fixed some compatibilities, added a dedicated fft node
This commit is contained in:
kantsche
@@ -36,13 +36,13 @@ class MixModGuiderComponentNode:
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"prev": prev_component
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}
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return (component,)
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class MixModGuiderNode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"component": ("COMPONENT",),
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"mode": (["team", "2model_fft"],),
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}
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}
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@@ -50,21 +50,42 @@ class MixModGuiderNode:
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FUNCTION = "create_guider"
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CATEGORY = "mixmod/conditioning"
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def create_guider(self, component, mode, image=None,):
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def create_guider(self, component, image=None,):
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# This is a placeholder for the actual guider implementation
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# The actual implementation will be filled in by the user
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guider = MixModGuider(component, mode, image, component)
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guider = MixModGuider(component, mode="team", image=image)
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return (guider,)
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class MixModFFTGuiderNode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"component": ("COMPONENT",),
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"mode": (["teamfft", "2model_fft"]),
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"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("GUIDER",)
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FUNCTION = "create_guider"
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CATEGORY = "mixmod/conditioning"
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def create_guider(self, component, mode="team", image=None, ratio=1.0):
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# This is a placeholder for the actual guider implementation
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# The actual implementation will be filled in by the user
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guider = MixModGuider(component, mode=mode, image=image, ratio=ratio)
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return (guider,)
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class MixModGuider(comfy.samplers.CFGGuider):
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def __init__(self, component, mode, vae, image=None, prev_guider=None):
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def __init__(self, component, mode="team", vae=None, image=None, prev_guider=None, ratio=1.0):
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self.component = component
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self.mode = mode
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self.vae = vae
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self.image = image
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self.prev_guider = prev_guider
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self.ratio = ratio
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self.original_conds = {}
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self.prepared_models = []
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# Initialize other necessary attributes
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@@ -102,29 +123,29 @@ class MixModGuider(comfy.samplers.CFGGuider):
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self.mode = mode
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def predict_noise(self, x, timestep, model_options={}, seed=None):
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pred = []
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for i in range(len(self.models)):
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model_options = self.models[i].model_options
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cond = [self.conds.get(f"positive_{i+1}", None), self.conds.get(f"negative_{i+1}", None)]
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pred.append(comfy.samplers.calc_cond_batch(self.prepared_models[i][0], cond, x, timestep, model_options))
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for fn in model_options.get("sampler_pre_cfg_function", []):
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args = {"conds":cond, "conds_out": out, "cond_scale": self.cfgs[i], "timestep": timestep,
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args = {"conds":cond, "conds_out": pred, "cond_scale": self.cfgs[i], "timestep": timestep,
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"input": x, "sigma": timestep, "model": self.prepared_models[i][0], "model_options": model_options}
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out = fn(args)
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pred[i] = fn(args)
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return self.mix_function(pred, x, timestep)
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def mix_function(self, pred, x, timestep):
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return self.mix_function(self.prepared_models[0][0], pred, x, timestep, model_options)
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def mix_function(self, model, pred, x, timestep, model_options):
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if(self.mode == "team"):
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# mix = (uncond_pred * ratio + baduncond * (1.0 - ratio)) + (cond_pred - uncond_pred) * cond_scale + (badcond - baduncond) * cond_scale_2
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# Use pred array with self.weights and self.cfgs
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# First, initialize with the unconditioned prediction from the first model
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mix = torch.zeros_like(pred[0][1])
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c_predf = torch.zeros_like(pred[0][0])
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u_predf = torch.zeros_like(pred[0][1])
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for i in range(len(pred)):
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mix = mix + pred[i][1] * self.weights[i]
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@@ -134,17 +155,150 @@ class MixModGuider(comfy.samplers.CFGGuider):
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# Extract conditional and unconditional predictions for this model
