GLIGEN (Grounded Language-to-Image Generation)

The GLIGEN model was created by researchers and engineers from University of Wisconsin-Madison, Columbia University, and Microsoft. The StableDiffusionGLIGENPipeline and StableDiffusionGLIGENTextImagePipeline can generate photorealistic images conditioned on grounding inputs. Along with text and bounding boxes with StableDiffusionGLIGENPipeline, if input images are given, StableDiffusionGLIGENTextImagePipeline can insert objects described by text at the region defined by bounding boxes. Otherwise, it'll generate an image described by the caption/prompt and insert objects described by text at the region defined by bounding boxes. It's trained on COCO2014D and COCO2014CD datasets, and the model uses a frozen CLIP ViT-L/14 text encoder to condition itself on grounding inputs.

The abstract from the paper is:

Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN’s zeroshot performance on COCO and LVIS outperforms existing supervised layout-to-image baselines by a large margin.

Tip

Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality and how to reuse pipeline components efficiently!

If you want to use one of the official checkpoints for a task, explore the gligen Hub organizations!

mindone.diffusers.StableDiffusionGLIGENPipeline

Bases: DiffusionPipeline, StableDiffusionMixin

Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).

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.).

PARAMETER	DESCRIPTION
vae	Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. TYPE: [`AutoencoderKL`]
text_encoder	Frozen text-encoder (clip-vit-large-patch14). TYPE: [`~transformers.CLIPTextModel`]
tokenizer	A CLIPTokenizer to tokenize text. TYPE: [`~transformers.CLIPTokenizer`]
unet	A UNet2DConditionModel to denoise the encoded image latents. TYPE: [`UNet2DConditionModel`]
scheduler	A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of [DDIMScheduler], [LMSDiscreteScheduler], or [PNDMScheduler]. TYPE: [`SchedulerMixin`]
safety_checker	Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the model card for more details about a model's potential harms. TYPE: [`StableDiffusionSafetyChecker`]
feature_extractor	A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker. TYPE: [`~transformers.CLIPImageProcessor`]

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py

class StableDiffusionGLIGENPipeline(DiffusionPipeline, StableDiffusionMixin):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).

    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.).

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        text_encoder ([`~transformers.CLIPTextModel`]):
            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer ([`~transformers.CLIPTokenizer`]):
            A `CLIPTokenizer` to tokenize text.
        unet ([`UNet2DConditionModel`]):
            A `UNet2DConditionModel` 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`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
    """

    _optional_components = ["safety_checker", "feature_extractor"]
    model_cpu_offload_seq = "text_encoder->unet->vae"
    _exclude_from_cpu_offload = ["safety_checker"]

    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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
    def _encode_prompt(
        self,
        prompt,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        lora_scale: Optional[float] = None,
        **kwargs,
    ):
        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."  # noqa: E501
        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)

        prompt_embeds_tuple = self.encode_prompt(
            prompt=prompt,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=lora_scale,
            **kwargs,
        )

        # concatenate for backwards comp
        prompt_embeds = ops.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])

        return prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
    def encode_prompt(
        self,
        prompt,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = 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
            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`).
            prompt_embeds (`ms.Tensor`, *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 (`ms.Tensor`, *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.
            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.
        """
        # 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, LoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            scale_lora_layers(self.text_encoder, lora_scale)

        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]

        if prompt_embeds is None:
            # textual inversion: process multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="np",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="np").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not np.array_equal(
                text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = ms.Tensor(text_inputs.attention_mask)
            else:
                attention_mask = None

            if clip_skip is None:
                prompt_embeds = self.text_encoder(ms.Tensor(text_input_ids), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                prompt_embeds = self.text_encoder(
                    ms.Tensor(text_input_ids), attention_mask=attention_mask, output_hidden_states=True
                )
                # Access the `hidden_states` first, that contains a tuple of
                # all the hidden states from the encoder layers. Then index into
                # the tuple to access the hidden states from the desired layer.
                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
                # We also need to apply the final LayerNorm here to not mess with the
                # representations. The `last_hidden_states` that we typically use for
                # obtaining the final prompt representations passes through the LayerNorm
                # layer.
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)

        if self.text_encoder is not None:
            prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None:
            prompt_embeds_dtype = self.unet.dtype
        else:
            prompt_embeds_dtype = prompt_embeds.dtype

        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.tile((1, num_images_per_prompt, 1))
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif 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 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

