Add semantic guidance pipeline (#2223)

* Add semantic guidance pipeline

* Fix style

* Refactor Pipeline

* Pipeline documentation

* Add documentation

* Fix style and quality

* Fix doctree

* Add tests for SEGA

* Update src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Make compatible with half precision

* Change deprecation warning to throw an exception

* update

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/en/_toctree.yml

@@ -134,6 +134,8 @@
       title: Safe Stable Diffusion
     - local: api/pipelines/score_sde_ve
       title: Score SDE VE
+    - local: api/pipelines/semantic_stable_diffusion
+      title: Semantic Guidance
     - sections:
       - local: api/pipelines/stable_diffusion/overview
         title: Overview

docs/source/en/api/pipelines/semantic_stable_diffusion.mdx

+<!--Copyright 2022 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.
+-->
+
+# Semantic Guidance
+
+Semantic Guidance for Diffusion Models was proposed in [SEGA: Instructing Diffusion using Semantic Dimensions](https://arxiv.org/abs/2301.12247) and provides strong semantic control over the image generation.
+Small changes to the text prompt usually result in entirely different output images. However, with SEGA a variety of changes to the image are enabled that can be controlled easily and intuitively, and stay true to the original image composition.
+
+The abstract of the paper is the following:
+
+*Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on a variety of tasks and provide evidence for its versatility and flexibility.*
+
+
+*Overview*:
+
+| Pipeline | Tasks | Colab | Demo
+|---|---|:---:|:---:|
+| [pipeline_semantic_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion) | *Text-to-Image Generation* |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/semantic-image-editing/blob/main/examples/SemanticGuidance.ipynb) | [Coming Soon](https://huggingface.co/AIML-TUDA)
+
+## Tips
+
+- The Semantic Guidance pipeline can be used with any [Stable Diffusion](./api/pipelines/stable_diffusion/text2img) checkpoint.
+
+### Run Semantic Guidance
+
+The interface of [`SemanticStableDiffusionPipeline`] provides several additional parameters to influence the image generation.
+Exemplary usage may look like this:
+
+```python
+import torch
+from diffusers import SemanticStableDiffusionPipeline
+
+pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+out = pipe(
+    prompt="a photo of the face of a woman",
+    num_images_per_prompt=1,
+    guidance_scale=7,
+    editing_prompt=[
+        "smiling, smile",  # Concepts to apply
+        "glasses, wearing glasses",
+        "curls, wavy hair, curly hair",
+        "beard, full beard, mustache",
+    ],
+    reverse_editing_direction=[False, False, False, False],  # Direction of guidance i.e. increase all concepts
+    edit_warmup_steps=[10, 10, 10, 10],  # Warmup period for each concept
+    edit_guidance_scale=[4, 5, 5, 5.4],  # Guidance scale for each concept
+    edit_threshold=[
+        0.99,
+        0.975,
+        0.925,
+        0.96,
+    ],  # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
+    edit_momentum_scale=0.3,  # Momentum scale that will be added to the latent guidance
+    edit_mom_beta=0.6,  # Momentum beta
+    edit_weights=[1, 1, 1, 1, 1],  # Weights of the individual concepts against each other
+)
+```
+
+For more examples check the colab notebook.
+
+## StableDiffusionSafePipelineOutput
+[[autodoc]] pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput
+	- all
+
+## SemanticStableDiffusionPipeline
+[[autodoc]] SemanticStableDiffusionPipeline
+	- all
+	- __call__

docs/source/en/api/pipelines/stable_diffusion_safe.mdx

@@ -24,7 +24,7 @@ The abstract of the paper is the following:
 
 | Pipeline | Tasks | Colab | Demo
 |---|---|:---:|:---:|
-| [pipeline_stable_diffusion_safe.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py) | *Text-to-Image Generation* |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/safe-latent-diffusion/blob/main/examples/Safe%20Latent%20Diffusion.ipynb) | -
+| [pipeline_stable_diffusion_safe.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py) | *Text-to-Image Generation* |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/safe-latent-diffusion/blob/main/examples/Safe%20Latent%20Diffusion.ipynb) | [![Huggingface Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/AIML-TUDA/unsafe-vs-safe-stable-diffusion)
 
