From b80769939de09e1430844631cb7dc2e52c041d49 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=A9dric=20Deltheil?= Date: Mon, 9 Oct 2023 16:57:58 +0200 Subject: [PATCH] add support for self-attention guidance See https://arxiv.org/abs/2210.00939 --- .../foundationals/latent_diffusion/model.py | 18 +++- .../self_attention_guidance.py | 101 ++++++++++++++++++ .../stable_diffusion_1/model.py | 42 ++++++++ .../self_attention_guidance.py | 41 +++++++ .../stable_diffusion_xl/model.py | 64 ++++++++++- .../self_attention_guidance.py | 41 +++++++ tests/e2e/test_diffusion.py | 75 +++++++++++++ tests/e2e/test_diffusion_ref/README.md | 2 + ...cted_cutecat_sdxl_ddim_random_init_sag.png | Bin 0 -> 1711741 bytes .../expected_std_random_init_sag.png | Bin 0 -> 504639 bytes 10 files changed, 380 insertions(+), 4 deletions(-) create mode 100644 src/refiners/foundationals/latent_diffusion/self_attention_guidance.py create mode 100644 src/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py create mode 100644 src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py create mode 100644 tests/e2e/test_diffusion_ref/expected_cutecat_sdxl_ddim_random_init_sag.png create mode 100644 tests/e2e/test_diffusion_ref/expected_std_random_init_sag.png diff --git a/src/refiners/foundationals/latent_diffusion/model.py b/src/refiners/foundationals/latent_diffusion/model.py index 353a598..7882dba 100644 --- a/src/refiners/foundationals/latent_diffusion/model.py +++ b/src/refiners/foundationals/latent_diffusion/model.py @@ -7,7 +7,6 @@ import refiners.fluxion.layers as fl from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler - T = TypeVar("T", bound="fl.Module") @@ -68,6 +67,17 @@ class LatentDiffusionModel(fl.Module, ABC): @abstractmethod def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None: ... + @abstractmethod + def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: ... + + @abstractmethod + def has_self_attention_guidance(self) -> bool: ... + + @abstractmethod + def compute_self_attention_guidance( + self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor + ) -> Tensor: ... + def forward( self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **kwargs: Tensor ) -> Tensor: @@ -80,6 +90,12 @@ class LatentDiffusionModel(fl.Module, ABC): # classifier-free guidance noise = unconditional_prediction + condition_scale * (conditional_prediction - unconditional_prediction) x = x.narrow(dim=1, start=0, length=4) # support > 4 channels for inpainting + + if self.has_self_attention_guidance(): + noise += self.compute_self_attention_guidance( + x=x, noise=unconditional_prediction, step=step, clip_text_embedding=clip_text_embedding, **kwargs + ) + return self.scheduler(x, noise=noise, step=step) def structural_copy(self: TLatentDiffusionModel) -> TLatentDiffusionModel: diff --git a/src/refiners/foundationals/latent_diffusion/self_attention_guidance.py b/src/refiners/foundationals/latent_diffusion/self_attention_guidance.py new file mode 100644 index 0000000..711e730 --- /dev/null +++ b/src/refiners/foundationals/latent_diffusion/self_attention_guidance.py @@ -0,0 +1,101 @@ +from typing import Any, Generic, TypeVar, TYPE_CHECKING +import math + +from torch import Tensor, Size +from jaxtyping import Float +import torch + +from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler +from refiners.fluxion.adapters.adapter import Adapter +from refiners.fluxion.context import Contexts +from refiners.fluxion.utils import interpolate, gaussian_blur +import refiners.fluxion.layers as fl + +if TYPE_CHECKING: + from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet + from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet + +T = TypeVar("T", bound="SD1UNet | SDXLUNet") +TSAGAdapter = TypeVar("TSAGAdapter", bound="SAGAdapter[Any]") # Self (see PEP 673) + + +class SelfAttentionMap(fl.Passthrough): + def __init__(self, num_heads: int, context_key: str) -> None: + self.num_heads = num_heads + self.context_key = context_key + super().