diff --git "a/v0.26.0/pipeline_sdxl_style_aligned.py" "b/v0.26.0/pipeline_sdxl_style_aligned.py"
new file mode 100644--- /dev/null
+++ "b/v0.26.0/pipeline_sdxl_style_aligned.py"
@@ -0,0 +1,2025 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# 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.
+#
+# Based on [Style Aligned Image Generation via Shared Attention](https://arxiv.org/abs/2312.02133).
+# Authors: Amir Hertz, Andrey Voynov, Shlomi Fruchter, Daniel Cohen-Or
+# Project Page: https://style-aligned-gen.github.io/
+# Code: https://github.com/google/style-aligned
+#
+# Adapted to Diffusers by [Aryan V S](https://github.com/a-r-r-o-w/).
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from transformers import (
+ CLIPImageProcessor,
+ CLIPTextModel,
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+ CLIPVisionModelWithProjection,
+)
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+ FromSingleFileMixin,
+ IPAdapterMixin,
+ StableDiffusionXLLoraLoaderMixin,
+ TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import (
+ Attention,
+ AttnProcessor2_0,
+ FusedAttnProcessor2_0,
+ LoRAAttnProcessor2_0,
+ LoRAXFormersAttnProcessor,
+ XFormersAttnProcessor,
+)
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+ USE_PEFT_BACKEND,
+ deprecate,
+ is_invisible_watermark_available,
+ is_torch_xla_available,
+ logging,
+ replace_example_docstring,
+ scale_lora_layers,
+ unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+
+
+if is_invisible_watermark_available():
+ from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
+
+if is_torch_xla_available():
+ import torch_xla.core.xla_model as xm
+
+ XLA_AVAILABLE = True
+else:
+ XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> from typing import List
+
+ >>> import torch
+ >>> from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+ >>> from PIL import Image
+
+ >>> model_id = "a-r-r-o-w/dreamshaper-xl-turbo"
+ >>> pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", custom_pipeline="pipeline_sdxl_style_aligned")
+ >>> pipe = pipe.to("cuda")
+
+ # Enable memory saving techniques
+ >>> pipe.enable_vae_slicing()
+ >>> pipe.enable_vae_tiling()
+
+ >>> prompt = [
+ ... "a toy train. macro photo. 3d game asset",
+ ... "a toy airplane. macro photo. 3d game asset",
+ ... "a toy bicycle. macro photo. 3d game asset",
+ ... "a toy car. macro photo. 3d game asset",
+ ... ]
+ >>> negative_prompt = "low quality, worst quality, "
+
+ >>> # Enable StyleAligned
+ >>> pipe.enable_style_aligned(
+ ... share_group_norm=False,
+ ... share_layer_norm=False,
+ ... share_attention=True,
+ ... adain_queries=True,
+ ... adain_keys=True,
+ ... adain_values=False,
+ ... full_attention_share=False,
+ ... shared_score_scale=1.0,
+ ... shared_score_shift=0.0,
+ ... only_self_level=0.0,
+ >>> )
+
+ >>> # Run inference
+ >>> images = pipe(
+ ... prompt=prompt,
+ ... negative_prompt=negative_prompt,
+ ... guidance_scale=2,
+ ... height=1024,
+ ... width=1024,
+ ... num_inference_steps=10,
+ ... generator=torch.Generator().manual_seed(42),
+ >>> ).images
+
+ >>> # Disable StyleAligned if you do not wish to use it anymore
+ >>> pipe.disable_style_aligned()
+ ```
+"""
+
+
+def expand_first(feat: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
+ b = feat.shape[0]
+ feat_style = torch.stack((feat[0], feat[b // 2])).unsqueeze(1)
+ if scale == 1:
+ feat_style = feat_style.expand(2, b // 2, *feat.shape[1:])
+ else:
+ feat_style = feat_style.repeat(1, b // 2, 1, 1, 1)
+ feat_style = torch.cat([feat_style[:, :1], scale * feat_style[:, 1:]], dim=1)
+ return feat_style.reshape(*feat.shape)
+
+
+def concat_first(feat: torch.Tensor, dim: int = 2, scale: float = 1.0) -> torch.Tensor:
+ feat_style = expand_first(feat, scale=scale)
+ return torch.cat((feat, feat_style), dim=dim)
+
+
+def calc_mean_std(feat: torch.Tensor, eps: float = 1e-5) -> tuple[torch.Tensor, torch.Tensor]:
+ feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()
+ feat_mean = feat.mean(dim=-2, keepdims=True)
+ return feat_mean, feat_std
+
+
+def adain(feat: torch.Tensor) -> torch.Tensor:
+ feat_mean, feat_std = calc_mean_std(feat)
+ feat_style_mean = expand_first(feat_mean)
+ feat_style_std = expand_first(feat_std)
+ feat = (feat - feat_mean) / feat_std
+ feat = feat * feat_style_std + feat_style_mean
+ return feat
+
+
+def get_switch_vec(total_num_layers, level):
+ if level == 0:
+ return torch.zeros(total_num_layers, dtype=torch.bool)
+ if level == 1:
+ return torch.ones(total_num_layers, dtype=torch.bool)
+ to_flip = level > 0.5
+ if to_flip:
+ level = 1 - level
+ num_switch = int(level * total_num_layers)
+ vec = torch.arange(total_num_layers)
+ vec = vec % (total_num_layers // num_switch)
+ vec = vec == 0
+ if to_flip:
+ vec = ~vec
+ return vec
+
+
+class SharedAttentionProcessor(AttnProcessor2_0):
+ def __init__(
+ self,
+ share_attention: bool = True,
+ adain_queries: bool = True,
+ adain_keys: bool = True,
+ adain_values: bool = False,
+ full_attention_share: bool = False,
+ shared_score_scale: float = 1.0,
+ shared_score_shift: float = 0.0,
+ ):
+ r"""Shared Attention Processor as proposed in the StyleAligned paper."""
