forked from ClownsharkBatwing/RES4LYF
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsamplers.py
279 lines (238 loc) · 12.1 KB
/
samplers.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
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
from .extra_samplers import prepare_noise
from .noise_classes import *
import comfy.samplers
import comfy.sample
import comfy.sampler_helpers
import latent_preview
import torch
def initialize_or_scale(tensor, value, steps):
if tensor is None:
return torch.full((steps,), value)
else:
return value * tensor
def move_to_same_device(*tensors):
if not tensors:
return tensors
device = tensors[0].device
return tuple(tensor.to(device) for tensor in tensors)
class ClownSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"momentum": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
"ita": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000.0, "step": 0.01}),
"c2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000.0, "step": 0.01}),
"clownseed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"offset": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"guide_1": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"guide_2": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"guide_mode_1": ("INT", {"default": 0, "min": -10000, "max": 10000}),
"guide_mode_2": ("INT", {"default": 0, "min": -10000, "max": 10000}),
"noise_sampler_type": (NOISE_GENERATOR_NAMES, ),
"denoise_to_zero": ("BOOLEAN", {"default": True}),
"simple_phi_calc": ("BOOLEAN", {"default": False}),
"guide_1_Luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.1}),
"guide_1_CyanRed": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.1}),
"guide_1_LimePurple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.1}),
"guide_1_PatternStruct": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.1}),
"alpha": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.1}),
"k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 2}),
},
"optional": {
"momentums": ("SIGMAS", ),
"itas": ("SIGMAS", ),
"c2s": ("SIGMAS", ),
"offsets": ("SIGMAS", ),
"guides_1": ("SIGMAS", ),
"guides_2": ("SIGMAS", ),
"alphas": ("SIGMAS", ),
"latent_guide_1": ("LATENT", ),
"latent_guide_2": ("LATENT", ),
"latent_noise": ("LATENT", ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, clownseed, noise_sampler_type, denoise_to_zero, simple_phi_calc, momentum, c2, ita, offset,
guide_1, guide_2, guide_mode_1, guide_mode_2,
guide_1_Luminosity, guide_1_CyanRed, guide_1_LimePurple, guide_1_PatternStruct,
alpha, k,
alphas=None, latent_noise=None,
guides_1=None, guides_2=None, latent_guide_1=None, latent_guide_2=None,
momentums=None, c2s=None, itas=None, offsets=None):
steps = 10000
momentums = initialize_or_scale(momentums, momentum, steps)
itas = initialize_or_scale(itas, ita, steps)
c2s = initialize_or_scale(c2s, c2, steps)
offsets = initialize_or_scale(offsets, offset, steps)
guides_1 = initialize_or_scale(guides_1, guide_1, steps)
guides_2 = initialize_or_scale(guides_2, guide_2, steps)
alphas = initialize_or_scale(alphas, alpha, steps)
#import pdb; pdb.set_trace()
if latent_guide_1 is not None:
latent_guide_1 = latent_guide_1["samples"]
if latent_guide_2 is not None:
latent_guide_2 = latent_guide_2["samples"]
guide_1_channels = torch.tensor([guide_1_Luminosity, guide_1_CyanRed, guide_1_LimePurple, guide_1_PatternStruct])
latent_noise_samples = latent_noise["samples"] if latent_noise and "samples" in latent_noise else None
sampler = comfy.samplers.ksampler(
"res_momentumized_advanced",
{
"noise_sampler_type": noise_sampler_type,
"denoise_to_zero": denoise_to_zero,
"simple_phi_calc": simple_phi_calc,
"momentums": momentums,
"itas": itas,
"c2s": c2s,
"offsets": offsets,
"guides_1": guides_1,
"guides_2": guides_2,
"latent_guide_1": latent_guide_1,
"latent_guide_2": latent_guide_2,
"guide_mode_1": guide_mode_1,
"guide_mode_2": guide_mode_2,
"guide_1_channels": guide_1_channels,
