363 lines
17 KiB
Python
363 lines
17 KiB
Python
import torch
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from comfy.k_diffusion.sampling import trange, to_d
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import comfy.model_patcher
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import comfy.samplers
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from comfy.k_diffusion import sampling
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from comfy import model_sampling
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from math import pi
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mmnorm = lambda x: (x - x.min()) / (x.max() - x.min())
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selfnorm = lambda x: x / x.norm()
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EPSILON = 1e-4
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@torch.no_grad()
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def matrix_batch_slerp(t, tn, w):
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dots = torch.mul(tn.unsqueeze(0), tn.unsqueeze(1)).sum(dim=[-1,-2], keepdim=True).clamp(min=-1.0 + EPSILON, max=1.0 - EPSILON)
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mask = ~torch.eye(tn.shape[0], dtype=torch.bool, device=tn.device)
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A, B, *rest = dots.shape
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rest_1s = (1,) * len(rest)
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dots = dots[mask].reshape(A, B - 1, *rest)
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omegas = dots.acos()
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sin_omega = omegas.sin()
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res = t.unsqueeze(1).repeat(1, B - 1, *rest_1s) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, *rest_1s) * omegas) / sin_omega
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res = res.sum(dim=[0, 1]).unsqueeze(0)
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return res
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@torch.no_grad()
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def fast_distance_weights(t, use_softmax=False, use_slerp=False, uncond=None):
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orig_shape = t.shape[1:]
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if t.shape[1] == 1 and t.ndim == 4:
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t = t.squeeze(1)
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elif t.ndim < 3:
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raise ValueError("Can't handle input with dimensions < 3")
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else:
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t = t.reshape(t.shape[0], -1, *t.shape[-2 if t.ndim > 3 else -1:])
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if t.ndim == 3:
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t = t.unsqueeze(-1)
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if uncond is not None:
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uncond = uncond.reshape(1, *t.shape[1:])
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norm = torch.linalg.matrix_norm(t, keepdim=True)
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n = t.shape[0]
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tn = t.div(norm)
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distances = (tn.unsqueeze(0) - tn.unsqueeze(1)).abs().sum(dim=0)
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distances = distances.max(dim=0, keepdim=True).values - distances
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if uncond != None:
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uncond = uncond.div(torch.linalg.matrix_norm(uncond, keepdim=True))
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distances += tn.sub(uncond).abs() #.div(n)
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if use_softmax:
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distances = distances.mul(n).softmax(dim=0)
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else:
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distances = distances.div(distances.max(dim=0).values).pow(2)
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distances = distances / distances.sum(dim=0)
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if use_slerp:
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res = matrix_batch_slerp(t, tn, distances)
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else:
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res = (t * distances).sum(dim=0).unsqueeze(0)
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res = res.div(torch.linalg.matrix_norm(res, keepdim=True)).mul(norm.mul(distances).sum(dim=0).unsqueeze(0))
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return res if res.shape == orig_shape else res.reshape(orig_shape)
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@torch.no_grad()
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def normalize_adjust(a,b,strength=1):
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c = a.clone()
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norm_a = a.norm(dim=1,keepdim=True)
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a = a / norm_a
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b = b / b.norm(dim=1,keepdim=True)
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d = mmnorm((a - b).abs())
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a = a - b * d * strength
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a = a * norm_a / a.norm(dim=1,keepdim=True)
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if a.isnan().any():
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a[~torch.isfinite(a)] = c[~torch.isfinite(a)]
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return a
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def get_ancestral_step_ext(sigma, sigma_next, eta=1.0, is_rf=False):
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if sigma_next == 0 or eta == 0:
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return sigma_next, sigma_next * 0.0, 1.0
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if not is_rf:
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return (*sampling.get_ancestral_step(sigma, sigma_next, eta=eta), 1.0)
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# Referenced from ComfyUI.
