Spaces:
Runtime error
Runtime error
File size: 10,031 Bytes
f2dbf59 |
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 |
import numpy as np
import torch
import comfy.sample
import comfy.samplers
import comfy.utils
import latent_preview
from comfy_extras.nodes_custom_sampler import Noise_EmptyNoise, Noise_RandomNoise
from nodes import common_ksampler
_CATEGORY = 'fnodes/Samplers'
NOISE_LEVELS = {
'SD1': [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582],
'SDXL': [14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582],
'SVD': [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002],
}
def common_sampling_logic(model, add_noise, noise_seed, cfg, positive, negative, sampler_name, steps, latent_image, sigmas):
sampler = comfy.samplers.sampler_object(sampler_name)
if isinstance(latent_image, dict) and 'samples' in latent_image:
latent = latent_image.copy()
latent_samples = latent_image['samples']
else:
latent = {'samples': latent_image}
latent_samples = latent_image
latent_samples = comfy.sample.fix_empty_latent_channels(model, latent_samples)
latent['samples'] = latent_samples
if not add_noise:
noise = Noise_EmptyNoise().generate_noise(latent)
else:
noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
noise_mask = latent.get('noise_mask')
x0_output = {}
callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = comfy.sample.sample_custom(
model,
noise,
cfg,
sampler,
sigmas,
positive,
negative,
latent_samples,
noise_mask=noise_mask,
callback=callback,
disable_pbar=disable_pbar,
seed=noise_seed,
)
out = {'samples': samples}
if 'x0' in x0_output:
out_denoised = {'samples': model.model.process_latent_out(x0_output['x0'].cpu())}
else:
out_denoised = out
return out, out_denoised
class ScheduleSamplerCustomTurbo:
@classmethod
def INPUT_TYPES(cls):
return {
'required': {
'model': ('MODEL',),
'add_noise': ('BOOLEAN', {'default': True}),
'noise_seed': (
'INT',
{'default': 0, 'min': 0, 'max': 0xFFFFFFFFFFFFFFFF},
),
'cfg': (
'FLOAT',
{
'default': 8.0,
'min': 0.0,
'max': 100.0,
'step': 0.1,
'round': 0.01,
},
),
'positive': ('CONDITIONING',),
'negative': ('CONDITIONING',),
'sampler_name': (comfy.samplers.SAMPLER_NAMES,),
'steps': ('INT', {'default': 4, 'min': 1, 'max': 10}),
'denoise_schedule': ('STRING', {'default': '0.5,0.25'}),
'latent_image': ('LATENT',),
}
}
RETURN_TYPES = ('LATENT', 'LATENT')
RETURN_NAMES = ('output', 'denoised_output')
FUNCTION = 'sample'
CATEGORY = _CATEGORY
def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler_name, steps, denoise_schedule, latent_image):
denoise_values = [float(x.strip()) for x in denoise_schedule.split(',')]
for i, denoise in enumerate(denoise_values):
current_noise_seed = noise_seed + i
start_step = 10 - int(10 * denoise)
timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step : start_step + steps]
sigmas = model.get_model_object('model_sampling').sigma(timesteps)
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
latent_image, out_denoised = common_sampling_logic(model, add_noise, current_noise_seed, cfg, positive, negative, sampler_name, steps, latent_image, sigmas)
return latent_image, out_denoised
def loglinear_interp(t_steps, num_steps):
"""
Performs log-linear interpolation of a given array of decreasing numbers.
