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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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