 ## Tips
 
@@ -58,7 +58,7 @@ You may use the 4 configurations defined in the [Safe Latent Diffusion paper](ht
 >>> out = pipeline(prompt=prompt, **SafetyConfig.MAX)
 ```
 
-The following configurations are available: `SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONg`, and `SafetyConfig.MAX`.
+The following configurations are available: `SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONG`, and `SafetyConfig.MAX`.
 
 ### How to load and use different schedulers.
 

docs/source/en/index.mdx

@@ -47,6 +47,7 @@ available a colab notebook to directly try them out.
 | [pndm](./api/pipelines/pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation | 
 | [score_sde_ve](./api/pipelines/score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
 | [score_sde_vp](./api/pipelines/score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [**Semantic Guidance**](https://arxiv.org/abs/2301.12247) | Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/semantic-image-editing/blob/main/examples/SemanticGuidance.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion/text2img) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion/img2img) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
 | [stable_diffusion](./api/pipelines/stable_diffusion/inpaint) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)

src/diffusers/__init__.py

@@ -110,6 +110,7 @@ else:
         CycleDiffusionPipeline,
         LDMTextToImagePipeline,
         PaintByExamplePipeline,
+        SemanticStableDiffusionPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionDepth2ImgPipeline,
         StableDiffusionImageVariationPipeline,

src/diffusers/pipelines/__init__.py

@@ -44,6 +44,7 @@ else:
     from .alt_diffusion import AltDiffusionImg2ImgPipeline, AltDiffusionPipeline
     from .latent_diffusion import LDMTextToImagePipeline
     from .paint_by_example import PaintByExamplePipeline
+    from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
     from .stable_diffusion import (
         CycleDiffusionPipeline,
         StableDiffusionAttendAndExcitePipeline,

src/diffusers/pipelines/semantic_stable_diffusion/__init__.py

+from dataclasses import dataclass
+from enum import Enum
+from typing import List, Optional, Union
+
+import numpy as np
+import PIL
+from PIL import Image
+
+from ...utils import BaseOutput, is_torch_available, is_transformers_available
+
+
+@dataclass
+class SemanticStableDiffusionPipelineOutput(BaseOutput):
+    """
+    Output class for Stable Diffusion pipelines.
+
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
+            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
+        nsfw_content_detected (`List[bool]`)
+            List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, or `None` if safety checking could not be performed.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]
+    nsfw_content_detected: Optional[List[bool]]
+
+
+if is_transformers_available() and is_torch_available():
+    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py

+import inspect
+from itertools import repeat
+from typing import Callable, List, Optional, Union
+
+import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+from ...models import AutoencoderKL, UNet2DConditionModel
+from ...pipeline_utils import DiffusionPipeline
+from ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from ...schedulers import KarrasDiffusionSchedulers
+from ...utils import logging, randn_tensor
+from . import SemanticStableDiffusionPipelineOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import SemanticStableDiffusionPipeline
+
+        >>> pipe = SemanticStableDiffusionPipeline.from_pretrained(
+        ...     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
+        ... )
+        >>> pipe = pipe.to("cuda")
+
+        >>> out = pipe(
+        ...     prompt="a photo of the face of a woman",
+        ...     num_images_per_prompt=1,
+        ...     guidance_scale=7,
+        ...     editing_prompt=[
+        ...         "smiling, smile",  # Concepts to apply
+        ...         "glasses, wearing glasses",
+        ...         "curls, wavy hair, curly hair",
+        ...         "beard, full beard, mustache",
+        ...     ],
+        ...     reverse_editing_direction=[
+        ...         False,
+        ...         False,
+        ...         False,
+        ...         False,
+        ...     ],  # Direction of guidance i.e. increase all concepts
+        ...     edit_warmup_steps=[10, 10, 10, 10],  # Warmup period for each concept
+        ...     edit_guidance_scale=[4, 5, 5, 5.4],  # Guidance scale for each concept
+        ...     edit_threshold=[
+        ...         0.99,
+        ...         0.975,
+        ...         0.925,
+        ...         0.96,
+        ...     ],  # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
+        ...     edit_momentum_scale=0.3,  # Momentum scale that will be added to the latent guidance
+        ...     edit_mom_beta=0.6,  # Momentum beta
+        ...     edit_weights=[1, 1, 1, 1, 1],  # Weights of the individual concepts against each other
+        ... )
+        >>> image = out.images[0]
+        ```
+"""
+
+
+class SemanticStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation with latent editing.
+
+    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.)
+
+    This model builds on the implementation of ['StableDiffusionPipeline']
+
+    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 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.
+        tokenizer (`CLIPTokenizer`):
+            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 latens. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`Q16SafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    # 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
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=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 None) or (