+ super().__init__()
+ self.share_attention = share_attention
+ self.adain_queries = adain_queries
+ self.adain_keys = adain_keys
+ self.adain_values = adain_values
+ self.full_attention_share = full_attention_share
+ self.shared_score_scale = shared_score_scale
+ self.shared_score_shift = shared_score_shift
+
+ def shifted_scaled_dot_product_attention(
+ self, attn: Attention, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+ ) -> torch.Tensor:
+ logits = torch.einsum("bhqd,bhkd->bhqk", query, key) * attn.scale
+ logits[:, :, :, query.shape[2] :] += self.shared_score_shift
+ probs = logits.softmax(-1)
+ return torch.einsum("bhqk,bhkd->bhqd", probs, value)
+
+ def shared_call(
+ self,
+ attn: Attention,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ):
+ residual = hidden_states
+ input_ndim = hidden_states.ndim
+ if input_ndim == 4:
+ batch_size, channel, height, width = hidden_states.shape
+ hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+ batch_size, sequence_length, _ = (
+ hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+ )
+
+ if attention_mask is not None:
+ attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+ # scaled_dot_product_attention expects attention_mask shape to be
+ # (batch, heads, source_length, target_length)
+ attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+ if attn.group_norm is not None:
+ hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+ query = attn.to_q(hidden_states)
+ key = attn.to_k(hidden_states)
+ value = attn.to_v(hidden_states)
+ inner_dim = key.shape[-1]
+ head_dim = inner_dim // attn.heads
+
+ query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+ key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+ value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+ if self.adain_queries:
+ query = adain(query)
+ if self.adain_keys:
+ key = adain(key)
+ if self.adain_values:
+ value = adain(value)
+ if self.share_attention:
+ key = concat_first(key, -2, scale=self.shared_score_scale)
+ value = concat_first(value, -2)
+ if self.shared_score_shift != 0:
+ hidden_states = self.shifted_scaled_dot_product_attention(attn, query, key, value)
+ else:
+ hidden_states = F.scaled_dot_product_attention(
+ query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+ )
+ else:
+ hidden_states = F.scaled_dot_product_attention(
+ query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+ )
+
+ hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+ hidden_states = hidden_states.to(query.dtype)
+
+ # linear proj
+ hidden_states = attn.to_out[0](hidden_states)
+ # dropout
+ hidden_states = attn.to_out[1](hidden_states)
+
+ if input_ndim == 4:
+ hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+ if attn.residual_connection:
+ hidden_states = hidden_states + residual
+
+ hidden_states = hidden_states / attn.rescale_output_factor
+ return hidden_states
+
+ def __call__(
+ self,
+ attn: Attention,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ):
+ if self.full_attention_share:
+ b, n, d = hidden_states.shape
+ k = 2
+ hidden_states = hidden_states.view(k, b, n, d).permute(0, 1, 3, 2).contiguous().view(-1, n, d)
+ # hidden_states = einops.rearrange(hidden_states, "(k b) n d -> k (b n) d", k=2)
+ hidden_states = super().__call__(
+ attn,
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ **kwargs,
+ )
+ hidden_states = hidden_states.view(k, b, n, d).permute(0, 1, 3, 2).contiguous().view(-1, n, d)
+ # hidden_states = einops.rearrange(hidden_states, "k (b n) d -> (k b) n d", n=n)
+ else:
+ hidden_states = self.shared_call(attn, hidden_states, hidden_states, attention_mask, **kwargs)
+
+ return hidden_states
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+ """
+ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+ Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+ """
+ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+ std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+ # rescale the results from guidance (fixes overexposure)
+ noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+ # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+ noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+ return noise_cfg
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+ scheduler,
+ num_inference_steps: Optional[int] = None,
+ device: Optional[Union[str, torch.device]] = None,
+ timesteps: Optional[List[int]] = None,
+ **kwargs,
+):
+ """
+ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+ custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+ Args:
+ scheduler (`SchedulerMixin`):
+ The scheduler to get timesteps from.
+ num_inference_steps (`int`):
+ The number of diffusion steps used when generating samples with a pre-trained model. If used,
+ `timesteps` must be `None`.
+ device (`str` or `torch.device`, *optional*):
+ The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+ timesteps (`List[int]`, *optional*):
+ Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
+ timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
+ must be `None`.
+
+ Returns:
+ `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+ second element is the number of inference steps.
+ """
+ if timesteps is not None:
+ accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+ if not accepts_timesteps:
+ raise ValueError(
+ f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+ f" timestep schedules. Please check whether you are using the correct scheduler."
+ )
+ scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ num_inference_steps = len(timesteps)
+ else:
+ scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ return timesteps, num_inference_steps
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+ encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+ if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+ return encoder_output.latent_dist.sample(generator)
+ elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+ return encoder_output.latent_dist.mode()
+ elif hasattr(encoder_output, "latents"):
+ return encoder_output.latents
+ else:
+ raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class StyleAlignedSDXLPipeline(
+ DiffusionPipeline,
+ FromSingleFileMixin,
+ StableDiffusionXLLoraLoaderMixin,
+ TextualInversionLoaderMixin,
+ IPAdapterMixin,
+):
+ r"""
+ Pipeline for text-to-image generation using Stable Diffusion XL.
+
+ This pipeline also adds experimental support for [StyleAligned](https://arxiv.org/abs/2312.02133). It can
+ be enabled/disabled using `.enable_style_aligned()` or `.disable_style_aligned()` respectively.
+
+ This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+ library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+ The pipeline also inherits the following loading methods:
+ - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+ - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+ - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+ - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+ - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
+
+ Args:
+ vae ([`AutoencoderKL`]):
+ Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+ text_encoder ([`CLIPTextModel`]):
+ Frozen text-encoder. Stable Diffusion XL uses the text portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+ the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+ text_encoder_2 ([` CLIPTextModelWithProjection`]):
+ Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+ specifically the
+ [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+ variant.
+ tokenizer (`CLIPTokenizer`):
+ Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ tokenizer_2 (`CLIPTokenizer`):
+ Second Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+ scheduler ([`SchedulerMixin`]):
+ A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+ [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+ force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
+ Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
+ `stabilityai/stable-diffusion-xl-base-1-0`.
+ add_watermarker (`bool`, *optional*):
+ Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
+ watermark output images. If not defined, it will default to True if the package is installed, otherwise no
+ watermarker will be used.