__init__( + fl.Lambda(func=self.compute_attention_scores), + fl.SetContext(context="self_attention_map", key=context_key), + ) + + def split_to_multi_head( + self, x: Float[Tensor, "batch_size sequence_length embedding_dim"] + ) -> Float[Tensor, "batch_size num_heads sequence_length (embedding_dim//num_heads)"]: + assert ( + len(x.shape) == 3 + ), f"Expected tensor with shape (batch_size sequence_length embedding_dim), got {x.shape}" + assert ( + x.shape[-1] % self.num_heads == 0 + ), f"Embedding dim (x.shape[-1]={x.shape[-1]}) must be divisible by num heads" + return x.reshape(x.shape[0], x.shape[1], self.num_heads, x.shape[-1] // self.num_heads).transpose(1, 2) + + def compute_attention_scores(self, query: Tensor, key: Tensor, value: Tensor) -> Tensor: + query, key = self.split_to_multi_head(query), self.split_to_multi_head(key) + _, _, _, dim = query.shape + attention = query @ key.permute(0, 1, 3, 2) + attention = attention / math.sqrt(dim) + return torch.softmax(input=attention, dim=-1) + + +class SelfAttentionShape(fl.Passthrough): + def __init__(self, context_key: str) -> None: + self.context_key = context_key + super().__init__( + fl.SetContext(context="self_attention_map", key=context_key, callback=self.register_shape), + ) + + def register_shape(self, shapes: list[Size], x: Tensor) -> None: + assert x.ndim == 4, f"Expected 4D tensor, got {x.ndim}D with shape {x.shape}" + shapes.append(x.shape[-2:]) + + +class SAGAdapter(Generic[T], fl.Chain, Adapter[T]): + def __init__(self, target: T, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None: + self.scale = scale + self.kernel_size = kernel_size + self.sigma = sigma + with self.setup_adapter(target): + super().__init__(target) + + def inject(self: "TSAGAdapter", parent: fl.Chain | None = None) -> "TSAGAdapter": + return super().inject(parent) + + def eject(self) -> None: + super().eject() + + def compute_sag_mask( + self, latents: Float[Tensor, "batch_size channels height width"], classifier_free_guidance: bool = True + ) -> Float[Tensor, "batch_size channels height width"]: + attn_map = self.use_context("self_attention_map")["middle_block_attn_map"] + if classifier_free_guidance: + unconditional_attn, _ = attn_map.chunk(2) + attn_map = unconditional_attn + attn_shape = self.use_context("self_attention_map")["middle_block_attn_shape"].pop() + assert len(attn_shape) == 2 + b, c, h, w = latents.shape + attn_h, attn_w = attn_shape + attn_mask = attn_map.mean(dim=1, keepdim=False).sum(dim=1, keepdim=False) > 1.0 + attn_mask = attn_mask.reshape(b, attn_h, attn_w).unsqueeze(1).repeat(1, c, 1, 1).type(attn_map.dtype) + return interpolate(attn_mask, Size((h, w))) + + def compute_degraded_latents( + self, scheduler: Scheduler, latents: Tensor, noise: Tensor, step: int, classifier_free_guidance: bool = True + ) -> Tensor: + sag_mask = self.compute_sag_mask(latents=latents, classifier_free_guidance=classifier_free_guidance) + original_latents = scheduler.remove_noise(x=latents, noise=noise, step=step) + degraded_latents = gaussian_blur(original_latents, kernel_size=self.kernel_size, sigma=self.sigma) + degraded_latents = degraded_latents * sag_mask + original_latents * (1 - sag_mask) + return scheduler.add_noise(degraded_latents, noise=noise, step=step) + + def init_context(self) -> Contexts: + return {"self_attention_map": {"middle_block_attn_map": None, "middle_block_attn_shape": []}} diff --git a/src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py b/src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py index 3bf5945..1604f64 100644 --- a/src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py +++ b/src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py @@ -6,6 +6,7 @@ from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet +from refiners.foundationals.latent_diffusion.stable_diffusion_1.self_attention_guidance import SD1SAGAdapter from PIL import Image import numpy as np from torch import device as Device, dtype as DType, Tensor @@ -54,6 +55,47 @@ class