+            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 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 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)}")
+
+        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."
+            )
+
+        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}."
+                )
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=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
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: int = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        editing_prompt: Optional[Union[str, List[str]]] = None,
+        editing_prompt_embeddings: Optional[torch.Tensor] = None,
+        reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,
+        edit_guidance_scale: Optional[Union[float, List[float]]] = 5,
+        edit_warmup_steps: Optional[Union[int, List[int]]] = 10,
+        edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
+        edit_threshold: Optional[Union[float, List[float]]] = 0.9,
+        edit_momentum_scale: Optional[float] = 0.1,
+        edit_mom_beta: Optional[float] = 0.4,
+        edit_weights: Optional[List[float]] = None,
+        sem_guidance: Optional[List[torch.Tensor]] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            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.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                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. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            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`, *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`.
+            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.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            editing_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to use for Semantic guidance. Semantic guidance is disabled by setting
+                `editing_prompt = None`. Guidance direction of prompt should be specified via
+                `reverse_editing_direction`.
+            editing_prompt_embeddings (`torch.Tensor>`, *optional*):
+                Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
+                specified via `reverse_editing_direction`.
+            reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
+                Whether the corresponding prompt in `editing_prompt` should be increased or decreased.
+            edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
+                Guidance scale for semantic guidance. If provided as list values should correspond to `editing_prompt`.
+                `edit_guidance_scale` is defined as `s_e` of equation 6 of [SEGA
+                Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
+                Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum
+                will still be calculated for those steps and applied once all warmup periods are over.
+                `edit_warmup_steps` is defined as `delta` (δ) of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
+                Number of diffusion steps (for each prompt) after which semantic guidance will no longer be applied.
+            edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
+                Threshold of semantic guidance.
+            edit_momentum_scale (`float`, *optional*, defaults to 0.1):
+                Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0
+                momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
+                than `sld_warmup_steps`. Momentum will only be added to latent guidance once all warmup periods are
+                finished. `edit_momentum_scale` is defined as `s_m` of equation 7 of [SEGA
+                Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            edit_mom_beta (`float`, *optional*, defaults to 0.4):
+                Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous
+                momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
+                than `edit_warmup_steps`. `edit_mom_beta` is defined as `beta_m` (β) of equation 8 of [SEGA
+                Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            edit_weights (`List[float]`, *optional*, defaults to `None`):
+                Indicates how much each individual concept should influence the overall guidance. If no weights are
+                provided all concepts are applied equally. `edit_mom_beta` is defined as `g_i` of equation 9 of [SEGA
+                Paper](https://arxiv.org/pdf/2301.12247.pdf).
+            sem_guidance (`List[torch.Tensor]`, *optional*):
+                List of pre-generated guidance vectors to be applied at generation. Length of the list has to
+                correspond to `num_inference_steps`.
+
+        Returns:
+            [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] if `return_dict` is True,
+            otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the
+            second element is a list of `bool`s denoting whether the corresponding generated image likely represents
+            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+
+        if editing_prompt:
+            enable_edit_guidance = True
+            if isinstance(editing_prompt, str):
+                editing_prompt = [editing_prompt]
+            enabled_editing_prompts = len(editing_prompt)
+        elif editing_prompt_embeddings is not None:
+            enable_edit_guidance = True
+            enabled_editing_prompts = editing_prompt_embeddings.shape[0]
+        else:
+            enabled_editing_prompts = 0
+            enable_edit_guidance = False
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if enable_edit_guidance:
+            # get safety text embeddings
+            if editing_prompt_embeddings is None:
+                edit_concepts_input = self.tokenizer(
+                    [x for item in editing_prompt for x in repeat(item, batch_size)],
