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add support for self-attention guidance
See https://arxiv.org/abs/2210.00939
This commit is contained in:
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@ -7,7 +7,6 @@ import refiners.fluxion.layers as fl
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from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
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from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
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T = TypeVar("T", bound="fl.Module")
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@ -68,6 +67,17 @@ class LatentDiffusionModel(fl.Module, ABC):
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@abstractmethod
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def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None: ...
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@abstractmethod
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def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: ...
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@abstractmethod
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def has_self_attention_guidance(self) -> bool: ...
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@abstractmethod
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def compute_self_attention_guidance(
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self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor
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) -> Tensor: ...
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def forward(
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self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **kwargs: Tensor
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) -> Tensor:
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@ -80,6 +90,12 @@ class LatentDiffusionModel(fl.Module, ABC):
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# classifier-free guidance
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noise = unconditional_prediction + condition_scale * (conditional_prediction - unconditional_prediction)
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x = x.narrow(dim=1, start=0, length=4) # support > 4 channels for inpainting
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if self.has_self_attention_guidance():
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noise += self.compute_self_attention_guidance(
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x=x, noise=unconditional_prediction, step=step, clip_text_embedding=clip_text_embedding, **kwargs
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)
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return self.scheduler(x, noise=noise, step=step)
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def structural_copy(self: TLatentDiffusionModel) -> TLatentDiffusionModel:
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@ -0,0 +1,101 @@
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from typing import Any, Generic, TypeVar, TYPE_CHECKING
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import math
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from torch import Tensor, Size
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from jaxtyping import Float
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import torch
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from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
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from refiners.fluxion.adapters.adapter import Adapter
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from refiners.fluxion.context import Contexts
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from refiners.fluxion.utils import interpolate, gaussian_blur
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import refiners.fluxion.layers as fl
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if TYPE_CHECKING:
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
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T = TypeVar("T", bound="SD1UNet | SDXLUNet")
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TSAGAdapter = TypeVar("TSAGAdapter", bound="SAGAdapter[Any]") # Self (see PEP 673)
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class SelfAttentionMap(fl.Passthrough):
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def __init__(self, num_heads: int, context_key: str) -> None:
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self.num_heads = num_heads
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self.context_key = context_key
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super().__init__(
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fl.Lambda(func=self.compute_attention_scores),
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fl.SetContext(context="self_attention_map", key=context_key),
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)
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def split_to_multi_head(
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self, x: Float[Tensor, "batch_size sequence_length embedding_dim"]
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) -> Float[Tensor, "batch_size num_heads sequence_length (embedding_dim//num_heads)"]:
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assert (
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len(x.shape) == 3
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), f"Expected tensor with shape (batch_size sequence_length embedding_dim), got {x.shape}"
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assert (
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x.shape[-1] % self.num_heads == 0
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), f"Embedding dim (x.shape[-1]={x.shape[-1]}) must be divisible by num heads"
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return x.reshape(x.shape[0], x.shape[1], self.num_heads, x.shape[-1] // self.num_heads).transpose(1, 2)
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def compute_attention_scores(self, query: Tensor, key: Tensor, value: Tensor) -> Tensor:
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query, key = self.split_to_multi_head(query), self.split_to_multi_head(key)
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_, _, _, dim = query.shape
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attention = query @ key.permute(0, 1, 3, 2)
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attention = attention / math.sqrt(dim)
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return torch.softmax(input=attention, dim=-1)
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class SelfAttentionShape(fl.Passthrough):
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def __init__(self, context_key: str) -> None:
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self.context_key = context_key
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super().__init__(
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fl.SetContext(context="self_attention_map", key=context_key, callback=self.register_shape),
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)
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def register_shape(self, shapes: list[Size], x: Tensor) -> None:
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assert x.ndim == 4, f"Expected 4D tensor, got {x.ndim}D with shape {x.shape}"
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shapes.append(x.shape[-2:])
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class SAGAdapter(Generic[T], fl.Chain, Adapter[T]):