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downstep_ratio = 1.0 + (sigma_next / sigma - 1.0) * eta
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sigma_down = sigma_next * downstep_ratio
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alpha_ip1, alpha_down = 1.0 - sigma_next, 1.0 - sigma_down
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sigma_up = (sigma_next**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5
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x_coeff = alpha_ip1 / alpha_down
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return sigma_down, sigma_up, x_coeff
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def internal_step(x, d, dt, sigma, sigma_next, sigma_up, x_coeff, noise_sampler):
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x = x + d * dt
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if sigma_up == 0 or noise_sampler is None:
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return x
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noise = noise_sampler(sigma, sigma_next).mul_(sigma_up)
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if x_coeff != 1:
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# x gets scaled for flow models.
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x *= x_coeff
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return x.add_(noise)
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def fix_step_range(steps, start, end):
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if start < 0:
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start = steps + start
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if end < 0:
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end = steps + end
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start = max(0, min(steps - 1, start))
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end = max(0, min(steps - 1, end))
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return (end, start) if start > end else (start, end)
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# Euler and CFGpp part taken from comfy_extras/nodes_advanced_samplers
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def distance_wrap(
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resample, resample_end=-1, cfgpp=False, sharpen=False, use_softmax=False,
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distance_first=0, distance_last=-1, eta_first=0, eta_last=-1, distance_eta_first=0, distance_eta_last=-1,
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use_slerp=False, perp_step=False, smooth=False, use_negative=False, eta=0.0, s_noise=1.0,
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distance_step_eta=0.0, distance_step_s_noise=1.0, distance_step_seed_offset=42,
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):
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@torch.no_grad()
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def sample_distance_advanced(model, x, sigmas, eta=eta, s_noise=s_noise, noise_sampler=None, distance_step_noise_sampler=None, extra_args=None, callback=None, disable=None):
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nonlocal distance_first, distance_last, eta_first, eta_last, distance_eta_first, distance_eta_last
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extra_args = {} if extra_args is None else extra_args
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seed = extra_args.get("seed")
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dstep_noise_sampler = None if distance_step_eta == 0 else distance_step_noise_sampler or noise_sampler or sampling.default_noise_sampler(x, seed=seed + distance_step_seed_offset if seed is not None else None)
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noise_sampler = None if eta == 0 else noise_sampler or sampling.default_noise_sampler(x, seed=seed)
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is_rf = isinstance(model.inner_model.inner_model.model_sampling, model_sampling.CONST)
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uncond = None
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steps = len(sigmas) - 1
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distance_first, distance_last = fix_step_range(steps, distance_first, distance_last)
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eta_first, eta_last = fix_step_range(steps, eta_first, eta_last)
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distance_eta_first, distance_eta_last = fix_step_range(steps, distance_eta_first, distance_eta_last)
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if cfgpp or use_negative:
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uncond = None
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def post_cfg_function(args):
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nonlocal uncond
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uncond = 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)
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s_min, s_max = sigmas[sigmas > 0].min(), sigmas.max()
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progression = lambda x, y=0.5: max(0,min(1,((x - s_min) / (s_max - s_min)) ** y))
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d_prev = None
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if resample == -1:
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current_resample = min(10, sigmas.shape[0] // 2)
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else:
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current_resample = resample
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total = 0
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s_in = x.new_ones([x.shape[0]])
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for i in trange(steps, disable=disable):
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use_distance = distance_first <= i <= distance_last
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use_eta = eta_first <= i <= eta_last
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use_distance_eta = distance_eta_first <= i <= distance_eta_last
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sigma, sigma_next = sigmas[i:i + 2]
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sigma_down, sigma_up, x_coeff = get_ancestral_step_ext(sigma, sigma_next, eta=eta if use_eta else 0.0, is_rf=is_rf)
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sigma_up *= s_noise
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dstep_sigma_down, dstep_sigma_up, dstep_x_coeff = get_ancestral_step_ext(sigma, sigma_next, eta=distance_step_eta if use_distance_eta else 0.0, is_rf=is_rf)
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dstep_sigma_up *= distance_step_s_noise
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res_mul = progression(sigma)
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if resample_end >= 0:
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resample_steps = max(min(current_resample,resample_end),min(max(current_resample,resample_end),int(current_resample * res_mul + resample_end * (1 - res_mul))))
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else:
