comfy.supported_models
self.original_supported_models = comfy.supported_models.models
comfy.supported_models.models = [Kolors]
self.original_load_clipvision_from_sd = comfy.clip_vision.load_clipvision_from_sd
comfy.clip_vision.load_clipvision_from_sd = load_clipvision_vitl_336
def __exit__(self, type, value, traceback):
import comfy.ldm.modules.diffusionmodules.openaimodel
import comfy.utils
import comfy.supported_models
import comfy.clip_vision
comfy.utils.UNET_MAP_BASIC = self.original_UNET_MAP_BASIC
model_detection.unet_config_from_diffusers_unet = self.original_unet_config_from_diffusers_unet
comfy.supported_models.models = self.original_supported_models
comfy.clip_vision.load_clipvision_from_sd = self.original_load_clipvision_from_sd
def is_kolors_model(model):
unet_config = model.model.model_config.unet_config
if unet_config and "adm_in_channels" in unet_config and unet_config["adm_in_channels"] == 5632:
return True
else:
return False |