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def __init__(self, target: T, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None:
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self.scale = scale
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self.kernel_size = kernel_size
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self.sigma = sigma
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with self.setup_adapter(target):
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super().__init__(target)
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def inject(self: "TSAGAdapter", parent: fl.Chain | None = None) -> "TSAGAdapter":
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return super().inject(parent)
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def eject(self) -> None:
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super().eject()
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def compute_sag_mask(
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self, latents: Float[Tensor, "batch_size channels height width"], classifier_free_guidance: bool = True
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) -> Float[Tensor, "batch_size channels height width"]:
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attn_map = self.use_context("self_attention_map")["middle_block_attn_map"]
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if classifier_free_guidance:
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unconditional_attn, _ = attn_map.chunk(2)
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attn_map = unconditional_attn
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attn_shape = self.use_context("self_attention_map")["middle_block_attn_shape"].pop()
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assert len(attn_shape) == 2
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b, c, h, w = latents.shape
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attn_h, attn_w = attn_shape
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attn_mask = attn_map.mean(dim=1, keepdim=False).sum(dim=1, keepdim=False) > 1.0
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attn_mask = attn_mask.reshape(b, attn_h, attn_w).unsqueeze(1).repeat(1, c, 1, 1).type(attn_map.dtype)
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return interpolate(attn_mask, Size((h, w)))
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def compute_degraded_latents(
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self, scheduler: Scheduler, latents: Tensor, noise: Tensor, step: int, classifier_free_guidance: bool = True
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) -> Tensor:
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sag_mask = self.compute_sag_mask(latents=latents, classifier_free_guidance=classifier_free_guidance)
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original_latents = scheduler.remove_noise(x=latents, noise=noise, step=step)
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degraded_latents = gaussian_blur(original_latents, kernel_size=self.kernel_size, sigma=self.sigma)
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degraded_latents = degraded_latents * sag_mask + original_latents * (1 - sag_mask)
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return scheduler.add_noise(degraded_latents, noise=noise, step=step)
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def init_context(self) -> Contexts:
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return {"self_attention_map": {"middle_block_attn_map": None, "middle_block_attn_shape": []}}
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@ -6,6 +6,7 @@ from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
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from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver
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from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.self_attention_guidance import SD1SAGAdapter
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from PIL import Image
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import numpy as np
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from torch import device as Device, dtype as DType, Tensor
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@ -54,6 +55,47 @@ class StableDiffusion_1(LatentDiffusionModel):
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self.unet.set_timestep(timestep=timestep)
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self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
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def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
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if enable:
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if sag := self._find_sag_adapter():
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sag.scale = scale
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else:
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sag = SD1SAGAdapter(target=self.unet, scale=scale)
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sag.inject()
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else:
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if sag := self._find_sag_adapter():
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sag.eject()
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def has_self_attention_guidance(self) -> bool:
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return self._find_sag_adapter() is not None
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def _find_sag_adapter(self) -> SD1SAGAdapter | None:
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for p in self.unet.get_parents():
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if isinstance(p, SD1SAGAdapter):
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return p
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return None
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def compute_self_attention_guidance(
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self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor
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) -> Tensor:
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sag = self._find_sag_adapter()
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assert sag is not None
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degraded_latents = sag.compute_degraded_latents(
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scheduler=self.scheduler,
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latents=x,
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noise=noise,
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step=step,
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classifier_free_guidance=True,
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)
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negative_embedding, _ = clip_text_embedding.chunk(2)
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timestep = self.scheduler.timesteps[step].unsqueeze(dim=0)
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self.set_unet_context(timestep=timestep, clip_text_embedding=negative_embedding, **kwargs)
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degraded_noise = self.unet(degraded_latents)
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return sag.scale * (noise - degraded_noise)
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class StableDiffusion_1_Inpainting(StableDiffusion_1):
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def __init__(
