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]