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c_pred = pred[i][0]
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u_pred = pred[i][1]
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c_predf = c_predf + c_pred
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u_predf = u_predf + u_pred
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# Apply the CFG formula with the corresponding weight and cfg
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# Add the weighted difference between conditional and unconditional
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mix = mix + (c_pred - u_pred) * self.cfgs[i]
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for fn in model_options.get("sampler_post_cfg_function", []):
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args = {"denoised": mix, "cond": self.conds.get(f"positive_1", None), "uncond": self.conds.get(f"negative_1", None), "cond_scale": self.cfgs[0], "model": model, "uncond_denoised": pred[0][1], "cond_denoised": pred[0][0],
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"sigma": timestep, "model_options": model_options, "input": x}
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mix = fn(args)
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if(self.mode == "teamfft"):
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# mix = (uncond_pred * ratio + baduncond * (1.0 - ratio)) + (cond_pred - uncond_pred) * cond_scale + (badcond - baduncond) * cond_scale_2
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# Use pred array with self.weights and self.cfgs
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# First, get shape information from the first model's predictions
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_, _, h, w = pred[0][0].shape
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# Initialize an empty tensor in frequency domain for the result
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fft_mix = torch.zeros_like(torch.fft.rfft2(pred[0][1]))
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# First loop: mix the unconditioned predictions in frequency domain
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for i in range(len(pred)):
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# Convert unconditional prediction to frequency domain
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fft_u_pred = torch.fft.rfft2(pred[i][1])
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# Add weighted contribution
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fft_mix = fft_mix + fft_u_pred * self.weights[i]
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# Second loop: apply the guidance in frequency domain
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for i in range(len(pred)):
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# Convert conditional and unconditional predictions to frequency domain
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fft_c_pred = torch.fft.rfft2(pred[i][0])
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fft_u_pred = torch.fft.rfft2(pred[i][1])
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# Apply the CFG formula in frequency domain
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fft_mix = fft_mix + (fft_c_pred - fft_u_pred) * self.cfgs[i]
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# Convert the final result back to spatial domain
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mix = torch.fft.irfft2(fft_mix, s=(h, w))
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if(self.mode == "teamfft4freq"):
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# Get shape information from the first model's predictions
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_, _, h, w = pred[0][0].shape
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# Initialize an empty tensor in frequency domain for the result
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fft_mix = torch.zeros_like(torch.fft.rfft2(pred[0][1]))
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# Get frequency domain dimensions
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freq_h, freq_w = fft_mix.shape[2], fft_mix.shape[3]
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# Create frequency band masks - dividing into 4 frequency bands
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# Low, low-mid, high-mid, and high frequencies
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masks = []
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# Calculate coordinates for frequency space
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h_coords = torch.linspace(0, 1, freq_h, device=fft_mix.device)
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w_coords = torch.linspace(0, 1, freq_w, device=fft_mix.device)
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h_grid, w_grid = torch.meshgrid(h_coords, w_coords, indexing='ij')
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# Calculate frequency magnitude (distance from DC component)
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freq_magnitude = torch.sqrt(h_grid**2 + w_grid**2)
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# Normalize to [0,1] range
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max_freq = torch.sqrt(torch.tensor(2.0, device=fft_mix.device))
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normalized_freq = freq_magnitude / max_freq
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# Define frequency band boundaries
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band_boundaries = [0.0, 0.25, 0.5, 0.75, 1.0]
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# Create masks for each frequency band with smooth transitions
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transition_width = 0.05 # 5% transition width
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for i in range(4): # 4 frequency bands
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band_start = band_boundaries[i]
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band_end = band_boundaries[i+1]
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# Calculate transition regions
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trans_start = max(0.0, band_start - transition_width/2)
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trans_end = min(1.0, band_end + transition_width/2)
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# Create mask
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mask = torch.zeros_like(normalized_freq)
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# Full strength in the main band region
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main_region = (normalized_freq >= band_start) & (normalized_freq <= band_end)
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mask = torch.where(main_region, torch.ones_like(mask), mask)
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# Smooth transition at the start of the band
