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