teacache tweaks

Author: kijai

custom_cogvideox_transformer_3d.py

@@ -692,6 +692,7 @@ def forward(
                     self.accumulated_rel_l1_distance += poly1d(self.teacache_coefficients, ((emb-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()))
                     if self.accumulated_rel_l1_distance < self.teacache_rel_l1_thresh:
                         should_calc = False
+                        self.teacache_counter += 1
                     else:
                         should_calc = True
                         self.accumulated_rel_l1_distance = 0

nodes.py

@@ -782,6 +782,9 @@ def process(self, model, positive, negative, steps, cfg, seed, scheduler, num_fr
                     block.cached_encoder_hidden_states = None
 
         print_memory(device)
+
+        if teacache_args is not None:
+            log.info(f"TeaCache skipped steps: {pipe.transformer.teacache_counter}")
         mm.soft_empty_cache()
         try:
             torch.cuda.reset_peak_memory_stats(device)
@@ -960,7 +963,8 @@ def sample(self, samples, seed, min_val, max_val, r_bias, g_bias, b_bias):
         latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
 
         #[[0.0658900170023352, 0.04687556512203313, -0.056971557475649186], [-0.01265770449940036, -0.02814809569100843, -0.0768912512529372], [0.061456544746314665, 0.0005511617552452358, -0.0652574975291287], [-0.09020669168815276, -0.004755440180558637, -0.023763970904494294], [0.031766964513999865, -0.030959599938418375, 0.08654669098083616], [-0.005981764690055846, -0.08809119252349802, -0.06439852368217663], [-0.0212114426433989, 0.08894281999597677, 0.05155629477559985], [-0.013947446911030725, -0.08987475069900677, -0.08923124751217484], [-0.08235967967978511, 0.07268025379974379, 0.08830486164536037], [-0.08052049179735378, -0.050116143175332195, 0.02023752569687405], [-0.07607527759162447, 0.06827156419895981, 0.08678111754261035], [-0.04689089232553825, 0.017294986041038893, -0.10280492336438908], [-0.06105783150270304, 0.07311850680875913, 0.019995735372550075], [-0.09232589996527711, -0.012869815059053047, -0.04355587834255975], [-0.06679931010802251, 0.018399815879067458, 0.06802404982033876], [-0.013062632927118165, -0.04292991477896661, 0.07476243356192845]]
-        latent_rgb_factors =[[0.11945946736445662, 0.09919175788574555, -0.004832707433877734], [-0.0011977028264356232, 0.05496505130267682, 0.021321622433638193], [-0.014088548986590666, -0.008701477861945644, -0.020991313281459367], [0.03063921972519621, 0.12186477097625073, 0.0139593690235148], [0.0927403067854673, 0.030293187650929136, 0.05083134241694003], [0.0379112441305742, 0.04935199882777209, 0.058562766246777774], [0.017749911959153715, 0.008839453404921545, 0.036005638019226294], [0.10610119248526109, 0.02339855688237826, 0.057154257614084596], [0.1273639464837117, -0.010959856130713416, 0.043268631260428896], [-0.01873510946881321, 0.08220930648486932, 0.10613256772247093], [0.008429116376722327, 0.07623856561000408, 0.09295712117576727], [0.12938137079617007, 0.12360403483892413, 0.04478930933220116], [0.04565908794779364, 0.041064156741596365, -0.017695041535528512], [0.00019003240570281826, -0.013965147883381978, 0.05329669529635849], [0.08082391586738358, 0.11548306825496074, -0.021464170006615893], [-0.01517932393230994, -0.0057985555313003236, 0.07216646476618871]]
+        #latent_rgb_factors =[[0.11945946736445662, 0.09919175788574555, -0.004832707433877734], [-0.0011977028264356232, 0.05496505130267682, 0.021321622433638193], [-0.014088548986590666, -0.008701477861945644, -0.020991313281459367], [0.03063921972519621, 0.12186477097625073, 0.0139593690235148], [0.0927403067854673, 0.030293187650929136, 0.05083134241694003], [0.0379112441305742, 0.04935199882777209, 0.058562766246777774], [0.017749911959153715, 0.008839453404921545, 0.036005638019226294], [0.10610119248526109, 0.02339855688237826, 0.057154257614084596], [0.1273639464837117, -0.010959856130713416, 0.043268631260428896], [-0.01873510946881321, 0.08220930648486932, 0.10613256772247093], [0.008429116376722327, 0.07623856561000408, 0.09295712117576727], [0.12938137079617007, 0.12360403483892413, 0.04478930933220116], [0.04565908794779364, 0.041064156741596365, -0.017695041535528512], [0.00019003240570281826, -0.013965147883381978, 0.05329669529635849], [0.08082391586738358, 0.11548306825496074, -0.021464170006615893], [-0.01517932393230994, -0.0057985555313003236, 0.07216646476618871]]
+        latent_rgb_factors = [[0.03197404301362048, 0.04091260743347359, 0.0015679806301828524], [0.005517101026578029, 0.0052348639043457755, -0.005613441650464035], [0.0012485338264583965, -0.016096744206117782, 0.025023940031635054], [0.01760126794276171, 0.0036818415416642893, -0.0006019202528157255], [0.000444954842288864, 0.006102128982092191, 0.0008457999272962447], [-0.010531904354560697, -0.0032275501924977175, -0.00886595780267917], [-0.0001454543946122991, 0.010199210750845965, -0.00012702234832386188], [0.02078497279904325, -0.001669617778939972, 0.006712703698951264], [0.005529571599763264, 0.009733929789086743, 0.001887302765339838], [0.012138415094654218, 0.024684961927224837, 0.037211249767461915], [0.0010364484570000384, 0.01983636315929172, 0.009864602025627755], [0.006802862648143341, -0.0010509255113510681, -0.007026003345126021], [0.0003532208468418043, 0.005351971582801936, -0.01845912126717106], [-0.009045079994694397, -0.01127941143183089, 0.0042294057970470806], [0.002548289972720752, 0.025224244654428216, -0.0006086130121693347], [-0.011135669222532816, 0.0018181308593668505, 0.02794541485349922]]
         import random
         random.seed(seed)
         latent_rgb_factors = [[random.uniform(min_val, max_val) for _ in range(3)] for _ in range(16)]

pipeline_cogvideox.py

@@ -625,6 +625,11 @@ def __call__(
         else:
             disable_enhance()
 
+        # reset TeaCache
+        if hasattr(self.transformer, 'accumulated_rel_l1_distance'):
+            delattr(self.transformer, 'accumulated_rel_l1_distance')
+        self.transformer.teacache_counter = 0
+
         # 11. Denoising loop
         #from .latent_preview import prepare_callback
         #callback = prepare_callback(self.transformer, num_inference_steps)