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@ -0,0 +1,41 @@
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from refiners.foundationals.latent_diffusion.self_attention_guidance import (
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SAGAdapter,
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SelfAttentionShape,
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SelfAttentionMap,
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)
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet, MiddleBlock, ResidualBlock
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from refiners.fluxion.layers.attentions import ScaledDotProductAttention
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import refiners.fluxion.layers as fl
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class SD1SAGAdapter(SAGAdapter[SD1UNet]):
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def __init__(self, target: SD1UNet, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None:
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super().__init__(
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target=target,
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scale=scale,
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kernel_size=kernel_size,
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sigma=sigma,
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)
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def inject(self: "SD1SAGAdapter", parent: fl.Chain | None = None) -> "SD1SAGAdapter":
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middle_block = self.target.ensure_find(MiddleBlock)
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middle_block.insert_after_type(ResidualBlock, SelfAttentionShape(context_key="middle_block_attn_shape"))
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# An alternative would be to replace the ScaledDotProductAttention with a version which records the attention
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# scores to avoid computing these scores twice
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self_attn = middle_block.ensure_find(fl.SelfAttention)
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self_attn.insert_before_type(
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ScaledDotProductAttention,
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SelfAttentionMap(num_heads=self_attn.num_heads, context_key="middle_block_attn_map"),
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)
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return super().inject(parent)
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def eject(self) -> None:
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middle_block = self.target.ensure_find(MiddleBlock)
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middle_block.remove(middle_block.ensure_find(SelfAttentionShape))
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self_attn = middle_block.ensure_find(fl.SelfAttention)
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self_attn.remove(self_attn.ensure_find(SelfAttentionMap))
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super().eject()
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@ -4,6 +4,7 @@ from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
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from refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM
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from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.self_attention_guidance import SDXLSAGAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
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from torch import device as Device, dtype as DType, Tensor
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@ -67,7 +68,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
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clip_text_embedding: Tensor,
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pooled_text_embedding: Tensor,
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time_ids: Tensor,
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**_: Tensor
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**_: Tensor,
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) -> None:
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self.unet.set_timestep(timestep=timestep)
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self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
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@ -83,7 +84,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
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pooled_text_embedding: Tensor,
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time_ids: Tensor,
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condition_scale: float = 5.0,
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**kwargs: Tensor
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**kwargs: Tensor,
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) -> Tensor:
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return super().forward(
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x=x,
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@ -92,5 +93,62 @@ class StableDiffusion_XL(LatentDiffusionModel):
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pooled_text_embedding=pooled_text_embedding,
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time_ids=time_ids,
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condition_scale=condition_scale,
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**kwargs
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**kwargs,
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)
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def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
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if enable:
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if sag := self._find_sag_adapter():
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sag.scale = scale
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else:
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sag = SDXLSAGAdapter(target=self.unet, scale=scale)
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sag.inject()
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else:
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if sag := self._find_sag_adapter():
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sag.eject()
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def has_self_attention_guidance(self) -> bool:
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return self._find_sag_adapter() is not None
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def _find_sag_adapter(self) -> SDXLSAGAdapter | None:
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for p in self.unet.get_parents():
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if isinstance(p, SDXLSAGAdapter):
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return p
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return None
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def compute_self_attention_guidance(
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self,
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x: Tensor,
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noise: Tensor,
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step: int,
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*,
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clip_text_embedding: Tensor,
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pooled_text_embedding: Tensor,