+                    padding="max_length",
+                    max_length=self.tokenizer.model_max_length,
+                    return_tensors="pt",
+                )
+
+                edit_concepts_input_ids = edit_concepts_input.input_ids
+
+                if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length:
+                    removed_text = self.tokenizer.batch_decode(
+                        edit_concepts_input_ids[:, self.tokenizer.model_max_length :]
+                    )
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+                    edit_concepts_input_ids = edit_concepts_input_ids[:, : self.tokenizer.model_max_length]
+                edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0]
+            else:
+                edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1)
+
+            # duplicate text embeddings for each generation per prompt, using mps friendly method
+            bs_embed_edit, seq_len_edit, _ = edit_concepts.shape
+            edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1)
+            edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1)
+
+        # 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.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""]
+            elif 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 isinstance(negative_prompt, str):
+                uncond_tokens = [negative_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
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            if enable_edit_guidance:
+                text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts])
+            else:
+                text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+        # get the initial random noise unless the user supplied it
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            text_embeddings.dtype,
+            self.device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # Initialize edit_momentum to None
+        edit_momentum = None
+
+        self.uncond_estimates = None
+        self.text_estimates = None
+        self.edit_estimates = None
+        self.sem_guidance = None
+
+        for i, t in enumerate(self.progress_bar(timesteps)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = (
+                torch.cat([latents] * (2 + enabled_editing_prompts)) if do_classifier_free_guidance else latents
+            )
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_out = noise_pred.chunk(2 + enabled_editing_prompts)  # [b,4, 64, 64]
+                noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
+                noise_pred_edit_concepts = noise_pred_out[2:]
+
+                # default text guidance
+                noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)
+                # noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0])
+
+                if self.uncond_estimates is None:
+                    self.uncond_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_uncond.shape))
+                self.uncond_estimates[i] = noise_pred_uncond.detach().cpu()
+
+                if self.text_estimates is None:
+                    self.text_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
+                self.text_estimates[i] = noise_pred_text.detach().cpu()
+
+                if self.edit_estimates is None and enable_edit_guidance:
+                    self.edit_estimates = torch.zeros(
+                        (num_inference_steps + 1, len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
+                    )
+
+                if self.sem_guidance is None:
+                    self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
+
+                if edit_momentum is None:
+                    edit_momentum = torch.zeros_like(noise_guidance)
+
+                if enable_edit_guidance:
+                    concept_weights = torch.zeros(
+                        (len(noise_pred_edit_concepts), noise_guidance.shape[0]),
+                        device=self.device,
+                        dtype=noise_guidance.dtype,
+                    )
+                    noise_guidance_edit = torch.zeros(
+                        (len(noise_pred_edit_concepts), *noise_guidance.shape),
+                        device=self.device,
+                        dtype=noise_guidance.dtype,
+                    )
+                    # noise_guidance_edit = torch.zeros_like(noise_guidance)
+                    warmup_inds = []
+                    for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
+                        self.edit_estimates[i, c] = noise_pred_edit_concept
+                        if isinstance(edit_guidance_scale, list):
+                            edit_guidance_scale_c = edit_guidance_scale[c]
+                        else:
+                            edit_guidance_scale_c = edit_guidance_scale
+
+                        if isinstance(edit_threshold, list):
+                            edit_threshold_c = edit_threshold[c]
+                        else:
+                            edit_threshold_c = edit_threshold
+                        if isinstance(reverse_editing_direction, list):
+                            reverse_editing_direction_c = reverse_editing_direction[c]
+                        else:
+                            reverse_editing_direction_c = reverse_editing_direction
+                        if edit_weights:
+                            edit_weight_c = edit_weights[c]
+                        else:
+                            edit_weight_c = 1.0
+                        if isinstance(edit_warmup_steps, list):
+                            edit_warmup_steps_c = edit_warmup_steps[c]
+                        else:
+                            edit_warmup_steps_c = edit_warmup_steps
+
+                        if isinstance(edit_cooldown_steps, list):
