Datasets:

WerddMana

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ComfyUI

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import torch class DifferentialDiffusion(): @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL", ), }} RETURN_TYPES = ("MODEL",) FUNCTION = "apply" CATEGORY = "_for_testing" INIT = False def apply(self, model): model = model.clone() model.set_model_denoise_mask_function(self.forward) return (model,) def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): model = extra_options["model"] step_sigmas = extra_options["sigmas"] sigma_to = model.inner_model.model_sampling.sigma_min if step_sigmas[-1] > sigma_to: sigma_to = step_sigmas[-1] sigma_from = step_sigmas[0] ts_from = model.inner_model.model_sampling.timestep(sigma_from) ts_to = model.inner_model.model_sampling.timestep(sigma_to) current_ts = model.inner_model.model_sampling.timestep(sigma[0]) threshold = (current_ts - ts_to) / (ts_from - ts_to) return (denoise_mask >= threshold).to(denoise_mask.dtype) NODE_CLASS_MAPPINGS = { "DifferentialDiffusion": DifferentialDiffusion, } NODE_DISPLAY_NAME_MAPPINGS = { "DifferentialDiffusion": "Differential Diffusion", }

import torch import logging def Fourier_filter(x, threshold, scale): x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) B, C, H, W = x_freq.shape mask = torch.ones((B, C, H, W), device=x.device) crow, ccol = H mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale x_freq = x_freq * mask x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real return x_filtered.to(x.dtype) class FreeU: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, b1, b2, s1, s2): model_channels = model.model.model_config.unet_config["model_channels"] scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} on_cpu_devices = {} def output_block_patch(h, hsp, transformer_options): scale = scale_dict.get(int(h.shape[1]), None) if scale is not None: h[:,:h.shape[1] if hsp.device not in on_cpu_devices: try: hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) except: logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) on_cpu_devices[hsp.device] = True hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) else: hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) return h, hsp m = model.clone() m.set_model_output_block_patch(output_block_patch) return (m, ) class FreeU_V2: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, b1, b2, s1, s2): model_channels = model.model.model_config.unet_config["model_channels"] scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} on_cpu_devices = {} def output_block_patch(h, hsp, transformer_options): scale = scale_dict.get(int(h.shape[1]), None) if scale is not None: hidden_mean = h.mean(1).unsqueeze(1) B = hidden_mean.shape[0] hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) h[:,:h.shape[1] if hsp.device not in on_cpu_devices: try: hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) except: logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) on_cpu_devices[hsp.device] = True hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) else: hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) return h, hsp m = model.clone() m.set_model_output_block_patch(output_block_patch) return (m, ) NODE_CLASS_MAPPINGS = { "FreeU": FreeU, "FreeU_V2": FreeU_V2, }

import numpy as np import torch def loglinear_interp(t_steps, num_steps): """ Performs log-linear interpolation of a given array of decreasing numbers. """ xs = np.linspace(0, 1, len(t_steps)) ys = np.log(t_steps[::-1]) new_xs = np.linspace(0, 1, num_steps) new_ys = np.interp(new_xs, xs, ys) interped_ys = np.exp(new_ys)[::-1].copy() return interped_ys NOISE_LEVELS = { 0.80: [ [14.61464119, 7.49001646, 0.02916753], [14.61464119, 11.54541874, 6.77309084, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 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6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753], ], 0.85: [ [14.61464119, 7.49001646, 0.02916753], [14.61464119, 7.49001646, 1.84880662, 0.02916753], [14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753], [14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 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0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], ], 1.50: [ [14.61464119, 0.54755926, 0.02916753], [14.61464119, 0.803307, 0.25053367, 0.02916753], [14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753], [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753], [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753], [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753], [14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753], [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], ], } class GITSScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}), "steps": ("INT", {"default": 10, "min": 2, "max": 1000}), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, coeff, steps, denoise): total_steps = steps if denoise < 1.0: if denoise <= 0.0: return (torch.FloatTensor([]),) total_steps = round(steps * denoise) if steps <= 20: sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:] else: sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:] sigmas = loglinear_interp(sigmas, steps + 1) sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 return (torch.FloatTensor(sigmas), ) NODE_CLASS_MAPPINGS = { "GITSScheduler": GITSScheduler, }

import comfy.utils import folder_paths import torch import logging def load_hypernetwork_patch(path, strength): sd = comfy.utils.load_torch_file(path, safe_load=True) activation_func = sd.get('activation_func', 'linear') is_layer_norm = sd.get('is_layer_norm', False) use_dropout = sd.get('use_dropout', False) activate_output = sd.get('activate_output', False) last_layer_dropout = sd.get('last_layer_dropout', False) valid_activation = { "linear": torch.nn.Identity, "relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU, "swish": torch.nn.Hardswish, "tanh": torch.nn.Tanh, "sigmoid": torch.nn.Sigmoid, "softsign": torch.nn.Softsign, "mish": torch.nn.Mish, } if activation_func not in valid_activation: logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {} {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)) return None out = {} for d in sd: try: dim = int(d) except: continue output = [] for index in [0, 1]: attn_weights = sd[dim][index] keys = attn_weights.keys() linears = filter(lambda a: a.endswith(".weight"), keys) linears = list(map(lambda a: a[:-len(".weight")], linears)) layers = [] i = 0 while i < len(linears): lin_name = linears[i] last_layer = (i == (len(linears) - 1)) penultimate_layer = (i == (len(linears) - 2)) lin_weight = attn_weights['{}.weight'.format(lin_name)] lin_bias = attn_weights['{}.bias'.format(lin_name)] layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0]) layer.load_state_dict({"weight": lin_weight, "bias": lin_bias}) layers.append(layer) if activation_func != "linear": if (not last_layer) or (activate_output): layers.append(valid_activation[activation_func]()) if is_layer_norm: i += 1 ln_name = linears[i] ln_weight = attn_weights['{}.weight'.format(ln_name)] ln_bias = attn_weights['{}.bias'.format(ln_name)] ln = torch.nn.LayerNorm(ln_weight.shape[0]) ln.load_state_dict({"weight": ln_weight, "bias": ln_bias}) layers.append(ln) if use_dropout: if (not last_layer) and (not penultimate_layer or last_layer_dropout): layers.append(torch.nn.Dropout(p=0.3)) i += 1 output.append(torch.nn.Sequential(*layers)) out[dim] = torch.nn.ModuleList(output) class hypernetwork_patch: def __init__(self, hypernet, strength): self.hypernet = hypernet self.strength = strength def __call__(self, q, k, v, extra_options): dim = k.shape[-1] if dim in self.hypernet: hn = self.hypernet[dim] k = k + hn[0](k) * self.strength v = v + hn[1](v) * self.strength return q, k, v def to(self, device): for d in self.hypernet.keys(): self.hypernet[d] = self.hypernet[d].to(device) return self return hypernetwork_patch(out, strength) class HypernetworkLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ), "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_hypernetwork" CATEGORY = "loaders" def load_hypernetwork(self, model, hypernetwork_name, strength): hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name) model_hypernetwork = model.clone() patch = load_hypernetwork_patch(hypernetwork_path, strength) if patch is not None: model_hypernetwork.set_model_attn1_patch(patch) model_hypernetwork.set_model_attn2_patch(patch) return (model_hypernetwork,) NODE_CLASS_MAPPINGS = { "HypernetworkLoader": HypernetworkLoader }

import math from einops import rearrange from torch import randint def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: min_value = min(min_value, value) divisors = [i for i in range(min_value, value + 1) if value % i == 0] ns = [value if len(ns) - 1 > 0: idx = randint(low=0, high=len(ns) - 1, size=(1,)).item() else: idx = 0 return ns[idx] class HyperTile: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), "scale_depth": ("BOOLEAN", {"default": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, tile_size, swap_size, max_depth, scale_depth): model_channels = model.model.model_config.unet_config["model_channels"] latent_tile_size = max(32, tile_size) self.temp = None def hypertile_in(q, k, v, extra_options): model_chans = q.shape[-2] orig_shape = extra_options['original_shape'] apply_to = [] for i in range(max_depth + 1): apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i))) if model_chans in apply_to: shape = extra_options["original_shape"] aspect_ratio = shape[-1] / shape[-2] hw = q.size(1) h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1 nh = random_divisor(h, latent_tile_size * factor, swap_size) nw = random_divisor(w, latent_tile_size * factor, swap_size) if nh * nw > 1: q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h self.temp = (nh, nw, h, w) return q, k, v return q, k, v def hypertile_out(out, extra_options): if self.temp is not None: nh, nw, h, w = self.temp self.temp = None out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h return out m = model.clone() m.set_model_attn1_patch(hypertile_in) m.set_model_attn1_output_patch(hypertile_out) return (m, ) NODE_CLASS_MAPPINGS = { "HyperTile": HyperTile, }

import nodes import folder_paths from comfy.cli_args import args from PIL import Image from PIL.PngImagePlugin import PngInfo import numpy as np import json import os MAX_RESOLUTION = nodes.MAX_RESOLUTION class ImageCrop: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "crop" CATEGORY = "image/transform" def crop(self, image, width, height, x, y): x = min(x, image.shape[2] - 1) y = min(y, image.shape[1] - 1) to_x = width + x to_y = height + y img = image[:,y:to_y, x:to_x, :] return (img,) class RepeatImageBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "amount": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "repeat" CATEGORY = "image/batch" def repeat(self, image, amount): s = image.repeat((amount, 1,1,1)) return (s,) class ImageFromBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}), "length": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "frombatch" CATEGORY = "image/batch" def frombatch(self, image, batch_index, length): s_in = image batch_index = min(s_in.shape[0] - 1, batch_index) length = min(s_in.shape[0] - batch_index, length) s = s_in[batch_index:batch_index + length].clone() return (s,) class SaveAnimatedWEBP: def __init__(self): self.output_dir = folder_paths.get_output_directory() self.type = "output" self.prefix_append = "" methods = {"default": 4, "fastest": 0, "slowest": 6} @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "ComfyUI"}), "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), "lossless": ("BOOLEAN", {"default": True}), "quality": ("INT", {"default": 80, "min": 0, "max": 100}), "method": (list(s.methods.keys()),), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "image/animation" def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None): method = self.methods.get(method) filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() pil_images = [] for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pil_images.append(img) metadata = pil_images[0].getexif() if not args.disable_metadata: if prompt is not None: metadata[0x0110] = "prompt:{}".format(json.dumps(prompt)) if extra_pnginfo is not None: inital_exif = 0x010f for x in extra_pnginfo: metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x])) inital_exif -= 1 if num_frames == 0: num_frames = len(pil_images) c = len(pil_images) for i in range(0, c, num_frames): file = f"{filename}_{counter:05}_.webp" pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method) results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) counter += 1 animated = num_frames != 1 return { "ui": { "images": results, "animated": (animated,) } } class SaveAnimatedPNG: def __init__(self): self.output_dir = folder_paths.get_output_directory() self.type = "output" self.prefix_append = "" @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "ComfyUI"}), "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), "compress_level": ("INT", {"default": 4, "min": 0, "max": 9}) }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "image/animation" def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() pil_images = [] for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pil_images.append(img) metadata = None if not args.disable_metadata: metadata = PngInfo() if prompt is not None: metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True) if extra_pnginfo is not None: for x in extra_pnginfo: metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True) file = f"{filename}_{counter:05}_.png" pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:]) results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) return { "ui": { "images": results, "animated": (True,)} } NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, "ImageFromBatch": ImageFromBatch, "SaveAnimatedWEBP": SaveAnimatedWEBP, "SaveAnimatedPNG": SaveAnimatedPNG, }

