Spaces:
Building
on
A10G
Building
on
A10G
File size: 14,506 Bytes
eb9ca51 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 |
import torch
from . import model_base
from . import utils
from . import sd1_clip
from . import sd2_clip
from . import sdxl_clip
from . import supported_models_base
from . import latent_formats
from . import diffusers_convert
class SD15(supported_models_base.BASE):
unet_config = {
"context_dim": 768,
"model_channels": 320,
"use_linear_in_transformer": False,
"adm_in_channels": None,
"use_temporal_attention": False,
}
unet_extra_config = {
"num_heads": 8,
"num_head_channels": -1,
}
latent_format = latent_formats.SD15
def process_clip_state_dict(self, state_dict):
k = list(state_dict.keys())
for x in k:
if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
state_dict[y] = state_dict.pop(x)
if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:
ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']
if ids.dtype == torch.float32:
state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
replace_prefix = {}
replace_prefix["cond_stage_model."] = "clip_l."
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {"clip_l.": "cond_stage_model."}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
def clip_target(self):
return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
class SD20(supported_models_base.BASE):
unet_config = {
"context_dim": 1024,
"model_channels": 320,
"use_linear_in_transformer": True,
"adm_in_channels": None,
"use_temporal_attention": False,
}
latent_format = latent_formats.SD15
def model_type(self, state_dict, prefix=""):
if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
out = state_dict[k]
if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
return model_base.ModelType.V_PREDICTION
return model_base.ModelType.EPS
def process_clip_state_dict(self, state_dict):
replace_prefix = {}
replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format
replace_prefix["cond_stage_model.model."] = "clip_h."
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {}
replace_prefix["clip_h"] = "cond_stage_model.model"
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
return state_dict
def clip_target(self):
return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
class SD21UnclipL(SD20):
unet_config = {
"context_dim": 1024,
"model_channels": 320,
"use_linear_in_transformer": True,
"adm_in_channels": 1536,
"use_temporal_attention": False,
}
clip_vision_prefix = "embedder.model.visual."
noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768}
class SD21UnclipH(SD20):
unet_config = {
"context_dim": 1024,
"model_channels": 320,
"use_linear_in_transformer": True,
"adm_in_channels": 2048,
"use_temporal_attention": False,
}
clip_vision_prefix = "embedder.model.visual."
noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024}
class SDXLRefiner(supported_models_base.BASE):
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_formats.SDXL
def get_model(self, state_dict, prefix="", device=None):
return model_base.SDXLRefiner(self, device=device)
def process_clip_state_dict(self, state_dict):
keys_to_replace = {}
replace_prefix = {}
replace_prefix["conditioner.embedders.0.model."] = "clip_g."
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {}
state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g:
state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
replace_prefix["clip_g"] = "conditioner.embedders.0.model"
state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
return state_dict_g
def clip_target(self):
return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
class SDXL(supported_models_base.BASE):
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_formats.SDXL
def model_type(self, state_dict, prefix=""):
if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5
self.latent_format = latent_formats.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 model_base.ModelType.EDM
elif "v_pred" in state_dict:
return model_base.ModelType.V_PREDICTION
else:
return model_base.ModelType.EPS
def get_model(self, state_dict, prefix="", device=None):
out = model_base.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):
keys_to_replace = {}
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 = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {}
keys_to_replace = {}
state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
for k in state_dict:
if k.startswith("clip_l"):
state_dict_g[k] = state_dict[k]
state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1))
pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
for p in pop_keys:
if p in state_dict_g:
state_dict_g.pop(p)
replace_prefix["clip_g"] = "conditioner.embedders.1.model"
replace_prefix["clip_l"] = "conditioner.embedders.0"
state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
return state_dict_g
def clip_target(self):
return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
class SSD1B(SDXL):
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):
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):
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):
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,
}
class SVD_img2vid(supported_models_base.BASE):
unet_config = {
"model_channels": 320,
"in_channels": 8,
"use_linear_in_transformer": True,
"transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
"context_dim": 1024,
"adm_in_channels": 768,
"use_temporal_attention": True,
"use_temporal_resblock": True
}
clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual."
latent_format = latent_formats.SD15
sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002}
def get_model(self, state_dict, prefix="", device=None):
out = model_base.SVD_img2vid(self, device=device)
return out
def clip_target(self):
return None
class Stable_Zero123(supported_models_base.BASE):
unet_config = {
"context_dim": 768,
"model_channels": 320,
"use_linear_in_transformer": False,
"adm_in_channels": None,
"use_temporal_attention": False,
"in_channels": 8,
}
unet_extra_config = {
"num_heads": 8,
"num_head_channels": -1,
}
clip_vision_prefix = "cond_stage_model.model.visual."
latent_format = latent_formats.SD15
def get_model(self, state_dict, prefix="", device=None):
out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"])
return out
def clip_target(self):
return None
class SD_X4Upscaler(SD20):
unet_config = {
"context_dim": 1024,
"model_channels": 256,
'in_channels': 7,
"use_linear_in_transformer": True,
"adm_in_channels": None,
"use_temporal_attention": False,
}
unet_extra_config = {
"disable_self_attentions": [True, True, True, False],
"num_classes": 1000,
"num_heads": 8,
"num_head_channels": -1,
}
latent_format = latent_formats.SD_X4
sampling_settings = {
"linear_start": 0.0001,
"linear_end": 0.02,
}
def get_model(self, state_dict, prefix="", device=None):
out = model_base.SD_X4Upscaler(self, device=device)
return out
class Stable_Cascade_C(supported_models_base.BASE):
unet_config = {
"stable_cascade_stage": 'c',
}
unet_extra_config = {}
latent_format = latent_formats.SC_Prior
supported_inference_dtypes = [torch.bfloat16, torch.float32]
sampling_settings = {
"shift": 2.0,
}
vae_key_prefix = ["vae."]
text_encoder_key_prefix = ["text_encoder."]
clip_vision_prefix = "clip_l_vision."
def process_unet_state_dict(self, state_dict):
key_list = list(state_dict.keys())
for y in ["weight", "bias"]:
suffix = "in_proj_{}".format(y)
keys = filter(lambda a: a.endswith(suffix), key_list)
for k_from in keys:
weights = state_dict.pop(k_from)
prefix = k_from[:-(len(suffix) + 1)]
shape_from = weights.shape[0] // 3
for x in range(3):
p = ["to_q", "to_k", "to_v"]
k_to = "{}.{}.{}".format(prefix, p[x], y)
state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)]
return state_dict
def process_clip_state_dict(self, state_dict):
state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
if "clip_g.text_projection" in state_dict:
state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1)
return state_dict
def get_model(self, state_dict, prefix="", device=None):
out = model_base.StableCascade_C(self, device=device)
return out
def clip_target(self):
return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel)
class Stable_Cascade_B(Stable_Cascade_C):
unet_config = {
"stable_cascade_stage": 'b',
}
unet_extra_config = {}
latent_format = latent_formats.SC_B
supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
sampling_settings = {
"shift": 1.0,
}
clip_vision_prefix = None
def get_model(self, state_dict, prefix="", device=None):
out = model_base.StableCascade_B(self, device=device)
return out
models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B]
models += [SVD_img2vid]
|