+ """
+
+ model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
+ _optional_components = [
+ "tokenizer",
+ "tokenizer_2",
+ "text_encoder",
+ "text_encoder_2",
+ "image_encoder",
+ "feature_extractor",
+ ]
+ _callback_tensor_inputs = [
+ "latents",
+ "prompt_embeds",
+ "negative_prompt_embeds",
+ "add_text_embeds",
+ "add_time_ids",
+ "negative_pooled_prompt_embeds",
+ "negative_add_time_ids",
+ ]
+
+ def __init__(
+ self,
+ vae: AutoencoderKL,
+ text_encoder: CLIPTextModel,
+ text_encoder_2: CLIPTextModelWithProjection,
+ tokenizer: CLIPTokenizer,
+ tokenizer_2: CLIPTokenizer,
+ unet: UNet2DConditionModel,
+ scheduler: KarrasDiffusionSchedulers,
+ image_encoder: CLIPVisionModelWithProjection = None,
+ feature_extractor: CLIPImageProcessor = None,
+ force_zeros_for_empty_prompt: bool = True,
+ add_watermarker: Optional[bool] = None,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ text_encoder=text_encoder,
+ text_encoder_2=text_encoder_2,
+ tokenizer=tokenizer,
+ tokenizer_2=tokenizer_2,
+ unet=unet,
+ scheduler=scheduler,
+ image_encoder=image_encoder,
+ feature_extractor=feature_extractor,
+ )
+ self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+ self.mask_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+ )
+
+ self.default_sample_size = self.unet.config.sample_size
+
+ add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+ if add_watermarker:
+ self.watermark = StableDiffusionXLWatermarker()
+ else:
+ self.watermark = None
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+ def enable_vae_slicing(self):
+ r"""
+ Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+ compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+ """
+ self.vae.enable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+ def disable_vae_slicing(self):
+ r"""
+ Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+ def enable_vae_tiling(self):
+ r"""
+ Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+ compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+ processing larger images.
+ """
+ self.vae.enable_tiling()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+ def disable_vae_tiling(self):
+ r"""
+ Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_tiling()
+
+ def encode_prompt(
+ self,
+ prompt: str,
+ prompt_2: Optional[str] = None,
+ device: Optional[torch.device] = None,
+ num_images_per_prompt: int = 1,
+ do_classifier_free_guidance: bool = True,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ lora_scale: Optional[float] = None,
+ clip_skip: Optional[int] = None,
+ ):
+ r"""
+ Encodes the prompt into text encoder hidden states.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ prompt to be encoded
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ device: (`torch.device`):
+ torch device
+ num_images_per_prompt (`int`):
+ number of images that should be generated per prompt
+ do_classifier_free_guidance (`bool`):
+ whether to use classifier free guidance or not
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation. If not defined, one has to pass
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+ provided, text embeddings will be generated from `prompt` input argument.
+ negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+ argument.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ lora_scale (`float`, *optional*):
+ A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+ clip_skip (`int`, *optional*):
+ Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+ the output of the pre-final layer will be used for computing the prompt embeddings.
+ """
+ device = device or self._execution_device
+
+ # set lora scale so that monkey patched LoRA
+ # function of text encoder can correctly access it
+ if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
+ self._lora_scale = lora_scale
+
+ # dynamically adjust the LoRA scale
+ if self.text_encoder is not None:
+ if not USE_PEFT_BACKEND:
+ adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+ else:
+ scale_lora_layers(self.text_encoder, lora_scale)
+
+ if self.text_encoder_2 is not None:
+ if not USE_PEFT_BACKEND:
+ adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+ else:
+ scale_lora_layers(self.text_encoder_2, lora_scale)
+
+ prompt = [prompt] if isinstance(prompt, str) else prompt
+
+ if prompt is not None:
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ # Define tokenizers and text encoders
+ tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+ text_encoders = (
+ [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+ )
+
+ if prompt_embeds is None:
+ prompt_2 = prompt_2 or prompt
+ prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+ # textual inversion: procecss multi-vector tokens if necessary
+ prompt_embeds_list = []
+ prompts = [prompt, prompt_2]
+ for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+ text_inputs = tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+ if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+ text_input_ids, untruncated_ids
+ ):
+ removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+ logger.warning(
+ "The following part of your input was truncated because CLIP can only handle sequences up to"
+ f" {tokenizer.model_max_length} tokens: {removed_text}"
+ )
+
+ prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ pooled_prompt_embeds = prompt_embeds[0]
+ if clip_skip is None:
+ prompt_embeds = prompt_embeds.hidden_states[-2]
+ else:
+ # "2" because SDXL always indexes from the penultimate layer.
+ prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+ prompt_embeds_list.append(prompt_embeds)
+
+ prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+ # get unconditional embeddings for classifier free guidance
+ zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+ if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+ negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+ negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+ elif do_classifier_free_guidance and negative_prompt_embeds is None:
+ negative_prompt = negative_prompt or ""
+ negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+ # normalize str to list
+ negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+ negative_prompt_2 = (
+ batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+ )
+
+ uncond_tokens: List[str]
+ if prompt is not None and type(prompt) is not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif batch_size != len(negative_prompt):
+ raise ValueError(
+ f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+ f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+ " the batch size of `prompt`."
+ )
+ else:
+ uncond_tokens = [negative_prompt, negative_prompt_2]
+
+ negative_prompt_embeds_list = []
+ for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+ max_length = prompt_embeds.shape[1]
+ uncond_input = tokenizer(
+ negative_prompt,
+ padding="max_length",
+ max_length=max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ negative_prompt_embeds = text_encoder(
+ uncond_input.input_ids.to(device),
+ output_hidden_states=True,
+ )
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+ negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+ negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+ negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+ if self.text_encoder_2 is not None:
+ prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+ else:
+ prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+ bs_embed, seq_len, _ = prompt_embeds.shape
+ # duplicate text embeddings for each generation per prompt, using mps friendly method
+ prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+ if do_classifier_free_guidance:
+ # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+ seq_len = negative_prompt_embeds.shape[1]
+
+ if self.text_encoder_2 is not None:
+ negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+ else:
+ negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+ negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+ pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+ if do_classifier_free_guidance:
+ negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+
+ if self.text_encoder is not None:
+ if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+ # Retrieve the original scale by scaling back the LoRA layers
+ unscale_lora_layers(self.text_encoder, lora_scale)
+
+ if self.text_encoder_2 is not None:
+ if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+ # Retrieve the original scale by scaling back the LoRA layers
+ unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+ return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+ def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
+ dtype = next(self.image_encoder.parameters()).dtype
+
+ if not isinstance(image, torch.Tensor):
+ image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+ image = image.to(device=device, dtype=dtype)
+ if output_hidden_states:
+ image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+ image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+ uncond_image_enc_hidden_states = self.image_encoder(
+ torch.zeros_like(image), output_hidden_states=True
+ ).hidden_states[-2]
+ uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+ num_images_per_prompt, dim=0
+ )
+ return image_enc_hidden_states, uncond_image_enc_hidden_states
+ else:
+ image_embeds = self.image_encoder(image).image_embeds
+ image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+ uncond_image_embeds = torch.zeros_like(image_embeds)
+
+ return image_embeds, uncond_image_embeds
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+ def prepare_extra_step_kwargs(self, generator, eta):
+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+ # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+ # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+ # and should be between [0, 1]
+
+ accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+ extra_step_kwargs = {}
+ if accepts_eta:
+ extra_step_kwargs["eta"] = eta
+
+ # check if the scheduler accepts generator
+ accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+ if accepts_generator:
+ extra_step_kwargs["generator"] = generator
+ return extra_step_kwargs
+
+ def check_inputs(
+ self,
+ prompt,
+ prompt_2,
+ height,
+ width,
+ callback_steps,
+ negative_prompt=None,
+ negative_prompt_2=None,
+ prompt_embeds=None,
+ negative_prompt_embeds=None,
+ pooled_prompt_embeds=None,
+ negative_pooled_prompt_embeds=None,
+ callback_on_step_end_tensor_inputs=None,
+ ):
+ if height % 8 != 0 or width % 8 != 0:
+ raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+ if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
+ raise ValueError(
+ f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+ f" {type(callback_steps)}."
