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b701455 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 | """SDXL model configurations for LightDiffusion.
This module provides SDXL and SDXL Refiner model configurations, adapted from
ComfyUI's implementation but using local LightDiffusion modules.
"""
from src.Model import ModelBase
from src.Utilities import Latent, util
from src.SD15 import SDXLClip
from src.clip import Clip
from src.sample import sampling
class SDXLRefiner(ModelBase.BASE):
"""SDXL Refiner model configuration."""
unet_config = {
"model_channels": 384,
"use_linear_in_transformer": True,
"context_dim": 1280,
"adm_in_channels": 2560,
"transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0],
"use_temporal_attention": False,
}
latent_format = Latent.SDXL
memory_usage_factor = 1.0
def get_model(self, state_dict, prefix="", device=None):
"""Get the refiner model instance.
Args:
state_dict: Model state dictionary
prefix: Key prefix for state dict
device: Device to load model on
Returns:
SDXLRefiner model instance
"""
return ModelBase.SDXLRefiner(self, device=device)
def process_clip_state_dict(self, state_dict):
"""Process CLIP state dict for refiner (G model only).
Args:
state_dict: Raw state dictionary
Returns:
Processed state dictionary
"""
replace_prefix = {}
replace_prefix["conditioner.embedders.0.model."] = "clip_g."
state_dict = util.state_dict_prefix_replace(
state_dict, replace_prefix, filter_keys=True
)
state_dict = util.clip_text_transformers_convert(
state_dict, "clip_g.", "clip_g.transformer."
)
return state_dict
def clip_target(self, state_dict=None):
"""Return the CLIP target for refiner.
Args:
state_dict: Optional state dictionary
Returns:
ClipTarget for SDXL Refiner (G model only)
"""
return Clip.ClipTarget(SDXLClip.SDXLTokenizer, SDXLClip.SDXLRefinerClipModel)
class SDXL(ModelBase.BASE):
"""SDXL model configuration."""
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 2, 2, 10, 10],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
latent_format = Latent.SDXL
memory_usage_factor = 0.8
def process_vae_state_dict(self, state_dict):
"""Process VAE state dict for SDXL.
Detects if the VAE is a 'flux-style' VAE (missing post_quant_conv)
and sets the flag for decoding logic.
"""
if "post_quant_conv.weight" not in state_dict:
# If missing post_quant_conv, it's a Flux-style VAE
self.vae_config = {"flux": True}
return state_dict
def model_type(self, state_dict, prefix=""):
"""Detect the model type from state dict.
Args:
state_dict: Model state dictionary
prefix: Key prefix
Returns:
ModelType enum value
"""
# Check for Playground V2.5
if "edm_mean" in state_dict and "edm_std" in state_dict:
self.latent_format = Latent.SDXL_Playground_2_5()
self.sampling_settings["sigma_data"] = 0.5
self.sampling_settings["sigma_max"] = 80.0
self.sampling_settings["sigma_min"] = 0.002
return sampling.ModelType.EDM
# Check for V-prediction EDM variant
elif "edm_vpred.sigma_max" in state_dict:
self.sampling_settings["sigma_max"] = float(
state_dict["edm_vpred.sigma_max"].item()
)
if "edm_vpred.sigma_min" in state_dict:
self.sampling_settings["sigma_min"] = float(
state_dict["edm_vpred.sigma_min"].item()
)
return sampling.ModelType.V_PREDICTION_EDM
# Check for V-prediction
elif "v_pred" in state_dict:
if "ztsnr" in state_dict: # Some zsnr anime checkpoints
self.sampling_settings["zsnr"] = True
return sampling.ModelType.V_PREDICTION
else:
return sampling.ModelType.EPS
def get_model(self, state_dict, prefix="", device=None):
"""Get the SDXL model instance.
Args:
state_dict: Model state dictionary
prefix: Key prefix for state dict
device: Device to load model on
Returns:
SDXL model instance
"""
out = ModelBase.SDXL(
self, model_type=self.model_type(state_dict, prefix), device=device
)
if self.inpaint_model():
out.set_inpaint()
return out
def process_clip_state_dict(self, state_dict):
"""Process CLIP state dict for SDXL (dual L+G models).
Args:
state_dict: Raw state dictionary
Returns:
Processed state dictionary
"""
replace_prefix = {}
replace_prefix[
"conditioner.embedders.0.transformer.text_model"
] = "clip_l.transformer.text_model"
replace_prefix["conditioner.embedders.1.model."] = "clip_g."
state_dict = util.state_dict_prefix_replace(
state_dict, replace_prefix, filter_keys=True
)
state_dict = util.clip_text_transformers_convert(
state_dict, "clip_g.", "clip_g.transformer."
)
return state_dict
def clip_target(self, state_dict=None):
"""Return the CLIP target for SDXL.
Args:
state_dict: Optional state dictionary
Returns:
ClipTarget for SDXL (dual L+G models)
"""
return Clip.ClipTarget(SDXLClip.SDXLTokenizer, SDXLClip.SDXLClipModel)
class SSD1B(SDXL):
"""SSD-1B model configuration (SDXL variant with fewer transformer blocks)."""
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 2, 2, 4, 4],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
class Segmind_Vega(SDXL):
"""Segmind Vega model configuration (SDXL variant)."""
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 1, 1, 2, 2],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
class KOALA_700M(SDXL):
"""KOALA 700M model configuration (SDXL variant)."""
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 2, 5],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
class KOALA_1B(SDXL):
"""KOALA 1B model configuration (SDXL variant)."""
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 2, 6],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
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