import torch class InstructPixToPixConditioning: @classmethod def INPUT_TYPES(s): return {"required": {"positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "vae": ("VAE", ), "pixels": ("IMAGE", ), }} RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/instructpix2pix" def encode(self, positive, negative, pixels, vae): x = (pixels.shape[1] y = (pixels.shape[2] if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % 8) y_offset = (pixels.shape[2] % 8) pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] concat_latent = vae.encode(pixels) out_latent = {} out_latent["samples"] = torch.zeros_like(concat_latent) out = [] for conditioning in [positive, negative]: c = [] for t in conditioning: d = t[1].copy() d["concat_latent_image"] = concat_latent n = [t[0], d] c.append(n) out.append(c) return (out[0], out[1], out_latent) NODE_CLASS_MAPPINGS = { "InstructPixToPixConditioning": InstructPixToPixConditioning, }

import comfy.utils import torch def reshape_latent_to(target_shape, latent): if latent.shape[1:] != target_shape[1:]: latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") return comfy.utils.repeat_to_batch_size(latent, target_shape[0]) class LatentAdd: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) samples_out["samples"] = s1 + s2 return (samples_out,) class LatentSubtract: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) samples_out["samples"] = s1 - s2 return (samples_out,) class LatentMultiply: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples, multiplier): samples_out = samples.copy() s1 = samples["samples"] samples_out["samples"] = s1 * multiplier return (samples_out,) class LatentInterpolate: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2, ratio): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) m1 = torch.linalg.vector_norm(s1, dim=(1)) m2 = torch.linalg.vector_norm(s2, dim=(1)) s1 = torch.nan_to_num(s1 / m1) s2 = torch.nan_to_num(s2 / m2) t = (s1 * ratio + s2 * (1.0 - ratio)) mt = torch.linalg.vector_norm(t, dim=(1)) st = torch.nan_to_num(t / mt) samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio)) return (samples_out,) class LatentBatch: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "batch" CATEGORY = "latent/batch" def batch(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] if s1.shape[1:] != s2.shape[1:]: s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center") s = torch.cat((s1, s2), dim=0) samples_out["samples"] = s samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])]) return (samples_out,) class LatentBatchSeedBehavior: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "seed_behavior": (["random", "fixed"],{"default": "fixed"}),}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples, seed_behavior): samples_out = samples.copy() latent = samples["samples"] if seed_behavior == "random": if 'batch_index' in samples_out: samples_out.pop('batch_index') elif seed_behavior == "fixed": batch_number = samples_out.get("batch_index", [0])[0] samples_out["batch_index"] = [batch_number] * latent.shape[0] return (samples_out,) NODE_CLASS_MAPPINGS = { "LatentAdd": LatentAdd, "LatentSubtract": LatentSubtract, "LatentMultiply": LatentMultiply, "LatentInterpolate": LatentInterpolate, "LatentBatch": LatentBatch, "LatentBatchSeedBehavior": LatentBatchSeedBehavior, }

import numpy as np import scipy.ndimage import torch import comfy.utils from nodes import MAX_RESOLUTION def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): source = source.to(destination.device) if resize_source: source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") source = comfy.utils.repeat_to_batch_size(source, destination.shape[0]) x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) left, top = (x right, bottom = (left + source.shape[3], top + source.shape[2],) if mask is None: mask = torch.ones_like(source) else: mask = mask.to(destination.device, copy=True) mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0]) visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) mask = mask[:, :, :visible_height, :visible_width] inverse_mask = torch.ones_like(mask) - mask source_portion = mask * source[:, :, :visible_height, :visible_width] destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] destination[:, :, top:bottom, left:right] = source_portion + destination_portion return destination class LatentCompositeMasked: @classmethod def INPUT_TYPES(s): return { "required": { "destination": ("LATENT",), "source": ("LATENT",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "resize_source": ("BOOLEAN", {"default": False}), }, "optional": { "mask": ("MASK",), } } RETURN_TYPES = ("LATENT",) FUNCTION = "composite" CATEGORY = "latent" def composite(self, destination, source, x, y, resize_source, mask = None): output = destination.copy() destination = destination["samples"].clone() source = source["samples"] output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) return (output,) class ImageCompositeMasked: @classmethod def INPUT_TYPES(s): return { "required": { "destination": ("IMAGE",), "source": ("IMAGE",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "resize_source": ("BOOLEAN", {"default": False}), }, "optional": { "mask": ("MASK",), } } RETURN_TYPES = ("IMAGE",) FUNCTION = "composite" CATEGORY = "image" def composite(self, destination, source, x, y, resize_source, mask = None): destination = destination.clone().movedim(-1, 1) output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) return (output,) class MaskToImage: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), } } CATEGORY = "mask" RETURN_TYPES = ("IMAGE",) FUNCTION = "mask_to_image" def mask_to_image(self, mask): result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) return (result,) class ImageToMask: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "channel": (["red", "green", "blue", "alpha"],), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "image_to_mask" def image_to_mask(self, image, channel): channels = ["red", "green", "blue", "alpha"] mask = image[:, :, :, channels.index(channel)] return (mask,) class ImageColorToMask: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "image_to_mask" def image_to_mask(self, image, color): temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] mask = torch.where(temp == color, 255, 0).float() return (mask,) class SolidMask: @classmethod def INPUT_TYPES(cls): return { "required": { "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "solid" def solid(self, value, width, height): out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") return (out,) class InvertMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "invert" def invert(self, mask): out = 1.0 - mask return (out,) class CropMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "crop" def crop(self, mask, x, y, width, height): mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) out = mask[:, y:y + height, x:x + width] return (out,) class MaskComposite: @classmethod def INPUT_TYPES(cls): return { "required": { "destination": ("MASK",), "source": ("MASK",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "operation": (["multiply", "add", "subtract", "and", "or", "xor"],), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "combine" def combine(self, destination, source, x, y, operation): output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() source = source.reshape((-1, source.shape[-2], source.shape[-1])) left, top = (x, y,) right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) visible_width, visible_height = (right - left, bottom - top,) source_portion = source[:, :visible_height, :visible_width] destination_portion = destination[:, top:bottom, left:right] if operation == "multiply": output[:, top:bottom, left:right] = destination_portion * source_portion elif operation == "add": output[:, top:bottom, left:right] = destination_portion + source_portion elif operation == "subtract": output[:, top:bottom, left:right] = destination_portion - source_portion elif operation == "and": output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() elif operation == "or": output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() elif operation == "xor": output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() output = torch.clamp(output, 0.0, 1.0) return (output,) class FeatherMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "feather" def feather(self, mask, left, top, right, bottom): output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() left = min(left, output.shape[-1]) right = min(right, output.shape[-1]) top = min(top, output.shape[-2]) bottom = min(bottom, output.shape[-2]) for x in range(left): feather_rate = (x + 1.0) / left output[:, :, x] *= feather_rate for x in range(right): feather_rate = (x + 1) / right output[:, :, -x] *= feather_rate for y in range(top): feather_rate = (y + 1) / top output[:, y, :] *= feather_rate for y in range(bottom): feather_rate = (y + 1) / bottom output[:, -y, :] *= feather_rate return (output,) class GrowMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), "tapered_corners": ("BOOLEAN", {"default": True}), }, } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "expand_mask" def expand_mask(self, mask, expand, tapered_corners): c = 0 if tapered_corners else 1 kernel = np.array([[c, 1, c], [1, 1, 1], [c, 1, c]]) mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) out = [] for m in mask: output = m.numpy() for _ in range(abs(expand)): if expand < 0: output = scipy.ndimage.grey_erosion(output, footprint=kernel) else: output = scipy.ndimage.grey_dilation(output, footprint=kernel) output = torch.from_numpy(output) out.append(output) return (torch.stack(out, dim=0),) class ThresholdMask: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "image_to_mask" def image_to_mask(self, mask, value): mask = (mask > value).float() return (mask,) NODE_CLASS_MAPPINGS = { "LatentCompositeMasked": LatentCompositeMasked, "ImageCompositeMasked": ImageCompositeMasked, "MaskToImage": MaskToImage, "ImageToMask": ImageToMask, "ImageColorToMask": ImageColorToMask, "SolidMask": SolidMask, "InvertMask": InvertMask, "CropMask": CropMask, "MaskComposite": MaskComposite, "FeatherMask": FeatherMask, "GrowMask": GrowMask, "ThresholdMask": ThresholdMask, } NODE_DISPLAY_NAME_MAPPINGS = { "ImageToMask": "Convert Image to Mask", "MaskToImage": "Convert Mask to Image", }