            # textual inversion: process multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="np",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = ms.Tensor(uncond_input.attention_mask)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                ms.Tensor(uncond_input.input_ids),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype)

            negative_prompt_embeds = negative_prompt_embeds.tile((1, num_images_per_prompt, 1))
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        if self.text_encoder is not None:
            if isinstance(self, LoraLoaderMixin):
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder, lora_scale)

        return prompt_embeds, negative_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
    def run_safety_checker(self, image, dtype):
        if self.safety_checker is None:
            has_nsfw_concept = None
        else:
            if ops.is_tensor(image):
                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
            else:
                feature_extractor_input = self.image_processor.numpy_to_pil(image)
            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="np")
            image, has_nsfw_concept = self.safety_checker(
                images=image, clip_input=ms.Tensor(safety_checker_input.pixel_values).to(dtype)
            )

            # Warning for safety checker operations here as it couldn't been done in construct()
            if ops.any(has_nsfw_concept):
                logger.warning(
                    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
                    " Try again with a different prompt and/or seed."
                )
        return image, has_nsfw_concept

    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,
        height,
        width,
        callback_steps,
        gligen_phrases,
        gligen_boxes,
        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}."
                )

        if len(gligen_phrases) != len(gligen_boxes):
            raise ValueError(
                "length of `gligen_phrases` and `gligen_boxes` has to be same, but"
                f" got: `gligen_phrases` {len(gligen_phrases)} != `gligen_boxes` {len(gligen_boxes)}"
            )

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
        shape = (
            batch_size,
            num_channels_latents,
            int(height) // self.vae_scale_factor,
            int(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, dtype=dtype)
        else:
            latents = latents.to(dtype)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        # wtf? The above line changes the dtype of latents from fp16 to fp32, so we need a casting.
        latents = latents.to(dtype=dtype)
        return latents

    def enable_fuser(self, enabled=True):
        for sub_name, module in self.unet.cells_and_names():
            if type(module) is GatedSelfAttentionDense:
                module.enabled = enabled

    def draw_inpaint_mask_from_boxes(self, boxes, size):
        inpaint_mask = ops.ones((size[0], size[1]))
        for box in boxes:
            x0, x1 = box[0] * size[0], box[2] * size[0]
            y0, y1 = box[1] * size[1], box[3] * size[1]
            inpaint_mask[int(y0) : int(y1), int(x0) : int(x1)] = 0
        return inpaint_mask

    def crop(self, im, new_width, new_height):
        width, height = im.size
        left = (width - new_width) / 2
        top = (height - new_height) / 2
        right = (width + new_width) / 2
        bottom = (height + new_height) / 2
        return im.crop((left, top, right, bottom))

    def target_size_center_crop(self, im, new_hw):
        width, height = im.size
        if width != height:
            im = self.crop(im, min(height, width), min(height, width))
        return im.resize((new_hw, new_hw), PIL.Image.LANCZOS)

    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        gligen_scheduled_sampling_beta: float = 0.3,
        gligen_phrases: List[str] = None,
        gligen_boxes: List[List[float]] = None,
        gligen_inpaint_image: Optional[PIL.Image.Image] = None,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
        latents: Optional[ms.Tensor] = None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = False,
        callback: Optional[Callable[[int, int, ms.Tensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        clip_skip: Optional[int] = None,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
            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):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            gligen_phrases (`List[str]`):
                The phrases to guide what to include in each of the regions defined by the corresponding
                `gligen_boxes`. There should only be one phrase per bounding box.
            gligen_boxes (`List[List[float]]`):
                The bounding boxes that identify rectangular regions of the image that are going to be filled with the
                content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a
                `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
            gligen_inpaint_image (`PIL.Image.Image`, *optional*):
                The input image, if provided, is inpainted with objects described by the `gligen_boxes` and
                `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.
            gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
                Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
                Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
                scheduled sampling during inference for improved quality and controllability.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
                A [`np.random.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            latents (`ms.Tensor`, *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 is generated by sampling using the supplied random `generator`.
            prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            negative_prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is called with the
                following arguments: `callback(step: int, timestep: int, latents: ms.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function is called. If not specified, the callback is called at
                every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
                [`self.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 from [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.
            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.
        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list with the generated images and the
                second element is a list of `bool`s indicating whether the corresponding generated image contains
                "not-safe-for-work" (nsfw) content.
        """
        # 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,
            gligen_phrases,
            gligen_boxes,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        # 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]

