Fix some more video tiled encode issues.

The downscale_ratio formula for the temporal had issues with some frame
numbers.

comfy/sd.py

@@ -12,6 +12,7 @@
 import comfy.ldm.genmo.vae.model
 import comfy.ldm.lightricks.vae.causal_video_autoencoder
 import yaml
+import math
 
 import comfy.utils
 
@@ -336,7 +337,7 @@ def __init__(self, sd=None, device=None, config=None, dtype=None):
                 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
-                self.downscale_ratio = (lambda a: max(0, (a + 3) / 6), 8, 8)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 5) / 6)), 8, 8)
                 self.working_dtypes = [torch.float16, torch.float32]
             elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv
                 self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE()
@@ -345,14 +346,14 @@ def __init__(self, sd=None, device=None, config=None, dtype=None):
                 self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)
-                self.downscale_ratio = (lambda a: max(0, (a + 4) / 8), 32, 32)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
             elif "decoder.conv_in.conv.weight" in sd:
                 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}
                 ddconfig["conv3d"] = True
                 ddconfig["time_compress"] = 4
                 self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
-                self.downscale_ratio = (lambda a: max(0, (a + 2) / 4), 8, 8)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
                 self.latent_dim = 3
                 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
                 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])