+ )
+
+ if callback_on_step_end_tensor_inputs is not None and not all(
+ k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+ ):
+ raise ValueError(
+ f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+ )
+
+ if prompt is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+ " only forward one of the two."
+ )
+ elif prompt_2 is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+ " only forward one of the two."
+ )
+ elif prompt is None and prompt_embeds is None:
+ raise ValueError(
+ "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+ )
+ elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+ raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+ elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+ raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+ if negative_prompt is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+ elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+
+ if prompt_embeds is not None and negative_prompt_embeds is not None:
+ if prompt_embeds.shape != negative_prompt_embeds.shape:
+ raise ValueError(
+ "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+ f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+ f" {negative_prompt_embeds.shape}."
+ )
+
+ if prompt_embeds is not None and pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+ )
+
+ if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+ )
+
+ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
+ # get the original timestep using init_timestep
+ if denoising_start is None:
+ init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+ t_start = max(num_inference_steps - init_timestep, 0)
+ else:
+ t_start = 0
+
+ timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+ # Strength is irrelevant if we directly request a timestep to start at;
+ # that is, strength is determined by the denoising_start instead.
+ if denoising_start is not None:
+ discrete_timestep_cutoff = int(
+ round(
+ self.scheduler.config.num_train_timesteps
+ - (denoising_start * self.scheduler.config.num_train_timesteps)
+ )
+ )
+
+ num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+ if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
+ # if the scheduler is a 2nd order scheduler we might have to do +1
+ # because `num_inference_steps` might be even given that every timestep
+ # (except the highest one) is duplicated. If `num_inference_steps` is even it would
+ # mean that we cut the timesteps in the middle of the denoising step
+ # (between 1st and 2nd devirative) which leads to incorrect results. By adding 1
+ # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
+ num_inference_steps = num_inference_steps + 1
+
+ # because t_n+1 >= t_n, we slice the timesteps starting from the end
+ timesteps = timesteps[-num_inference_steps:]
+ return timesteps, num_inference_steps
+
+ return timesteps, num_inference_steps - t_start
+
+ def prepare_latents(
+ self,
+ image,
+ mask,
+ width,
+ height,
+ num_channels_latents,
+ timestep,
+ batch_size,
+ num_images_per_prompt,
+ dtype,
+ device,
+ generator=None,
+ add_noise=True,
+ latents=None,
+ is_strength_max=True,
+ return_noise=False,
+ return_image_latents=False,
+ ):
+ batch_size *= num_images_per_prompt
+
+ if image is None:
+ shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if latents is None:
+ latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ else:
+ latents = latents.to(device)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ elif mask is None:
+ if not isinstance(image, (torch.Tensor, Image.Image, list)):
+ raise ValueError(
+ f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+ )
+
+ # Offload text encoder if `enable_model_cpu_offload` was enabled
+ if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+ self.text_encoder_2.to("cpu")
+ torch.cuda.empty_cache()
+
+ image = image.to(device=device, dtype=dtype)
+
+ if image.shape[1] == 4:
+ init_latents = image
+
+ else:
+ # make sure the VAE is in float32 mode, as it overflows in float16
+ if self.vae.config.force_upcast:
+ image = image.float()
+ self.vae.to(dtype=torch.float32)
+
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ elif isinstance(generator, list):
+ init_latents = [
+ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+ for i in range(batch_size)
+ ]
+ init_latents = torch.cat(init_latents, dim=0)
+ else:
+ init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+
+ if self.vae.config.force_upcast:
+ self.vae.to(dtype)
+
+ init_latents = init_latents.to(dtype)
+ init_latents = self.vae.config.scaling_factor * init_latents
+
+ if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+ # expand init_latents for batch_size
+ additional_image_per_prompt = batch_size // init_latents.shape[0]
+ init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+ elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+ raise ValueError(
+ f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+ )
+ else:
+ init_latents = torch.cat([init_latents], dim=0)
+
+ if add_noise:
+ shape = init_latents.shape
+ noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ # get latents
+ init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+
+ latents = init_latents
+ return latents
+
+ else:
+ shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if (image is None or timestep is None) and not is_strength_max:
+ raise ValueError(
+ "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
+ "However, either the image or the noise timestep has not been provided."
+ )
+
+ if image.shape[1] == 4:
+ image_latents = image.to(device=device, dtype=dtype)
+ image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
+ elif return_image_latents or (latents is None and not is_strength_max):
+ image = image.to(device=device, dtype=dtype)
+ image_latents = self._encode_vae_image(image=image, generator=generator)
+ image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
+
+ if latents is None and add_noise:
+ noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ # if strength is 1. then initialise the latents to noise, else initial to image + noise
+ latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
+ # if pure noise then scale the initial latents by the Scheduler's init sigma
+ latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
+ elif add_noise:
+ noise = latents.to(device)
+ latents = noise * self.scheduler.init_noise_sigma
+ else:
+ noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ latents = image_latents.to(device)
+
+ outputs = (latents,)
+
+ if return_noise:
+ outputs += (noise,)
+
+ if return_image_latents:
+ outputs += (image_latents,)
+
+ return outputs
+
+ def prepare_mask_latents(
+ self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+ ):
+ # resize the mask to latents shape as we concatenate the mask to the latents
+ # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+ # and half precision
+ mask = torch.nn.functional.interpolate(
+ mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
+ )
+ mask = mask.to(device=device, dtype=dtype)
+
+ # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+ if mask.shape[0] < batch_size:
+ if not batch_size % mask.shape[0] == 0:
+ raise ValueError(
+ "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+ f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+ " of masks that you pass is divisible by the total requested batch size."
