forked from AUTOMATIC1111/stable-diffusion-webui
-
Notifications
You must be signed in to change notification settings - Fork 0
/
img2imgalt.py
183 lines (123 loc) · 6.97 KB
/
img2imgalt.py
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
from collections import namedtuple
import numpy as np
from tqdm import trange
import modules.scripts as scripts
import gradio as gr
from modules import processing, shared, sd_samplers, prompt_parser
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state
import torch
import k_diffusion as K
from PIL import Image
from torch import autocast
from einops import rearrange, repeat
def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
x = p.init_latent
s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(shared.sd_model)
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
for i in trange(1, len(sigmas)):
shared.state.sampling_step += 1
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigmas[i] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
t = dnw.sigma_to_t(sigma_in)
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
d = (x - denoised) / sigmas[i]
dt = sigmas[i] - sigmas[i - 1]
x = x + d * dt
sd_samplers.store_latent(x)
# This shouldn't be necessary, but solved some VRAM issues
del x_in, sigma_in, cond_in, c_out, c_in, t,
del eps, denoised_uncond, denoised_cond, denoised, d, dt
shared.state.nextjob()
return x / x.std()
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])
# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
x = p.init_latent
s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(shared.sd_model)
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
for i in trange(1, len(sigmas)):
shared.state.sampling_step += 1
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
if i == 1:
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
else:
t = dnw.sigma_to_t(sigma_in)
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
if i == 1:
d = (x - denoised) / (2 * sigmas[i])
else:
d = (x - denoised) / sigmas[i - 1]
dt = sigmas[i] - sigmas[i - 1]
x = x + d * dt
sd_samplers.store_latent(x)
# This shouldn't be necessary, but solved some VRAM issues
del x_in, sigma_in, cond_in, c_out, c_in, t,
del eps, denoised_uncond, denoised_cond, denoised, d, dt
shared.state.nextjob()
return x / sigmas[-1]
class Script(scripts.Script):
def __init__(self):
self.cache = None
def title(self):
return "img2img alternative test"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
original_prompt = gr.Textbox(label="Original prompt", lines=1)
original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
p.batch_size = 1
p.batch_count = 1
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
lat = (p.init_latent.cpu().numpy() * 10).astype(int)
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
and self.cache.original_prompt == original_prompt \
and self.cache.original_negative_prompt == original_negative_prompt \
and self.cache.sigma_adjustment == sigma_adjustment
same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
if same_everything:
rec_noise = self.cache.noise
else:
shared.state.job_count += 1
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
if sigma_adjustment:
rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
else:
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
sigmas = sampler.model_wrap.get_sigmas(p.steps)
noise_dt = combined_noise - (p.init_latent / sigmas[0])
p.seed = p.seed + 1
return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning)
p.sample = sample_extra
p.extra_generation_params["Decode prompt"] = original_prompt
p.extra_generation_params["Decode negative prompt"] = original_negative_prompt
p.extra_generation_params["Decode CFG scale"] = cfg
p.extra_generation_params["Decode steps"] = st
p.extra_generation_params["Randomness"] = randomness
p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
processed = processing.process_images(p)
return processed