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import comfy.samplers
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import comfy.utils
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import torch
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import numpy as np
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from tqdm.auto import trange, tqdm
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import math
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@torch.no_grad()
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def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
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extra_args = {} if extra_args is None else extra_args
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if upscale_steps is None:
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upscale_steps = max(len(sigmas) // 2 + 1, 2)
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else:
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upscale_steps += 1
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upscale_steps = min(upscale_steps, len(sigmas) + 1)
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upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
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orig_shape = x.size()
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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denoised = model(x, sigmas[i] * s_in, **extra_args)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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x = denoised
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if i < len(upscales):
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x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
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if sigmas[i + 1] > 0:
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x += sigmas[i + 1] * torch.randn_like(x)
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return x
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class SamplerLCMUpscale:
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upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}),
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"scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}),
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"upscale_method": (s.upscale_methods,),
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}
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "sampling/custom_sampling/samplers"
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FUNCTION = "get_sampler"
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def get_sampler(self, scale_ratio, scale_steps, upscale_method):
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if scale_steps < 0:
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scale_steps = None
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sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
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return (sampler, )
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from comfy.k_diffusion.sampling import to_d
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import comfy.model_patcher
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@torch.no_grad()
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def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
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extra_args = {} if extra_args is None else extra_args
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temp = [0]
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def post_cfg_function(args):
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temp[0] = args["uncond_denoised"]
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return args["denoised"]
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model_options = extra_args.get("model_options", {}).copy()
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extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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sigma_hat = sigmas[i]
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denoised = model(x, sigma_hat * s_in, **extra_args)
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d = to_d(x - denoised + temp[0], sigmas[i], denoised)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
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dt = sigmas[i + 1] - sigma_hat
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x = x + d * dt
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return x
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class SamplerEulerCFGpp:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"version": (["regular", "alternative"],),}
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "_for_testing"
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FUNCTION = "get_sampler"
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def get_sampler(self, version):
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if version == "alternative":
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sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
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else:
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sampler = comfy.samplers.ksampler("euler_cfg_pp")
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return (sampler, )
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NODE_CLASS_MAPPINGS = {
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"SamplerLCMUpscale": SamplerLCMUpscale,
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"SamplerEulerCFGpp": SamplerEulerCFGpp,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SamplerEulerCFGpp": "SamplerEulerCFG++",
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}
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