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time_ids: Tensor,
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**kwargs: Tensor,
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) -> Tensor:
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sag = self._find_sag_adapter()
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assert sag is not None
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degraded_latents = sag.compute_degraded_latents(
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scheduler=self.scheduler,
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latents=x,
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noise=noise,
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step=step,
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classifier_free_guidance=True,
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)
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negative_embedding, _ = clip_text_embedding.chunk(2)
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negative_pooled_embedding, _ = pooled_text_embedding.chunk(2)
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timestep = self.scheduler.timesteps[step].unsqueeze(dim=0)
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time_ids, _ = time_ids.chunk(2)
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self.set_unet_context(
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timestep=timestep,
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clip_text_embedding=negative_embedding,
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pooled_text_embedding=negative_pooled_embedding,
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time_ids=time_ids,
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**kwargs,
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)
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degraded_noise = self.unet(degraded_latents)
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return sag.scale * (noise - degraded_noise)
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@ -0,0 +1,41 @@
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from refiners.foundationals.latent_diffusion.self_attention_guidance import (
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SAGAdapter,
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SelfAttentionShape,
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SelfAttentionMap,
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)
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet, MiddleBlock, ResidualBlock
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from refiners.fluxion.layers.attentions import ScaledDotProductAttention
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import refiners.fluxion.layers as fl
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class SDXLSAGAdapter(SAGAdapter[SDXLUNet]):
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def __init__(self, target: SDXLUNet, scale: float = 1.0, kernel_size: int = 9, sigma: float = 1.0) -> None:
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super().__init__(
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target=target,
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scale=scale,
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kernel_size=kernel_size,
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sigma=sigma,
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)
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def inject(self: "SDXLSAGAdapter", parent: fl.Chain | None = None) -> "SDXLSAGAdapter":
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middle_block = self.target.ensure_find(MiddleBlock)
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middle_block.insert_after_type(ResidualBlock, SelfAttentionShape(context_key="middle_block_attn_shape"))
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# An alternative would be to replace the ScaledDotProductAttention with a version which records the attention
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# scores to avoid computing these scores twice
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self_attn = middle_block.ensure_find(fl.SelfAttention)
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self_attn.insert_before_type(
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ScaledDotProductAttention,
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SelfAttentionMap(num_heads=self_attn.num_heads, context_key="middle_block_attn_map"),
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)
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return super().inject(parent)
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def eject(self) -> None:
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middle_block = self.target.ensure_find(MiddleBlock)
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middle_block.remove(middle_block.ensure_find(SelfAttentionShape))
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self_attn = middle_block.ensure_find(fl.SelfAttention)
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self_attn.remove(self_attn.ensure_find(SelfAttentionMap))
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super().eject()
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@ -64,6 +64,11 @@ def expected_image_std_random_init(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "expected_std_random_init.png").convert("RGB")
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@pytest.fixture
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def expected_image_std_random_init_sag(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "expected_std_random_init_sag.png").convert("RGB")
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@pytest.fixture
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def expected_image_std_init_image(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "expected_std_init_image.png").convert("RGB")
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@ -109,6 +114,11 @@ def expected_sdxl_ddim_random_init(ref_path: Path) -> Image.Image:
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return Image.open(fp=ref_path / "expected_cutecat_sdxl_ddim_random_init.png").convert(mode="RGB")
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@pytest.fixture
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def expected_sdxl_ddim_random_init_sag(ref_path: Path) -> Image.Image:
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return Image.open(fp=ref_path / "expected_cutecat_sdxl_ddim_random_init_sag.png").convert(mode="RGB")
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@pytest.fixture(scope="module", params=["canny", "depth", "lineart", "normals", "sam"])
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def controlnet_data(
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ref_path: Path, test_weights_path: Path, request: pytest.FixtureRequest
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||||
|
@ -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)
|
||||
|
|
|
@ -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`
|
||||
- `expected_t2i_adapter_xl_canny.png`
|
||||
- `expected_image_sdxl_ip_adapter_plus_woman.png`
|
||||
- `expected_cutecat_sdxl_ddim_random_init_sag.png`
|
||||
|
||||
## Other images
|
||||
|
||||
|
|
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tests/e2e/test_diffusion_ref/expected_std_random_init_sag.png
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BIN
tests/e2e/test_diffusion_ref/expected_std_random_init_sag.png
Normal file
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Loading…
Reference in a new issue