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import folder_paths
import fcbh.sd
import fcbh.model_sampling
import torch
class LCM(fcbh.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 ModelSamplingDiscreteLCM(torch.nn.Module):
def __init__(self):
super().__init__()
self.sigma_data = 1.0
timesteps = 1000
beta_start = 0.00085
beta_end = 0.012
betas = torch.linspace(beta_start**0.5, beta_end**0.5, timesteps, dtype=torch.float32) ** 2
alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
original_timesteps = 50
self.skip_steps = timesteps // original_timesteps
alphas_cumprod_valid = torch.zeros((original_timesteps), dtype=torch.float32)
for x in range(original_timesteps):
alphas_cumprod_valid[original_timesteps - 1 - x] = alphas_cumprod[timesteps - 1 - x * self.skip_steps]
sigmas = ((1 - alphas_cumprod_valid) / alphas_cumprod_valid) ** 0.5
self.set_sigmas(sigmas)
def set_sigmas(self, sigmas):
self.register_buffer('sigmas', sigmas)
self.register_buffer('log_sigmas', sigmas.log())
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
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)
def sigma(self, timestep):
t = torch.clamp(((timestep - (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()
def percent_to_sigma(self, percent):
if percent <= 0.0:
return 999999999.9
if percent >= 1.0:
return 0.0
percent = 1.0 - percent
return self.sigma(torch.tensor(percent * 999.0)).item()
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"],),
"zsnr": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, sampling, zsnr):
m = model.clone()
sampling_base = fcbh.model_sampling.ModelSamplingDiscrete
if sampling == "eps":
sampling_type = fcbh.model_sampling.EPS
elif sampling == "v_prediction":
sampling_type = fcbh.model_sampling.V_PREDICTION
elif sampling == "lcm":
sampling_type = LCM
sampling_base = ModelSamplingDiscreteLCM
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced()
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 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,
"RescaleCFG": RescaleCFG,
}
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