File size: 10,996 Bytes
ac6acf2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 |
import folder_paths
import comfy.sd
import comfy.model_sampling
import comfy.latent_formats
import torch
class LCM(comfy.model_sampling.EPS):
def calculate_denoised(self, sigma, model_output, model_input):
timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
x0 = model_input - model_output * sigma
sigma_data = 0.5
scaled_timestep = timestep * 10.0 #timestep_scaling
c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
return c_out * x0 + c_skip * model_input
class X0(comfy.model_sampling.EPS):
def calculate_denoised(self, sigma, model_output, model_input):
return model_output
class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
original_timesteps = 50
def __init__(self, model_config=None):
super().__init__(model_config)
self.skip_steps = self.num_timesteps // self.original_timesteps
sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
for x in range(self.original_timesteps):
sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps]
self.set_sigmas(sigmas_valid)
def timestep(self, sigma):
log_sigma = sigma.log()
dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device)
def sigma(self, timestep):
t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1))
low_idx = t.floor().long()
high_idx = t.ceil().long()
w = t.frac()
log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
return log_sigma.exp().to(timestep.device)
def rescale_zero_terminal_snr_sigmas(sigmas):
alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
alphas_bar_sqrt = alphas_cumprod.sqrt()
# Store old values.
alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
# Shift so the last timestep is zero.
alphas_bar_sqrt -= (alphas_bar_sqrt_T)
# Scale so the first timestep is back to the old value.
alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
# Convert alphas_bar_sqrt to betas
alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
alphas_bar[-1] = 4.8973451890853435e-08
return ((1 - alphas_bar) / alphas_bar) ** 0.5
class ModelSamplingDiscrete:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["eps", "v_prediction", "lcm", "x0"],),
"zsnr": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, sampling, zsnr):
m = model.clone()
sampling_base = comfy.model_sampling.ModelSamplingDiscrete
if sampling == "eps":
sampling_type = comfy.model_sampling.EPS
elif sampling == "v_prediction":
sampling_type = comfy.model_sampling.V_PREDICTION
elif sampling == "lcm":
sampling_type = LCM
sampling_base = ModelSamplingDiscreteDistilled
elif sampling == "x0":
sampling_type = X0
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
if zsnr:
model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class ModelSamplingStableCascade:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, shift):
m = model.clone()
sampling_base = comfy.model_sampling.StableCascadeSampling
sampling_type = comfy.model_sampling.EPS
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class ModelSamplingSD3:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, shift, multiplier=1000):
m = model.clone()
sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
sampling_type = comfy.model_sampling.CONST
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift=shift, multiplier=multiplier)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class ModelSamplingAuraFlow(ModelSamplingSD3):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"shift": ("FLOAT", {"default": 1.73, "min": 0.0, "max": 100.0, "step":0.01}),
}}
FUNCTION = "patch_aura"
def patch_aura(self, model, shift):
return self.patch(model, shift, multiplier=1.0)
class ModelSamplingContinuousEDM:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
"sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, sampling, sigma_max, sigma_min):
m = model.clone()
latent_format = None
sigma_data = 1.0
if sampling == "eps":
sampling_type = comfy.model_sampling.EPS
elif sampling == "v_prediction":
sampling_type = comfy.model_sampling.V_PREDICTION
elif sampling == "edm_playground_v2.5":
sampling_type = comfy.model_sampling.EDM
sigma_data = 0.5
latent_format = comfy.latent_formats.SDXL_Playground_2_5()
class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
m.add_object_patch("model_sampling", model_sampling)
if latent_format is not None:
m.add_object_patch("latent_format", latent_format)
return (m, )
class ModelSamplingContinuousV:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["v_prediction"],),
"sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, sampling, sigma_max, sigma_min):
m = model.clone()
latent_format = None
sigma_data = 1.0
if sampling == "v_prediction":
sampling_type = comfy.model_sampling.V_PREDICTION
class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class RescaleCFG:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, multiplier):
def rescale_cfg(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
sigma = args["sigma"]
sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
x_orig = args["input"]
#rescale cfg has to be done on v-pred model output
x = x_orig / (sigma * sigma + 1.0)
cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
#rescalecfg
x_cfg = uncond + cond_scale * (cond - uncond)
ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
x_rescaled = x_cfg * (ro_pos / ro_cfg)
x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)
m = model.clone()
m.set_model_sampler_cfg_function(rescale_cfg)
return (m, )
NODE_CLASS_MAPPINGS = {
"ModelSamplingDiscrete": ModelSamplingDiscrete,
"ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
"ModelSamplingContinuousV": ModelSamplingContinuousV,
"ModelSamplingStableCascade": ModelSamplingStableCascade,
"ModelSamplingSD3": ModelSamplingSD3,
"ModelSamplingAuraFlow": ModelSamplingAuraFlow,
"RescaleCFG": RescaleCFG,
}
|