        # 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

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            clip_skip=clip_skip,
        )
        # 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 do_classifier_free_guidance:
            prompt_embeds = ops.cat([negative_prompt_embeds, prompt_embeds])

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            generator,
            latents,
        )

        # 5.1 Prepare GLIGEN variables
        max_objs = 30
        if len(gligen_boxes) > max_objs:
            warnings.warn(
                f"More that {max_objs} objects found. Only first {max_objs} objects will be processed.",
                FutureWarning,
            )
            gligen_phrases = gligen_phrases[:max_objs]
            gligen_boxes = gligen_boxes[:max_objs]
        # prepare batched input to the GLIGENTextBoundingboxProjection (boxes, phrases, mask)
        # Get tokens for phrases from pre-trained CLIPTokenizer
        tokenizer_inputs = self.tokenizer(gligen_phrases, padding=True, return_tensors="np")
        for k, v in tokenizer_inputs.items():
            tokenizer_inputs[k] = ms.Tensor.from_numpy(v)
        # For the token, we use the same pre-trained text encoder
        # to obtain its text feature
        _text_embeddings = self.text_encoder(**tokenizer_inputs)[1]
        n_objs = len(gligen_boxes)
        # For each entity, described in phrases, is denoted with a bounding box,
        # we represent the location information as (xmin,ymin,xmax,ymax)
        boxes = ops.zeros((max_objs, 4), dtype=self.text_encoder.dtype)
        boxes[:n_objs] = ms.Tensor(gligen_boxes)
        text_embeddings = ops.zeros((max_objs, self.unet.config.cross_attention_dim), dtype=self.text_encoder.dtype)
        text_embeddings[:n_objs] = _text_embeddings
        # Generate a mask for each object that is entity described by phrases
        masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
        masks[:n_objs] = 1

        repeat_batch = batch_size * num_images_per_prompt
        boxes = boxes.unsqueeze(0).broadcast_to((repeat_batch, -1, -1)).copy()
        text_embeddings = text_embeddings.unsqueeze(0).broadcast_to((repeat_batch, -1, -1)).copy()
        masks = masks.unsqueeze(0).broadcast_to((repeat_batch, -1)).copy()
        if do_classifier_free_guidance:
            repeat_batch = repeat_batch * 2
            boxes = ops.cat([boxes] * 2)
            text_embeddings = ops.cat([text_embeddings] * 2)
            masks = ops.cat([masks] * 2)
            masks[: repeat_batch // 2] = 0
        if cross_attention_kwargs is None:
            cross_attention_kwargs = {}
        cross_attention_kwargs["gligen"] = {"boxes": boxes, "positive_embeddings": text_embeddings, "masks": masks}

        # Prepare latent variables for GLIGEN inpainting
        if gligen_inpaint_image is not None:
            # if the given input image is not of the same size as expected by VAE
            # center crop and resize the input image to expected shape
            if gligen_inpaint_image.size != (self.vae.sample_size, self.vae.sample_size):
                gligen_inpaint_image = self.target_size_center_crop(gligen_inpaint_image, self.vae.sample_size)
            # Convert a single image into a batch of images with a batch size of 1
            # The resulting shape becomes (1, C, H, W), where C is the number of channels,
            # and H and W are the height and width of the image.
            # scales the pixel values to a range [-1, 1]
            gligen_inpaint_image = self.image_processor.preprocess(gligen_inpaint_image)
            gligen_inpaint_image = gligen_inpaint_image.to(dtype=self.vae.dtype)
            # Run AutoEncoder to get corresponding latents
            gligen_inpaint_latent = self.vae.encode(gligen_inpaint_image)[0]
            gligen_inpaint_latent = self.vae.diag_gauss_dist.sample(gligen_inpaint_latent)
            gligen_inpaint_latent = self.vae.config.scaling_factor * gligen_inpaint_latent
            # Generate an inpainting mask
            # pixel value = 0, where the object is present (defined by bounding boxes above)
            #               1, everywhere else
            gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(gligen_boxes, gligen_inpaint_latent.shape[2:])
            gligen_inpaint_mask = gligen_inpaint_mask.to(dtype=gligen_inpaint_latent.dtype)
            gligen_inpaint_mask = gligen_inpaint_mask[None, None]
            gligen_inpaint_mask_addition = ops.cat(
                (gligen_inpaint_latent * gligen_inpaint_mask, gligen_inpaint_mask), axis=1
            )
            # Convert a single mask into a batch of masks with a batch size of 1
            gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.broadcast_to((repeat_batch, -1, -1, -1)).copy()

        num_grounding_steps = int(gligen_scheduled_sampling_beta * len(timesteps))
        self.enable_fuser(True)