+ )
+ mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+
+ mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+
+ if masked_image is not None and masked_image.shape[1] == 4:
+ masked_image_latents = masked_image
+ else:
+ masked_image_latents = None
+
+ if masked_image is not None:
+ if masked_image_latents is None:
+ masked_image = masked_image.to(device=device, dtype=dtype)
+ masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
+
+ if masked_image_latents.shape[0] < batch_size:
+ if not batch_size % masked_image_latents.shape[0] == 0:
+ raise ValueError(
+ "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+ f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+ " Make sure the number of images that you pass is divisible by the total requested batch size."
+ )
+ masked_image_latents = masked_image_latents.repeat(
+ batch_size // masked_image_latents.shape[0], 1, 1, 1
+ )
+
+ masked_image_latents = (
+ torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+ )
+
+ # aligning device to prevent device errors when concating it with the latent model input
+ masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+
+ return mask, masked_image_latents
+
+ def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+ dtype = image.dtype
+ if self.vae.config.force_upcast:
+ image = image.float()
+ self.vae.to(dtype=torch.float32)
+
+ if isinstance(generator, list):
+ image_latents = [
+ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+ for i in range(image.shape[0])
+ ]
+ image_latents = torch.cat(image_latents, dim=0)
+ else:
+ image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+
+ if self.vae.config.force_upcast:
+ self.vae.to(dtype)
+
+ image_latents = image_latents.to(dtype)
+ image_latents = self.vae.config.scaling_factor * image_latents
+
+ return image_latents
+
+ def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
+ add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+ passed_add_embed_dim = (
+ self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
+ )
+ expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+ if expected_add_embed_dim != passed_add_embed_dim:
+ raise ValueError(
+ f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+ )
+
+ add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+ return add_time_ids
+
+ def upcast_vae(self):
+ dtype = self.vae.dtype
+ self.vae.to(dtype=torch.float32)
+ use_torch_2_0_or_xformers = isinstance(
+ self.vae.decoder.mid_block.attentions[0].processor,
+ (
+ AttnProcessor2_0,
+ XFormersAttnProcessor,
+ LoRAXFormersAttnProcessor,
+ LoRAAttnProcessor2_0,
+ FusedAttnProcessor2_0,
+ ),
+ )
+ # if xformers or torch_2_0 is used attention block does not need
+ # to be in float32 which can save lots of memory
+ if use_torch_2_0_or_xformers:
+ self.vae.post_quant_conv.to(dtype)
+ self.vae.decoder.conv_in.to(dtype)
+ self.vae.decoder.mid_block.to(dtype)
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
+ def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
+ r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
+
+ The suffixes after the scaling factors represent the stages where they are being applied.
+
+ Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
+ that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+ Args:
+ s1 (`float`):
+ Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+ mitigate "oversmoothing effect" in the enhanced denoising process.
+ s2 (`float`):
+ Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+ mitigate "oversmoothing effect" in the enhanced denoising process.
+ b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+ b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+ """
+ if not hasattr(self, "unet"):
+ raise ValueError("The pipeline must have `unet` for using FreeU.")
+ self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
+ def disable_freeu(self):
+ """Disables the FreeU mechanism if enabled."""
+ self.unet.disable_freeu()
+
+ def _enable_shared_attention_processors(
+ self,
+ share_attention: bool,
+ adain_queries: bool,
+ adain_keys: bool,
+ adain_values: bool,
+ full_attention_share: bool,
+ shared_score_scale: float,
+ shared_score_shift: float,
+ only_self_level: float,
+ ):
+ r"""Helper method to enable usage of Shared Attention Processor."""
+ attn_procs = {}
+ num_self_layers = len([name for name in self.unet.attn_processors.keys() if "attn1" in name])
+
+ only_self_vec = get_switch_vec(num_self_layers, only_self_level)
+
+ for i, name in enumerate(self.unet.attn_processors.keys()):
+ is_self_attention = "attn1" in name
+ if is_self_attention:
+ if only_self_vec[i // 2]:
+ attn_procs[name] = AttnProcessor2_0()
+ else:
+ attn_procs[name] = SharedAttentionProcessor(
+ share_attention=share_attention,
+ adain_queries=adain_queries,
+ adain_keys=adain_keys,
+ adain_values=adain_values,
+ full_attention_share=full_attention_share,
+ shared_score_scale=shared_score_scale,
+ shared_score_shift=shared_score_shift,
+ )
+ else:
+ attn_procs[name] = AttnProcessor2_0()
+
+ self.unet.set_attn_processor(attn_procs)
+
+ def _disable_shared_attention_processors(self):
+ r"""
+ Helper method to disable usage of the Shared Attention Processor. All processors
+ are reset to the default Attention Processor for pytorch versions above 2.0.
+ """
+ attn_procs = {}
+
+ for i, name in enumerate(self.unet.attn_processors.keys()):
+ attn_procs[name] = AttnProcessor2_0()
+
+ self.unet.set_attn_processor(attn_procs)
+
+ def _register_shared_norm(self, share_group_norm: bool = True, share_layer_norm: bool = True):
+ r"""Helper method to register shared group/layer normalization layers."""
