-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathutils.py
194 lines (161 loc) · 9.04 KB
/
utils.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
184
185
186
187
188
189
190
191
192
193
194
import torch
import comfy
from comfy import model_management
from tqdm.auto import trange
import comfy.k_diffusion.utils as utils
import latent_preview
from comfy.samplers import KSAMPLER, ksampler, CFGGuider
from comfy.extra_samplers import uni_pc
def sampler_object(name):
if name == "uni_pc":
sampler = KSAMPLER(uni_pc.sample_unipc)
elif name == "uni_pc_bh2":
sampler = KSAMPLER(uni_pc.sample_unipc_bh2)
elif name == "ddim":
sampler = ksampler("euler", inpaint_options={"random": True})
else:
sampler = ksampler(name)
return sampler
def sample_(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
cfg_guider = CFGGuider(model)
cfg_guider.set_conds(positive, negative)
cfg_guider.set_cfg(cfg)
return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None,
force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
if sigmas is None:
sigmas = self.sigmas
if last_step is not None and last_step < (len(sigmas) - 1):
sigmas = sigmas[:last_step + 1]
if force_full_denoise:
sigmas[-1] = 0
if start_step is not None:
if start_step < (len(sigmas) - 1):
sigmas = sigmas[start_step:]
else:
if latent_image is not None:
return latent_image
else:
return torch.zeros_like(noise)
sampler = sampler_object(self.sampler)
sampler.sampler_function = sample_euler_blend
return sample_(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options,
latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar,
seed=seed)
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
callback = latent_preview.prepare_callback(model, steps)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
sampler.sample = sample.__get__(sampler, KSAMPLER)
samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=None, callback=callback, disable_pbar=disable_pbar, seed=seed)
samples = samples.to(comfy.model_management.intermediate_device())
out = latent.copy()
out["samples"] = samples
return (out, )
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / utils.append_dims(sigma, x.ndim)
@torch.no_grad()
def blend_h(a, b, blend_extent):
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
for x in range(blend_extent):
b[:, :, :, x] = a[:, :, :, -blend_extent
+ x] * (1 - x / blend_extent) + b[:, :, :, x] * (
x / blend_extent)
return b
@torch.no_grad()
def sample_euler_blend(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
w = x.shape[-1]
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
sigma_hat = sigmas[i] * (gamma + 1)
if gamma > 0:
eps = torch.randn_like(x) * s_noise
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x, sigma_hat, denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
# Euler method
x = x + d * dt
x = blend_h(x, x, 4)
x = blend_h(x, x, 4)
x = x[:, :, :, :w]
return x
def decode_tiled_blended_(self, samples, tile_x=64, tile_y=64, overlap=16):
steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y,
overlap)
steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2,
tile_y * 2, overlap)
steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2,
tile_y // 2, overlap)
pbar = comfy.utils.ProgressBar(steps)
decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
output = self.process_output(
(tiled_scale_blended(samples, decode_fn, tile_x // 2, tile_y * 2, overlap,
upscale_amount=self.upscale_ratio, output_device=self.output_device,
pbar=pbar) +
tiled_scale_blended(samples, decode_fn, tile_x * 2, tile_y // 2, overlap,
upscale_amount=self.upscale_ratio, output_device=self.output_device,
pbar=pbar) +
tiled_scale_blended(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount=self.upscale_ratio,
output_device=self.output_device, pbar=pbar))
/ 3.0)
return output
def decode_tiled_blended(self, samples, tile_x=64, tile_y=64, overlap = 16):
model_management.load_model_gpu(self.patcher)
output = self.decode_tiled_blended_(samples, tile_x, tile_y, overlap)
return output.movedim(1, -1)
def blend_h(a, b, blend_extent):
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
for x in range(blend_extent):
b[:, :, :, x] = a[:, :, :, -blend_extent
+ x] * (1 - x / blend_extent) + b[:, :, :, x] * (
x / blend_extent)
return b
def tiled_scale_blended(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device)
for b in range(samples.shape[0]):
s = samples[b:b+1]
out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
w = samples.shape[3]
samples = torch.cat([samples, samples[:, :, :, :w // 4]], dim=-1)
s = samples[b:b + 1]
for y in range(0, s.shape[2], tile_y - overlap):
# for x in range(0, s.shape[3], tile_x - overlap):
# x = max(0, min(s.shape[-1] - overlap, x))
y = max(0, min(s.shape[-2] - overlap, y))
s_in = s[:,:,y:y+tile_y,:]
ps = function(s_in).to(output_device)
ps = blend_h(
ps[:,:,:,w*upscale_amount:],
ps[:,:,:,:w*upscale_amount],
w // 4 * upscale_amount
)
mask = torch.ones_like(ps)
feather = round(overlap * upscale_amount)
for t in range(feather):
mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),:] += ps * mask
out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),:] += mask
if pbar is not None:
pbar.update(1)
output[b:b+1] = out/out_div
return output