        # 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)

        # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
        # to the unet and will raise RuntimeError.
        lora_scale = cross_attention_kwargs.pop("scale", None) if cross_attention_kwargs is not None else None
        if lora_scale is not None:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self.unet, lora_scale)

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # Scheduled sampling
                if i == num_grounding_steps:
                    self.enable_fuser(False)

                if latents.shape[1] != 4:
                    latents = ops.randn_like(latents[:, :4], dtype=latents.dtype)

                if gligen_inpaint_image is not None:
                    gligen_inpaint_latent_with_noise = (
                        self.scheduler.add_noise(
                            gligen_inpaint_latent,
                            ops.randn_like(gligen_inpaint_latent, dtype=gligen_inpaint_latent.dtype),
                            t[None],
                        )
                        .broadcast_to((latents.shape[0], -1, -1, -1))
                        .copy()
                    )
                    latents = gligen_inpaint_latent_with_noise * gligen_inpaint_mask + latents * (
                        1 - gligen_inpaint_mask
                    )

                # expand the latents if we are doing classifier free guidance
                latent_model_input = ops.cat([latents] * 2) if do_classifier_free_guidance else latents
                # TODO: method of scheduler should not change the dtype of input.
                #  Remove the casting after cuiyushi confirm that.
                tmp_dtype = latent_model_input.dtype
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                latent_model_input = latent_model_input.to(tmp_dtype)

                if gligen_inpaint_image is not None:
                    latent_model_input = ops.cat((latent_model_input, gligen_inpaint_mask_addition), axis=1)

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=ms.mutable(cross_attention_kwargs),
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                # TODO: method of scheduler should not change the dtype of input.
                #  Remove the casting after cuiyushi confirm that.
                tmp_dtype = latents.dtype
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
                latents = latents.to(tmp_dtype)

                # 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 lora_scale is not None:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self.unet, lora_scale)

        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, prompt_embeds.dtype)
        else:
            image = latents
            has_nsfw_concept = None

        if has_nsfw_concept is None:
            do_denormalize = [True] * image.shape[0]
        else:
            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

mindone.diffusers.StableDiffusionGLIGENPipeline.__call__(prompt=None, height=None, width=None, num_inference_steps=50, guidance_scale=7.5, gligen_scheduled_sampling_beta=0.3, gligen_phrases=None, gligen_boxes=None, gligen_inpaint_image=None, negative_prompt=None, num_images_per_prompt=1, eta=0.0, generator=None, latents=None, prompt_embeds=None, negative_prompt_embeds=None, output_type='pil', return_dict=False, callback=None, callback_steps=1, cross_attention_kwargs=None, clip_skip=None)

The call function to the pipeline for generation.

PARAMETER	DESCRIPTION
prompt	The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
height	The height in pixels of the generated image. TYPE: `int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor` DEFAULT: None
width	The width in pixels of the generated image. TYPE: `int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor` DEFAULT: None
num_inference_steps	The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. TYPE: `int`, *optional*, defaults to 50 DEFAULT: 50
guidance_scale	A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. TYPE: `float`, *optional*, defaults to 7.5 DEFAULT: 7.5
gligen_phrases	The phrases to guide what to include in each of the regions defined by the corresponding gligen_boxes. There should only be one phrase per bounding box. TYPE: `List[str]` DEFAULT: None
gligen_boxes	The bounding boxes that identify rectangular regions of the image that are going to be filled with the content described by the corresponding gligen_phrases. Each rectangular box is defined as a List[float] of 4 elements [xmin, ymin, xmax, ymax] where each value is between [0,1]. TYPE: `List[List[float]]` DEFAULT: None
gligen_inpaint_image	The input image, if provided, is inpainted with objects described by the gligen_boxes and gligen_phrases. Otherwise, it is treated as a generation task on a blank input image. TYPE: `PIL.Image.Image`, *optional* DEFAULT: None
gligen_scheduled_sampling_beta	Scheduled Sampling factor from GLIGEN: Open-Set Grounded Text-to-Image Generation. Scheduled Sampling factor is only varied for scheduled sampling during inference for improved quality and controllability. TYPE: `float`, defaults to 0.3 DEFAULT: 0.3
negative_prompt	The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). TYPE: `str` or `List[str]`, *optional* DEFAULT: None
num_images_per_prompt	The number of images to generate per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
eta	Corresponds to parameter eta (η) from the DDIM paper. Only applies to the [~schedulers.DDIMScheduler], and is ignored in other schedulers. TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0
generator	A np.random.Generator to make generation deterministic. TYPE: `np.random.Generator` or `List[np.random.Generator]`, *optional* DEFAULT: None
latents	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 is generated by sampling using the supplied random generator. TYPE: `ms.Tensor`, *optional* DEFAULT: None
prompt_embeds	Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_prompt_embeds	Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument. TYPE: `ms.Tensor`, *optional* DEFAULT: None
output_type	The output format of the generated image. Choose between PIL.Image or np.array. TYPE: `str`, *optional*, defaults to `"pil"` DEFAULT: 'pil'
return_dict	Whether or not to return a [~pipelines.stable_diffusion.StableDiffusionPipelineOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
callback	A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: ms.Tensor). TYPE: `Callable`, *optional* DEFAULT: None
callback_steps	The frequency at which the callback function is called. If not specified, the callback is called at every step. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
cross_attention_kwargs	A kwargs dictionary that if specified is passed along to the [AttentionProcessor] as defined in self.processor. TYPE: `dict`, *optional* DEFAULT: None
guidance_rescale	Guidance rescale factor from Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR. TYPE: `float`, *optional*, defaults to 0.0
clip_skip	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. TYPE: `int`, *optional* DEFAULT: None