+                            edit_cooldown_steps_c = edit_cooldown_steps[c]
+                        elif edit_cooldown_steps is None:
+                            edit_cooldown_steps_c = i + 1
+                        else:
+                            edit_cooldown_steps_c = edit_cooldown_steps
+                        if i >= edit_warmup_steps_c:
+                            warmup_inds.append(c)
+                        if i >= edit_cooldown_steps_c:
+                            noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)
+                            continue
+
+                        noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
+                        # tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))
+                        tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3))
+
+                        tmp_weights = torch.full_like(tmp_weights, edit_weight_c)  # * (1 / enabled_editing_prompts)
+                        if reverse_editing_direction_c:
+                            noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
+                        concept_weights[c, :] = tmp_weights
+
+                        noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c
+
+                        # torch.quantile function expects float32
+                        if noise_guidance_edit_tmp.dtype == torch.float32:
+                            tmp = torch.quantile(
+                                torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2),
+                                edit_threshold_c,
+                                dim=2,
+                                keepdim=False,
+                            )
+                        else:
+                            tmp = torch.quantile(
+                                torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32),
+                                edit_threshold_c,
+                                dim=2,
+                                keepdim=False,
+                            ).to(noise_guidance_edit_tmp.dtype)
+
+                        noise_guidance_edit_tmp = torch.where(
+                            torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None],
+                            noise_guidance_edit_tmp,
+                            torch.zeros_like(noise_guidance_edit_tmp),
+                        )
+                        noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp
+
+                        # noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp
+
+                    warmup_inds = torch.tensor(warmup_inds).to(self.device)
+                    if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
+                        concept_weights = concept_weights.to("cpu")  # Offload to cpu
+                        noise_guidance_edit = noise_guidance_edit.to("cpu")
+
+                        concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds)
+                        concept_weights_tmp = torch.where(
+                            concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
+                        )
+                        concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
+                        # concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)
+
+                        noise_guidance_edit_tmp = torch.index_select(
+                            noise_guidance_edit.to(self.device), 0, warmup_inds
+                        )
+                        noise_guidance_edit_tmp = torch.einsum(
+                            "cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp
+                        )
+                        noise_guidance_edit_tmp = noise_guidance_edit_tmp
+                        noise_guidance = noise_guidance + noise_guidance_edit_tmp
+
+                        self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu()
+
+                        del noise_guidance_edit_tmp
+                        del concept_weights_tmp
+                        concept_weights = concept_weights.to(self.device)
+                        noise_guidance_edit = noise_guidance_edit.to(self.device)
+
+                    concept_weights = torch.where(
+                        concept_weights < 0, torch.zeros_like(concept_weights), concept_weights
+                    )
+
+                    concept_weights = torch.nan_to_num(concept_weights)
+
+                    noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit)
+
+                    noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum
+
+                    edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit
+
+                    if warmup_inds.shape[0] == len(noise_pred_edit_concepts):
+                        noise_guidance = noise_guidance + noise_guidance_edit
+                        self.sem_guidance[i] = noise_guidance_edit.detach().cpu()
+
+                if sem_guidance is not None:
+                    edit_guidance = sem_guidance[i].to(self.device)
+                    noise_guidance = noise_guidance + edit_guidance
+
+                noise_pred = noise_pred_uncond + noise_guidance
+
+                # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        # 8. Post-processing
+        image = self.decode_latents(latents)
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return SemanticStableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -77,6 +77,21 @@ class PaintByExamplePipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class SemanticStableDiffusionPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class StableDiffusionAttendAndExcitePipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# 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.
+
+import gc
+import random
+import tempfile
+import unittest
+
+import numpy as np
+import torch
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
+from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
