Adding Tailored ControlNet Implementations into Generative Model Application

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace

monai/apps/generation/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

monai/apps/generation/maisi/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

monai/apps/generation/maisi/networks/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

monai/apps/generation/maisi/networks/controlnet_maisi.py

@@ -0,0 +1,178 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Sequence, cast
+
+import torch
+
+from monai.utils import optional_import
+
+ControlNet, has_controlnet = optional_import("generative.networks.nets.controlnet", name="ControlNet")
+get_timestep_embedding, has_get_timestep_embedding = optional_import(
+    "generative.networks.nets.diffusion_model_unet", name="get_timestep_embedding"
+)
+
+if TYPE_CHECKING:
+    from generative.networks.nets.controlnet import ControlNet as ControlNetType
+else:
+    ControlNetType = cast(type, ControlNet)
+
+
+class ControlNetMaisi(ControlNetType):
+    """
+    Control network for diffusion models based on Zhang and Agrawala "Adding Conditional Control to Text-to-Image
+    Diffusion Models" (https://arxiv.org/abs/2302.05543)
+
+    Args:
+        spatial_dims: number of spatial dimensions.
+        in_channels: number of input channels.
+        num_res_blocks: number of residual blocks (see ResnetBlock) per level.
+        num_channels: tuple of block output channels.
+        attention_levels: list of levels to add attention.
+        norm_num_groups: number of groups for the normalization.
+        norm_eps: epsilon for the normalization.
+        resblock_updown: if True use residual blocks for up/downsampling.
+        num_head_channels: number of channels in each attention head.
+        with_conditioning: if True add spatial transformers to perform conditioning.
+        transformer_num_layers: number of layers of Transformer blocks to use.
+        cross_attention_dim: number of context dimensions to use.
+        num_class_embeds: if specified (as an int), then this model will be class-conditional with `num_class_embeds`
+            classes.
+        upcast_attention: if True, upcast attention operations to full precision.
+        use_flash_attention: if True, use flash attention for a memory efficient attention mechanism.
+        conditioning_embedding_in_channels: number of input channels for the conditioning embedding.
+        conditioning_embedding_num_channels: number of channels for the blocks in the conditioning embedding.
+        use_checkpointing: if True, use activation checkpointing to save memory.
+    """
+
+    def __init__(
+        self,
+        spatial_dims: int,
+        in_channels: int,
+        num_res_blocks: Sequence[int] | int = (2, 2, 2, 2),
+        num_channels: Sequence[int] = (32, 64, 64, 64),
+        attention_levels: Sequence[bool] = (False, False, True, True),
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-6,
+        resblock_updown: bool = False,
+        num_head_channels: int | Sequence[int] = 8,
+        with_conditioning: bool = False,
+        transformer_num_layers: int = 1,
+        cross_attention_dim: int | None = None,
+        num_class_embeds: int | None = None,
+        upcast_attention: bool = False,
+        use_flash_attention: bool = False,
+        conditioning_embedding_in_channels: int = 1,
+        conditioning_embedding_num_channels: Sequence[int] | None = (16, 32, 96, 256),
+        use_checkpointing: bool = True,
+    ) -> None:
+        super().__init__(
+            spatial_dims,
+            in_channels,
+            num_res_blocks,
+            num_channels,
+            attention_levels,
+            norm_num_groups,
+            norm_eps,
+            resblock_updown,
+            num_head_channels,
+            with_conditioning,
+            transformer_num_layers,
+            cross_attention_dim,
+            num_class_embeds,
+            upcast_attention,
+            use_flash_attention,
+            conditioning_embedding_in_channels,
+            conditioning_embedding_num_channels,
+        )
+        self.use_checkpointing = use_checkpointing
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        controlnet_cond: torch.Tensor,
+        conditioning_scale: float = 1.0,
+        context: torch.Tensor | None = None,
+        class_labels: torch.Tensor | None = None,
+    ) -> tuple[Sequence[torch.Tensor], torch.Tensor]:
+        emb = self._prepare_time_and_class_embedding(x, timesteps, class_labels)
+        h = self._apply_initial_convolution(x)
+        if self.use_checkpointing:
+            controlnet_cond = torch.utils.checkpoint.checkpoint(
+                self.controlnet_cond_embedding, controlnet_cond, use_reentrant=False
+            )
+        else:
+            controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
+        h += controlnet_cond
+        down_block_res_samples, h = self._apply_down_blocks(emb, context, h)
+        h = self._apply_mid_block(emb, context, h)
+        down_block_res_samples, mid_block_res_sample = self._apply_controlnet_blocks(h, down_block_res_samples)
+        # scaling
+        down_block_res_samples = [h * conditioning_scale for h in down_block_res_samples]
+        mid_block_res_sample *= conditioning_scale
+
+        return down_block_res_samples, mid_block_res_sample
+
+    def _prepare_time_and_class_embedding(self, x, timesteps, class_labels):
+        # 1. time
+        t_emb = get_timestep_embedding(timesteps, self.block_out_channels[0])
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=x.dtype)
+        emb = self.time_embed(t_emb)
+
+        # 2. class
+        if self.num_class_embeds is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+            class_emb = self.class_embedding(class_labels)
+            class_emb = class_emb.to(dtype=x.dtype)
+            emb = emb + class_emb
+
+        return emb
+
+    def _apply_initial_convolution(self, x):
+        # 3. initial convolution
+        h = self.conv_in(x)
+        return h
+
+    def _apply_down_blocks(self, emb, context, h):
+        # 4. down
+        if context is not None and self.with_conditioning is False:
+            raise ValueError("model should have with_conditioning = True if context is provided")
+        down_block_res_samples: list[torch.Tensor] = [h]
+        for downsample_block in self.down_blocks:
+            h, res_samples = downsample_block(hidden_states=h, temb=emb, context=context)
+            for residual in res_samples:
+                down_block_res_samples.append(residual)
+
+        return down_block_res_samples, h
+
+    def _apply_mid_block(self, emb, context, h):
+        # 5. mid
+        h = self.middle_block(hidden_states=h, temb=emb, context=context)
+        return h
+
+    def _apply_controlnet_blocks(self, h, down_block_res_samples):
+        # 6. Control net blocks
+        controlnet_down_block_res_samples = []
+        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
+            down_block_res_sample = controlnet_block(down_block_res_sample)
+            controlnet_down_block_res_samples.append(down_block_res_sample)
+
+        mid_block_res_sample = self.controlnet_mid_block(h)
+
+        return controlnet_down_block_res_samples, mid_block_res_sample

monai/data/torchscript_utils.py

@@ -116,7 +116,7 @@ def load_net_with_metadata(
     Returns:
         Triple containing loaded object, metadata dict, and extra files dict containing other file data if present
     """
-    extra_files = {f: "" for f in more_extra_files}
+    extra_files = dict.fromkeys(more_extra_files, "")
     extra_files[METADATA_FILENAME] = ""
 
     jit_obj = torch.jit.load(filename_prefix_or_stream, map_location, extra_files)

requirements-dev.txt

@@ -58,3 +58,4 @@ lpips==0.1.4
 nvidia-ml-py
 huggingface_hub
 pyamg>=5.0.0
+git+https://github.com/KumoLiu/GenerativeModels.git@cuda#egg=monai-generative