"alphas": alphas,
"alpha": alpha,
"k": k,
"clownseed": clownseed,
"latent_noise": latent_noise_samples,
}
)
return (sampler, )
class SharkSampler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"add_noise": ("BOOLEAN", {"default": True}),
"noise_is_latent": ("BOOLEAN", {"default": False}),
"noise_type": (NOISE_GENERATOR_NAMES, ),
"alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": 0.01}),
"k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": 0.01}),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"sampler": ("SAMPLER", ),
"sigmas": ("SIGMAS", ),
"latent_image": ("LATENT", ),
},
"optional":
{"latent_noise": ("LATENT", ),
}
}
RETURN_TYPES = ("LATENT","LATENT","LATENT")
RETURN_NAMES = ("output", "denoised_output", "latent_batch")
FUNCTION = "sample"
CATEGORY = "sampling/custom_sampling"
@cast_fp64
def sample(self, model, add_noise, noise_is_latent, noise_type, noise_seed, cfg, alpha, k, positive, negative, sampler,
sigmas, latent_image, latent_noise=None):
latent = latent_image
latent_image = latent["samples"].to(torch.float64)
if not add_noise:
torch.manual_seed(noise_seed)
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
elif latent_noise is None:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = prepare_noise(latent_image, noise_seed, noise_type, batch_inds, alpha, k)
else:
noise = latent_noise["samples"].to(torch.float64)
if noise_is_latent:
noise += latent_image.cpu()
noise.sub_(noise.mean()).div_(noise.std())
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
x0_output = {}
callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
disable_pbar = False
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image,
noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar,
seed=noise_seed)
out = latent.copy()
out["samples"] = samples
if "x0" in x0_output:
out_denoised = latent.copy()
out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
else:
out_denoised = out
return (out, out_denoised)
class SamplerDPMPP_SDE_ADVANCED:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"alpha": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step":0.1, "round": False}),
"k": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step":2.0, "round": False}),
"noise_device": (['gpu', 'cpu'], ),
"noise_sampler_type": (NOISE_GENERATOR_NAMES, ),
},
"optional":
{
"alphas": ("SIGMAS", ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise, r, alpha, k, noise_device, noise_sampler_type, alphas=None):
if noise_device == 'cpu':
sampler_name = "dpmpp_sde_advanced"
else:
sampler_name = "dpmpp_sde_gpu_advanced"
steps = 10000
alphas = initialize_or_scale(alphas, alpha, steps)
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r, "alpha": alphas, "k": k, "noise_sampler_type": noise_sampler_type})
return (sampler, )
class SamplerDPMPP_DUALSDE_MOMENTUMIZED_ADVANCED:
@classmethod
def INPUT_TYPES(s):
return {
"required":
{
"noise_sampler_type": (NOISE_GENERATOR_NAMES, ),
"momentum": ("FLOAT", {"default": 0.5, "min": -1.0, "max": 1.0, "step":0.01}),
"eta": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
"s_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
"r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}),
},
"optional":
{
"momentums": ("SIGMAS", ),
"etas": ("SIGMAS", ),
"s_noises": ("SIGMAS", ),
"rs": ("SIGMAS", ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, momentum, eta, s_noise, r, momentums=None, etas=None, s_noises=None, rs=None):
scheduled_r=False
steps = 10000
momentums = initialize_or_scale(momentums, momentum, steps)
etas = initialize_or_scale(etas, eta, steps)
s_noises = initialize_or_scale(s_noises, s_noise, steps)
if rs is None:
rs = torch.full((steps,), r)
else:
rs = r * rs
scheduled_r = True
sampler = comfy.samplers.ksampler(
"dpmpp_dualsde_momentumized_advanced",
{
"noise_sampler_type": noise_sampler_type,
"momentums": momentums,
"etas": etas,
"s_noises": s_noises,
"rs": rs,
"scheduled_r": scheduled_r
}
)
return (sampler, )