+
+ def register_norm_forward(norm_layer: Union[nn.GroupNorm, nn.LayerNorm]) -> Union[nn.GroupNorm, nn.LayerNorm]:
+ if not hasattr(norm_layer, "orig_forward"):
+ setattr(norm_layer, "orig_forward", norm_layer.forward)
+ orig_forward = norm_layer.orig_forward
+
+ def forward_(hidden_states: torch.Tensor) -> torch.Tensor:
+ n = hidden_states.shape[-2]
+ hidden_states = concat_first(hidden_states, dim=-2)
+ hidden_states = orig_forward(hidden_states)
+ return hidden_states[..., :n, :]
+
+ norm_layer.forward = forward_
+ return norm_layer
+
+ def get_norm_layers(pipeline_, norm_layers_: Dict[str, List[Union[nn.GroupNorm, nn.LayerNorm]]]):
+ if isinstance(pipeline_, nn.LayerNorm) and share_layer_norm:
+ norm_layers_["layer"].append(pipeline_)
+ if isinstance(pipeline_, nn.GroupNorm) and share_group_norm:
+ norm_layers_["group"].append(pipeline_)
+ else:
+ for layer in pipeline_.children():
+ get_norm_layers(layer, norm_layers_)
+
+ norm_layers = {"group": [], "layer": []}
+ get_norm_layers(self.unet, norm_layers)
+
+ norm_layers_list = []
+ for key in ["group", "layer"]:
+ for layer in norm_layers[key]:
+ norm_layers_list.append(register_norm_forward(layer))
+
+ return norm_layers_list
+
+ @property
+ def style_aligned_enabled(self):
+ r"""Returns whether StyleAligned has been enabled in the pipeline or not."""
+ return hasattr(self, "_style_aligned_norm_layers") and self._style_aligned_norm_layers is not None
+
+ def enable_style_aligned(
+ self,
+ share_group_norm: bool = True,
+ share_layer_norm: bool = True,
+ share_attention: bool = True,
+ adain_queries: bool = True,
+ adain_keys: bool = True,
+ adain_values: bool = False,
+ full_attention_share: bool = False,
+ shared_score_scale: float = 1.0,
+ shared_score_shift: float = 0.0,
+ only_self_level: float = 0.0,
+ ):
+ r"""
+ Enables the StyleAligned mechanism as in https://arxiv.org/abs/2312.02133.
+
+ Args:
+ share_group_norm (`bool`, defaults to `True`):
+ Whether or not to use shared group normalization layers.
+ share_layer_norm (`bool`, defaults to `True`):
+ Whether or not to use shared layer normalization layers.
+ share_attention (`bool`, defaults to `True`):
+ Whether or not to use attention sharing between batch images.
+ adain_queries (`bool`, defaults to `True`):
+ Whether or not to apply the AdaIn operation on attention queries.
+ adain_keys (`bool`, defaults to `True`):
+ Whether or not to apply the AdaIn operation on attention keys.
+ adain_values (`bool`, defaults to `False`):
+ Whether or not to apply the AdaIn operation on attention values.
+ full_attention_share (`bool`, defaults to `False`):
+ Whether or not to use full attention sharing between all images in a batch. Can
+ lead to content leakage within each batch and some loss in diversity.
+ shared_score_scale (`float`, defaults to `1.0`):
+ Scale for shared attention.
+ """
+ self._style_aligned_norm_layers = self._register_shared_norm(share_group_norm, share_layer_norm)
+ self._enable_shared_attention_processors(
+ share_attention=share_attention,
+ adain_queries=adain_queries,
+ adain_keys=adain_keys,
+ adain_values=adain_values,
+ full_attention_share=full_attention_share,
+ shared_score_scale=shared_score_scale,
+ shared_score_shift=shared_score_shift,
+ only_self_level=only_self_level,
+ )
+
+ def disable_style_aligned(self):
+ r"""Disables the StyleAligned mechanism if it had been previously enabled."""
+ if self.style_aligned_enabled:
+ for layer in self._style_aligned_norm_layers:
+ layer.forward = layer.orig_forward
+
+ self._style_aligned_norm_layers = None
+ self._disable_shared_attention_processors()
+
+ def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):
+ """
+ Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
+ key, value) are fused. For cross-attention modules, key and value projection matrices are fused.
+
+ <Tip warning={true}>
+
+ This API is 🧪 experimental.
+
+ </Tip>
+
+ Args:
+ unet (`bool`, defaults to `True`): To apply fusion on the UNet.
+ vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+ """
+ self.fusing_unet = False
+ self.fusing_vae = False
+
+ if unet:
+ self.fusing_unet = True
+ self.unet.fuse_qkv_projections()
+ self.unet.set_attn_processor(FusedAttnProcessor2_0())
+
+ if vae:
+ if not isinstance(self.vae, AutoencoderKL):
+ raise ValueError("`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.")
+
+ self.fusing_vae = True
+ self.vae.fuse_qkv_projections()
+ self.vae.set_attn_processor(FusedAttnProcessor2_0())
+
+ def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):
+ """Disable QKV projection fusion if enabled.
+
+ <Tip warning={true}>
+
+ This API is 🧪 experimental.
+
+ </Tip>
+
+ Args:
+ unet (`bool`, defaults to `True`): To apply fusion on the UNet.
+ vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+
+ """
+ if unet:
+ if not self.fusing_unet:
+ logger.warning("The UNet was not initially fused for QKV projections. Doing nothing.")
+ else:
+ self.unet.unfuse_qkv_projections()
+ self.fusing_unet = False
+
+ if vae:
+ if not self.fusing_vae:
+ logger.warning("The VAE was not initially fused for QKV projections. Doing nothing.")
+ else:
+ self.vae.unfuse_qkv_projections()
+ self.fusing_vae = False
+
+ # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+ def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+ """
+ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+ Args:
+ timesteps (`torch.Tensor`):
+ generate embedding vectors at these timesteps
+ embedding_dim (`int`, *optional*, defaults to 512):
+ dimension of the embeddings to generate
+ dtype:
+ data type of the generated embeddings
+
+ Returns:
+ `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+ """
+ assert len(w.shape) == 1
+ w = w * 1000.0
+
+ half_dim = embedding_dim // 2
+ emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+ emb = w.to(dtype)[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0, 1))
+ assert emb.shape == (w.shape[0], embedding_dim)
+ return emb
+
+ @property
+ def guidance_scale(self):
+ return self._guidance_scale
+
+ @property
+ def guidance_rescale(self):
+ return self._guidance_rescale
+
+ @property
+ def clip_skip(self):
+ return self._clip_skip
+
+ # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+ # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+ # corresponds to doing no classifier free guidance.
