Base of modular backend

invokeai/app/invocations/denoise_latents.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 import inspect
+import os
 from contextlib import ExitStack
 from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
@@ -39,6 +40,7 @@
 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.denoise_context import DenoiseContext, DenoiseInputs
 from invokeai.backend.stable_diffusion.diffusers_pipeline import (
     ControlNetData,
     StableDiffusionGeneratorPipeline,
@@ -53,6 +55,11 @@
     TextConditioningData,
     TextConditioningRegions,
 )
+from invokeai.backend.stable_diffusion.diffusion.custom_atttention import CustomAttnProcessor2_0
+from invokeai.backend.stable_diffusion.diffusion_backend import StableDiffusionBackend
+from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
+from invokeai.backend.stable_diffusion.extensions.preview import PreviewExt
+from invokeai.backend.stable_diffusion.extensions_manager import ExtensionsManager
 from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_NAME_VALUES
 from invokeai.backend.util.devices import TorchDevice
@@ -314,9 +321,10 @@ def get_conditioning_data(
         context: InvocationContext,
         positive_conditioning_field: Union[ConditioningField, list[ConditioningField]],
         negative_conditioning_field: Union[ConditioningField, list[ConditioningField]],
-        unet: UNet2DConditionModel,
         latent_height: int,
         latent_width: int,
+        device: torch.device,
+        dtype: torch.dtype,
         cfg_scale: float | list[float],
         steps: int,
         cfg_rescale_multiplier: float,
@@ -330,25 +338,25 @@ def get_conditioning_data(
             uncond_list = [uncond_list]
 
         cond_text_embeddings, cond_text_embedding_masks = DenoiseLatentsInvocation._get_text_embeddings_and_masks(
-            cond_list, context, unet.device, unet.dtype
+            cond_list, context, device, dtype
         )
         uncond_text_embeddings, uncond_text_embedding_masks = DenoiseLatentsInvocation._get_text_embeddings_and_masks(
-            uncond_list, context, unet.device, unet.dtype
+            uncond_list, context, device, dtype
         )
 
         cond_text_embedding, cond_regions = DenoiseLatentsInvocation._concat_regional_text_embeddings(
             text_conditionings=cond_text_embeddings,
             masks=cond_text_embedding_masks,
             latent_height=latent_height,
             latent_width=latent_width,
-            dtype=unet.dtype,
+            dtype=dtype,
         )
         uncond_text_embedding, uncond_regions = DenoiseLatentsInvocation._concat_regional_text_embeddings(
             text_conditionings=uncond_text_embeddings,
             masks=uncond_text_embedding_masks,
             latent_height=latent_height,
             latent_width=latent_width,
-            dtype=unet.dtype,
+            dtype=dtype,
         )
 
         if isinstance(cfg_scale, list):
@@ -707,9 +715,108 @@ def prepare_noise_and_latents(
 
