[pull] master from comfyanonymous:master

comfy/model_management.py

@@ -345,9 +345,9 @@ def amd_min_version(device=None, min_rdna_version=0):
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
                     if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
-#                if torch_version_numeric >= (2, 8):
-#                    if any((a in arch) for a in ["gfx1201"]):
-#                        ENABLE_PYTORCH_ATTENTION = True
+                if rocm_version >= (7, 0):
+                   if any((a in arch) for a in ["gfx1201"]):
+                       ENABLE_PYTORCH_ATTENTION = True
         if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4):
             if any((a in arch) for a in ["gfx1200", "gfx1201", "gfx942", "gfx950"]):  # TODO: more arches
                 SUPPORT_FP8_OPS = True

comfy/ops.py

@@ -24,6 +24,8 @@
 import comfy.rmsnorm
 import contextlib
 
+def run_every_op():
+    comfy.model_management.throw_exception_if_processing_interrupted()
 
 def scaled_dot_product_attention(q, k, v, *args, **kwargs):
     return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
@@ -109,6 +111,7 @@ def forward_comfy_cast_weights(self, input):
             return torch.nn.functional.linear(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -123,6 +126,7 @@ def forward_comfy_cast_weights(self, input):
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -137,6 +141,7 @@ def forward_comfy_cast_weights(self, input):
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -151,6 +156,7 @@ def forward_comfy_cast_weights(self, input):
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -165,6 +171,7 @@ def forward_comfy_cast_weights(self, input):
             return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -183,6 +190,7 @@ def forward_comfy_cast_weights(self, input):
             return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -202,6 +210,7 @@ def forward_comfy_cast_weights(self, input):
             # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -223,6 +232,7 @@ def forward_comfy_cast_weights(self, input, output_size=None):
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -244,6 +254,7 @@ def forward_comfy_cast_weights(self, input, output_size=None):
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -262,6 +273,7 @@ def forward_comfy_cast_weights(self, input, out_dtype=None):
             return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:

comfy/sd.py

@@ -276,8 +276,13 @@ def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None)
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
-        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
-        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
+        if model_management.is_amd():
+            VAE_KL_MEM_RATIO = 2.73
+        else:
+            VAE_KL_MEM_RATIO = 1.0
+
+        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO #These are for AutoencoderKL and need tweaking (should be lower)
+        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4

comfy/utils.py

@@ -39,7 +39,11 @@ class ModelCheckpoint:
         pass
     ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint"
 
-    from numpy.core.multiarray import scalar
+    def scalar(*args, **kwargs):
+        from numpy.core.multiarray import scalar as sc
+        return sc(*args, **kwargs)
+    scalar.__module__ = "numpy.core.multiarray"
+
     from numpy import dtype
     from numpy.dtypes import Float64DType
     from _codecs import encode

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.64"
+__version__ = "0.3.65"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.64"
+version = "0.3.65"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"