StableDiffusion_1(LatentDiffusionModel): self.unet.set_timestep(timestep=timestep) self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding) + def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: + if enable: + if sag := self._find_sag_adapter(): + sag.scale = scale + else: + sag = SD1SAGAdapter(target=self.unet, scale=scale) + sag.inject() + else: + if sag := self._find_sag_adapter(): + sag.eject() + + def has_self_attention_guidance(self) -> bool: + return self._find_sag_adapter() is not None + + def _find_sag_adapter(self) -> SD1SAGAdapter | None: + for p in self.unet.get_parents(): + if isinstance(p, SD1SAGAdapter): + return p + return None + + def compute_self_attention_guidance( + self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor + ) -> Tensor: + sag = self._find_sag_adapter() + assert sag is not None + + degraded_latents = sag.compute_degraded_latents( + scheduler=self.scheduler, + latents=x, + noise=noise, + step=step, + classifier_free_guidance=True, + ) + + negative_embedding, _ = clip_text_embedding.chunk(2) + timestep = self.scheduler.timesteps[step].unsqueeze(dim=0) + self.set_unet_context(timestep=timestep, clip_text_embedding=negative_embedding, **kwargs) + degraded_noise = self.unet(degraded_latents) + + return sag.scale * (noise - degraded_noise) + class StableDiffusion_1_Inpainting(StableDiffusion_1): def __init__( diff --git a/src/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py b/src/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py new file mode 100644 index 0000000..e92471e --- /dev/null +++ b/src/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py @@ -0,0 +1,41 @@ +from refiners.foundationals.latent_diffusion.self_attention_guidance import ( + SAGAdapter, + SelfAttentionShape, + SelfAttentionMap, +) +from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet, MiddleBlock, ResidualBlock +from refiners.fluxion.layers.attentions import ScaledDotProductAttention +import refiners.fluxion.layers as fl + + +class SD1SAGAdapter(SAGAdapter[SD1UNet]): + def __init__(self, target: SD1UNet, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None: + super().__init__( + target=target, + scale=scale, + kernel_size=kernel_size, + sigma=sigma, + ) + + def inject(self: "SD1SAGAdapter", parent: fl.Chain | None = None) -> "SD1SAGAdapter": + middle_block = self.target.ensure_find(MiddleBlock) + middle_block.insert_after_type(ResidualBlock, SelfAttentionShape(context_key="middle_block_attn_shape")) + + # An alternative would be to replace the ScaledDotProductAttention with a version which records the attention + # scores to avoid computing these scores twice + self_attn = middle_block.ensure_find(fl.SelfAttention) + self_attn.insert_before_type( + ScaledDotProductAttention, + SelfAttentionMap(num_heads=self_attn.num_heads, context_key="middle_block_attn_map"), + ) + + return super().inject(parent) + + def eject(self) -> None: + middle_block = self.target.ensure_find(MiddleBlock) + middle_block.remove(middle_block.ensure_find(SelfAttentionShape)) + + self_attn = middle_block.ensure_find(fl.SelfAttention) + self_attn.remove(self_attn.ensure_find(SelfAttentionMap)) + + super().eject() diff --git a/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py b/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py index 0e4c783..819ff8a 100644 --- a/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py +++ b/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py @@ -4,6 +4,7 @@ from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel from refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet +from refiners.foundationals.latent_diffusion.stable_diffusion_xl.self_attention_guidance import SDXLSAGAdapter from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder from torch import device as Device, dtype as DType, Tensor @@ -67,7 +68,7 @@ class StableDiffusion_XL(LatentDiffusionModel): clip_text_embedding: Tensor, pooled_text_embedding: Tensor, time_ids: Tensor, - **_: Tensor + **_: Tensor, ) -> None: self.unet.set_timestep(timestep=timestep) self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding) @@ -83,7 +84,7 @@ class StableDiffusion_XL(LatentDiffusionModel): pooled_text_embedding: Tensor, time_ids: Tensor, condition_scale: float = 5.0, - **kwargs: Tensor + **kwargs: Tensor, ) -> Tensor: return super().forward( x=x, @@ -92,5 +93,62 @@ class StableDiffusion_XL(LatentDiffusionModel): pooled_text_embedding=pooled_text_embedding, time_ids=time_ids, condition_scale=condition_scale, - **kwargs + **kwargs, ) + + def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: + if enable: + if sag := self._find_sag_adapter(): + sag.scale = scale + else: + sag = SDXLSAGAdapter(target=self.unet, scale=scale) + sag.inject() + else: + if sag := self._find_sag_adapter(): + sag.eject() + + def has_self_attention_guidance(self) -> bool: + return self._find_sag_adapter() is not None + + def _find_sag_adapter(self) -> SDXLSAGAdapter | None: + for p in self.unet.get_parents(): + if isinstance(p, SDXLSAGAdapter): + return p + return None + + def compute_self_attention_guidance( + self, + x: Tensor, + noise: Tensor, + step: int, + *, + clip_text_embedding: Tensor, + pooled_text_embedding: Tensor, + time_ids: Tensor, + **kwargs: Tensor, + ) -> Tensor: + sag = self._find_sag_adapter() + assert sag is not None + + degraded_latents = sag.compute_degraded_latents( + scheduler=self.scheduler, + latents=x, + noise=noise, + step=step, + classifier_free_guidance=True, + ) + + negative_embedding, _ = clip_text_embedding.chunk(2) + negative_pooled_embedding, _ = pooled_text_embedding.chunk(2) + timestep = self.scheduler.timesteps[step].unsqueeze(dim=0) + time_ids, _ = time_ids.chunk(2) + self.set_unet_context( + timestep=timestep, + clip_text_embedding=negative_embedding, + pooled_text_embedding=negative_pooled_embedding, + time_ids=time_ids, + **kwargs, + ) + degraded_noise = self.unet(degraded_latents) + + return sag.scale * (noise - degraded_noise) diff --git a/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py b/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py new file mode 100644 index 0000000..d125156 --- /dev/null +++ b/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py @@ -0,0 +1,41 @@ +from refiners.foundationals.latent_diffusion.self_attention_guidance import ( + SAGAdapter, + SelfAttentionShape, + SelfAttentionMap, +) +from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet, MiddleBlock, ResidualBlock +from refiners.fluxion.layers.attentions import ScaledDotProductAttention +import refiners.fluxion.layers as fl + + +class SDXLSAGAdapter(SAGAdapter[SDXLUNet]): + def __init__(self, target: SDXLUNet, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None: + super().__init__( + target=target, + scale=scale, + kernel_size=kernel_size, + sigma=sigma, + ) + + def inject(self: "SDXLSAGAdapter", parent: fl.Chain | None = None) -> "SDXLSAGAdapter": + middle_block = self.target.ensure_find(MiddleBlock) + middle_block.insert_after_type(ResidualBlock, SelfAttentionShape(context_key="middle_block_attn_shape")) + + # An alternative would be to replace the ScaledDotProductAttention with a version which records the attention + # scores to avoid computing these scores twice + self_attn = middle_block.ensure_find(fl.SelfAttention) + self_attn.insert_before_type( + ScaledDotProductAttention, + SelfAttentionMap(num_heads=self_attn.num_heads, context_key="middle_block_attn_map"), + ) + + return super().inject(parent) + + def eject(self) -> None: + middle_block = self.target.ensure_find(MiddleBlock) + middle_block.remove(middle_block.ensure_find(SelfAttentionShape)) + + self_attn = middle_block.ensure_find(fl.SelfAttention) + self_attn.remove(self_attn.ensure_find(SelfAttentionMap)) + + super().eject() diff --git a/tests/e2e/test_diffusion.py b/tests/e2e/test_diffusion.py index 6e6b038..c5e8473 100644 --- a/tests/e2e/test_diffusion.py +++ b/tests/e2e/test_diffusion.py @@ -64,6 +64,11 @@ def expected_image_std_random_init(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_std_random_init.png").convert("RGB") +@pytest.fixture +def