import folder_paths import comfy.sd import comfy.model_sampling import comfy.latent_formats import torch class LCM(comfy.model_sampling.EPS): def calculate_denoised(self, sigma, model_output, model_input): timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) x0 = model_input - model_output * sigma sigma_data = 0.5 scaled_timestep = timestep * 10.0 c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 return c_out * x0 + c_skip * model_input class X0(comfy.model_sampling.EPS): def calculate_denoised(self, sigma, model_output, model_input): return model_output class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete): original_timesteps = 50 def __init__(self, model_config=None): super().__init__(model_config) self.skip_steps = self.num_timesteps sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32) for x in range(self.original_timesteps): sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps] self.set_sigmas(sigmas_valid) def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device) def sigma(self, timestep): t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] return log_sigma.exp().to(timestep.device) def rescale_zero_terminal_snr_sigmas(sigmas): alphas_cumprod = 1 / ((sigmas * sigmas) + 1) alphas_bar_sqrt = alphas_cumprod.sqrt() alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() alphas_bar_sqrt -= (alphas_bar_sqrt_T) alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) alphas_bar = alphas_bar_sqrt**2 alphas_bar[-1] = 4.8973451890853435e-08 return ((1 - alphas_bar) / alphas_bar) ** 0.5 class ModelSamplingDiscrete: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["eps", "v_prediction", "lcm", "x0"],), "zsnr": ("BOOLEAN", {"default": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, zsnr): m = model.clone() sampling_base = comfy.model_sampling.ModelSamplingDiscrete if sampling == "eps": sampling_type = comfy.model_sampling.EPS elif sampling == "v_prediction": sampling_type = comfy.model_sampling.V_PREDICTION elif sampling == "lcm": sampling_type = LCM sampling_base = ModelSamplingDiscreteDistilled elif sampling == "x0": sampling_type = X0 class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) if zsnr: model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas)) m.add_object_patch("model_sampling", model_sampling) return (m, ) class ModelSamplingStableCascade: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, shift): m = model.clone() sampling_base = comfy.model_sampling.StableCascadeSampling sampling_type = comfy.model_sampling.EPS class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(shift) m.add_object_patch("model_sampling", model_sampling) return (m, ) class ModelSamplingSD3: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, shift): m = model.clone() sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow sampling_type = comfy.model_sampling.CONST class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(shift=shift) m.add_object_patch("model_sampling", model_sampling) return (m, ) class ModelSamplingContinuousEDM: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],), "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() latent_format = None sigma_data = 1.0 if sampling == "eps": sampling_type = comfy.model_sampling.EPS elif sampling == "v_prediction": sampling_type = comfy.model_sampling.V_PREDICTION elif sampling == "edm_playground_v2.5": sampling_type = comfy.model_sampling.EDM sigma_data = 0.5 latent_format = comfy.latent_formats.SDXL_Playground_2_5() class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) m.add_object_patch("model_sampling", model_sampling) if latent_format is not None: m.add_object_patch("latent_format", latent_format) return (m, ) class ModelSamplingContinuousV: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["v_prediction"],), "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() latent_format = None sigma_data = 1.0 if sampling == "v_prediction": sampling_type = comfy.model_sampling.V_PREDICTION class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) m.add_object_patch("model_sampling", model_sampling) return (m, ) class RescaleCFG: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, multiplier): def rescale_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] sigma = args["sigma"] sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1)) x_orig = args["input"] x = x_orig / (sigma * sigma + 1.0) cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) x_cfg = uncond + cond_scale * (cond - uncond) ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) x_rescaled = x_cfg * (ro_pos / ro_cfg) x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) m = model.clone() m.set_model_sampler_cfg_function(rescale_cfg) return (m, ) NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, "ModelSamplingContinuousV": ModelSamplingContinuousV, "ModelSamplingStableCascade": ModelSamplingStableCascade, "ModelSamplingSD3": ModelSamplingSD3, "RescaleCFG": RescaleCFG, }

import torch import comfy.utils class PatchModelAddDownscale: upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), "downscale_after_skip": ("BOOLEAN", {"default": True}), "downscale_method": (s.upscale_methods,), "upscale_method": (s.upscale_methods,), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): model_sampling = model.get_model_object("model_sampling") sigma_start = model_sampling.percent_to_sigma(start_percent) sigma_end = model_sampling.percent_to_sigma(end_percent) def input_block_patch(h, transformer_options): if transformer_options["block"][1] == block_number: sigma = transformer_options["sigmas"][0].item() if sigma <= sigma_start and sigma >= sigma_end: h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") return h def output_block_patch(h, hsp, transformer_options): if h.shape[2] != hsp.shape[2]: h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") return h, hsp m = model.clone() if downscale_after_skip: m.set_model_input_block_patch_after_skip(input_block_patch) else: m.set_model_input_block_patch(input_block_patch) m.set_model_output_block_patch(output_block_patch) return (m, ) NODE_CLASS_MAPPINGS = { "PatchModelAddDownscale": PatchModelAddDownscale, } NODE_DISPLAY_NAME_MAPPINGS = { "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)", }

import comfy.sd import comfy.utils import comfy.model_base import comfy.model_management import comfy.model_sampling import torch import folder_paths import json import os from comfy.cli_args import args class ModelMergeSimple: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, ratio): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) class ModelSubtract: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, multiplier): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, - multiplier, multiplier) return (m, ) class ModelAdd: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, 1.0, 1.0) return (m, ) class CLIPMergeSimple: @classmethod def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",), "clip2": ("CLIP",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, clip1, clip2, ratio): m = clip1.clone() kp = clip2.get_key_patches() for k in kp: if k.endswith(".position_ids") or k.endswith(".logit_scale"): continue m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) class CLIPSubtract: @classmethod def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",), "clip2": ("CLIP",), "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, clip1, clip2, multiplier): m = clip1.clone() kp = clip2.get_key_patches() for k in kp: if k.endswith(".position_ids") or k.endswith(".logit_scale"): continue m.add_patches({k: kp[k]}, - multiplier, multiplier) return (m, ) class CLIPAdd: @classmethod def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",), "clip2": ("CLIP",), }} RETURN_TYPES = ("CLIP",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, clip1, clip2): m = clip1.clone() kp = clip2.get_key_patches() for k in kp: if k.endswith(".position_ids") or k.endswith(".logit_scale"): continue m.add_patches({k: kp[k]}, 1.0, 1.0) return (m, ) class ModelMergeBlocks: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, **kwargs): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") default_ratio = next(iter(kwargs.values())) for k in kp: ratio = default_ratio k_unet = k[len("diffusion_model."):] last_arg_size = 0 for arg in kwargs: if k_unet.startswith(arg) and last_arg_size < len(arg): ratio = kwargs[arg] last_arg_size = len(arg) m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} enable_modelspec = True if isinstance(model.model, comfy.model_base.SDXL): if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit" else: metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" elif isinstance(model.model, comfy.model_base.SDXLRefiner): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" elif isinstance(model.model, comfy.model_base.SVD_img2vid): metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1" elif isinstance(model.model, comfy.model_base.SD3): metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" else: enable_modelspec = False if enable_modelspec: metadata["modelspec.sai_model_spec"] = "1.0.0" metadata["modelspec.implementation"] = "sgm" metadata["modelspec.title"] = "{} {}".format(filename, counter) extra_keys = {} model_sampling = model.get_model_object("model_sampling") if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM): if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION): extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float() extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float() if model.model.model_type == comfy.model_base.ModelType.EPS: metadata["modelspec.predict_key"] = "epsilon" elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: metadata["modelspec.predict_key"] = "v" if not args.disable_metadata: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys) class CheckpointSave: def __init__(self): self.output_dir = folder_paths.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip": ("CLIP",), "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) return {} class CLIPSave: def __init__(self): self.output_dir = folder_paths.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP",), "filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} if not args.disable_metadata: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True) clip_sd = clip.get_sd() for prefix in ["clip_l.", "clip_g.", ""]: k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) current_clip_sd = {} for x in k: current_clip_sd[x] = clip_sd.pop(x) if len(current_clip_sd) == 0: continue p = prefix[:-1] replace_prefix = {} filename_prefix_ = filename_prefix if len(p) > 0: filename_prefix_ = "{}_{}".format(filename_prefix_, p) replace_prefix[prefix] = "" replace_prefix["transformer."] = "" full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) return {} class VAESave: def __init__(self): self.output_dir = folder_paths.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} if not args.disable_metadata: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) return {} NODE_CLASS_MAPPINGS = { "ModelMergeSimple": ModelMergeSimple, "ModelMergeBlocks": ModelMergeBlocks, "ModelMergeSubtract": ModelSubtract, "ModelMergeAdd": ModelAdd, "CheckpointSave": CheckpointSave, "CLIPMergeSimple": CLIPMergeSimple, "CLIPMergeSubtract": CLIPSubtract, "CLIPMergeAdd": CLIPAdd, "CLIPSave": CLIPSave, "VAESave": VAESave, }

import comfy_extras.nodes_model_merging class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): CATEGORY = "advanced/model_merging/model_specific" @classmethod def INPUT_TYPES(s): arg_dict = { "model1": ("MODEL",), "model2": ("MODEL",)} argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) arg_dict["time_embed."] = argument arg_dict["label_emb."] = argument for i in range(12): arg_dict["input_blocks.{}.".format(i)] = argument for i in range(3): arg_dict["middle_block.{}.".format(i)] = argument for i in range(12): arg_dict["output_blocks.{}.".format(i)] = argument arg_dict["out."] = argument return {"required": arg_dict} class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): CATEGORY = "advanced/model_merging/model_specific" @classmethod def INPUT_TYPES(s): arg_dict = { "model1": ("MODEL",), "model2": ("MODEL",)} argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) arg_dict["time_embed."] = argument arg_dict["label_emb."] = argument for i in range(9): arg_dict["input_blocks.{}".format(i)] = argument for i in range(3): arg_dict["middle_block.{}".format(i)] = argument for i in range(9): arg_dict["output_blocks.{}".format(i)] = argument arg_dict["out."] = argument return {"required": arg_dict} class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): CATEGORY = "advanced/model_merging/model_specific" @classmethod def INPUT_TYPES(s): arg_dict = { "model1": ("MODEL",), "model2": ("MODEL",)} argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) arg_dict["pos_embed."] = argument arg_dict["x_embedder."] = argument arg_dict["context_embedder."] = argument arg_dict["y_embedder."] = argument arg_dict["t_embedder."] = argument for i in range(24): arg_dict["joint_blocks.{}.".format(i)] = argument arg_dict["final_layer."] = argument return {"required": arg_dict} NODE_CLASS_MAPPINGS = { "ModelMergeSD1": ModelMergeSD1, "ModelMergeSD2": ModelMergeSD1, "ModelMergeSDXL": ModelMergeSDXL, "ModelMergeSD3_2B": ModelMergeSD3_2B, }

import torch import comfy.model_management from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat class Morphology: @classmethod def INPUT_TYPES(s): return {"required": {"image": ("IMAGE",), "operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],), "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "process" CATEGORY = "image/postprocessing" def process(self, image, operation, kernel_size): device = comfy.model_management.get_torch_device() kernel = torch.ones(kernel_size, kernel_size, device=device) image_k = image.to(device).movedim(-1, 1) if operation == "erode": output = erosion(image_k, kernel) elif operation == "dilate": output = dilation(image_k, kernel) elif operation == "open": output = opening(image_k, kernel) elif operation == "close": output = closing(image_k, kernel) elif operation == "gradient": output = gradient(image_k, kernel) elif operation == "top_hat": output = top_hat(image_k, kernel) elif operation == "bottom_hat": output = bottom_hat(image_k, kernel) else: raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'") img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1) return (img_out,) NODE_CLASS_MAPPINGS = { "Morphology": Morphology, } NODE_DISPLAY_NAME_MAPPINGS = { "Morphology": "ImageMorphology", }

