import torch import math import comfy.samplers import comfy.utils import nodes import logging class MixModGuiderComponentNode: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "positive": ("CONDITIONING",), "negative": ("CONDITIONING",), "base_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "cfg": ("FLOAT", {"default": 7.5, "min": -100.0, "max": 100.0, "step": 0.1}), }, "optional": { "prev_component": ("COMPONENT",), } } RETURN_TYPES = ("COMPONENT",) FUNCTION = "create_component" CATEGORY = "mixmod/conditioning" def create_component(self, model, positive, negative, base_weight, cfg, mask=None, prev_component=None): component = { "model": model, "positive": positive, "negative": negative, "weight": base_weight, "cfg": cfg, "mask": mask, "prev": prev_component } return (component,) class MixModGuiderNode: @classmethod def INPUT_TYPES(s): return {"required": {"component": ("COMPONENT",), "mode": (["team", "2model_fft"],), } } RETURN_TYPES = ("GUIDER",) FUNCTION = "create_guider" CATEGORY = "mixmod/conditioning" def create_guider(self, component, mode, image=None,): # This is a placeholder for the actual guider implementation # The actual implementation will be filled in by the user guider = MixModGuider(component, mode, image, component) return (guider,) class MixModGuider(comfy.samplers.CFGGuider): def __init__(self, component, mode, vae, image=None, prev_guider=None): self.component = component self.mode = mode self.vae = vae self.image = image self.prev_guider = prev_guider self.original_conds = {} self.prepared_models = [] # Initialize other necessary attributes self.models = [] self.weights = [] self.cfgs = [] self.masks = [] # Process component chain current = component conds = {} while current is not None: self.models.append(current["model"]) if len(self.models) == 1: self.model_patcher = self.models[0] self.model_options = self.models[0].model_options self.weights.append(current["weight"]) self.cfgs.append(current["cfg"]) self.masks.append(current["mask"]) # Add to original_conds with indexed keys idx = len(self.models) conds.update({f"positive_{idx}": current["positive"], f"negative_{idx}": current["negative"]}) current = current.get("prev") self.inner_set_conds(conds) weight_sum = sum(self.weights) # Normalize weights if sum is not zero if weight_sum > 0: self.weights = [w / weight_sum for w in self.weights] def set_mode(self, mode): self.mode = mode def predict_noise(self, x, timestep, model_options={}, seed=None): pred = [] for i in range(len(self.models)): model_options = self.models[i].model_options cond = [self.conds.get(f"positive_{i+1}", None), self.conds.get(f"negative_{i+1}", None)] pred.append(comfy.samplers.calc_cond_batch(self.prepared_models[i][0], cond, x, timestep, model_options)) for fn in model_options.get("sampler_pre_cfg_function", []): args = {"conds":cond, "conds_out": out, "cond_scale": self.cfgs[i], "timestep": timestep, "input": x, "sigma": timestep, "model": self.prepared_models[i][0], "model_options": model_options} out = fn(args) return self.mix_function(pred, x, timestep) def mix_function(self, pred, x, timestep): if(self.mode == "team"): # mix = (uncond_pred * ratio + baduncond * (1.0 - ratio)) + (cond_pred - uncond_pred) * cond_scale + (badcond - baduncond) * cond_scale_2 # Use pred array with self.weights and self.cfgs # First, initialize with the unconditioned prediction from the first model mix = torch.zeros_like(pred[0][1]) for i in range(len(pred)): mix = mix + pred[i][1] * self.weights[i] # For each model, apply the guidance according to weights and cfg values for i in range(len(pred)): # Extract conditional and unconditional predictions for this model c_pred = pred[i][0] u_pred = pred[i][1] # Apply the CFG formula with the corresponding weight and cfg # Add the weighted difference between conditional and unconditional mix = mix + (c_pred - u_pred) * self.cfgs[i] elif(self.mode == "2model_fft"): # Apply FFT-based frequency splitting # Need at least 2 models for FFT mode if len(pred) < 2: return pred[0][1] # Return unconditioned prediction if only one model # Get shapes from first prediction _, _, h, w = pred[0][0].shape # Use the first model for low frequencies and second model for high frequencies # Extract predictions from first model c_pred_1 = pred[0][0] # Conditioned prediction u_pred_1 = pred[0][1] # Unconditioned prediction # Extract predictions from second model c_pred_2 = pred[1][0] # Conditioned prediction u_pred_2 = pred[1][1] # Unconditioned prediction # Convert to frequency domain fft_u_pred_1 = torch.fft.rfft2(u_pred_1) fft_c_pred_1 = torch.fft.rfft2(c_pred_1) fft_u_pred_2 = torch.fft.rfft2(u_pred_2) fft_c_pred_2 = torch.fft.rfft2(c_pred_2) # Use weight of first model as frequency split ratio ratio = abs(self.weights[0]) # Calculate frequency split point split_h = int(h * ratio) split_w = int(w * ratio / 2) # rfft2 returns half the width in frequency domain # Create masks for low and high frequencies mask_low = torch.ones_like(fft_u_pred_1, dtype=torch.float32) mask_high = torch.ones_like(fft_u_pred_1, dtype=torch.float32) # Set high frequencies to zero in low mask mask_low[:, :, split_h:, :] = 0 mask_low[:, :, :, split_w:] = 0 # Set low frequencies to zero in high mask mask_high[:, :, :split_h, :split_w] = 0 # Apply masks and compute CFG in frequency domain using the corresponding CFG values fft_result_low = (fft_u_pred_1 * mask_low) + ((fft_c_pred_1 - fft_u_pred_1) * self.cfgs[0] * mask_low) fft_result_high = (fft_u_pred_2 * mask_high) + ((fft_c_pred_2 - fft_u_pred_2) * self.cfgs[1] * mask_high) # Combine low and high frequency results fft_result = fft_result_low + fft_result_high # Convert back to spatial domain mix = torch.fft.irfft2(fft_result, s=(h, w)) return mix def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): # Get the device from the first model device = self.models[0].load_device # Prepare the models, conditions, and loaded models for each model prepared_models = [] all_loaded_models = [] for i, model in enumerate(self.models): # Get relevant conditions for this model model_conds = {k: v for k, v in self.conds.items() if k.endswith(f"_{i+1}")} # Prepare sampling for this model inner_model, prepared_conds, loaded_models = comfy.sampler_helpers.prepare_sampling(model, noise.shape, model_conds, model.model_options) # Save the prepared data self.prepared_models.append((inner_model, prepared_conds)) all_loaded_models.extend(loaded_models) # Update the conditions for this model for k, v in prepared_conds.items(): self.conds[k] = v self.inner_model = self.prepared_models[0][0] # Prepare the device-related tensors if denoise_mask is not None: denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device) noise = noise.to(device) latent_image = latent_image.to(device) sigmas = sigmas.to(device) # Set model options for the main model patcher comfy.samplers.cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype()) try: # Pre-run all models for model in self.models: model.pre_run() # Set the inner_model to be a list of prepared models # Run the inner sample with prepared models output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) finally: # Clean up all models for model in self.models: model.cleanup() del self.inner_model # Clean up models and loaded models comfy.sampler_helpers.cleanup_models(self.conds, all_loaded_models) return output NODE_CLASS_MAPPINGS = { "MixModGuiderComponentNode": MixModGuiderComponentNode, "MixModGuiderNode": MixModGuiderNode, } NODE_DISPLAY_NAME_MAPPINGS = { "MixModGuiderComponentNode": "MixMod Guider Component", "MixModGuiderNode": "MixMod Guider", }