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resample_steps = current_resample
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denoised = model(x, sigma * s_in, **extra_args)
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total += 1
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if cfgpp and torch.any(uncond):
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d = to_d(x - denoised + uncond, sigma, denoised)
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else:
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d = to_d(x, sigma, denoised)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas, 'sigma_hat': sigma, 'denoised': denoised})
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dt = sigma_down - sigma
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dstep_dt = dstep_sigma_down - sigma
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if sigma_next == 0 or resample_steps == 0 or not use_distance:
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# Euler method
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x = internal_step(x, d, dt, sigma, sigma_next, sigma_up, x_coeff, noise_sampler)
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continue
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# not Euler method
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x_n = [d]
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for re_step in trange(resample_steps, initial=1, disable=disable or resample_steps < 2, leave=False, desc=" Distance"):
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x_new = internal_step(x, d, dstep_dt, sigma, sigma_next, dstep_sigma_up, dstep_x_coeff, dstep_noise_sampler)
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new_denoised = model(x_new, sigma_next * s_in, **extra_args)
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if smooth:
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new_denoised = new_denoised.abs().pow(1 / new_denoised.std().sqrt()) * new_denoised.sign()
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new_denoised = new_denoised.div(new_denoised.std().sqrt())
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total += 1
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if cfgpp and torch.any(uncond):
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new_d = to_d(x_new - new_denoised + uncond, sigma_next, new_denoised)
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else:
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new_d = to_d(x_new, sigma_next * s_in, new_denoised)
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x_n.append(new_d)
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if re_step == 0:
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d = (new_d + d) / 2
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continue
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u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
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d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
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if sharpen or perp_step:
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if sharpen and d_prev is not None:
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d = normalize_adjust(d, d_prev, 1)
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elif perp_step and d_prev is not None:
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d = diff_step(d, d_prev, 0.5)
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d_prev = d.clone()
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x_n.append(d)
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x = internal_step(x, d, dt, sigma, sigma_next, sigma_up, x_coeff, noise_sampler)
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return x
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return sample_distance_advanced
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def blend_add(t,v,s):
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tn = torch.linalg.norm(t)
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vn = torch.linalg.norm(v)
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vp = (v / vn - torch.dot(v / vn, t / tn) * t / tn) * tn
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return t + vp * s / 2
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@torch.no_grad()
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def diff_step(a, b, s):
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n = torch.linalg.matrix_norm(a, keepdim=True)
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x = a.div(n)
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y = b.div(torch.linalg.matrix_norm(b, keepdim=True))
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y = n * y.sub(x.mul(torch.mul(x, y).sum().clamp(min=-1.0, max=1.0)))
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return a - y * s
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def perp_step_wrap(s=0.5):
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@torch.no_grad()
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def perp_step(model, x, sigmas, extra_args=None, callback=None, disable=None):
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"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
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extra_args = {} if extra_args is None else extra_args
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s_in = x.new_ones([x.shape[0]])
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previous_step = None
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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, sigma_hat, denoised)
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dt = sigmas[i + 1] - sigma_hat
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if previous_step is not None and sigmas[i + 1] != 0:
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d = diff_step(d, previous_step, s)
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previous_step = d.clone()
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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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x = x + d * dt
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return x
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return perp_step
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# as a reference
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@torch.no_grad()
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def simplified_euler(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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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, sigma_hat, 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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# Euler method
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x = x + d * dt
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return x
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class SamplerDistanceBase:
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_DISTANCE_OPTIONS = None # All options by default.
