Remove old seamless class

invokeai/app/invocations/denoise_latents.py

@@ -39,7 +39,7 @@ from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
+from invokeai.backend.stable_diffusion import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext, DenoiseInputs
 from invokeai.backend.stable_diffusion.diffusers_pipeline import (
     ControlNetData,
@@ -920,7 +920,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             ExitStack() as exit_stack,
             unet_info.model_on_device() as (model_state_dict, unet),
             ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
-            set_seamless(unet, self.unet.seamless_axes),  # FIXME
+            SeamlessExt.static_patch_model(unet, self.unet.seamless_axes),  # FIXME
             # Apply the LoRA after unet has been moved to its target device for faster patching.
             ModelPatcher.apply_lora_unet(
                 unet,

invokeai/app/invocations/latents_to_image.py

@@ -24,7 +24,7 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.stable_diffusion import set_seamless
+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
 
@@ -59,7 +59,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-        with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
+        with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             latents = latents.to(vae.device)
             if self.fp32:

invokeai/backend/stable_diffusion/__init__.py

@@ -7,11 +7,9 @@ from invokeai.backend.stable_diffusion.diffusers_pipeline import (  # noqa: F401
     StableDiffusionGeneratorPipeline,
 )
 from invokeai.backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent  # noqa: F401
-from invokeai.backend.stable_diffusion.seamless import set_seamless  # noqa: F401
 
 __all__ = [
     "PipelineIntermediateState",
     "StableDiffusionGeneratorPipeline",
     "InvokeAIDiffuserComponent",
-    "set_seamless",
 ]

invokeai/backend/stable_diffusion/seamless.py

@@ -1,51 +0,0 @@
-from contextlib import contextmanager
-from typing import Callable, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
-from diffusers.models.lora import LoRACompatibleConv
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
-
-
-@contextmanager
-def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]):
-    if not seamless_axes:
-        yield
-        return
-
-    # override conv_forward
-    # https://github.com/huggingface/diffusers/issues/556#issuecomment-1993287019
-    def _conv_forward_asymmetric(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
-        self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
-        self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
-        working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
-        working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
-        return torch.nn.functional.conv2d(
-            working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups
-        )
-
-    original_layers: List[Tuple[nn.Conv2d, Callable]] = []
-
-    try:
-        x_mode = "circular" if "x" in seamless_axes else "constant"
-        y_mode = "circular" if "y" in seamless_axes else "constant"
-
-        conv_layers: List[torch.nn.Conv2d] = []
-
-        for module in model.modules():
-            if isinstance(module, torch.nn.Conv2d):
-                conv_layers.append(module)
-
-        for layer in conv_layers:
-            if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
-                layer.lora_layer = lambda *x: 0
-            original_layers.append((layer, layer._conv_forward))
-            layer._conv_forward = _conv_forward_asymmetric.__get__(layer, torch.nn.Conv2d)
-
-        yield
-
-    finally:
-        for layer, orig_conv_forward in original_layers:
-            layer._conv_forward = orig_conv_forward