209 lines
9.1 KiB
Python
209 lines
9.1 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 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, C, D, E = dots.shape
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dots = dots[mask].reshape(A, B - 1, C, D, E)
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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, 1, 1, 1) * torch.sin(w.div(B - 1).unsqueeze(1).repeat(1, B - 1, 1, 1, 1) * 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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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
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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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# Euler and CFGpp part taken from comfy_extras/nodes_advanced_samplers
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def distance_wrap(resample,resample_end=-1,cfgpp=False,sharpen=False,use_softmax=False,first_only=False,use_slerp=False,perp_step=False,smooth=False,use_negative=False):
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@torch.no_grad()
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def sample_distance_advanced(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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uncond = None
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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(len(sigmas) - 1, disable=disable):
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sigma_hat = sigmas[i]
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res_mul = progression(sigma_hat)
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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_hat * 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, sigmas[i], denoised)
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else:
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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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if sigmas[i + 1] == 0 or resample_steps == 0 or (i > 0 and first_only):
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# Euler method
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x = x + d * dt
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else:
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# not Euler method
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x_n = [d]
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for re_step in range(resample_steps):
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x_new = x + d * dt
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new_denoised = model(x_new, sigmas[i + 1] * 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, sigmas[i + 1], new_denoised)
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else:
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new_d = to_d(x_new, sigmas[i + 1] * 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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else:
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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).squeeze(1), 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 = x + d * dt
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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 SamplerDistanceAdvanced:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"resample": ("INT", {"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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"resample_end": ("INT", {"default": -1, "min": -1, "max": 32, "step": 1, "tooltip":"How many resamples for the end. -1 means constant."}),
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"cfgpp" : ("BOOLEAN", {"default": True}),
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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,resample,resample_end,cfgpp):
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sampler = comfy.samplers.KSAMPLER(
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distance_wrap(resample=resample,cfgpp=cfgpp,resample_end=resample_end))
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return (sampler, )
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