refactor: estimate working vae memory during encode/decode

invokeai/app/invocations/cogview4_image_to_latents.py

@@ -17,6 +17,7 @@
 from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_cogview4
 
 # TODO(ryand): This is effectively a copy of SD3ImageToLatentsInvocation and a subset of ImageToLatentsInvocation. We
 # should refactor to avoid this duplication.
@@ -36,18 +37,12 @@ class CogView4ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     image: ImageField = InputField(description="The image to encode.")
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
 
-    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoencoderKL) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # Encode operations use approximately 50% of the memory required for decode operations
-        h = image_tensor.shape[-2]
-        w = image_tensor.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 1100  # 50% of decode scaling constant (2200)
-        working_memory = h * w * element_size * scaling_constant
-        return int(working_memory)
-
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
+        assert isinstance(vae_info.model, AutoencoderKL)
+        estimated_working_memory = estimate_vae_working_memory_cogview4(
+            operation="encode", image_tensor=image_tensor, vae=vae_info.model
+        )
         with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoencoderKL)
 
@@ -74,10 +69,7 @@ def invoke(self, context: InvocationContext) -> LatentsOutput:
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, AutoencoderKL)
 
-        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
-        latents = self.vae_encode(
-            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
-        )
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/invocations/cogview4_latents_to_image.py

@@ -6,7 +6,6 @@
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -20,6 +19,7 @@
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_cogview4
 
 # TODO(ryand): This is effectively a copy of SD3LatentsToImageInvocation and a subset of LatentsToImageInvocation. We
 # should refactor to avoid this duplication.
@@ -39,22 +39,15 @@ class CogView4LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     latents: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
 
-    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoencoderKL) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
-        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 2200  # Determined experimentally.
-        working_memory = out_h * out_w * element_size * scaling_constant
-        return int(working_memory)
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL))
-        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
+        estimated_working_memory = estimate_vae_working_memory_cogview4(
+            operation="decode", image_tensor=latents, vae=vae_info.model
+        )
         with (
             SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
             vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),

invokeai/app/invocations/flux_vae_decode.py

@@ -3,7 +3,6 @@
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -18,6 +17,7 @@
 from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_flux
 
 
 @invocation(
@@ -39,17 +39,11 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
 
-    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoEncoder) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
-        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 2200  # Determined experimentally.
-        working_memory = out_h * out_w * element_size * scaling_constant
-        return int(working_memory)
-
     def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
-        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
+        assert isinstance(vae_info.model, AutoEncoder)
+        estimated_working_memory = estimate_vae_working_memory_flux(
+            operation="decode", image_tensor=latents, vae=vae_info.model
+        )
         with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoEncoder)
             vae_dtype = next(iter(vae.parameters())).dtype

invokeai/app/invocations/flux_vae_encode.py

@@ -15,6 +15,7 @@
 from invokeai.backend.model_manager import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_flux
 
 
 @invocation(
@@ -35,22 +36,16 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         input=Input.Connection,
     )
 
-    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoEncoder) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # Encode operations use approximately 50% of the memory required for decode operations
-        h = image_tensor.shape[-2]
-        w = image_tensor.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 1100  # 50% of decode scaling constant (2200)
-        working_memory = h * w * element_size * scaling_constant
-        return int(working_memory)
-
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
         # TODO(ryand): Expose seed parameter at the invocation level.
         # TODO(ryand): Write a util function for generating random tensors that is consistent across devices / dtypes.
         # There's a starting point in get_noise(...), but it needs to be extracted and generalized. This function
         # should be used for VAE encode sampling.
+        assert isinstance(vae_info.model, AutoEncoder)
+        estimated_working_memory = estimate_vae_working_memory_flux(
+            operation="encode", image_tensor=image_tensor, vae=vae_info.model
+        )
         generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
         with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoEncoder)
@@ -70,10 +65,7 @@ def invoke(self, context: InvocationContext) -> LatentsOutput:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
         context.util.signal_progress("Running VAE")
-        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
-        latents = self.vae_encode(
-            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
-        )
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/invocations/image_to_latents.py

@@ -27,6 +27,7 @@
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_sd15_sdxl
 
 
 @invocation(
@@ -52,47 +53,23 @@ class ImageToLatentsInvocation(BaseInvocation):
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
     fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
 