import comfy.model_patcher import comfy.samplers class PerturbedAttentionGuidance: @classmethod def INPUT_TYPES(s): return { "required": { "model": ("MODEL",), "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}), } } RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, scale): unet_block = "middle" unet_block_id = 0 m = model.clone() def perturbed_attention(q, k, v, extra_options, mask=None): return v def post_cfg_function(args): model = args["model"] cond_pred = args["cond_denoised"] cond = args["cond"] cfg_result = args["denoised"] sigma = args["sigma"] model_options = args["model_options"].copy() x = args["input"] if scale == 0: return cfg_result model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, perturbed_attention, "attn1", unet_block, unet_block_id) (pag,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) return cfg_result + (cond_pred - pag) * scale m.set_model_sampler_post_cfg_function(post_cfg_function) return (m,) NODE_CLASS_MAPPINGS = { "PerturbedAttentionGuidance": PerturbedAttentionGuidance, }

import torch import comfy.model_management import comfy.sampler_helpers import comfy.samplers import comfy.utils import node_helpers def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale): pos = noise_pred_pos - noise_pred_nocond neg = noise_pred_neg - noise_pred_nocond perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos perp_neg = perp * neg_scale cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) return cfg_result class PerpNeg: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL", ), "empty_conditioning": ("CONDITIONING", ), "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, empty_conditioning, neg_scale): m = model.clone() nocond = comfy.sampler_helpers.convert_cond(empty_conditioning) def cfg_function(args): model = args["model"] noise_pred_pos = args["cond_denoised"] noise_pred_neg = args["uncond_denoised"] cond_scale = args["cond_scale"] x = args["input"] sigma = args["sigma"] model_options = args["model_options"] nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options) cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale) return cfg_result m.set_model_sampler_cfg_function(cfg_function) return (m, ) class Guider_PerpNeg(comfy.samplers.CFGGuider): def set_conds(self, positive, negative, empty_negative_prompt): empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"}) self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative}) def set_cfg(self, cfg, neg_scale): self.cfg = cfg self.neg_scale = neg_scale def predict_noise(self, x, timestep, model_options={}, seed=None): positive_cond = self.conds.get("positive", None) negative_cond = self.conds.get("negative", None) empty_cond = self.conds.get("empty_negative_prompt", None) (noise_pred_pos, noise_pred_neg, noise_pred_empty) = \ comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options) cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg) for fn in model_options.get("sampler_post_cfg_function", []): args = { "denoised": cfg_result, "cond": positive_cond, "uncond": negative_cond, "model": self.inner_model, "uncond_denoised": noise_pred_neg, "cond_denoised": noise_pred_pos, "sigma": timestep, "model_options": model_options, "input": x, "empty_cond": empty_cond, "empty_cond_denoised": noise_pred_empty,} cfg_result = fn(args) return cfg_result class PerpNegGuider: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "empty_conditioning": ("CONDITIONING", ), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), } } RETURN_TYPES = ("GUIDER",) FUNCTION = "get_guider" CATEGORY = "_for_testing" def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale): guider = Guider_PerpNeg(model) guider.set_conds(positive, negative, empty_conditioning) guider.set_cfg(cfg, neg_scale) return (guider,) NODE_CLASS_MAPPINGS = { "PerpNeg": PerpNeg, "PerpNegGuider": PerpNegGuider, } NODE_DISPLAY_NAME_MAPPINGS = { "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)", }

import torch import torch.nn as nn import folder_paths import comfy.clip_model import comfy.clip_vision import comfy.ops VISION_CONFIG_DICT = { "hidden_size": 1024, "image_size": 224, "intermediate_size": 4096, "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "hidden_act": "quick_gelu", } class MLP(nn.Module): def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops): super().__init__() if use_residual: assert in_dim == out_dim self.layernorm = operations.LayerNorm(in_dim) self.fc1 = operations.Linear(in_dim, hidden_dim) self.fc2 = operations.Linear(hidden_dim, out_dim) self.use_residual = use_residual self.act_fn = nn.GELU() def forward(self, x): residual = x x = self.layernorm(x) x = self.fc1(x) x = self.act_fn(x) x = self.fc2(x) if self.use_residual: x = x + residual return x class FuseModule(nn.Module): def __init__(self, embed_dim, operations): super().__init__() self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations) self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations) self.layer_norm = operations.LayerNorm(embed_dim) def fuse_fn(self, prompt_embeds, id_embeds): stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds stacked_id_embeds = self.mlp2(stacked_id_embeds) stacked_id_embeds = self.layer_norm(stacked_id_embeds) return stacked_id_embeds def forward( self, prompt_embeds, id_embeds, class_tokens_mask, ) -> torch.Tensor: id_embeds = id_embeds.to(prompt_embeds.dtype) num_inputs = class_tokens_mask.sum().unsqueeze(0) batch_size, max_num_inputs = id_embeds.shape[:2] seq_length = prompt_embeds.shape[1] flat_id_embeds = id_embeds.view( -1, id_embeds.shape[-2], id_embeds.shape[-1] ) valid_id_mask = ( torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] < num_inputs[:, None] ) valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) class_tokens_mask = class_tokens_mask.view(-1) valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) image_token_embeds = prompt_embeds[class_tokens_mask] stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) return updated_prompt_embeds class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection): def __init__(self): self.load_device = comfy.model_management.text_encoder_device() offload_device = comfy.model_management.text_encoder_offload_device() dtype = comfy.model_management.text_encoder_dtype(self.load_device) super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast) self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False) self.fuse_module = FuseModule(2048, comfy.ops.manual_cast) def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): b, num_inputs, c, h, w = id_pixel_values.shape id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) shared_id_embeds = self.vision_model(id_pixel_values)[2] id_embeds = self.visual_projection(shared_id_embeds) id_embeds_2 = self.visual_projection_2(shared_id_embeds) id_embeds = id_embeds.view(b, num_inputs, 1, -1) id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) return updated_prompt_embeds class PhotoMakerLoader: @classmethod def INPUT_TYPES(s): return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}} RETURN_TYPES = ("PHOTOMAKER",) FUNCTION = "load_photomaker_model" CATEGORY = "_for_testing/photomaker" def load_photomaker_model(self, photomaker_model_name): photomaker_model_path = folder_paths.get_full_path("photomaker", photomaker_model_name) photomaker_model = PhotoMakerIDEncoder() data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True) if "id_encoder" in data: data = data["id_encoder"] photomaker_model.load_state_dict(data) return (photomaker_model,) class PhotoMakerEncode: @classmethod def INPUT_TYPES(s): return {"required": { "photomaker": ("PHOTOMAKER",), "image": ("IMAGE",), "clip": ("CLIP", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_photomaker" CATEGORY = "_for_testing/photomaker" def apply_photomaker(self, photomaker, image, clip, text): special_token = "photomaker" pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float() try: index = text.split(" ").index(special_token) + 1 except ValueError: index = -1 tokens = clip.tokenize(text, return_word_ids=True) out_tokens = {} for k in tokens: out_tokens[k] = [] for t in tokens[k]: f = list(filter(lambda x: x[2] != index, t)) while len(f) < len(t): f.append(t[-1]) out_tokens[k].append(f) cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True) if index > 0: token_index = index - 1 num_id_images = 1 class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)] out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device), class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0)) else: out = cond return ([[out, {"pooled_output": pooled}]], ) NODE_CLASS_MAPPINGS = { "PhotoMakerLoader": PhotoMakerLoader, "PhotoMakerEncode": PhotoMakerEncode, }

import numpy as np import torch import torch.nn.functional as F from PIL import Image import math import comfy.utils import comfy.model_management class Blend: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image1": ("IMAGE",), "image2": ("IMAGE",), "blend_factor": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01 }), "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "blend_images" CATEGORY = "image/postprocessing" def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): image2 = image2.to(image1.device) if image1.shape != image2.shape: image2 = image2.permute(0, 3, 1, 2) image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') image2 = image2.permute(0, 2, 3, 1) blended_image = self.blend_mode(image1, image2, blend_mode) blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor blended_image = torch.clamp(blended_image, 0, 1) return (blended_image,) def blend_mode(self, img1, img2, mode): if mode == "normal": return img2 elif mode == "multiply": return img1 * img2 elif mode == "screen": return 1 - (1 - img1) * (1 - img2) elif mode == "overlay": return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) elif mode == "soft_light": return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) elif mode == "difference": return img1 - img2 else: raise ValueError(f"Unsupported blend mode: {mode}") def g(self, x): return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) def gaussian_kernel(kernel_size: int, sigma: float, device=None): x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") d = torch.sqrt(x * x + y * y) g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) return g / g.sum() class Blur: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "blur_radius": ("INT", { "default": 1, "min": 1, "max": 31, "step": 1 }), "sigma": ("FLOAT", { "default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1 }), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "blur" CATEGORY = "image/postprocessing" def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): if blur_radius == 0: return (image,) image = image.to(comfy.model_management.get_torch_device()) batch_size, height, width, channels = image.shape kernel_size = blur_radius * 2 + 1 kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) image = image.permute(0, 3, 1, 2) padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') blurred = F.conv2d(padded_image, kernel, padding=kernel_size blurred = blurred.permute(0, 2, 3, 1) return (blurred.to(comfy.model_management.intermediate_device()),) class Quantize: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "colors": ("INT", { "default": 256, "min": 1, "max": 256, "step": 1 }), "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "quantize" CATEGORY = "image/postprocessing" def bayer(im, pal_im, order): def normalized_bayer_matrix(n): if n == 0: return np.zeros((1,1), "float32") else: q = 4 ** n m = q * normalized_bayer_matrix(n - 1) return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q num_colors = len(pal_im.getpalette()) spread = 2 * 256 / num_colors bayer_n = int(math.log2(order)) bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5) result = torch.from_numpy(np.array(im).astype(np.float32)) tw = math.ceil(result.shape[0] / bayer_matrix.shape[0]) th = math.ceil(result.shape[1] / bayer_matrix.shape[1]) tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1) result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255) result = result.to(dtype=torch.uint8) im = Image.fromarray(result.cpu().numpy()) im = im.quantize(palette=pal_im, dither=Image.Dither.NONE) return im def quantize(self, image: torch.Tensor, colors: int, dither: str): batch_size, height, width, _ = image.shape result = torch.zeros_like(image) for b in range(batch_size): im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB') pal_im = im.quantize(colors=colors) if dither == "none": quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE) elif dither == "floyd-steinberg": quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG) elif dither.startswith("bayer"): order = int(dither.split('-')[-1]) quantized_image = Quantize.bayer(im, pal_im, order) quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 result[b] = quantized_array return (result,) class Sharpen: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "sharpen_radius": ("INT", { "default": 1, "min": 1, "max": 31, "step": 1 }), "sigma": ("FLOAT", { "default": 1.0, "min": 0.1, "max": 10.0, "step": 0.01 }), "alpha": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "sharpen" CATEGORY = "image/postprocessing" def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): if sharpen_radius == 0: return (image,) batch_size, height, width, channels = image.shape image = image.to(comfy.model_management.get_torch_device()) kernel_size = sharpen_radius * 2 + 1 kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) center = kernel_size kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) tensor_image = image.permute(0, 3, 1, 2) tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] sharpened = sharpened.permute(0, 2, 3, 1) result = torch.clamp(sharpened, 0, 1) return (result.to(comfy.model_management.intermediate_device()),) class ImageScaleToTotalPixels: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] crop_methods = ["disabled", "center"] @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, image, upscale_method, megapixels): samples = image.movedim(-1,1) total = int(megapixels * 1024 * 1024) scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = s.movedim(1,-1) return (s,) NODE_CLASS_MAPPINGS = { "ImageBlend": Blend, "ImageBlur": Blur, "ImageQuantize": Quantize, "ImageSharpen": Sharpen, "ImageScaleToTotalPixels": ImageScaleToTotalPixels, }