expected_image_std_random_init_sag(ref_path: Path) -> Image.Image: + return Image.open(ref_path / "expected_std_random_init_sag.png").convert("RGB") + + @pytest.fixture def expected_image_std_init_image(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_std_init_image.png").convert("RGB") @@ -109,6 +114,11 @@ def expected_sdxl_ddim_random_init(ref_path: Path) -> Image.Image: return Image.open(fp=ref_path / "expected_cutecat_sdxl_ddim_random_init.png").convert(mode="RGB") +@pytest.fixture +def expected_sdxl_ddim_random_init_sag(ref_path: Path) -> Image.Image: + return Image.open(fp=ref_path / "expected_cutecat_sdxl_ddim_random_init_sag.png").convert(mode="RGB") + + @pytest.fixture(scope="module", params=["canny", "depth", "lineart", "normals", "sam"]) def controlnet_data( ref_path: Path, test_weights_path: Path, request: pytest.FixtureRequest @@ -514,6 +524,35 @@ def test_diffusion_std_random_init_float16( ensure_similar_images(predicted_image, expected_image_std_random_init, min_psnr=35, min_ssim=0.98) +@torch.no_grad() +def test_diffusion_std_random_init_sag( + sd15_std: StableDiffusion_1, expected_image_std_random_init_sag: Image.Image, test_device: torch.device +): + sd15 = sd15_std + n_steps = 30 + + prompt = "a cute cat, detailed high-quality professional image" + negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" + clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) + + sd15.set_num_inference_steps(n_steps) + sd15.set_self_attention_guidance(enable=True, scale=0.75) + + manual_seed(2) + x = torch.randn(1, 4, 64, 64, device=test_device) + + for step in sd15.steps: + x = sd15( + x, + step=step, + clip_text_embedding=clip_text_embedding, + condition_scale=7.5, + ) + predicted_image = sd15.lda.decode_latents(x) + + ensure_similar_images(predicted_image, expected_image_std_random_init_sag) + + @torch.no_grad() def test_diffusion_std_init_image( sd15_std: StableDiffusion_1, @@ -1364,6 +1403,42 @@ def test_sdxl_random_init( ensure_similar_images(img_1=predicted_image, img_2=expected_image, min_psnr=35, min_ssim=0.98) +@torch.no_grad() +def test_sdxl_random_init_sag( + sdxl_ddim: StableDiffusion_XL, expected_sdxl_ddim_random_init_sag: Image.Image, test_device: torch.device +) -> None: + sdxl = sdxl_ddim + expected_image = expected_sdxl_ddim_random_init_sag + n_steps = 30 + + prompt = "a cute cat, detailed high-quality professional image" + negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" + + clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding( + text=prompt, negative_text=negative_prompt + ) + time_ids = sdxl.default_time_ids + + sdxl.set_num_inference_steps(num_inference_steps=n_steps) + sdxl.set_self_attention_guidance(enable=True, scale=0.75) + + manual_seed(seed=2) + x = torch.randn(1, 4, 128, 128, device=test_device) + + for step in sdxl.steps: + x = sdxl( + x, + step=step, + clip_text_embedding=clip_text_embedding, + pooled_text_embedding=pooled_text_embedding, + time_ids=time_ids, + condition_scale=5, + ) + predicted_image = sdxl.lda.decode_latents(x=x) + + ensure_similar_images(img_1=predicted_image, img_2=expected_image) + + @torch.no_grad() def test_multi_diffusion(sd15_ddim: StableDiffusion_1, expected_multi_diffusion: Image.Image) -> None: manual_seed(seed=2) diff --git a/tests/e2e/test_diffusion_ref/README.md b/tests/e2e/test_diffusion_ref/README.md index ca58f65..f1feae8 100644 --- a/tests/e2e/test_diffusion_ref/README.md +++ b/tests/e2e/test_diffusion_ref/README.md @@ -34,6 +34,7 @@ output.images[0].save("std_random_init_expected.png") Special cases: +- For self-attention guidance, `StableDiffusionSAGPipeline` has been used instead of the default pipeline. - `expected_refonly.png` has been generated [with Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui). - The following references have been generated with refiners itself (and inspected so that they look reasonable): - `expected_inpainting_refonly.png`, @@ -42,6 +43,7 @@ Special cases: - `expected_ip_adapter_controlnet.png` - 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