rope, attetntion update | vae on cpu warning

comfy/ldm/modules/attention.py

@@ -416,7 +416,8 @@ except:
     pass
 
 @wrap_attn
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, var_length=False, **kwargs):
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     b = q.shape[0]
     dim_head = q.shape[-1]
     # check to make sure xformers isn't broken
@@ -506,7 +507,8 @@ else:
     SDP_BATCH_LIMIT = 2**31
 
 @wrap_attn
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, var_length=False, **kwargs):
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     if var_length:
         cu_seqlens_q, cu_seqlens_k, _, _ = var_attn_arg(kwargs)
         if not skip_reshape:
@@ -570,7 +572,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
     return out
 
 @wrap_attn
-def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, var_length = False, **kwargs):
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     exception_fallback = False
     if var_length:
         if not skip_reshape:
@@ -656,7 +659,8 @@ except AttributeError as error:
         assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
 
 @wrap_attn
-def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, var_length=False, **kwargs):
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     if var_length:
         cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = var_attn_arg(kwargs)
         return flash_attn_varlen_func(

comfy/ldm/seedvr/model.py

@@ -13,6 +13,7 @@ from comfy.rmsnorm import RMSNorm
 from torch.nn.modules.utils import _triple
 from torch import nn
 import math
+from comfy.ldm.flux.math import apply_rope1
 
 class Cache:
     def __init__(self, disable=False, prefix="", cache=None):
@@ -443,7 +444,6 @@ def apply_rotary_emb(
     freqs_seq_dim = None
 ):
     dtype = t.dtype
-
     if not exists(freqs_seq_dim):
         if freqs.ndim == 2 or t.ndim == 3:
             freqs_seq_dim = 0
@@ -452,20 +452,23 @@ def apply_rotary_emb(
         seq_len = t.shape[seq_dim]
         freqs = slice_at_dim(freqs, slice(-seq_len, None), dim = freqs_seq_dim)
 
-    rot_dim = freqs.shape[-1]
-    end_index = start_index + rot_dim
-
-    assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}'
+    rot_feats = freqs.shape[-1]
+    end_index = start_index + rot_feats
 
     t_left = t[..., :start_index]
     t_middle = t[..., start_index:end_index]
     t_right = t[..., end_index:]
 
-    freqs = freqs.to(t_middle.device)
-    t_transformed = (t_middle * freqs.cos() * scale) + (rotate_half(t_middle) * freqs.sin() * scale)
+    angles = freqs.to(t_middle.device)[..., ::2]
+    cos = torch.cos(angles) * scale
+    sin = torch.sin(angles) * scale
 
-    out = torch.cat((t_left, t_transformed, t_right), dim=-1)
+    col0 = torch.stack([cos, sin], dim=-1)
+    col1 = torch.stack([-sin, cos], dim=-1)
+    freqs_mat = torch.stack([col0, col1], dim=-1)
 
+    t_middle_out = apply_rope1(t_middle, freqs_mat)
+    out = torch.cat((t_left, t_middle_out, t_right), dim=-1)
     return out.type(dtype)
 
 class NaMMRotaryEmbedding3d(MMRotaryEmbeddingBase):

comfy/ldm/seedvr/vae.py

@@ -16,6 +16,7 @@ import math
 from enum import Enum
 from comfy.ops import NVIDIA_MEMORY_CONV_BUG_WORKAROUND
 
+import logging
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
@@ -446,6 +447,7 @@ class InflatedCausalConv3d(ops.Conv3d):
         self.memory_device = memory_device
         self.padding = (0, *self.padding[1:])
         self.memory_limit = float("inf")
+        self.logged_once = False
 
     def set_memory_limit(self, value: float):
         self.memory_limit = value
@@ -469,8 +471,16 @@ class InflatedCausalConv3d(ops.Conv3d):
                 return out
             except RuntimeError:
                 pass
-
-        return super()._conv_forward(input, weight, bias, *args, **kwargs)
+            except NotImplementedError:
+                pass
+        try:
+            return super()._conv_forward(input, weight, bias, *args, **kwargs)
+        except NotImplementedError:
+            # for: Could not run 'aten::cudnn_convolution' with arguments from the 'CPU' backend
+            if not self.logged_once:
+                logging.warning("VAE is on CPU for decoding. This is most likely due to being not enough memory")
+                self.logged_once = True
+            return F.conv3d(input, weight, bias, *args, **kwargs)
 
     def memory_limit_conv(
         self,

comfy/sd.py

@@ -382,8 +382,8 @@ class VAE:
                 self.latent_channels = 16
             elif "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd: # seedvr2
                 self.first_stage_model = comfy.ldm.seedvr.vae.VideoAutoencoderKLWrapper()
-                self.memory_used_decode = lambda shape, dtype: (10 * shape[1] * shape[2] * shape[3] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
-                self.memory_used_encode = lambda shape, dtype: (10 * max(shape[1], 5) * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (shape[1] * shape[2] * shape[3] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (max(shape[1], 5) * shape[2] * shape[3]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
                 self.downscale_index_formula = (4, 8, 8)