245 lines
8.8 KiB
Python
245 lines
8.8 KiB
Python
import folder_paths
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import comfy.sd
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import comfy.model_sampling
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import comfy.latent_formats
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import torch
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class LCM(comfy.model_sampling.EPS):
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def calculate_denoised(self, sigma, model_output, model_input):
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timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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x0 = model_input - model_output * sigma
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sigma_data = 0.5
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scaled_timestep = timestep * 10.0 #timestep_scaling
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c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
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c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
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return c_out * x0 + c_skip * model_input
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class X0(comfy.model_sampling.EPS):
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def calculate_denoised(self, sigma, model_output, model_input):
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return model_output
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class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
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original_timesteps = 50
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def __init__(self, model_config=None):
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super().__init__(model_config)
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self.skip_steps = self.num_timesteps // self.original_timesteps
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sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
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for x in range(self.original_timesteps):
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sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps]
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self.set_sigmas(sigmas_valid)
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def timestep(self, sigma):
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log_sigma = sigma.log()
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dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
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return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device)
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def sigma(self, timestep):
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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))
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low_idx = t.floor().long()
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high_idx = t.ceil().long()
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w = t.frac()
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log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
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return log_sigma.exp().to(timestep.device)
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def rescale_zero_terminal_snr_sigmas(sigmas):
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alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
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alphas_bar_sqrt = alphas_cumprod.sqrt()
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# Store old values.
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alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
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alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
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# Shift so the last timestep is zero.
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alphas_bar_sqrt -= (alphas_bar_sqrt_T)
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# Scale so the first timestep is back to the old value.
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alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
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# Convert alphas_bar_sqrt to betas
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alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
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alphas_bar[-1] = 4.8973451890853435e-08
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return ((1 - alphas_bar) / alphas_bar) ** 0.5
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class ModelSamplingDiscrete:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"sampling": (["eps", "v_prediction", "lcm", "x0"],),
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"zsnr": ("BOOLEAN", {"default": False}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "advanced/model"
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def patch(self, model, sampling, zsnr):
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m = model.clone()
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sampling_base = comfy.model_sampling.ModelSamplingDiscrete
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if sampling == "eps":
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sampling_type = comfy.model_sampling.EPS
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elif sampling == "v_prediction":
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sampling_type = comfy.model_sampling.V_PREDICTION
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elif sampling == "lcm":
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sampling_type = LCM
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sampling_base = ModelSamplingDiscreteDistilled
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elif sampling == "x0":
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sampling_type = X0
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class ModelSamplingAdvanced(sampling_base, sampling_type):
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pass
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model_sampling = ModelSamplingAdvanced(model.model.model_config)
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if zsnr:
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model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
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m.add_object_patch("model_sampling", model_sampling)
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return (m, )
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class ModelSamplingStableCascade:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "advanced/model"
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def patch(self, model, shift):
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m = model.clone()
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sampling_base = comfy.model_sampling.StableCascadeSampling
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sampling_type = comfy.model_sampling.EPS
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class ModelSamplingAdvanced(sampling_base, sampling_type):
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pass
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model_sampling = ModelSamplingAdvanced(model.model.model_config)
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model_sampling.set_parameters(shift)
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m.add_object_patch("model_sampling", model_sampling)
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return (m, )
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class ModelSamplingSD3:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "advanced/model"
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def patch(self, model, shift):
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m = model.clone()
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sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
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sampling_type = comfy.model_sampling.CONST
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class ModelSamplingAdvanced(sampling_base, sampling_type):
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pass
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model_sampling = ModelSamplingAdvanced(model.model.model_config)
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model_sampling.set_parameters(shift=shift)
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m.add_object_patch("model_sampling", model_sampling)
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return (m, )
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class ModelSamplingContinuousEDM:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
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"sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
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"sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "advanced/model"
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def patch(self, model, sampling, sigma_max, sigma_min):
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m = model.clone()
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latent_format = None
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sigma_data = 1.0
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if sampling == "eps":
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sampling_type = comfy.model_sampling.EPS
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elif sampling == "v_prediction":
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sampling_type = comfy.model_sampling.V_PREDICTION
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elif sampling == "edm_playground_v2.5":
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sampling_type = comfy.model_sampling.EDM
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sigma_data = 0.5
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latent_format = comfy.latent_formats.SDXL_Playground_2_5()
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class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type):
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pass
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model_sampling = ModelSamplingAdvanced(model.model.model_config)
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model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
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m.add_object_patch("model_sampling", model_sampling)
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if latent_format is not None:
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m.add_object_patch("latent_format", latent_format)
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return (m, )
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class RescaleCFG:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "advanced/model"
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def patch(self, model, multiplier):
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def rescale_cfg(args):
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cond = args["cond"]
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uncond = args["uncond"]
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cond_scale = args["cond_scale"]
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sigma = args["sigma"]
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sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
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x_orig = args["input"]
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#rescale cfg has to be done on v-pred model output
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x = x_orig / (sigma * sigma + 1.0)
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cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
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uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
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#rescalecfg
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x_cfg = uncond + cond_scale * (cond - uncond)
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ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
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ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
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x_rescaled = x_cfg * (ro_pos / ro_cfg)
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x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
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return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)
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m = model.clone()
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m.set_model_sampler_cfg_function(rescale_cfg)
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return (m, )
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NODE_CLASS_MAPPINGS = {
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"ModelSamplingDiscrete": ModelSamplingDiscrete,
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"ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
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"ModelSamplingStableCascade": ModelSamplingStableCascade,
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"ModelSamplingSD3": ModelSamplingSD3,
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"RescaleCFG": RescaleCFG,
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}
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