import torch class LatentRebatch: @classmethod def INPUT_TYPES(s): return {"required": { "latents": ("LATENT",), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("LATENT",) INPUT_IS_LIST = True OUTPUT_IS_LIST = (True, ) FUNCTION = "rebatch" CATEGORY = "latent/batch" @staticmethod def get_batch(latents, list_ind, offset): '''prepare a batch out of the list of latents''' samples = latents[list_ind]['samples'] shape = samples.shape mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu') if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]: torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear") if mask.shape[0] < samples.shape[0]: mask = mask.repeat((shape[0] - 1) if 'batch_index' in latents[list_ind]: batch_inds = latents[list_ind]['batch_index'] else: batch_inds = [x+offset for x in range(shape[0])] return samples, mask, batch_inds @staticmethod def get_slices(indexable, num, batch_size): '''divides an indexable object into num slices of length batch_size, and a remainder''' slices = [] for i in range(num): slices.append(indexable[i*batch_size:(i+1)*batch_size]) if num * batch_size < len(indexable): return slices, indexable[num * batch_size:] else: return slices, None @staticmethod def slice_batch(batch, num, batch_size): result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] return list(zip(*result)) @staticmethod def cat_batch(batch1, batch2): if batch1[0] is None: return batch2 result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] return result def rebatch(self, latents, batch_size): batch_size = batch_size[0] output_list = [] current_batch = (None, None, None) processed = 0 for i in range(len(latents)): next_batch = self.get_batch(latents, i, processed) processed += len(next_batch[2]) if current_batch[0] is None: current_batch = next_batch elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: sliced, _ = self.slice_batch(current_batch, 1, batch_size) output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) current_batch = next_batch else: current_batch = self.cat_batch(current_batch, next_batch) if current_batch[0].shape[0] > batch_size: num = current_batch[0].shape[0] sliced, remainder = self.slice_batch(current_batch, num, batch_size) for i in range(num): output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) current_batch = remainder if current_batch[0] is not None: sliced, _ = self.slice_batch(current_batch, 1, batch_size) output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) for s in output_list: if s['noise_mask'].mean() == 1.0: del s['noise_mask'] return (output_list,) class ImageRebatch: @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) INPUT_IS_LIST = True OUTPUT_IS_LIST = (True, ) FUNCTION = "rebatch" CATEGORY = "image/batch" def rebatch(self, images, batch_size): batch_size = batch_size[0] output_list = [] all_images = [] for img in images: for i in range(img.shape[0]): all_images.append(img[i:i+1]) for i in range(0, len(all_images), batch_size): output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) return (output_list,) NODE_CLASS_MAPPINGS = { "RebatchLatents": LatentRebatch, "RebatchImages": ImageRebatch, } NODE_DISPLAY_NAME_MAPPINGS = { "RebatchLatents": "Rebatch Latents", "RebatchImages": "Rebatch Images", }

import torch from torch import einsum import torch.nn.functional as F import math from einops import rearrange, repeat from comfy.ldm.modules.attention import optimized_attention import comfy.samplers def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None): b, _, dim_head = q.shape dim_head scale = dim_head ** -0.5 h = heads q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) if attn_precision == torch.float32: sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale else: sim = einsum('b i d, b j d -> b i j', q, k) * scale del q, k if mask is not None: mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) sim = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return (out, sim) def create_blur_map(x0, attn, sigma=3.0, threshold=1.0): _, hw1, hw2 = attn.shape b, _, lh, lw = x0.shape attn = attn.reshape(b, -1, hw1, hw2) mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length() mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)] mask = ( mask.reshape(b, *mid_shape) .unsqueeze(1) .type(attn.dtype) ) mask = F.interpolate(mask, (lh, lw)) blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma) blurred = blurred * mask + x0 * (1 - mask) return blurred def gaussian_blur_2d(img, kernel_size, sigma): ksize_half = (kernel_size - 1) * 0.5 x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) pdf = torch.exp(-0.5 * (x / sigma).pow(2)) x_kernel = pdf / pdf.sum() x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) padding = [kernel_size img = F.pad(img, padding, mode="reflect") img = F.conv2d(img, kernel2d, groups=img.shape[-3]) return img class SelfAttentionGuidance: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.1}), "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, scale, blur_sigma): m = model.clone() attn_scores = None def attn_and_record(q, k, v, extra_options): nonlocal attn_scores heads = extra_options["n_heads"] cond_or_uncond = extra_options["cond_or_uncond"] b = q.shape[0] if 1 in cond_or_uncond: uncond_index = cond_or_uncond.index(1) (out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) n_slices = heads * b attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] return out else: return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) def post_cfg_function(args): nonlocal attn_scores uncond_attn = attn_scores sag_scale = scale sag_sigma = blur_sigma sag_threshold = 1.0 model = args["model"] uncond_pred = args["uncond_denoised"] uncond = args["uncond"] cfg_result = args["denoised"] sigma = args["sigma"] model_options = args["model_options"] x = args["input"] if min(cfg_result.shape[2:]) <= 4: return cfg_result degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) degraded_noised = degraded + x - uncond_pred (sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options) return cfg_result + (degraded - sag) * sag_scale m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) return (m, ) NODE_CLASS_MAPPINGS = { "SelfAttentionGuidance": SelfAttentionGuidance, } NODE_DISPLAY_NAME_MAPPINGS = { "SelfAttentionGuidance": "Self-Attention Guidance", }

import folder_paths import comfy.sd import comfy.model_management import nodes import torch class TripleCLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), ) }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" def load_clip(self, clip_name1, clip_name2, clip_name3): clip_path1 = folder_paths.get_full_path("clip", clip_name1) clip_path2 = folder_paths.get_full_path("clip", clip_name2) clip_path3 = folder_paths.get_full_path("clip", clip_name3) clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings")) return (clip,) class EmptySD3LatentImage: def __init__(self): self.device = comfy.model_management.intermediate_device() @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} RETURN_TYPES = ("LATENT",) FUNCTION = "generate" CATEGORY = "latent/sd3" def generate(self, width, height, batch_size=1): latent = torch.ones([batch_size, 16, height return ({"samples":latent}, ) class CLIPTextEncodeSD3: @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP", ), "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), "empty_padding": (["none", "empty_prompt"], ) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "advanced/conditioning" def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding): no_padding = empty_padding == "none" tokens = clip.tokenize(clip_g) if len(clip_g) == 0 and no_padding: tokens["g"] = [] if len(clip_l) == 0 and no_padding: tokens["l"] = [] else: tokens["l"] = clip.tokenize(clip_l)["l"] if len(t5xxl) == 0 and no_padding: tokens["t5xxl"] = [] else: tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] if len(tokens["l"]) != len(tokens["g"]): empty = clip.tokenize("") while len(tokens["l"]) < len(tokens["g"]): tokens["l"] += empty["l"] while len(tokens["l"]) > len(tokens["g"]): tokens["g"] += empty["g"] cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled}]], ) NODE_CLASS_MAPPINGS = { "TripleCLIPLoader": TripleCLIPLoader, "EmptySD3LatentImage": EmptySD3LatentImage, "CLIPTextEncodeSD3": CLIPTextEncodeSD3, }

import torch import comfy.utils class SD_4XUpscale_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "positive": ("CONDITIONING",), "negative": ("CONDITIONING",), "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/upscale_diffusion" def encode(self, images, positive, negative, scale_ratio, noise_augmentation): width = max(1, round(images.shape[-2] * scale_ratio)) height = max(1, round(images.shape[-3] * scale_ratio)) pixels = comfy.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width out_cp = [] out_cn = [] for t in positive: n = [t[0], t[1].copy()] n[1]['concat_image'] = pixels n[1]['noise_augmentation'] = noise_augmentation out_cp.append(n) for t in negative: n = [t[0], t[1].copy()] n[1]['concat_image'] = pixels n[1]['noise_augmentation'] = noise_augmentation out_cn.append(n) latent = torch.zeros([images.shape[0], 4, height return (out_cp, out_cn, {"samples":latent}) NODE_CLASS_MAPPINGS = { "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, }

import torch import nodes import comfy.utils def camera_embeddings(elevation, azimuth): elevation = torch.as_tensor([elevation]) azimuth = torch.as_tensor([azimuth]) embeddings = torch.stack( [ torch.deg2rad( (90 - elevation) - (90) ), torch.sin(torch.deg2rad(azimuth)), torch.cos(torch.deg2rad(azimuth)), torch.deg2rad( 90 - torch.full_like(elevation, 0) ), ], dim=-1).unsqueeze(1) return embeddings class StableZero123_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/3d_models" def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) cam_embeds = camera_embeddings(elevation, azimuth) cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1) positive = [[cond, {"concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([batch_size, 4, height return (positive, negative, {"samples":latent}) class StableZero123_Conditioning_Batched: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/3d_models" def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) cam_embeds = [] for i in range(batch_size): cam_embeds.append(camera_embeddings(elevation, azimuth)) elevation += elevation_batch_increment azimuth += azimuth_batch_increment cam_embeds = torch.cat(cam_embeds, dim=0) cond = torch.cat([comfy.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1) positive = [[cond, {"concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([batch_size, 4, height return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) class SV3D_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}), "elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/3d_models" def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) azimuth = 0 azimuth_increment = 360 / (max(video_frames, 2) - 1) elevations = [] azimuths = [] for i in range(video_frames): elevations.append(elevation) azimuths.append(azimuth) azimuth += azimuth_increment positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]] latent = torch.zeros([video_frames, 4, height return (positive, negative, {"samples":latent}) NODE_CLASS_MAPPINGS = { "StableZero123_Conditioning": StableZero123_Conditioning, "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, "SV3D_Conditioning": SV3D_Conditioning, }