+from diffusers.utils import floats_tensor, nightly, torch_device
+from diffusers.utils.testing_utils import require_torch_gpu
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class SafeDiffusionPipelineFastTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    @property
+    def dummy_image(self):
+        batch_size = 1
+        num_channels = 3
+        sizes = (32, 32)
+
+        image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
+        return image
+
+    @property
+    def dummy_cond_unet(self):
+        torch.manual_seed(0)
+        model = UNet2DConditionModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            cross_attention_dim=32,
+        )
+        return model
+
+    @property
+    def dummy_vae(self):
+        torch.manual_seed(0)
+        model = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+        )
+        return model
+
+    @property
+    def dummy_text_encoder(self):
+        torch.manual_seed(0)
+        config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+        )
+        return CLIPTextModel(config)
+
+    @property
+    def dummy_extractor(self):
+        def extract(*args, **kwargs):
+            class Out:
+                def __init__(self):
+                    self.pixel_values = torch.ones([0])
+
+                def to(self, device):
+                    self.pixel_values.to(device)
+                    return self
+
+            return Out()
+
+        return extract
+
+    def test_semantic_diffusion_ddim(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        scheduler = DDIMScheduler(
+            beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            clip_sample=False,
+            set_alpha_to_one=False,
+        )
+
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
+        image = output.images
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        image_from_tuple = sd_pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            return_dict=False,
+        )[0]
+
+        image_slice = image[0, -3:, -3:, -1]
+        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 64, 64, 3)
+        expected_slice = np.array([0.5644, 0.6018, 0.4799, 0.5267, 0.5585, 0.4641, 0.516, 0.4964, 0.4792])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_semantic_diffusion_pndm(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
+
+        image = output.images
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        image_from_tuple = sd_pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            return_dict=False,
+        )[0]
+
+        image_slice = image[0, -3:, -3:, -1]
+        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 64, 64, 3)
+        expected_slice = np.array([0.5095, 0.5674, 0.4668, 0.5126, 0.5697, 0.4675, 0.5278, 0.4964, 0.4945])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_semantic_diffusion_no_safety_checker(self):
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
+        )
+        assert isinstance(pipe, StableDiffusionPipeline)
+        assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
+        assert pipe.safety_checker is None
+
+        image = pipe("example prompt", num_inference_steps=2).images[0]
+        assert image is not None
+
+        # check that there's no error when saving a pipeline with one of the models being None
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            pipe.save_pretrained(tmpdirname)
+            pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)
+
+        # sanity check that the pipeline still works
+        assert pipe.safety_checker is None
+        image = pipe("example prompt", num_inference_steps=2).images[0]
+        assert image is not None
+
+    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
+    def test_semantic_diffusion_fp16(self):
+        """Test that stable diffusion works with fp16"""
+        unet = self.dummy_cond_unet
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        # put models in fp16
+        unet = unet.half()
+        vae = vae.half()
+        bert = bert.half()
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+        image = sd_pipe([prompt], num_inference_steps=2, output_type="np").images
+
+        assert image.shape == (1, 64, 64, 3)
+
+
+@nightly
+@require_torch_gpu
+class SemanticDiffusionPipelineIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_positive_guidance(self):
+        torch_device = "cuda"
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        prompt = "a photo of a cat"
+        edit = {
+            "editing_prompt": ["sunglasses"],
+            "reverse_editing_direction": [False],
+            "edit_warmup_steps": 10,
+            "edit_guidance_scale": 6,
+            "edit_threshold": 0.95,
+            "edit_momentum_scale": 0.5,
+            "edit_mom_beta": 0.6,
+        }
+
+        seed = 3
+        guidance_scale = 7
+
+        # no sega enabled
+        generator = torch.Generator(torch_device)
+        generator.manual_seed(seed)
+        output = pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            output_type="np",
+            width=512,
+            height=512,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = [
+            0.34673113,
+            0.38492733,
+            0.37597352,
+            0.34086335,
+            0.35650748,
+            0.35579205,
+            0.3384763,
+            0.34340236,
+            0.3573271,
+        ]
+
+        assert image.shape == (1, 512, 512, 3)
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+        # with sega enabled
+        # generator = torch.manual_seed(seed)
+        generator.manual_seed(seed)
+        output = pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            output_type="np",
+            width=512,
+            height=512,
+            **edit,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = [
+            0.41887826,
