Merge branch 'comfyanonymous:master' into master

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -378,7 +378,7 @@ class Decoder(nn.Module):
             assert (
                 timestep is not None
             ), "should pass timestep with timestep_conditioning=True"
-            scaled_timestep = timestep * self.timestep_scale_multiplier
+            scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device)
 
         for up_block in self.up_blocks:
             if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
@@ -403,7 +403,7 @@ class Decoder(nn.Module):
             )
             ada_values = self.last_scale_shift_table[
                 None, ..., None, None, None
-            ] + embedded_timestep.reshape(
+            ].to(device=sample.device, dtype=sample.dtype) + embedded_timestep.reshape(
                 batch_size,
                 2,
                 -1,
@@ -697,7 +697,7 @@ class ResnetBlock3D(nn.Module):
             ), "should pass timestep with timestep_conditioning=True"
             ada_values = self.scale_shift_table[
                 None, ..., None, None, None
-            ] + timestep.reshape(
+            ].to(device=hidden_states.device, dtype=hidden_states.dtype) + timestep.reshape(
                 batch_size,
                 4,
                 -1,
@@ -715,7 +715,7 @@ class ResnetBlock3D(nn.Module):
 
         if self.inject_noise:
             hidden_states = self._feed_spatial_noise(
-                hidden_states, self.per_channel_scale1
+                hidden_states, self.per_channel_scale1.to(device=hidden_states.device, dtype=hidden_states.dtype)
             )
 
         hidden_states = self.norm2(hidden_states)
@@ -731,7 +731,7 @@ class ResnetBlock3D(nn.Module):
 
         if self.inject_noise:
             hidden_states = self._feed_spatial_noise(
-                hidden_states, self.per_channel_scale2
+                hidden_states, self.per_channel_scale2.to(device=hidden_states.device, dtype=hidden_states.dtype)
             )
 
         input_tensor = self.norm3(input_tensor)

comfy/ldm/pixart/blocks.py

@@ -6,16 +6,15 @@ import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
 
-from comfy import model_management
 from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, Mlp, timestep_embedding
 from comfy.ldm.modules.attention import optimized_attention
 
-if model_management.xformers_enabled():
-    import xformers.ops
-    if int((xformers.__version__).split(".")[2]) >= 28:
-        block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens
-    else:
-        block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens
+# if model_management.xformers_enabled():
+#     import xformers.ops
+#     if int((xformers.__version__).split(".")[2].split("+")[0]) >= 28:
+#         block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens
+#     else:
+#         block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens
 
 def modulate(x, shift, scale):
     return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)

comfy/model_management.py

@@ -534,7 +534,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
                 lowvram_model_memory = 0
 
         if vram_set_state == VRAMState.NO_VRAM:
-            lowvram_model_memory = 64 * 1024 * 1024
+            lowvram_model_memory = 0.1
 
         loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
         current_loaded_models.insert(0, loaded_model)