         return seed, noise, latents
 
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        if os.environ.get("USE_MODULAR_DENOISE", False):
+            return self._new_invoke(context)
+        else:
+            return self._old_invoke(context)
+
     @torch.no_grad()
     @SilenceWarnings()  # This quenches the NSFW nag from diffusers.
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
+    def _new_invoke(self, context: InvocationContext) -> LatentsOutput:
+        ext_manager = ExtensionsManager(is_canceled=context.util.is_canceled)
+
+        device = TorchDevice.choose_torch_device()
+        dtype = TorchDevice.choose_torch_dtype()
+
+        seed, noise, latents = self.prepare_noise_and_latents(context, self.noise, self.latents)
+        latents = latents.to(device=device, dtype=dtype)
+        if noise is not None:
+            noise = noise.to(device=device, dtype=dtype)
+
+        _, _, latent_height, latent_width = latents.shape
+
+        conditioning_data = self.get_conditioning_data(
+            context=context,
+            positive_conditioning_field=self.positive_conditioning,
+            negative_conditioning_field=self.negative_conditioning,
+            cfg_scale=self.cfg_scale,
+            steps=self.steps,
+            latent_height=latent_height,
+            latent_width=latent_width,
+            device=device,
+            dtype=dtype,
+            # TODO: old backend, remove
+            cfg_rescale_multiplier=self.cfg_rescale_multiplier,
+        )
+
+        scheduler = get_scheduler(
+            context=context,
+            scheduler_info=self.unet.scheduler,
+            scheduler_name=self.scheduler,
+            seed=seed,
+        )
+
+        timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
+            scheduler,
+            seed=seed,
+            device=device,
+            steps=self.steps,
+            denoising_start=self.denoising_start,
+            denoising_end=self.denoising_end,
+        )
+
+        denoise_ctx = DenoiseContext(
+            inputs=DenoiseInputs(
+                orig_latents=latents,
+                timesteps=timesteps,
+                init_timestep=init_timestep,
+                noise=noise,
+                seed=seed,
+                scheduler_step_kwargs=scheduler_step_kwargs,
+                conditioning_data=conditioning_data,
+                attention_processor_cls=CustomAttnProcessor2_0,
+            ),
+            unet=None,
+            scheduler=scheduler,
+        )
+
+        # get the unet's config so that we can pass the base to sd_step_callback()
+        unet_config = context.models.get_config(self.unet.unet.key)
+
+        ### preview
+        def step_callback(state: PipelineIntermediateState) -> None:
+            context.util.sd_step_callback(state, unet_config.base)
+
+        ext_manager.add_extension(PreviewExt(step_callback))
+
+        # ext: t2i/ip adapter
+        ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)
+
+        unet_info = context.models.load(self.unet.unet)
+        assert isinstance(unet_info.model, UNet2DConditionModel)
+        with (
+            unet_info.model_on_device() as (model_state_dict, unet),
+            ModelPatcher.patch_unet_attention_processor(unet, denoise_ctx.inputs.attention_processor_cls),
+            # ext: controlnet
+            ext_manager.patch_extensions(unet),
+            # ext: freeu, seamless, ip adapter, lora
+            ext_manager.patch_unet(model_state_dict, unet),
+        ):
+            sd_backend = StableDiffusionBackend(unet, scheduler)
+            denoise_ctx.unet = unet
+            result_latents = sd_backend.latents_from_embeddings(denoise_ctx, ext_manager)
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        result_latents = result_latents.detach().to("cpu")
+        TorchDevice.empty_cache()
+
+        name = context.tensors.save(tensor=result_latents)
+        return LatentsOutput.build(latents_name=name, latents=result_latents, seed=None)
+
+    @torch.no_grad()
+    @SilenceWarnings()  # This quenches the NSFW nag from diffusers.
+    def _old_invoke(self, context: InvocationContext) -> LatentsOutput:
         seed, noise, latents = self.prepare_noise_and_latents(context, self.noise, self.latents)
 
         mask, masked_latents, gradient_mask = self.prep_inpaint_mask(context, latents)
@@ -788,7 +895,8 @@ def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
                 context=context,
                 positive_conditioning_field=self.positive_conditioning,
                 negative_conditioning_field=self.negative_conditioning,
-                unet=unet,
+                device=unet.device,
+                dtype=unet.dtype,
                 latent_height=latent_height,
                 latent_width=latent_width,
                 cfg_scale=self.cfg_scale,

invokeai/backend/model_patcher.py

@@ -5,7 +5,7 @@
 
 import pickle
 from contextlib import contextmanager
-from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Union
+from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Type, Union
 
 import numpy as np
 import torch
@@ -32,8 +32,27 @@
 """
 
 
-# TODO: rename smth like ModelPatcher and add TI method?
 class ModelPatcher:
+    @staticmethod
+    @contextmanager
+    def patch_unet_attention_processor(unet: UNet2DConditionModel, processor_cls: Type[Any]):
+        """A context manager that patches `unet` with the provided attention processor.
+
+        Args:
+            unet (UNet2DConditionModel): The UNet model to patch.
+            processor (Type[Any]): Class which will be initialized for each key and passed to set_attn_processor(...).
+        """
+        unet_orig_processors = unet.attn_processors
+
+        # create separate instance for each attention, to be able modify each attention separately
+        unet_new_processors = {key: processor_cls() for key in unet_orig_processors.keys()}
+        try:
+            unet.set_attn_processor(unet_new_processors)
+            yield None
+
+        finally:
+            unet.set_attn_processor(unet_orig_processors)
+
     @staticmethod
     def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
         assert "." not in lora_key