+            0.37728766,
+            0.30138272,
+            0.41416335,
+            0.41664985,
+            0.36283392,
+            0.36191246,
+            0.43364465,
+            0.43001732,
+        ]
+
+        assert image.shape == (1, 512, 512, 3)
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_negative_guidance(self):
+        torch_device = "cuda"
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        prompt = "an image of a crowded boulevard, realistic, 4k"
+        edit = {
+            "editing_prompt": "crowd, crowded, people",
+            "reverse_editing_direction": True,
+            "edit_warmup_steps": 10,
+            "edit_guidance_scale": 8.3,
+            "edit_threshold": 0.9,
+            "edit_momentum_scale": 0.5,
+            "edit_mom_beta": 0.6,
+        }
+
+        seed = 9
+        guidance_scale = 7
+
+        # no sega enabled
+        generator = torch.Generator(torch_device)
+        generator.manual_seed(seed)
+        output = pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            output_type="np",
+            width=512,
+            height=512,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = [
+            0.43497998,
+            0.91814065,
+            0.7540739,
+            0.55580205,
+            0.8467265,
+            0.5389691,
+            0.62574506,
+            0.58897763,
+            0.50926757,
+        ]
+
+        assert image.shape == (1, 512, 512, 3)
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+        # with sega enabled
+        # generator = torch.manual_seed(seed)
+        generator.manual_seed(seed)
+        output = pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            output_type="np",
+            width=512,
+            height=512,
+            **edit,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = [
+            0.3089719,
+            0.30500144,
+            0.29016042,
+            0.30630964,
+            0.325687,
+            0.29419225,
+            0.2908091,
+            0.28723598,
+            0.27696294,
+        ]
+
+        assert image.shape == (1, 512, 512, 3)
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_multi_cond_guidance(self):
+        torch_device = "cuda"
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        prompt = "a castle next to a river"
+        edit = {
+            "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
+            "reverse_editing_direction": False,
+            "edit_warmup_steps": [15, 18],
+            "edit_guidance_scale": 6,
+            "edit_threshold": [0.9, 0.8],
+            "edit_momentum_scale": 0.5,
+            "edit_mom_beta": 0.6,
+        }
+
+        seed = 48
+        guidance_scale = 7
+
+        # no sega enabled
+        generator = torch.Generator(torch_device)
+        generator.manual_seed(seed)
+        output = pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            output_type="np",
+            width=512,
+            height=512,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = [
+            0.75163555,
+            0.76037145,
+            0.61785,
+            0.9189673,
+            0.8627701,
+            0.85189694,
+            0.8512813,
+            0.87012076,
+            0.8312857,
+        ]
+
+        assert image.shape == (1, 512, 512, 3)
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+        # with sega enabled
+        # generator = torch.manual_seed(seed)
+        generator.manual_seed(seed)
+        output = pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            output_type="np",
+            width=512,
+            height=512,
+            **edit,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = [
+            0.73553365,
+            0.7537271,
+            0.74341905,
+            0.66480356,
+            0.6472925,
+            0.63039416,
+            0.64812905,
+            0.6749717,
+            0.6517102,
+        ]
+
+        assert image.shape == (1, 512, 512, 3)
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_guidance_fp16(self):
+        torch_device = "cuda"
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        prompt = "a photo of a cat"
+        edit = {
+            "editing_prompt": ["sunglasses"],
+            "reverse_editing_direction": [False],
+            "edit_warmup_steps": 10,
+            "edit_guidance_scale": 6,
+            "edit_threshold": 0.95,
+            "edit_momentum_scale": 0.5,
+            "edit_mom_beta": 0.6,
+        }
+
+        seed = 3
+        guidance_scale = 7
+
+        # no sega enabled
+        generator = torch.Generator(torch_device)
+        generator.manual_seed(seed)
+        output = pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            output_type="np",
+            width=512,
+            height=512,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = [
+            0.34887695,
+            0.3876953,
+            0.375,
+            0.34423828,
+            0.3581543,
+            0.35717773,
+            0.3383789,
+            0.34570312,
+            0.359375,
+        ]
+
+        assert image.shape == (1, 512, 512, 3)
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+        # with sega enabled
+        # generator = torch.manual_seed(seed)
+        generator.manual_seed(seed)
+        output = pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            output_type="np",
+            width=512,
+            height=512,
+            **edit,
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+        expected_slice = [
+            0.42285156,
+            0.36914062,
+            0.29077148,
+            0.42041016,
+            0.41918945,
+            0.35498047,
+            0.3618164,
+            0.4423828,
+            0.43115234,
+        ]
+
+        assert image.shape == (1, 512, 512, 3)
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2