+ @property
+ def do_classifier_free_guidance(self):
+ return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+ @property
+ def cross_attention_kwargs(self):
+ return self._cross_attention_kwargs
+
+ @property
+ def denoising_end(self):
+ return self._denoising_end
+
+ @property
+ def denoising_start(self):
+ return self._denoising_start
+
+ @property
+ def num_timesteps(self):
+ return self._num_timesteps
+
+ @property
+ def interrupt(self):
+ return self._interrupt
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Union[str, List[str]] = None,
+ prompt_2: Optional[Union[str, List[str]]] = None,
+ image: Optional[PipelineImageInput] = None,
+ mask_image: Optional[PipelineImageInput] = None,
+ masked_image_latents: Optional[torch.FloatTensor] = None,
+ strength: float = 0.3,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
+ num_inference_steps: int = 50,
+ timesteps: List[int] = None,
+ denoising_start: Optional[float] = None,
+ denoising_end: Optional[float] = None,
+ guidance_scale: float = 5.0,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ negative_prompt_2: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ output_type: Optional[str] = "pil",
+ return_dict: bool = True,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ original_size: Optional[Tuple[int, int]] = None,
+ crops_coords_top_left: Tuple[int, int] = (0, 0),
+ target_size: Optional[Tuple[int, int]] = None,
+ clip_skip: Optional[int] = None,
+ callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+ callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+ **kwargs,
+ ):
+ r"""
+ Function invoked when calling the pipeline for generation.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+ instead.
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+ The height in pixels of the generated image. This is set to 1024 by default for the best results.
+ Anything below 512 pixels won't work well for
+ [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+ and checkpoints that are not specifically fine-tuned on low resolutions.
+ width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+ The width in pixels of the generated image. This is set to 1024 by default for the best results.
+ Anything below 512 pixels won't work well for
+ [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+ and checkpoints that are not specifically fine-tuned on low resolutions.
+ num_inference_steps (`int`, *optional*, defaults to 50):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ timesteps (`List[int]`, *optional*):
+ Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+ in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+ passed will be used. Must be in descending order.
+ denoising_end (`float`, *optional*):
+ When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+ completed before it is intentionally prematurely terminated. As a result, the returned sample will
+ still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+ scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+ "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+ Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+ guidance_scale (`float`, *optional*, defaults to 5.0):
+ Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+ `guidance_scale` is defined as `w` of equation 2. of [Imagen
+ Paper](https://arxiv.org/pdf/2205.11487.pdf). 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.
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation. If not defined, one has to pass
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ num_images_per_prompt (`int`, *optional*, defaults to 1):
+ The number of images to generate per prompt.
+ eta (`float`, *optional*, defaults to 0.0):
+ Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+ [`schedulers.DDIMScheduler`], will be ignored for others.
+ generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+ One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+ to make generation deterministic.
+ latents (`torch.FloatTensor`, *optional*):
+ Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+ generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+ tensor will ge generated by sampling using the supplied random `generator`.
+ prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+ provided, text embeddings will be generated from `prompt` input argument.
+ negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+ argument.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ ip_adapter_image: (`PipelineImageInput`, *optional*):
+ Optional image input to work with IP Adapters.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generate image. Choose between
+ [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+ of a plain tuple.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ guidance_rescale (`float`, *optional*, defaults to 0.0):
+ Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+ Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+ [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+ Guidance rescale factor should fix overexposure when using zero terminal SNR.
+ original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+ `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+ explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+ `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+ `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ For most cases, `target_size` should be set to the desired height and width of the generated image. If
+ not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+ section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+ micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+ information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+ negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+ micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+ information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+ negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ To negatively condition the generation process based on a target image resolution. It should be as same
+ as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+ information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+ callback_on_step_end (`Callable`, *optional*):
+ A function that calls at the end of each denoising steps during the inference. The function is called
+ with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+ callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+ `callback_on_step_end_tensor_inputs`.
+ callback_on_step_end_tensor_inputs (`List`, *optional*):
+ The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+ will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+ `._callback_tensor_inputs` attribute of your pipeline class.
+
+ Examples:
+
+ Returns:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+ `tuple`. When returning a tuple, the first element is a list with the generated images.
+ """
+
+ callback = kwargs.pop("callback", None)
+ callback_steps = kwargs.pop("callback_steps", None)
+
+ if callback is not None:
+ deprecate(
+ "callback",
+ "1.0.0",
+ "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+ )
+ if callback_steps is not None:
+ deprecate(
+ "callback_steps",
+ "1.0.0",
+ "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+ )
+
+ # 0. Default height and width to unet
+ height = height or self.default_sample_size * self.vae_scale_factor
+ width = width or self.default_sample_size * self.vae_scale_factor
+
+ original_size = original_size or (height, width)
+ target_size = target_size or (height, width)
+
+ # 1. Check inputs. Raise error if not correct
+ self.check_inputs(
+ prompt=prompt,
+ prompt_2=prompt_2,
+ height=height,
+ width=width,
+ callback_steps=callback_steps,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+ callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+ )
+
+ self._guidance_scale = guidance_scale
+ self._guidance_rescale = guidance_rescale
+ self._clip_skip = clip_skip
+ self._cross_attention_kwargs = cross_attention_kwargs
+ self._denoising_end = denoising_end
+ self._denoising_start = denoising_start
+ self._interrupt = False
+
+ # 2. Define call parameters
+ if prompt is not None and isinstance(prompt, str):
+ batch_size = 1
+ elif prompt is not None and isinstance(prompt, list):
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ device = self._execution_device
+
+ # 3. Encode input prompt
+ lora_scale = (
+ self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+ )
+
+ (
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ ) = self.encode_prompt(
+ prompt=prompt,
+ prompt_2=prompt_2,