invokeai/backend/stable_diffusion/denoise_context.py

@@ -0,0 +1,131 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, Union
+
+import torch
+from diffusers import UNet2DConditionModel
+from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
+
+if TYPE_CHECKING:
+    from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningMode, TextConditioningData
+
+
+@dataclass
+class UNetKwargs:
+    sample: torch.Tensor
+    timestep: Union[torch.Tensor, float, int]
+    encoder_hidden_states: torch.Tensor
+
+    class_labels: Optional[torch.Tensor] = None
+    timestep_cond: Optional[torch.Tensor] = None
+    attention_mask: Optional[torch.Tensor] = None
+    cross_attention_kwargs: Optional[Dict[str, Any]] = None
+    added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None
+    down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None
+    mid_block_additional_residual: Optional[torch.Tensor] = None
+    down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None
+    encoder_attention_mask: Optional[torch.Tensor] = None
+    # return_dict: bool = True
+
+
+@dataclass
+class DenoiseInputs:
+    """Initial variables passed to denoise. Supposed to be unchanged."""
+
+    # The latent-space image to denoise.
+    # Shape: [batch, channels, latent_height, latent_width]
+    # - If we are inpainting, this is the initial latent image before noise has been added.
+    # - If we are generating a new image, this should be initialized to zeros.
+    # - In some cases, this may be a partially-noised latent image (e.g. when running the SDXL refiner).
+    orig_latents: torch.Tensor
+
+    # kwargs forwarded to the scheduler.step() method.
+    scheduler_step_kwargs: dict[str, Any]
+
+    # Text conditionging data.
+    conditioning_data: TextConditioningData
+
+    # Noise used for two purposes:
+    # 1. Used by the scheduler to noise the initial `latents` before denoising.
+    # 2. Used to noise the `masked_latents` when inpainting.
+    # `noise` should be None if the `latents` tensor has already been noised.
+    # Shape: [1 or batch, channels, latent_height, latent_width]
+    noise: Optional[torch.Tensor]
+
+    # The seed used to generate the noise for the denoising process.
+    # HACK(ryand): seed is only used in a particular case when `noise` is None, but we need to re-generate the
+    # same noise used earlier in the pipeline. This should really be handled in a clearer way.
+    seed: int
+
+    # The timestep schedule for the denoising process.
+    timesteps: torch.Tensor
+
+    # The first timestep in the schedule. This is used to determine the initial noise level, so
+    # should be populated if you want noise applied *even* if timesteps is empty.
+    init_timestep: torch.Tensor
+
+    # Class of attention processor that is used.
+    attention_processor_cls: Type[Any]
+
+
+@dataclass
+class DenoiseContext:
+    """Context with all variables in denoise"""
+
+    # Initial variables passed to denoise. Supposed to be unchanged.
+    inputs: DenoiseInputs
+
+    # Scheduler which used to apply noise predictions.
+    scheduler: SchedulerMixin
+
+    # UNet model.
+    unet: Optional[UNet2DConditionModel] = None
+
+    # Current state of latent-space image in denoising process.
+    # None until `pre_denoise_loop` callback.
+    # Shape: [batch, channels, latent_height, latent_width]
+    latents: Optional[torch.Tensor] = None
+
+    # Current denoising step index.
+    # None until `pre_step` callback.
+    step_index: Optional[int] = None
+
+    # Current denoising step timestep.
+    # None until `pre_step` callback.
+    timestep: Optional[torch.Tensor] = None
+
+    # Arguments which will be passed to UNet model.
+    # Available in `pre_unet`/`post_unet` callbacks, otherwise will be None.
+    unet_kwargs: Optional[UNetKwargs] = None
+
+    # SchedulerOutput class returned from step function(normally, generated by scheduler).
+    # Supposed to be used only in `post_step` callback, otherwise can be None.
+    step_output: Optional[SchedulerOutput] = None
+
+    # Scaled version of `latents`, which will be passed to unet_kwargs initialization.
+    # Available in events inside step(between `pre_step` and `post_stop`).
+    # Shape: [batch, channels, latent_height, latent_width]
+    latent_model_input: Optional[torch.Tensor] = None
+
+    # [TMP] Defines on which conditionings current unet call will be runned.
+    # Available in `pre_unet`/`post_unet` callbacks, otherwise will be None.
+    conditioning_mode: Optional[ConditioningMode] = None
+
+    # [TMP] Noise predictions from negative conditioning.
+    # Available in `apply_cfg` and `post_apply_cfg` callbacks, otherwise will be None.
+    # Shape: [batch, channels, latent_height, latent_width]
+    negative_noise_pred: Optional[torch.Tensor] = None
+
+    # [TMP] Noise predictions from positive conditioning.
+    # Available in `apply_cfg` and `post_apply_cfg` callbacks, otherwise will be None.
+    # Shape: [batch, channels, latent_height, latent_width]
+    positive_noise_pred: Optional[torch.Tensor] = None
+
+    # Combined noise prediction from passed conditionings.
+    # Available in `apply_cfg` and `post_apply_cfg` callbacks, otherwise will be None.
+    # Shape: [batch, channels, latent_height, latent_width]
+    noise_pred: Optional[torch.Tensor] = None
+
+    # Dictionary for extensions to pass extra info about denoise process to other extensions.
+    extra: dict = field(default_factory=dict)

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -23,21 +23,12 @@
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import IPAdapterData, TextConditioningData
 from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher, UNetIPAdapterData
+from invokeai.backend.stable_diffusion.extensions.preview import PipelineIntermediateState
 from invokeai.backend.util.attention import auto_detect_slice_size
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.hotfixes import ControlNetModel
 
 
-@dataclass
-class PipelineIntermediateState:
-    step: int
-    order: int
-    total_steps: int
-    timestep: int
-    latents: torch.Tensor
-    predicted_original: Optional[torch.Tensor] = None
-
-
 @dataclass
 class AddsMaskGuidance:
     mask: torch.Tensor