-    def _estimate_working_memory(
-        self, image_tensor: torch.Tensor, use_tiling: bool, vae: AutoencoderKL | AutoencoderTiny
-    ) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # Encode operations use approximately 50% of the memory required for decode operations
-        element_size = 4 if self.fp32 else 2
-        scaling_constant = 1100  # 50% of decode scaling constant (2200)
-
-        if use_tiling:
-            tile_size = self.tile_size
-            if tile_size == 0:
-                tile_size = vae.tile_sample_min_size
-                assert isinstance(tile_size, int)
-            h = tile_size
-            w = tile_size
-            working_memory = h * w * element_size * scaling_constant
-
-            # We add 25% to the working memory estimate when tiling is enabled to account for factors like tile overlap
-            # and number of tiles. We could make this more precise in the future, but this should be good enough for
-            # most use cases.
-            working_memory = working_memory * 1.25
-        else:
-            h = image_tensor.shape[-2]
-            w = image_tensor.shape[-1]
-            working_memory = h * w * element_size * scaling_constant
-
-        if self.fp32:
-            # If we are running in FP32, then we should account for the likely increase in model size (~250MB).
-            working_memory += 250 * 2**20
-
-        return int(working_memory)
-
-    @staticmethod
+    @classmethod
     def vae_encode(
+        cls,
         vae_info: LoadedModel,
         upcast: bool,
         tiled: bool,
         image_tensor: torch.Tensor,
         tile_size: int = 0,
-        estimated_working_memory: int = 0,
     ) -> torch.Tensor:
+        assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
+        estimated_working_memory = estimate_vae_working_memory_sd15_sdxl(
+            operation="encode",
+            image_tensor=image_tensor,
+            vae=vae_info.model,
+            tile_size=tile_size if tiled else None,
+            fp32=upcast,
+        )
         with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             orig_dtype = vae.dtype
@@ -156,17 +133,13 @@ def invoke(self, context: InvocationContext) -> LatentsOutput:
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
-        use_tiling = self.tiled or context.config.get().force_tiled_decode
-        estimated_working_memory = self._estimate_working_memory(image_tensor, use_tiling, vae_info.model)
-
         context.util.signal_progress("Running VAE encoder")
         latents = self.vae_encode(
             vae_info=vae_info,
             upcast=self.fp32,
-            tiled=self.tiled,
+            tiled=self.tiled or context.config.get().force_tiled_decode,
             image_tensor=image_tensor,
             tile_size=self.tile_size,
-            estimated_working_memory=estimated_working_memory,
         )
 
         latents = latents.to("cpu")

invokeai/app/invocations/latents_to_image.py

@@ -27,6 +27,7 @@
 from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_sd15_sdxl
 
 
 @invocation(
@@ -53,39 +54,6 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
     fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
 
-    def _estimate_working_memory(
-        self, latents: torch.Tensor, use_tiling: bool, vae: AutoencoderKL | AutoencoderTiny
-    ) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # It was found experimentally that the peak working memory scales linearly with the number of pixels and the
-        # element size (precision). This estimate is accurate for both SD1 and SDXL.
-        element_size = 4 if self.fp32 else 2
-        scaling_constant = 2200  # Determined experimentally.
-
-        if use_tiling:
-            tile_size = self.tile_size
-            if tile_size == 0:
-                tile_size = vae.tile_sample_min_size
-                assert isinstance(tile_size, int)
-            out_h = tile_size
-            out_w = tile_size
-            working_memory = out_h * out_w * element_size * scaling_constant
-
-            # We add 25% to the working memory estimate when tiling is enabled to account for factors like tile overlap
-            # and number of tiles. We could make this more precise in the future, but this should be good enough for
-            # most use cases.
-            working_memory = working_memory * 1.25
-        else:
-            out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
-            out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
-            working_memory = out_h * out_w * element_size * scaling_constant
-
-        if self.fp32:
-            # If we are running in FP32, then we should account for the likely increase in model size (~250MB).
-            working_memory += 250 * 2**20
-
-        return int(working_memory)
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
@@ -94,8 +62,13 @@ def invoke(self, context: InvocationContext) -> ImageOutput:
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-
-        estimated_working_memory = self._estimate_working_memory(latents, use_tiling, vae_info.model)
+        estimated_working_memory = estimate_vae_working_memory_sd15_sdxl(
+            operation="decode",
+            image_tensor=latents,
+            vae=vae_info.model,
+            tile_size=self.tile_size if use_tiling else None,
+            fp32=self.fp32,
+        )
         with (
             SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
             vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),

invokeai/app/invocations/sd3_image_to_latents.py

@@ -17,6 +17,7 @@
 from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_sd3
 
 
 @invocation(
@@ -32,18 +33,12 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     image: ImageField = InputField(description="The image to encode")
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
 
-    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoencoderKL) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # Encode operations use approximately 50% of the memory required for decode operations
-        h = image_tensor.shape[-2]
-        w = image_tensor.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 1100  # 50% of decode scaling constant (2200)
-        working_memory = h * w * element_size * scaling_constant
-        return int(working_memory)
-
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
+        assert isinstance(vae_info.model, AutoencoderKL)
+        estimated_working_memory = estimate_vae_working_memory_sd3(
+            operation="encode", image_tensor=image_tensor, vae=vae_info.model
+        )
         with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoencoderKL)
 
@@ -70,10 +65,7 @@ def invoke(self, context: InvocationContext) -> LatentsOutput:
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, AutoencoderKL)
 
-        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
-        latents = self.vae_encode(
-            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
-        )
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)