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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 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 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,
"ModelSamplingStableCascade": ModelSamplingStableCascade,
"RescaleCFG": RescaleCFG,
}
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