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if band_start > 0:
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start_trans = (normalized_freq >= trans_start) & (normalized_freq < band_start)
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mask = torch.where(
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start_trans,
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(normalized_freq - trans_start) / (band_start - trans_start),
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mask
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)
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# Smooth transition at the end of the band
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if band_end < 1.0:
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end_trans = (normalized_freq > band_end) & (normalized_freq <= trans_end)
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mask = torch.where(
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end_trans,
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1.0 - (normalized_freq - band_end) / (trans_end - band_end),
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mask
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)
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masks.append(mask)
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# Ensure masks sum to 1.0 at each point for perfect reconstruction
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mask_sum = sum(masks)
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masks = [m / (mask_sum + 1e-8) for m in masks] # Add small epsilon to avoid division by zero
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# Process each frequency band separately
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for band_idx in range(4):
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band_mix = torch.zeros_like(fft_mix)
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# First loop: mix the unconditioned predictions in this frequency band
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for i in range(len(pred)):
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# Convert unconditional prediction to frequency domain
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fft_u_pred = torch.fft.rfft2(pred[i][1])
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# Add weighted contribution for this frequency band
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band_mix = band_mix + fft_u_pred * self.weights[i] * masks[band_idx]
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# Second loop: apply the guidance in this frequency band
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for i in range(len(pred)):
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# Convert conditional and unconditional predictions to frequency domain
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fft_c_pred = torch.fft.rfft2(pred[i][0])
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fft_u_pred = torch.fft.rfft2(pred[i][1])
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# Apply the CFG formula in this frequency band
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band_mix = band_mix + (fft_c_pred - fft_u_pred) * self.cfgs[i] * masks[band_idx]
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# Add this band's contribution to the final mix
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fft_mix = fft_mix + band_mix
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# Convert the final result back to spatial domain
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mix = torch.fft.irfft2(fft_mix, s=(h, w))
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elif(self.mode == "2model_fft"):
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# Apply FFT-based frequency splitting
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# Need at least 2 models for FFT mode
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if len(pred) < 2:
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return pred[0][1] # Return unconditioned prediction if only one model
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return pred[0][0] * self.cfgs[0] + pred[0][1] * (1 - self.cfgs[0]) # Return properly conditioned prediction
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# Get shapes from first prediction
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_, _, h, w = pred[0][0].shape
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@@ -164,7 +318,8 @@ class MixModGuider(comfy.samplers.CFGGuider):
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fft_c_pred_2 = torch.fft.rfft2(c_pred_2)
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# Use weight of first model as frequency split ratio
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ratio = abs(self.weights[0])
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# Ensure ratio is between 0.05 and 0.95 to guarantee some frequency coverage for both models
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ratio = max(0.05, min(0.95, abs(self.ratio)))
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# Calculate frequency split point
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split_h = int(h * ratio)
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@@ -174,12 +329,36 @@ class MixModGuider(comfy.samplers.CFGGuider):
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mask_low = torch.ones_like(fft_u_pred_1, dtype=torch.float32)
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mask_high = torch.ones_like(fft_u_pred_1, dtype=torch.float32)
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# Set high frequencies to zero in low mask
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mask_low[:, :, split_h:, :] = 0
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mask_low[:, :, :, split_w:] = 0
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# Create transition area for smooth blending
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transition_h = max(1, int(h * 0.05)) # 5% transition zone
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transition_w = max(1, int(w * 0.05 / 2)) # 5% transition zone
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# Set low frequencies to zero in high mask
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mask_high[:, :, :split_h, :split_w] = 0
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# Set high frequencies to zero in low mask with smooth transition
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for i in range(mask_low.shape[2]):
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for j in range(mask_low.shape[3]):
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if i >= split_h + transition_h or j >= split_w + transition_w:
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mask_low[:, :, i, j] = 0.0
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elif i >= split_h or j >= split_w:
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# Calculate smooth transition value