""" This file is part of ComfyUI. Copyright (C) 2024 Stability AI This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https: """ import torch import nodes import comfy.utils class StableCascade_EmptyLatentImage: def __init__(self, device="cpu"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}) }} RETURN_TYPES = ("LATENT", "LATENT") RETURN_NAMES = ("stage_c", "stage_b") FUNCTION = "generate" CATEGORY = "latent/stable_cascade" def generate(self, width, height, compression, batch_size=1): c_latent = torch.zeros([batch_size, 16, height b_latent = torch.zeros([batch_size, 4, height return ({ "samples": c_latent, }, { "samples": b_latent, }) class StableCascade_StageC_VAEEncode: def __init__(self, device="cpu"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "vae": ("VAE", ), "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), }} RETURN_TYPES = ("LATENT", "LATENT") RETURN_NAMES = ("stage_c", "stage_b") FUNCTION = "generate" CATEGORY = "latent/stable_cascade" def generate(self, image, vae, compression): width = image.shape[-2] height = image.shape[-3] out_width = (width out_height = (height s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1) c_latent = vae.encode(s[:,:,:,:3]) b_latent = torch.zeros([c_latent.shape[0], 4, (height return ({ "samples": c_latent, }, { "samples": b_latent, }) class StableCascade_StageB_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "conditioning": ("CONDITIONING",), "stage_c": ("LATENT",), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "set_prior" CATEGORY = "conditioning/stable_cascade" def set_prior(self, conditioning, stage_c): c = [] for t in conditioning: d = t[1].copy() d['stable_cascade_prior'] = stage_c['samples'] n = [t[0], d] c.append(n) return (c, ) class StableCascade_SuperResolutionControlnet: def __init__(self, device="cpu"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "vae": ("VAE", ), }} RETURN_TYPES = ("IMAGE", "LATENT", "LATENT") RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b") FUNCTION = "generate" CATEGORY = "_for_testing/stable_cascade" def generate(self, image, vae): width = image.shape[-2] height = image.shape[-3] batch_size = image.shape[0] controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1) c_latent = torch.zeros([batch_size, 16, height b_latent = torch.zeros([batch_size, 4, height return (controlnet_input, { "samples": c_latent, }, { "samples": b_latent, }) NODE_CLASS_MAPPINGS = { "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode, "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet, }

import torch from typing import Tuple, Callable import math def do_nothing(x: torch.Tensor, mode:str=None): return x def mps_gather_workaround(input, dim, index): if input.shape[-1] == 1: return torch.gather( input.unsqueeze(-1), dim - 1 if dim < 0 else dim, index.unsqueeze(-1) ).squeeze(-1) else: return torch.gather(input, dim, index) def bipartite_soft_matching_random2d(metric: torch.Tensor, w: int, h: int, sx: int, sy: int, r: int, no_rand: bool = False) -> Tuple[Callable, Callable]: """ Partitions the tokens into src and dst and merges r tokens from src to dst. Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. Args: - metric [B, N, C]: metric to use for similarity - w: image width in tokens - h: image height in tokens - sx: stride in the x dimension for dst, must divide w - sy: stride in the y dimension for dst, must divide h - r: number of tokens to remove (by merging) - no_rand: if true, disable randomness (use top left corner only) """ B, N, _ = metric.shape if r <= 0 or w == 1 or h == 1: return do_nothing, do_nothing gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather with torch.no_grad(): hsy, wsx = h if no_rand: rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) else: rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) if (hsy * sy) < h or (wsx * sx) < w: idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view else: idx_buffer = idx_buffer_view rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) del idx_buffer, idx_buffer_view num_dst = hsy * wsx a_idx = rand_idx[:, num_dst:, :] b_idx = rand_idx[:, :num_dst, :] def split(x): C = x.shape[-1] src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) return src, dst metric = metric / metric.norm(dim=-1, keepdim=True) a, b = split(metric) scores = a @ b.transpose(-1, -2) r = min(a.shape[1], r) node_max, node_idx = scores.max(dim=-1) edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] unm_idx = edge_idx[..., r:, :] src_idx = edge_idx[..., :r, :] dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx) def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: src, dst = split(x) n, t1, c = src.shape unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) return torch.cat([unm, dst], dim=1) def unmerge(x: torch.Tensor) -> torch.Tensor: unm_len = unm_idx.shape[1] unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] _, _, c = unm.shape src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm) out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src) return out return merge, unmerge def get_functions(x, ratio, original_shape): b, c, original_h, original_w = original_shape original_tokens = original_h * original_w downsample = int(math.ceil(math.sqrt(original_tokens stride_x = 2 stride_y = 2 max_downsample = 1 if downsample <= max_downsample: w = int(math.ceil(original_w / downsample)) h = int(math.ceil(original_h / downsample)) r = int(x.shape[1] * ratio) no_rand = False m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand) return m, u nothing = lambda y: y return nothing, nothing class TomePatchModel: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, ratio): self.u = None def tomesd_m(q, k, v, extra_options): m, self.u = get_functions(q, ratio, extra_options["original_shape"]) return m(q), k, v def tomesd_u(n, extra_options): return self.u(n) m = model.clone() m.set_model_attn1_patch(tomesd_m) m.set_model_attn1_output_patch(tomesd_u) return (m, ) NODE_CLASS_MAPPINGS = { "TomePatchModel": TomePatchModel, }

import os import logging from spandrel import ModelLoader, ImageModelDescriptor from comfy import model_management import torch import comfy.utils import folder_paths try: from spandrel_extra_arches import EXTRA_REGISTRY from spandrel import MAIN_REGISTRY MAIN_REGISTRY.add(*EXTRA_REGISTRY) logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.") except: pass class UpscaleModelLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ), }} RETURN_TYPES = ("UPSCALE_MODEL",) FUNCTION = "load_model" CATEGORY = "loaders" def load_model(self, model_name): model_path = folder_paths.get_full_path("upscale_models", model_name) sd = comfy.utils.load_torch_file(model_path, safe_load=True) if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""}) out = ModelLoader().load_from_state_dict(sd).eval() if not isinstance(out, ImageModelDescriptor): raise Exception("Upscale model must be a single-image model.") return (out, ) class ImageUpscaleWithModel: @classmethod def INPUT_TYPES(s): return {"required": { "upscale_model": ("UPSCALE_MODEL",), "image": ("IMAGE",), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, upscale_model, image): device = model_management.get_torch_device() memory_required = model_management.module_size(upscale_model.model) memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 memory_required += image.nelement() * image.element_size() model_management.free_memory(memory_required, device) upscale_model.to(device) in_img = image.movedim(-1,-3).to(device) tile = 512 overlap = 32 oom = True while oom: try: steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) pbar = comfy.utils.ProgressBar(steps) s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) oom = False except model_management.OOM_EXCEPTION as e: tile if tile < 128: raise e upscale_model.to("cpu") s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) return (s,) NODE_CLASS_MAPPINGS = { "UpscaleModelLoader": UpscaleModelLoader, "ImageUpscaleWithModel": ImageUpscaleWithModel }

import nodes import torch import comfy.utils import comfy.sd import folder_paths import comfy_extras.nodes_model_merging class ImageOnlyCheckpointLoader: @classmethod def INPUT_TYPES(s): return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "loaders/video_models" def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) return (out[0], out[3], out[2]) class SVD_img2vid_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/video_models" def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] if augmentation_level > 0: encode_pixels += torch.randn_like(pixels) * augmentation_level t = vae.encode(encode_pixels) positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([video_frames, 4, height return (positive, negative, {"samples":latent}) class VideoLinearCFGGuidance: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "sampling/video_models" def patch(self, model, min_cfg): def linear_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1)) return uncond + scale * (cond - uncond) m = model.clone() m.set_model_sampler_cfg_function(linear_cfg) return (m, ) class VideoTriangleCFGGuidance: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "sampling/video_models" def patch(self, model, min_cfg): def linear_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] period = 1.0 values = torch.linspace(0, 1, cond.shape[0], device=cond.device) values = 2 * (values / period - torch.floor(values / period + 0.5)).abs() scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1)) return uncond + scale * (cond - uncond) m = model.clone() m.set_model_sampler_cfg_function(linear_cfg) return (m, ) class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): CATEGORY = "_for_testing" @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip_vision": ("CLIP_VISION",), "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None): comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) return {} NODE_CLASS_MAPPINGS = { "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, "VideoLinearCFGGuidance": VideoLinearCFGGuidance, "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance, "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, } NODE_DISPLAY_NAME_MAPPINGS = { "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)", }

import nodes import folder_paths MAX_RESOLUTION = nodes.MAX_RESOLUTION class WebcamCapture(nodes.LoadImage): @classmethod def INPUT_TYPES(s): return { "required": { "image": ("WEBCAM", {}), "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "capture_on_queue": ("BOOLEAN", {"default": True}), } } RETURN_TYPES = ("IMAGE",) FUNCTION = "load_capture" CATEGORY = "image" def load_capture(s, image, **kwargs): return super().load_image(folder_paths.get_annotated_filepath(image)) NODE_CLASS_MAPPINGS = { "WebcamCapture": WebcamCapture, } NODE_DISPLAY_NAME_MAPPINGS = { "WebcamCapture": "Webcam Capture", }

from spandrel import ModelLoader def load_state_dict(state_dict): print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.") return ModelLoader().load_from_state_dict(state_dict).eval()

from PIL import Image, ImageOps from io import BytesIO import numpy as np import struct import comfy.utils import time class SaveImageWebsocket: @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ),} } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "api/image" def save_images(self, images): pbar = comfy.utils.ProgressBar(images.shape[0]) step = 0 for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pbar.update_absolute(step, images.shape[0], ("PNG", img, None)) step += 1 return {} def IS_CHANGED(s, images): return time.time() NODE_CLASS_MAPPINGS = { "SaveImageWebsocket": SaveImageWebsocket, }

model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder params: layer: "hidden" layer_idx: -2

model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder params: layer: "hidden" layer_idx: -2

model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_fp16: True image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder params: layer: "hidden" layer_idx: -2

model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_fp16: True image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

model: base_learning_rate: 7.5e-05 target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: hybrid monitor: val/loss_simple_ema scale_factor: 0.18215 finetune_keys: null scheduler_config: target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 2500 ] cycle_lengths: [ 10000000000000 ] f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 in_channels: 9 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"

model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: False image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"

model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"

model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: False image_size: 32 in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate"