"""
xs = np.linspace(0, 1, len(t_steps))
ys = np.log(t_steps[::-1])
new_xs = np.linspace(0, 1, num_steps)
new_ys = np.interp(new_xs, xs, ys)
interped_ys = np.exp(new_ys)[::-1].copy()
return interped_ys
class ScheduleSamplerCustomAYS:
@classmethod
def INPUT_TYPES(cls):
return {
'required': {
'model': ('MODEL',),
'add_noise': ('BOOLEAN', {'default': True}),
'noise_seed': (
'INT',
{'default': 0, 'min': 0, 'max': 0xFFFFFFFFFFFFFFFF},
),
'cfg': (
'FLOAT',
{
'default': 8.0,
'min': 0.0,
'max': 100.0,
'step': 0.1,
'round': 0.01,
},
),
'positive': ('CONDITIONING',),
'negative': ('CONDITIONING',),
'sampler_name': (comfy.samplers.SAMPLER_NAMES,),
'model_type': (['SD1', 'SDXL', 'SVD'],),
'steps': ('INT', {'default': 10, 'min': 10, 'max': 10000}),
'denoise_schedule': ('STRING', {'default': '0.5,0.25'}),
'latent_image': ('LATENT',),
}
}
RETURN_TYPES = ('LATENT', 'LATENT')
RETURN_NAMES = ('output', 'denoised_output')
FUNCTION = 'sample'
CATEGORY = _CATEGORY
def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler_name, model_type, steps, denoise_schedule, latent_image):
denoise_values = [float(x.strip()) for x in denoise_schedule.split(',')]
for i, denoise in enumerate(denoise_values):
current_noise_seed = noise_seed + i
total_steps = steps
if denoise < 1.0:
if denoise <= 0.0:
return (torch.FloatTensor([]),)
total_steps = round(steps * denoise)
sigmas = NOISE_LEVELS[model_type][:]
if (steps + 1) != len(sigmas):
sigmas = loglinear_interp(sigmas, steps + 1)
sigmas = sigmas[-(total_steps + 1) :]
sigmas[-1] = 0
sigmas = torch.FloatTensor(sigmas)
latent_image, out_denoised = common_sampling_logic(model, add_noise, current_noise_seed, cfg, positive, negative, sampler_name, total_steps, latent_image, sigmas)
return latent_image, out_denoised
class ScheduleSampler:
@classmethod
def INPUT_TYPES(cls):
return {
'required': {
'model': ('MODEL', {'tooltip': 'The model used for denoising the input latent.'}),
'seed': ('INT', {'default': 0, 'min': 0, 'max': 0xFFFFFFFFFFFFFFFF, 'tooltip': 'The random seed used for creating the noise.'}),
'steps': ('INT', {'default': 20, 'min': 1, 'max': 10000, 'tooltip': 'The number of steps used in the denoising process.'}),
'cfg': (
'FLOAT',
{
'default': 8.0,
'min': 0.0,
'max': 100.0,
'step': 0.1,
'round': 0.01,
'tooltip': 'The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality.',
},
),
'sampler_name': (comfy.samplers.KSampler.SAMPLERS, {'tooltip': 'The algorithm used when sampling, this can affect the quality, speed, and style of the generated output.'}),
'scheduler': (comfy.samplers.KSampler.SCHEDULERS, {'tooltip': 'The scheduler controls how noise is gradually removed to form the image.'}),
'positive': ('CONDITIONING', {'tooltip': 'The conditioning describing the attributes you want to include in the image.'}),
'negative': ('CONDITIONING', {'tooltip': 'The conditioning describing the attributes you want to exclude from the image.'}),
'latent_image': ('LATENT', {'tooltip': 'The latent image to denoise.'}),
'denoise_schedule': ('STRING', {'default': '0.5,0.25'}),
}
}
RETURN_TYPES = ('LATENT',)
OUTPUT_TOOLTIPS = ('The denoised latent.',)
FUNCTION = 'sample'
CATEGORY = _CATEGORY
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise_schedule):
denoise_values = [float(x.strip()) for x in denoise_schedule.split(',')]
for i, denoise in enumerate(denoise_values):
current_noise_seed = seed + i
current_steps = round(steps * denoise)
latent_image = common_ksampler(model, current_noise_seed, current_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
latent_image = latent_image[0]
return (latent_image,)
SAMPLER_CLASS_MAPPINGS = {
'ScheduleSamplerCustomTurbo-': ScheduleSamplerCustomTurbo,
'ScheduleSamplerCustomAYS-': ScheduleSamplerCustomAYS,
'ScheduleSampler-': ScheduleSampler,
}
SAMPLER_NAME_MAPPINGS = {
'ScheduleSamplerCustomTurbo-': 'ScheduleSamplerCustomTurbo',
'ScheduleSamplerCustomAYS-': 'ScheduleSamplerCustomAYS',
'ScheduleSampler-': 'ScheduleSampler',
}
|