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fade_h = 1.0 - min(1.0, (i - split_h) / transition_h) if i >= split_h else 1.0
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fade_w = 1.0 - min(1.0, (j - split_w) / transition_w) if j >= split_w else 1.0
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mask_low[:, :, i, j] = fade_h * fade_w
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# Set low frequencies to zero in high mask with inverse smooth transition
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for i in range(mask_high.shape[2]):
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for j in range(mask_high.shape[3]):
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if i <= split_h - transition_h and j <= split_w - transition_w:
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mask_high[:, :, i, j] = 0.0
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elif i <= split_h or j <= split_w:
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# Calculate smooth transition value - inverse of the low mask
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fade_h = min(1.0, (split_h - i) / transition_h) if i <= split_h else 0.0
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fade_w = min(1.0, (split_w - j) / transition_w) if j <= split_w else 0.0
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mask_high[:, :, i, j] = 1.0 - (fade_h * fade_w)
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# Ensure masks sum to 1.0 at each point for full spectrum coverage
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mask_sum = mask_low + mask_high
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mask_low = mask_low / mask_sum
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mask_high = mask_high / mask_sum
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# Apply masks and compute CFG in frequency domain using the corresponding CFG values
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fft_result_low = (fft_u_pred_1 * mask_low) + ((fft_c_pred_1 - fft_u_pred_1) * self.cfgs[0] * mask_low)
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@@ -191,6 +370,208 @@ class MixModGuider(comfy.samplers.CFGGuider):
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# Convert back to spatial domain
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mix = torch.fft.irfft2(fft_result, s=(h, w))
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elif(self.mode == "fft_overlap"):
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# Split each model into its own frequency band with overlap - optimized version
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_, _, h, w = pred[0][0].shape
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# Initialize result in frequency domain
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fft_result = torch.zeros_like(torch.fft.rfft2(pred[0][0]))
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# Convert all predictions to frequency domain
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fft_c_preds = []
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fft_u_preds = []
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valid_weights = []
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valid_cfgs = []
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for i in range(len(pred)):
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if self.weights[i] <= 0:
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continue
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# Extract predictions for this model
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c_pred = pred[i][0]
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u_pred = pred[i][1]
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# Convert to frequency domain
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fft_c_preds.append(torch.fft.rfft2(c_pred))
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fft_u_preds.append(torch.fft.rfft2(u_pred))
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valid_weights.append(self.weights[i])
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valid_cfgs.append(self.cfgs[i])
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# Get number of valid models
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num_models = len(valid_weights)
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if num_models == 0:
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# Handle edge case with no valid models
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return pred[0][0] # Return first model's conditioned output
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elif num_models == 1:
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# With only one model, use it for the full spectrum
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mix = pred[0][0] * valid_cfgs[0] + pred[0][1] * (1 - valid_cfgs[0])
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return mix
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# Calculate frequency coordinates
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freq_h, freq_w = fft_result.shape[2], fft_result.shape[3]
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# Create normalized coordinate grids - vectorized
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h_coords = torch.linspace(0, 1, freq_h, device=fft_result.device)
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w_coords = torch.linspace(0, 1, freq_w, device=fft_result.device)
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# Create meshgrid of coordinates
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h_grid, w_grid = torch.meshgrid(h_coords, w_coords, indexing='ij')
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# Calculate frequency magnitude for all points
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freq_magnitude = torch.sqrt(h_grid**2 + w_grid**2)
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# Normalize frequency magnitude to range [0, 1]
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max_freq = torch.sqrt(torch.tensor(2.0, device=fft_result.device)) # Maximum possible frequency magnitude
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normalized_freq = freq_magnitude / max_freq
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# Define overlap ratio (0.2 means 20% overlap between bands)
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overlap_ratio = 0.2
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# Calculate band width for each model, accounting for overlap
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band_width = (1.0 + overlap_ratio) / num_models
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# Calculate frequency masks for each model - vectorized
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model_masks = []
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for i in range(num_models):
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# Calculate band start and end points