model: base_learning_rate: 5.0e-05 target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: hybrid scale_factor: 0.18215 monitor: val/loss_simple_ema finetune_keys: null use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True image_size: 32 in_channels: 9 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [ ] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: target: ldm.data.laion.WebDataModuleFromConfig params: tar_base: null p_unsafe_threshold: 0.1 filter_word_list: "data/filters.yaml" max_pwatermark: 0.45 batch_size: 8 num_workers: 6 multinode: True min_size: 512 train: shards: - "pipe:aws s3 cp s3: - "pipe:aws s3 cp s3: - "pipe:aws s3 cp s3: - "pipe:aws s3 cp s3: - "pipe:aws s3 cp s3: shuffle: 10000 image_key: jpg image_transforms: - target: torchvision.transforms.Resize params: size: 512 interpolation: 3 - target: torchvision.transforms.RandomCrop params: size: 512 postprocess: target: ldm.data.laion.AddMask params: mode: "512train-large" p_drop: 0.25 validation: shards: - "pipe:aws s3 cp s3: shuffle: 0 image_key: jpg image_transforms: - target: torchvision.transforms.Resize params: size: 512 interpolation: 3 - target: torchvision.transforms.CenterCrop params: size: 512 postprocess: target: ldm.data.laion.AddMask params: mode: "512train-large" p_drop: 0.25 lightning: find_unused_parameters: True modelcheckpoint: params: every_n_train_steps: 5000 callbacks: metrics_over_trainsteps_checkpoint: params: every_n_train_steps: 10000 image_logger: target: main.ImageLogger params: enable_autocast: False disabled: False batch_frequency: 1000 max_images: 4 increase_log_steps: False log_first_step: False log_images_kwargs: use_ema_scope: False inpaint: False plot_progressive_rows: False plot_diffusion_rows: False N: 4 unconditional_guidance_scale: 5.0 unconditional_guidance_label: [""] ddim_steps: 50 ddim_eta: 0.0 trainer: benchmark: True val_check_interval: 5000000 num_sanity_val_steps: 0 accumulate_grad_batches: 1

import json from urllib import request, parse import random prompt_text = """ { "3": { "class_type": "KSampler", "inputs": { "cfg": 8, "denoise": 1, "latent_image": [ "5", 0 ], "model": [ "4", 0 ], "negative": [ "7", 0 ], "positive": [ "6", 0 ], "sampler_name": "euler", "scheduler": "normal", "seed": 8566257, "steps": 20 } }, "4": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "v1-5-pruned-emaonly.ckpt" } }, "5": { "class_type": "EmptyLatentImage", "inputs": { "batch_size": 1, "height": 512, "width": 512 } }, "6": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "masterpiece best quality girl" } }, "7": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "bad hands" } }, "8": { "class_type": "VAEDecode", "inputs": { "samples": [ "3", 0 ], "vae": [ "4", 2 ] } }, "9": { "class_type": "SaveImage", "inputs": { "filename_prefix": "ComfyUI", "images": [ "8", 0 ] } } } """ def queue_prompt(prompt): p = {"prompt": prompt} data = json.dumps(p).encode('utf-8') req = request.Request("http: request.urlopen(req) prompt = json.loads(prompt_text) prompt["6"]["inputs"]["text"] = "masterpiece best quality man" prompt["3"]["inputs"]["seed"] = 5 queue_prompt(prompt)

import websocket import uuid import json import urllib.request import urllib.parse server_address = "127.0.0.1:8188" client_id = str(uuid.uuid4()) def queue_prompt(prompt): p = {"prompt": prompt, "client_id": client_id} data = json.dumps(p).encode('utf-8') req = urllib.request.Request("http: return json.loads(urllib.request.urlopen(req).read()) def get_image(filename, subfolder, folder_type): data = {"filename": filename, "subfolder": subfolder, "type": folder_type} url_values = urllib.parse.urlencode(data) with urllib.request.urlopen("http: return response.read() def get_history(prompt_id): with urllib.request.urlopen("http: return json.loads(response.read()) def get_images(ws, prompt): prompt_id = queue_prompt(prompt)['prompt_id'] output_images = {} while True: out = ws.recv() if isinstance(out, str): message = json.loads(out) if message['type'] == 'executing': data = message['data'] if data['node'] is None and data['prompt_id'] == prompt_id: break else: continue history = get_history(prompt_id)[prompt_id] for o in history['outputs']: for node_id in history['outputs']: node_output = history['outputs'][node_id] if 'images' in node_output: images_output = [] for image in node_output['images']: image_data = get_image(image['filename'], image['subfolder'], image['type']) images_output.append(image_data) output_images[node_id] = images_output return output_images prompt_text = """ { "3": { "class_type": "KSampler", "inputs": { "cfg": 8, "denoise": 1, "latent_image": [ "5", 0 ], "model": [ "4", 0 ], "negative": [ "7", 0 ], "positive": [ "6", 0 ], "sampler_name": "euler", "scheduler": "normal", "seed": 8566257, "steps": 20 } }, "4": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "v1-5-pruned-emaonly.ckpt" } }, "5": { "class_type": "EmptyLatentImage", "inputs": { "batch_size": 1, "height": 512, "width": 512 } }, "6": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "masterpiece best quality girl" } }, "7": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "bad hands" } }, "8": { "class_type": "VAEDecode", "inputs": { "samples": [ "3", 0 ], "vae": [ "4", 2 ] } }, "9": { "class_type": "SaveImage", "inputs": { "filename_prefix": "ComfyUI", "images": [ "8", 0 ] } } } """ prompt = json.loads(prompt_text) prompt["6"]["inputs"]["text"] = "masterpiece best quality man" prompt["3"]["inputs"]["seed"] = 5 ws = websocket.WebSocket() ws.connect("ws: images = get_images(ws, prompt)

import websocket import uuid import json import urllib.request import urllib.parse server_address = "127.0.0.1:8188" client_id = str(uuid.uuid4()) def queue_prompt(prompt): p = {"prompt": prompt, "client_id": client_id} data = json.dumps(p).encode('utf-8') req = urllib.request.Request("http: return json.loads(urllib.request.urlopen(req).read()) def get_image(filename, subfolder, folder_type): data = {"filename": filename, "subfolder": subfolder, "type": folder_type} url_values = urllib.parse.urlencode(data) with urllib.request.urlopen("http: return response.read() def get_history(prompt_id): with urllib.request.urlopen("http: return json.loads(response.read()) def get_images(ws, prompt): prompt_id = queue_prompt(prompt)['prompt_id'] output_images = {} current_node = "" while True: out = ws.recv() if isinstance(out, str): message = json.loads(out) if message['type'] == 'executing': data = message['data'] if data['prompt_id'] == prompt_id: if data['node'] is None: break else: current_node = data['node'] else: if current_node == 'save_image_websocket_node': images_output = output_images.get(current_node, []) images_output.append(out[8:]) output_images[current_node] = images_output return output_images prompt_text = """ { "3": { "class_type": "KSampler", "inputs": { "cfg": 8, "denoise": 1, "latent_image": [ "5", 0 ], "model": [ "4", 0 ], "negative": [ "7", 0 ], "positive": [ "6", 0 ], "sampler_name": "euler", "scheduler": "normal", "seed": 8566257, "steps": 20 } }, "4": { "class_type": "CheckpointLoaderSimple", "inputs": { "ckpt_name": "v1-5-pruned-emaonly.ckpt" } }, "5": { "class_type": "EmptyLatentImage", "inputs": { "batch_size": 1, "height": 512, "width": 512 } }, "6": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "masterpiece best quality girl" } }, "7": { "class_type": "CLIPTextEncode", "inputs": { "clip": [ "4", 1 ], "text": "bad hands" } }, "8": { "class_type": "VAEDecode", "inputs": { "samples": [ "3", 0 ], "vae": [ "4", 2 ] } }, "save_image_websocket_node": { "class_type": "SaveImageWebsocket", "inputs": { "images": [ "8", 0 ] } } } """ prompt = json.loads(prompt_text) prompt["6"]["inputs"]["text"] = "masterpiece best quality man" prompt["3"]["inputs"]["seed"] = 5 ws = websocket.WebSocket() ws.connect("ws: images = get_images(ws, prompt)

import os import pytest def pytest_addoption(parser): parser.addoption('--output_dir', action="store", default='tests/inference/samples', help='Output directory for generated images') parser.addoption("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") parser.addoption("--port", type=int, default=8188, help="Set the listen port.") @pytest.fixture(scope="session", autouse=True) def args_pytest(pytestconfig): args = {} args['output_dir'] = pytestconfig.getoption('output_dir') args['listen'] = pytestconfig.getoption('listen') args['port'] = pytestconfig.getoption('port') os.makedirs(args['output_dir'], exist_ok=True) return args def pytest_collection_modifyitems(items): LAST_TESTS = ['test_quality'] last_items = [] for test_name in LAST_TESTS: for item in items.copy(): print(item.module.__name__, item) if item.module.__name__ == test_name: last_items.append(item) items.remove(item) items.extend(last_items)

Additional requirements for running tests: ``` pip install pytest pip install websocket-client==1.6.1 opencv-python==4.6.0.66 scikit-image==0.21.0 ``` Run inference tests: ``` pytest tests/inference ``` Compares images in 2 directories to ensure they are the same 1) Run an inference test to save a directory of "ground truth" images ``` pytest tests/inference --output_dir tests/inference/baseline ``` 2) Make code edits 3) Run inference and quality comparison tests ``` pytest ```

import os import pytest def pytest_addoption(parser): parser.addoption('--baseline_dir', action="store", default='tests/inference/baseline', help='Directory for ground-truth images') parser.addoption('--test_dir', action="store", default='tests/inference/samples', help='Directory for images to test') parser.addoption('--metrics_file', action="store", default='tests/metrics.md', help='Output file for metrics') parser.addoption('--img_output_dir', action="store", default='tests/compare/samples', help='Output directory for diff metric images') @pytest.fixture(scope="session", autouse=True) def args_pytest(pytestconfig): args = {} args['baseline_dir'] = pytestconfig.getoption('baseline_dir') args['test_dir'] = pytestconfig.getoption('test_dir') args['metrics_file'] = pytestconfig.getoption('metrics_file') args['img_output_dir'] = pytestconfig.getoption('img_output_dir') with open(args['metrics_file'], 'a') as f: if os.stat(args['metrics_file']).st_size == 0: f.write("| date | run | file | status | value | \n") f.write("| --- | --- | --- | --- | --- | \n") return args def gather_file_basenames(directory: str): files = [] for file in os.listdir(directory): if file.endswith(".png"): files.append(file) return files def pytest_generate_tests(metafunc): if "baseline_fname" in metafunc.fixturenames: baseline_fnames = gather_file_basenames(metafunc.config.getoption("baseline_dir")) metafunc.parametrize("baseline_fname", baseline_fnames)