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_DISTANCE_PARAMS = {
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"resample": ("INT", {
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"default": 3, "min": -1, "max": 32, "step": 1,
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"tooltip": "0 all along gives Euler. 1 gives Heun.\nAnything starting from 2 will use the distance method.\n-1 will do remaining steps + 1 as the resample value. This can be pretty slow.",
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}),
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"resample_end": ("INT", {
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"default": -1, "min": -1, "max": 32, "step": 1,
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"tooltip": "How many resamples for the end. -1 means constant.",
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}),
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"cfgpp": ("BOOLEAN", {
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"default": True,
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"tooltip": "Controls whether to use CFG++ sampling. When enabled, you should set CFG to a fairly low value.",
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}),
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"eta": ("FLOAT", {
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"default": 0.0, "min": 0.0, "max": 32.0, "step": 0.01,
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"tooltip": "Controls the ancestralness of the main sampler steps. 0.0 means to use non-ancestral sampling. Note: May not work well with some of the other options.",
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}),
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"s_noise": ("FLOAT", {
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"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01,
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"tooltip": "Scale factor for ancestral noise added during sampling. Generally should be left at 1.0 and only has an effect when ancestral sampling is used.",
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}),
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"distance_step_eta": ("FLOAT", {
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"default": 0.0, "min": 0.0, "max": 32.0, "step": 0.01,
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"tooltip": "Experimental option that allows using ancestral sampling for the internal distance steps. When used, should generally be a fairly low value such as 0.25. 0.0 means to use non-ancestral sampling for the internal distance steps.",
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}),
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"distance_step_s_noise": ("FLOAT", {
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"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01,
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"tooltip": "Scale factor for ancestral noise added in the internal distance steps. Generally should be left at 1.0 and only has an effect when distance_step_eta is non-zero.",
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}),
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"use_softmax": ("BOOLEAN", {
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"default": False,
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"tooltip": "Rather than using a min/max normalization and an exponent will use a softmax instead.",
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}),
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"use_slerp": ("BOOLEAN", {
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"default": False,
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"tooltip": "Will SLERP the predictions instead of doing a weighted average. The difference is more obvious when using use_negative.",
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}),
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"perp_step": ("BOOLEAN", {
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"default": False,
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"tooltip": "Experimental, not yet recommended.",
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}),
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"use_negative": ("BOOLEAN", {
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"default": False,
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"tooltip": "Will use the negative prediction to prepare the distance scores. This tends to give images with less errors from my testing.",
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}),
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"smooth": ("BOOLEAN", {
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"default": False,
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"tooltip": "Not recommended, will make everything brighter. Not smoother.",
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}),
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"sharpen": ("BOOLEAN", {
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"default": False,
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"tooltip": "Not recommended, attempts to sharpen the results but instead tends to make things fuzzy.",
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}),
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"distance_first": ("INT", {
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"default": 0, "min": -10000, "max": 10000, "step": 1,
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"tooltip": "First step to use distance sampling. You can use negative values to count from the end. Note: Steps are zero-based.",
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}),
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"distance_last": ("INT", {
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"default": -1, "min": -10000, "max": 10000, "step": 1,
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"tooltip": "Last step to use distance sampling. You can use negative values to count from the end. Note: Steps are zero-based.",
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}),
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"eta_first": ("INT", {
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"default": 0, "min": -10000, "max": 10000, "step": 1,
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"tooltip": "First step to use ancestral sampling. Only applies when ETA is non-zero. You can use negative values to count from the end. Note: Steps are zero-based.",
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}),
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"eta_last": ("INT", {
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"default": -1, "min": -10000, "max": 10000, "step": 1,
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"tooltip": "Last step to use ancestral sampling. Only applies when ETA is non-zero. You can use negative values to count from the end. Note: Steps are zero-based.",
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}),
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"distance_eta_first": ("INT", {
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"default": 0, "min": -10000, "max": 10000, "step": 1,
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"tooltip": "First step to use ancestral sampling for the distance steps. Only applies when distance ETA is non-zero. You can use negative values to count from the end. Note: Steps are zero-based.",
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}),
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"distance_eta_last": ("INT", {
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"default": -1, "min": -10000, "max": 10000, "step": 1,
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"tooltip": "Last step to use ancestral sampling for the distance steps. Only applies when distance ETA is non-zero. You can use negative values to count from the end. Note: Steps are zero-based.",
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}),
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}
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@classmethod
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def INPUT_TYPES(s):
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if s._DISTANCE_OPTIONS is None:
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return {"required": s._DISTANCE_PARAMS.copy()}
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return {"required": {k: s._DISTANCE_PARAMS[k] for k in s._DISTANCE_OPTIONS}}
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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, **kwargs):
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sampler = comfy.samplers.KSAMPLER(distance_wrap(**kwargs))
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return (sampler, )
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class SamplerDistance(SamplerDistanceBase):
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_DISTANCE_OPTIONS = ("resample", "resample_end", "cfgpp")
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class SamplerDistanceAdvanced(SamplerDistanceBase):
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pass # Includes all options by default.
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