+ device=device,
+ num_images_per_prompt=num_images_per_prompt,
+ do_classifier_free_guidance=self.do_classifier_free_guidance,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+ lora_scale=lora_scale,
+ clip_skip=self.clip_skip,
+ )
+
+ # 4. Preprocess image and mask_image
+ if image is not None:
+ image = self.image_processor.preprocess(image, height=height, width=width)
+ image = image.to(device=self.device, dtype=prompt_embeds.dtype)
+
+ if mask_image is not None:
+ mask = self.mask_processor.preprocess(mask_image, height=height, width=width)
+ mask = mask.to(device=self.device, dtype=prompt_embeds.dtype)
+
+ if masked_image_latents is not None:
+ masked_image = masked_image_latents
+ elif image.shape[1] == 4:
+ # if image is in latent space, we can't mask it
+ masked_image = None
+ else:
+ masked_image = image * (mask < 0.5)
+ else:
+ mask = None
+
+ # 4. Prepare timesteps
+ def denoising_value_valid(dnv):
+ return isinstance(self.denoising_end, float) and 0 < dnv < 1
+
+ timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+
+ if image is not None:
+ timesteps, num_inference_steps = self.get_timesteps(
+ num_inference_steps,
+ strength,
+ device,
+ denoising_start=self.denoising_start if denoising_value_valid else None,
+ )
+
+ # check that number of inference steps is not < 1 - as this doesn't make sense
+ if num_inference_steps < 1:
+ raise ValueError(
+ f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
+ f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
+ )
+
+ latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+ is_strength_max = strength == 1.0
+ add_noise = True if self.denoising_start is None else False
+
+ # 5. Prepare latent variables
+ num_channels_latents = self.unet.config.in_channels
+ num_channels_unet = self.unet.config.in_channels
+ return_image_latents = num_channels_unet == 4
+
+ latents = self.prepare_latents(
+ image=image,
+ mask=mask,
+ width=width,
+ height=height,
+ num_channels_latents=num_channels_latents,
+ timestep=latent_timestep,
+ batch_size=batch_size * num_images_per_prompt,
+ num_images_per_prompt=num_images_per_prompt,
+ dtype=prompt_embeds.dtype,
+ device=device,
+ generator=generator,
+ add_noise=add_noise,
+ latents=latents,
+ is_strength_max=is_strength_max,
+ return_noise=True,
+ return_image_latents=return_image_latents,
+ )
+
+ if mask is not None:
+ if return_image_latents:
+ latents, noise, image_latents = latents
+ else:
+ latents, noise = latents
+
+ mask, masked_image_latents = self.prepare_mask_latents(
+ mask=mask,
+ masked_image=masked_image,
+ batch_size=batch_size * num_images_per_prompt,
+ height=height,
+ width=width,
+ dtype=prompt_embeds.dtype,
+ device=device,
+ generator=generator,
+ do_classifier_free_guidance=self.do_classifier_free_guidance,
+ )
+
+ # Check that sizes of mask, masked image and latents match
+ if num_channels_unet == 9:
+ # default case for runwayml/stable-diffusion-inpainting
+ num_channels_mask = mask.shape[1]
+ num_channels_masked_image = masked_image_latents.shape[1]
+ if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet:
+ raise ValueError(
+ f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+ f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+ f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
+ f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+ " `pipeline.unet` or your `mask_image` or `image` input."
+ )
+ elif num_channels_unet != 4:
+ raise ValueError(
+ f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
+ )
+
+ # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+ extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+ height, width = latents.shape[-2:]
+ height = height * self.vae_scale_factor
+ width = width * self.vae_scale_factor
+
+ original_size = original_size or (height, width)
+ target_size = target_size or (height, width)
+
+ # 7. Prepare added time ids & embeddings
+ add_text_embeds = pooled_prompt_embeds
+ add_time_ids = self._get_add_time_ids(
+ original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
+ )
+
+ if self.do_classifier_free_guidance:
+ prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+ add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+ add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+
+ prompt_embeds = prompt_embeds.to(device)
+ add_text_embeds = add_text_embeds.to(device)
+ add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+ if ip_adapter_image is not None:
+ output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
+ image_embeds, negative_image_embeds = self.encode_image(
+ ip_adapter_image, device, num_images_per_prompt, output_hidden_state
+ )
+ if self.do_classifier_free_guidance:
+ image_embeds = torch.cat([negative_image_embeds, image_embeds])
+ image_embeds = image_embeds.to(device)
+
+ # 8. Denoising loop
+ num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+ # 8.1 Apply denoising_end
+ if (
+ self.denoising_end is not None
+ and isinstance(self.denoising_end, float)
+ and self.denoising_end > 0
+ and self.denoising_end < 1
+ ):
+ discrete_timestep_cutoff = int(
+ round(
+ self.scheduler.config.num_train_timesteps
+ - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+ )
+ )
+ num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+ timesteps = timesteps[:num_inference_steps]
+
+ # 9. Optionally get Guidance Scale Embedding
+ timestep_cond = None
+ if self.unet.config.time_cond_proj_dim is not None:
+ guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+ timestep_cond = self.get_guidance_scale_embedding(
+ guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+ ).to(device=device, dtype=latents.dtype)
+
+ self._num_timesteps = len(timesteps)
+
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+ if self.interrupt:
+ continue
+
+ # expand the latents if we are doing classifier free guidance
+ latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+
+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+ # predict the noise residual
+ added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+ if ip_adapter_image is not None:
+ added_cond_kwargs["image_embeds"] = image_embeds
+
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=prompt_embeds,
+ timestep_cond=timestep_cond,
+ cross_attention_kwargs=self.cross_attention_kwargs,
+ added_cond_kwargs=added_cond_kwargs,
+ return_dict=False,
+ )[0]
+
+ # perform guidance
+ if self.do_classifier_free_guidance:
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+ if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+ noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+ if mask is not None and num_channels_unet == 4:
+ init_latents_proper = image_latents
+
+ if self.do_classifier_free_guidance:
+ init_mask, _ = mask.chunk(2)
+ else:
+ init_mask = mask
+
+ if i < len(timesteps) - 1:
+ noise_timestep = timesteps[i + 1]
+ init_latents_proper = self.scheduler.add_noise(
+ init_latents_proper, noise, torch.tensor([noise_timestep])
+ )
+
+ latents = (1 - init_mask) * init_latents_proper + init_mask * latents
+
+ if callback_on_step_end is not None:
+ callback_kwargs = {}
+ for k in callback_on_step_end_tensor_inputs:
+ callback_kwargs[k] = locals()[k]
+ callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+ latents = callback_outputs.pop("latents", latents)
+ prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+ negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+ add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+ negative_pooled_prompt_embeds = callback_outputs.pop(
+ "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+ )
+ add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+
+ # call the callback, if provided
+ if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, t, latents)
+
+ if XLA_AVAILABLE:
+ xm.mark_step()
+
+ if not output_type == "latent":
+ # make sure the VAE is in float32 mode, as it overflows in float16
+ needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+ if needs_upcasting:
+ self.upcast_vae()
+ latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+ image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+ # cast back to fp16 if needed
+ if needs_upcasting:
+ self.vae.to(dtype=torch.float16)
+ else:
+ image = latents
+
+ if not output_type == "latent":
+ # apply watermark if available
+ if self.watermark is not None:
+ image = self.watermark.apply_watermark(image)
+
+ image = self.image_processor.postprocess(image, output_type=output_type)
+
+ # Offload all models
+ self.maybe_free_model_hooks()
+
+ if not return_dict:
+ return (image,)
+
+ return StableDiffusionXLPipelineOutput(images=image)