import datetime import numpy as np import os from PIL import Image import pytest from pytest import fixture from typing import Tuple, List from cv2 import imread, cvtColor, COLOR_BGR2RGB from skimage.metrics import structural_similarity as ssim """ This test suite compares images in 2 directories by file name The directories are specified by the command line arguments --baseline_dir and --test_dir """ def ssim_score(img0: np.ndarray, img1: np.ndarray) -> Tuple[float, np.ndarray]: score, diff = ssim(img0, img1, channel_axis=-1, full=True) diff = (diff * 255).astype("uint8") return score, diff METRICS = {"ssim": ssim_score} METRICS_PASS_THRESHOLD = {"ssim": 0.95} class TestCompareImageMetrics: @fixture(scope="class") def test_file_names(self, args_pytest): test_dir = args_pytest['test_dir'] fnames = self.gather_file_basenames(test_dir) yield fnames del fnames @fixture(scope="class", autouse=True) def teardown(self, args_pytest): yield baseline_dir = args_pytest['baseline_dir'] test_dir = args_pytest['test_dir'] img_output_dir = args_pytest['img_output_dir'] metrics_file = args_pytest['metrics_file'] grid_dir = os.path.join(img_output_dir, "grid") os.makedirs(grid_dir, exist_ok=True) for metric_dir in METRICS.keys(): metric_path = os.path.join(img_output_dir, metric_dir) for file in os.listdir(metric_path): if file.endswith(".png"): score = self.lookup_score_from_fname(file, metrics_file) image_file_list = [] image_file_list.append([ os.path.join(baseline_dir, file), os.path.join(test_dir, file), os.path.join(metric_path, file) ]) image_list = [[Image.open(file) for file in files] for files in image_file_list] grid = self.image_grid(image_list) grid.save(os.path.join(grid_dir, f"{metric_dir}_{score:.3f}_{file}")) @fixture() def fname(self, baseline_fname): yield baseline_fname del baseline_fname def test_directories_not_empty(self, args_pytest): baseline_dir = args_pytest['baseline_dir'] test_dir = args_pytest['test_dir'] assert len(os.listdir(baseline_dir)) != 0, f"Baseline directory {baseline_dir} is empty" assert len(os.listdir(test_dir)) != 0, f"Test directory {test_dir} is empty" def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest): baseline_file_path = os.path.join(args_pytest['baseline_dir'], fname) file_paths = [os.path.join(args_pytest['test_dir'], f) for f in test_file_names] file_match = self.find_file_match(baseline_file_path, file_paths) assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}" @pytest.mark.parametrize("metric", METRICS.keys()) def test_pipeline_compare( self, args_pytest, fname, test_file_names, metric, ): baseline_dir = args_pytest['baseline_dir'] test_dir = args_pytest['test_dir'] metrics_output_file = args_pytest['metrics_file'] img_output_dir = args_pytest['img_output_dir'] baseline_file_path = os.path.join(baseline_dir, fname) file_paths = [os.path.join(test_dir, f) for f in test_file_names] test_file = self.find_file_match(baseline_file_path, file_paths) sample_baseline = self.read_img(baseline_file_path) sample_secondary = self.read_img(test_file) score, metric_img = METRICS[metric](sample_baseline, sample_secondary) metric_status = score > METRICS_PASS_THRESHOLD[metric] with open(metrics_output_file, 'a') as f: run_info = os.path.splitext(fname)[0] metric_status_str = "PASS ✅" if metric_status else "FAIL ❌" date_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") f.write(f"| {date_str} | {run_info} | {metric} | {metric_status_str} | {score} | \n") metric_img_dir = os.path.join(img_output_dir, metric) os.makedirs(metric_img_dir, exist_ok=True) output_filename = f'{fname}' Image.fromarray(metric_img).save(os.path.join(metric_img_dir, output_filename)) assert score > METRICS_PASS_THRESHOLD[metric] def read_img(self, filename: str) -> np.ndarray: cvImg = imread(filename) cvImg = cvtColor(cvImg, COLOR_BGR2RGB) return cvImg def image_grid(self, img_list: list[list[Image.Image]]): rows = len(img_list) cols = len(img_list[0]) w, h = img_list[0][0].size grid = Image.new('RGB', size=(cols*w, rows*h)) for i, row in enumerate(img_list): for j, img in enumerate(row): grid.paste(img, box=(j*w, i*h)) return grid def lookup_score_from_fname(self, fname: str, metrics_output_file: str ) -> float: fname_basestr = os.path.splitext(fname)[0] with open(metrics_output_file, 'r') as f: for line in f: if fname_basestr in line: score = float(line.split('|')[5]) return score raise ValueError(f"Could not find score for {fname} in {metrics_output_file}") def gather_file_basenames(self, directory: str): files = [] for file in os.listdir(directory): if file.endswith(".png"): files.append(file) return files def read_file_prompt(self, fname:str) -> str: img = Image.open(fname) img.load() return img.info['prompt'] def find_file_match(self, baseline_file: str, file_paths: List[str]): baseline_prompt = self.read_file_prompt(baseline_file) if baseline_prompt is None or baseline_prompt == "": return None basename = os.path.basename(baseline_file) file_path_basenames = [os.path.basename(f) for f in file_paths] if basename in file_path_basenames: match_index = file_path_basenames.index(basename) file_paths.insert(0, file_paths.pop(match_index)) for f in file_paths: test_file_prompt = self.read_file_prompt(f) if baseline_prompt == test_file_prompt: return f

from copy import deepcopy from io import BytesIO from urllib import request import numpy import os from PIL import Image import pytest from pytest import fixture import time import torch from typing import Union import json import subprocess import websocket import uuid import urllib.request import urllib.parse from comfy.samplers import KSampler """ These tests generate and save images through a range of parameters """ class ComfyGraph: def __init__(self, graph: dict, sampler_nodes: list[str], ): self.graph = graph self.sampler_nodes = sampler_nodes def set_prompt(self, prompt, negative_prompt=None): for node in self.sampler_nodes: prompt_node = self.graph[node]['inputs']['positive'][0] self.graph[prompt_node]['inputs']['text'] = prompt if negative_prompt: negative_prompt_node = self.graph[node]['inputs']['negative'][0] self.graph[negative_prompt_node]['inputs']['text'] = negative_prompt def set_sampler_name(self, sampler_name:str, ): for node in self.sampler_nodes: self.graph[node]['inputs']['sampler_name'] = sampler_name def set_scheduler(self, scheduler:str): for node in self.sampler_nodes: self.graph[node]['inputs']['scheduler'] = scheduler def set_filename_prefix(self, prefix:str): for node in self.graph: if self.graph[node]['class_type'] == 'SaveImage': self.graph[node]['inputs']['filename_prefix'] = prefix class ComfyClient: def connect(self, listen:str = '127.0.0.1', port:Union[str,int] = 8188, client_id: str = str(uuid.uuid4()) ): self.client_id = client_id self.server_address = f"{listen}:{port}" ws = websocket.WebSocket() ws.connect("ws: self.ws = ws def queue_prompt(self, prompt): p = {"prompt": prompt, "client_id": self.client_id} data = json.dumps(p).encode('utf-8') req = urllib.request.Request("http: return json.loads(urllib.request.urlopen(req).read()) def get_image(self, filename, subfolder, folder_type): data = {"filename": filename, "subfolder": subfolder, "type": folder_type} url_values = urllib.parse.urlencode(data) with urllib.request.urlopen("http: return response.read() def get_history(self, prompt_id): with urllib.request.urlopen("http: return json.loads(response.read()) def get_images(self, graph, save=True): prompt = graph if not save: prompt_str = json.dumps(prompt) prompt_str = prompt_str.replace('SaveImage', 'PreviewImage') prompt = json.loads(prompt_str) prompt_id = self.queue_prompt(prompt)['prompt_id'] output_images = {} while True: out = self.ws.recv() if isinstance(out, str): message = json.loads(out) if message['type'] == 'executing': data = message['data'] if data['node'] is None and data['prompt_id'] == prompt_id: break else: continue history = self.get_history(prompt_id)[prompt_id] for o in history['outputs']: for node_id in history['outputs']: node_output = history['outputs'][node_id] if 'images' in node_output: images_output = [] for image in node_output['images']: image_data = self.get_image(image['filename'], image['subfolder'], image['type']) images_output.append(image_data) output_images[node_id] = images_output return output_images default_graph_file = 'tests/inference/graphs/default_graph_sdxl1_0.json' with open(default_graph_file, 'r') as file: default_graph = json.loads(file.read()) DEFAULT_COMFY_GRAPH = ComfyGraph(graph=default_graph, sampler_nodes=['10','14']) DEFAULT_COMFY_GRAPH_ID = os.path.splitext(os.path.basename(default_graph_file))[0] comfy_graph_list = [DEFAULT_COMFY_GRAPH] comfy_graph_ids = [DEFAULT_COMFY_GRAPH_ID] prompt_list = [ 'a painting of a cat', ] sampler_list = KSampler.SAMPLERS scheduler_list = KSampler.SCHEDULERS @pytest.mark.inference @pytest.mark.parametrize("sampler", sampler_list) @pytest.mark.parametrize("scheduler", scheduler_list) @pytest.mark.parametrize("prompt", prompt_list) class TestInference: @fixture(scope="class", autouse=True) def _server(self, args_pytest): p = subprocess.Popen([ 'python','main.py', '--output-directory', args_pytest["output_dir"], '--listen', args_pytest["listen"], '--port', str(args_pytest["port"]), ]) yield p.kill() torch.cuda.empty_cache() def start_client(self, listen:str, port:int): comfy_client = ComfyClient() n_tries = 5 for i in range(n_tries): time.sleep(4) try: comfy_client.connect(listen=listen, port=port) except ConnectionRefusedError as e: print(e) print(f"({i+1}/{n_tries}) Retrying...") else: break return comfy_client @fixture(scope="class", params=comfy_graph_list, ids=comfy_graph_ids, autouse=True) def _client_graph(self, request, args_pytest, _server) -> (ComfyClient, ComfyGraph): comfy_graph = request.param comfy_client = self.start_client(args_pytest["listen"], args_pytest["port"]) comfy_client.get_images(graph=comfy_graph.graph, save=False) yield comfy_client, comfy_graph del comfy_client del comfy_graph torch.cuda.empty_cache() @fixture def client(self, _client_graph): client = _client_graph[0] yield client @fixture def comfy_graph(self, _client_graph): graph = deepcopy(_client_graph[1]) yield graph def test_comfy( self, client, comfy_graph, sampler, scheduler, prompt, request ): test_info = request.node.name comfy_graph.set_filename_prefix(test_info) comfy_graph.set_sampler_name(sampler) comfy_graph.set_scheduler(scheduler) comfy_graph.set_prompt(prompt) images = client.get_images(comfy_graph.graph) assert len(images) != 0, "No images generated" for images_output in images.values(): for image_data in images_output: pil_image = Image.open(BytesIO(image_data)) assert numpy.array(pil_image).any() != 0, "Image is blank"

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