File size: 7,174 Bytes
d661b19
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import torch
import numpy as np
from einops import rearrange
from torch import autocast
from contextlib import nullcontext
from math import sqrt
from adapt import ScoreAdapter

from cldm.model import create_model, load_state_dict

from lora_util import *
import warnings
from transformers import logging
warnings.filterwarnings("ignore", category=DeprecationWarning)
logging.set_verbosity_error()

device = torch.device("cuda")


def _sqrt(x):
    if isinstance(x, float):
        return sqrt(x)
    else:
        assert isinstance(x, torch.Tensor)
        return torch.sqrt(x)

def load_embedding(model,embedding):
    length=len(embedding['string_to_param']['*'])
    voc=[]
    for i in range(length):
        voc.append(f'<{str(i)}>')
    print(f"Added Token: {voc}")
    model.cond_stage_model.tokenizer._add_tokens(voc)

    x=torch.nn.Embedding(model.cond_stage_model.tokenizer.__len__(),768)
    
    for params in x.parameters():
        params.requires_grad=False

    x.weight[:-length]=model.cond_stage_model.transformer.text_model.embeddings.token_embedding.weight
    x.weight[-length:]=embedding['string_to_param']['*']
    model.cond_stage_model.transformer.text_model.embeddings.token_embedding=x
    
def load_3DFuse(control,dir,alpha):
    ######################LOADCONTROL###########################
    model = create_model(control['control_yaml']).cpu()
    model.load_state_dict(load_state_dict(control['control_weight'], location='cuda'))
    state_dict, l = merge("runwayml/stable-diffusion-v1-5",dir,alpha)
    
    #######################OVERRIDE#############################
    model.load_state_dict(state_dict,strict=False)
    
    #######################ADDEMBBEDDING########################
    load_embedding(model,l)
    ###############################################################
    return model

class StableDiffusion(ScoreAdapter):
    def __init__(self, variant, v2_highres, prompt, scale, precision, dir, alpha=1.0):
               
        model=load_3DFuse(self.checkpoint_root(),dir,alpha)
        self.model = model.cuda()
        
        H , W = (512, 512)

        ae_resolution_f = 8

        self._device = self.model._device

        self.prompt = prompt
        self.scale = scale
        self.precision = precision
        self.precision_scope = autocast if self.precision == "autocast" else nullcontext
        self._data_shape = (4, H // ae_resolution_f, W // ae_resolution_f)

        self.cond_func = self.model.get_learned_conditioning
        self.M = 1000
        noise_schedule = "linear"
        self.noise_schedule = noise_schedule
        self.us = self.linear_us(self.M)

    def data_shape(self):
        return self._data_shape

    @property
    def σ_max(self):
        return self.us[0]

    @property
    def σ_min(self):
        return self.us[-1]

    @torch.no_grad()
    def denoise(self, xs, σ,control, **model_kwargs):
        with self.precision_scope("cuda"):
            with self.model.ema_scope():
                N = xs.shape[0]
                c = model_kwargs.pop('c')
                uc = model_kwargs.pop('uc')
                conditional_conditioning = {"c_concat": [control], "c_crossattn": [c]}
                unconditional_conditioning = {"c_concat": [control], "c_crossattn": [uc]}

                cond_t, σ = self.time_cond_vec(N, σ)
                unscaled_xs = xs
                xs = xs / _sqrt(1 + σ**2)
                if uc is None or self.scale == 1.:
                    output = self.model.apply_model(xs, cond_t, c)
                else:
                    x_in = torch.cat([xs] * 2)
                    t_in = torch.cat([cond_t] * 2)
                    c_in = dict()
                    for k in conditional_conditioning:
                        if isinstance(conditional_conditioning[k], list):
                            c_in[k] = [torch.cat([
                                unconditional_conditioning[k][i],
                                conditional_conditioning[k][i]]) for i in range(len(conditional_conditioning[k]))]
                        else:
                            c_in[k] = torch.cat([
                                    unconditional_conditioning[k],
                                    conditional_conditioning[k]])
                    
                    e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
                    output = e_t_uncond + self.scale * (e_t - e_t_uncond)

                if self.model.parameterization == "v":
                    output = self.model.predict_eps_from_z_and_v(xs, cond_t, output)
                else:

                    output = output

                Ds = unscaled_xs - σ * output
                return Ds

    def cond_info(self, batch_size):
        prompts = batch_size * [self.prompt]
        return self.prompts_emb(prompts)

    @torch.no_grad()
    def prompts_emb(self, prompts):
        assert isinstance(prompts, list)
        batch_size = len(prompts)
        with self.precision_scope("cuda"):
            with self.model.ema_scope():
                cond = {}
                c = self.cond_func(prompts)
                cond['c'] = c
                uc = None
                if self.scale != 1.0:
                    uc = self.cond_func(batch_size * [""])
                cond['uc'] = uc
                return cond

    def unet_is_cond(self):
        return True

    def use_cls_guidance(self):
        return False

    def snap_t_to_nearest_tick(self, t):
        j = np.abs(t - self.us).argmin()
        return self.us[j], j

    def time_cond_vec(self, N, σ):
        if isinstance(σ, float):
            σ, j = self.snap_t_to_nearest_tick(σ)  # σ might change due to snapping
            cond_t = (self.M - 1) - j
            cond_t = torch.tensor([cond_t] * N, device=self.device)
            return cond_t, σ
        else:
            assert isinstance(σ, torch.Tensor)
            σ = σ.reshape(-1).cpu().numpy()
            σs = []
            js = []
            for elem in σ:
                _σ, _j = self.snap_t_to_nearest_tick(elem)
                σs.append(_σ)
                js.append((self.M - 1) - _j)

            cond_t = torch.tensor(js, device=self.device)
            σs = torch.tensor(σs, device=self.device, dtype=torch.float32).reshape(-1, 1, 1, 1)
            return cond_t, σs

    @staticmethod
    def linear_us(M=1000):
        assert M == 1000
        β_start = 0.00085
        β_end = 0.0120
        βs = np.linspace(β_start**0.5, β_end**0.5, M, dtype=np.float64)**2
        αs = np.cumprod(1 - βs)
        us = np.sqrt((1 - αs) / αs)
        us = us[::-1]
        return us

    @torch.no_grad()
    def encode(self, xs):
        model = self.model
        with self.precision_scope("cuda"):
            with self.model.ema_scope():
                zs = model.get_first_stage_encoding(
                    model.encode_first_stage(xs)
                )
        return zs

    @torch.no_grad()
    def decode(self, xs):
        with self.precision_scope("cuda"):
            with self.model.ema_scope():
                xs = self.model.decode_first_stage(xs)
                return xs