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
 from invokeai.backend.stable_diffusion.diffusers_pipeline import (
     ControlNetData,
     StableDiffusionGeneratorPipeline,
@@ -53,6 +55,10 @@
     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.extensions 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 +320,10 @@
         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 +337,25 @@
             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 +714,105 @@
 
         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:
+        with ExitStack() as exit_stack:
+            ext_manager = ExtensionsManager()
+
+            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(
+                latents=latents,
+                timesteps=timesteps,
+                init_timestep=init_timestep,
+                noise=noise,
+                seed=seed,
+                scheduler_step_kwargs=scheduler_step_kwargs,
+                conditioning_data=conditioning_data,
+                unet=None,
+                scheduler=scheduler,
+            )
+
+            ### preview
+            def step_callback(state: PipelineIntermediateState) -> None:
+                context.util.sd_step_callback(state, unet_config.base)
+
+            ext_manager.add_extension(PreviewExt(step_callback))
+
+            # 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)
+
+            # ext: t2i/ip adapter
+            ext_manager.callbacks.setup(denoise_ctx, ext_manager)
+
+            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),
+                # ext: controlnet
+                ext_manager.patch_attention_processor(unet, CustomAttnProcessor2_0),
+                # 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.to("cpu")  # TODO: detach?
+        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 +891,8 @@
                 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/stable_diffusion/denoise_context.py

@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, 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 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 DenoiseContext:
+    latents: torch.Tensor
+    scheduler_step_kwargs: dict[str, Any]
+    conditioning_data: TextConditioningData
+    noise: Optional[torch.Tensor]
+    seed: int
+    timesteps: torch.Tensor
+    init_timestep: torch.Tensor
+
+    scheduler: SchedulerMixin
+    unet: Optional[UNet2DConditionModel] = None
+
+    orig_latents: Optional[torch.Tensor] = None
+    step_index: Optional[int] = None
+    timestep: Optional[torch.Tensor] = None
+    unet_kwargs: Optional[UNetKwargs] = None
+    step_output: Optional[SchedulerOutput] = None
+
+    latent_model_input: Optional[torch.Tensor] = None
+    conditioning_mode: Optional[str] = None
+    negative_noise_pred: Optional[torch.Tensor] = None
+    positive_noise_pred: Optional[torch.Tensor] = None
+    noise_pred: Optional[torch.Tensor] = None
+
+    extra: dict = field(default_factory=dict)
+
+    def __delattr__(self, name: str):
+        setattr(self, name, None)

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -23,19 +23,19 @@
 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 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 PipelineIntermediateState:
+#    step: int
+#    order: int
+#    total_steps: int
+#    timestep: int
+#    latents: torch.Tensor
+#    predicted_original: Optional[torch.Tensor] = None
 
 
 @dataclass

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -5,6 +5,7 @@
 import torch
 
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
+from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
 
 
 @dataclass
@@ -103,7 +104,7 @@ def __init__(
         uncond_regions: Optional[TextConditioningRegions],
         cond_regions: Optional[TextConditioningRegions],
         guidance_scale: Union[float, List[float]],
-        guidance_rescale_multiplier: float = 0,
+        guidance_rescale_multiplier: float = 0,  # TODO: old backend, remove
     ):
         self.uncond_text = uncond_text
         self.cond_text = cond_text
@@ -114,10 +115,96 @@ def __init__(
         # Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
         # images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
         self.guidance_scale = guidance_scale
+        # TODO: old backend, remove
         # For models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7.
         # See [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
         self.guidance_rescale_multiplier = guidance_rescale_multiplier
 
     def is_sdxl(self):
         assert isinstance(self.uncond_text, SDXLConditioningInfo) == isinstance(self.cond_text, SDXLConditioningInfo)
         return isinstance(self.cond_text, SDXLConditioningInfo)
+
+    def to_unet_kwargs(self, unet_kwargs, conditioning_mode):
+        _, _, h, w = unet_kwargs.sample.shape
+        device = unet_kwargs.sample.device
+        dtype = unet_kwargs.sample.dtype
+
+        # TODO: combine regions with conditionings
+        if conditioning_mode == "both":
+            conditionings = [self.uncond_text.embeds, self.cond_text.embeds]
+            c_regions = [self.uncond_regions, self.cond_regions]
+        elif conditioning_mode == "positive":
+            conditionings = [self.cond_text.embeds]
+            c_regions = [self.cond_regions]
+        else:
+            conditionings = [self.uncond_text.embeds]
+            c_regions = [self.uncond_regions]
+
+        encoder_hidden_states, encoder_attention_mask = self._concat_conditionings_for_batch(conditionings)
+
+        unet_kwargs.encoder_hidden_states = encoder_hidden_states
+        unet_kwargs.encoder_attention_mask = encoder_attention_mask
+
+        if self.is_sdxl():
+            added_cond_kwargs = dict(  # noqa: C408
+                text_embeds=torch.cat([c.pooled_embeds for c in conditionings]),
+                time_ids=torch.cat([c.add_time_ids for c in conditionings]),
+            )
+
+            unet_kwargs.added_cond_kwargs = added_cond_kwargs
+
+        if any(r is not None for r in c_regions):
+            tmp_regions = []
+            for c, r in zip(conditionings, c_regions, strict=True):
+                if r is None:
+                    r = TextConditioningRegions(
+                        masks=torch.ones((1, 1, h, w), dtype=dtype),
+                        ranges=[Range(start=0, end=c.embeds.shape[1])],
+                    )
+                tmp_regions.append(r)
+
+            if unet_kwargs.cross_attention_kwargs is None:
+                unet_kwargs.cross_attention_kwargs = {}
+
+            unet_kwargs.cross_attention_kwargs.update(
+                regional_prompt_data=RegionalPromptData(regions=tmp_regions, device=device, dtype=dtype),
+            )
+
+    def _concat_conditionings_for_batch(self, conditionings):
+        def _pad_zeros(t: torch.Tensor, pad_shape: tuple, dim: int):
+            return torch.cat([t, torch.zeros(pad_shape, device=t.device, dtype=t.dtype)], dim=dim)
+
+        def _pad_conditioning(cond, target_len, encoder_attention_mask):
+            conditioning_attention_mask = torch.ones(
+                (cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype
+            )
+
+            if cond.shape[1] < max_len:
+                conditioning_attention_mask = _pad_zeros(
+                    conditioning_attention_mask,
+                    pad_shape=(cond.shape[0], max_len - cond.shape[1]),
+                    dim=1,
+                )
+
+                cond = _pad_zeros(
+                    cond,
+                    pad_shape=(cond.shape[0], max_len - cond.shape[1], cond.shape[2]),
+                    dim=1,
+                )
+
+            if encoder_attention_mask is None:
+                encoder_attention_mask = conditioning_attention_mask
+            else:
+                encoder_attention_mask = torch.cat([encoder_attention_mask, conditioning_attention_mask])
+
+            return cond, encoder_attention_mask
+
+        encoder_attention_mask = None
+        max_len = max([c.shape[1] for c in conditionings])
+        if any(c.shape[1] != max_len for c in conditionings):
+            for i in range(len(conditionings)):
+                conditionings[i], encoder_attention_mask = _pad_conditioning(
+                    conditionings[i], max_len, encoder_attention_mask
+                )
+
+        return torch.cat(conditionings), encoder_attention_mask