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band_start = max(0.0, i * band_width - (overlap_ratio * band_width / 2))
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band_end = min(1.0, (i + 1) * band_width + (overlap_ratio * band_width / 2))
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# Create initial mask where freq is within the band
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mask = torch.zeros_like(normalized_freq)
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in_band = (normalized_freq >= band_start) & (normalized_freq <= band_end)
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# Define transition regions
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start_trans = band_start + (overlap_ratio * band_width / 2)
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end_trans = band_end - (overlap_ratio * band_width / 2)
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# Create mask for different regions
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full_region = (normalized_freq >= start_trans) & (normalized_freq <= end_trans)
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start_region = (normalized_freq >= band_start) & (normalized_freq < start_trans)
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end_region = (normalized_freq > end_trans) & (normalized_freq <= band_end)
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# Set full strength in middle region
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mask[full_region] = 1.0
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# Calculate and set smooth transitions at edges
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if torch.any(start_region):
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trans_width = start_trans - band_start
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mask[start_region] = (normalized_freq[start_region] - band_start) / trans_width
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if torch.any(end_region):
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trans_width = band_end - end_trans
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mask[end_region] = (band_end - normalized_freq[end_region]) / trans_width
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model_masks.append(mask)
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# Stack masks for easier normalization
|
||||
stacked_masks = torch.stack(model_masks)
|
||||
|
||||
# Normalize masks to ensure they sum to 1.0 across all models
|
||||
sum_masks = torch.sum(stacked_masks, dim=0)
|
||||
sum_masks = torch.clamp(sum_masks, min=1e-10)
|
||||
|
||||
# Normalize each mask
|
||||
normalized_stacked_masks = stacked_masks / sum_masks
|
||||
|
||||
# Apply CFG to each frequency band and combine - vectorized
|
||||
for i in range(num_models):
|
||||
# Get mask, add batch and channel dimensions
|
||||
mask = normalized_stacked_masks[i].unsqueeze(0).unsqueeze(0)
|
||||
|
||||
# Apply CFG formula with frequency band mask
|
||||
fft_u = fft_u_preds[i]
|
||||
fft_c = fft_c_preds[i]
|
||||
cfg_scale = valid_cfgs[i]
|
||||
|
||||
# Add weighted contribution to result
|
||||
fft_result += (fft_u + (fft_c - fft_u) * cfg_scale) * mask
|
||||
|
||||
# Convert back to spatial domain
|
||||
mix = torch.fft.irfft2(fft_result, s=(h, w))
|
||||
|
||||
elif(self.mode == "fft_full"):
|
||||
# Full spectrum frequency mixing implementation - optimized version
|
||||
_, _, h, w = pred[0][0].shape
|
||||
|
||||
# Initialize result in frequency domain
|
||||
fft_result = torch.zeros_like(torch.fft.rfft2(pred[0][0]))
|
||||
|
||||
# First, convert all predictions to frequency domain
|
||||
fft_c_preds = []
|
||||
fft_u_preds = []
|
||||
valid_model_indices = []
|
||||
|
||||
for i in range(len(pred)):
|
||||
if self.weights[i] <= 0:
|
||||
# Add placeholders for zero-weight models
|
||||
fft_c_preds.append(None)
|
||||
fft_u_preds.append(None)
|
||||
continue
|
||||
|
||||
# Extract predictions for this model
|
||||
c_pred = pred[i][0]
|
||||
u_pred = pred[i][1]
|
||||
|
||||
# Convert to frequency domain
|
||||
fft_c_preds.append(torch.fft.rfft2(c_pred))
|
||||
fft_u_preds.append(torch.fft.rfft2(u_pred))
|
||||
valid_model_indices.append(i)
|
||||
|
||||
# Calculate frequency coordinates
|
||||
frequency_h, frequency_w = fft_result.shape[2], fft_result.shape[3]
|
||||
|
||||
# Create normalized coordinate grids (vectorized)
|
||||
h_coords = torch.arange(frequency_h, device=fft_result.device).float() / frequency_h
|
||||
w_coords = torch.arange(frequency_w, device=fft_result.device).float() / frequency_w
|
||||
|
||||
# Create meshgrid of coordinates
|
||||
h_grid, w_grid = torch.meshgrid(h_coords, w_coords, indexing='ij')
|
||||
|
||||
# Calculate frequency magnitude for all points at once
|
||||
freq_magnitude = torch.sqrt(h_grid**2 + w_grid**2)
|
||||
|
||||
# Process each valid model
|
||||
if valid_model_indices:
|
||||
# Pre-calculate model weights for all frequency points
|
||||
all_model_weights = []
|
||||
for i in valid_model_indices:
|
||||
# Calculate model influence: higher weights → lower frequencies, lower weights → higher frequencies
|
||||
model_weight = self.weights[i] * (1.0 - freq_magnitude) + (1.0 - self.weights[i]) * freq_magnitude
|
||||
all_model_weights.append(model_weight)
|
||||
|
||||
# Convert to tensor for easier operations
|
||||
all_model_weights = torch.stack(all_model_weights)
|
||||
|
||||
# Normalize weights along model dimension
|
||||
weight_sum = all_model_weights.sum(dim=0, keepdim=True)
|
||||
# Avoid division by zero
|
||||
weight_sum = torch.clamp(weight_sum, min=1e-10)
|
||||
normalized_weights = all_model_weights / weight_sum
|
||||
|
||||
# Apply weights and CFG to each frequency component
|
||||
for idx, i in enumerate(valid_model_indices):
|
||||
# Get model weights for this model
|
||||
model_weights = normalized_weights[idx]
|
||||
|
||||
# Apply weights to each frequency point (broadcasting)
|
||||
model_weights = model_weights.unsqueeze(0).unsqueeze(0) # Add batch and channel dimensions
|
||||
|
||||
# Get unconditioned and conditioned predictions
|
||||
fft_u = fft_u_preds[i]
|
||||
fft_c = fft_c_preds[i]
|
||||
|
||||
# Apply CFG with model-specific weight
|
||||
contrib = fft_u * model_weights + (fft_c - fft_u) * self.cfgs[i] * model_weights
|
||||
|
||||
# Add to result
|
||||
fft_result += contrib
|
||||
|
||||
# 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):
|
||||
@@ -255,9 +636,11 @@ class MixModGuider(comfy.samplers.CFGGuider):
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"MixModGuiderComponentNode": MixModGuiderComponentNode,
|
||||
"MixModGuiderNode": MixModGuiderNode,
|
||||
"MixModFFTGuiderNode": MixModFFTGuiderNode,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"MixModGuiderComponentNode": "MixMod Guider Component",
|
||||
"MixModGuiderNode": "MixMod Guider",
|
||||
"MixModFFTGuiderNode": "MixMod FFT Guider",
|
||||
}
|
||||
Reference in New Issue
Block a user