RETURNS	DESCRIPTION
	[~pipelines.stable_diffusion.StableDiffusionPipelineOutput] or tuple: If return_dict is True, [~pipelines.stable_diffusion.StableDiffusionPipelineOutput] is returned, otherwise a tuple is returned where the first element is a list with the generated images and the second element is a list of bools indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py

def __call__(
    self,
    prompt: Union[str, List[str]] = None,
    height: Optional[int] = None,
    width: Optional[int] = None,
    num_inference_steps: int = 50,
    guidance_scale: float = 7.5,
    gligen_scheduled_sampling_beta: float = 0.3,
    gligen_phrases: List[str] = None,
    gligen_boxes: List[List[float]] = None,
    gligen_inpaint_image: Optional[PIL.Image.Image] = None,
    negative_prompt: Optional[Union[str, List[str]]] = None,
    num_images_per_prompt: Optional[int] = 1,
    eta: float = 0.0,
    generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
    latents: Optional[ms.Tensor] = None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = False,
    callback: Optional[Callable[[int, int, ms.Tensor], None]] = None,
    callback_steps: int = 1,
    cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    clip_skip: Optional[int] = None,
):
    r"""
    The call function to the pipeline for generation.

    Args:
        prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
        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):
            A higher guidance scale value encourages the model to generate images closely linked to the text
            `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
        gligen_phrases (`List[str]`):
            The phrases to guide what to include in each of the regions defined by the corresponding
            `gligen_boxes`. There should only be one phrase per bounding box.
        gligen_boxes (`List[List[float]]`):
            The bounding boxes that identify rectangular regions of the image that are going to be filled with the
            content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a
            `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
        gligen_inpaint_image (`PIL.Image.Image`, *optional*):
            The input image, if provided, is inpainted with objects described by the `gligen_boxes` and
            `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.
        gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
            Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
            Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
            scheduled sampling during inference for improved quality and controllability.
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to guide what to not include in image generation. If not defined, you need to
            pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
            to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
        generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
            A [`np.random.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
            generation deterministic.
        latents (`ms.Tensor`, *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 is generated by sampling using the supplied random `generator`.
        prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
            provided, text embeddings are generated from the `prompt` input argument.
        negative_prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
            not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generated image. Choose between `PIL.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `False`):
            Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
            plain tuple.
        callback (`Callable`, *optional*):
            A function that calls every `callback_steps` steps during inference. The function is called with the
            following arguments: `callback(step: int, timestep: int, latents: ms.Tensor)`.
        callback_steps (`int`, *optional*, defaults to 1):
            The frequency at which the `callback` function is called. If not specified, the callback is called at
            every step.
        cross_attention_kwargs (`dict`, *optional*):
            A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
            [`self.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 from [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.
        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.
    Examples:

    Returns:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
            otherwise a `tuple` is returned where the first element is a list with the generated images and the
            second element is a list of `bool`s indicating whether the corresponding generated image contains
            "not-safe-for-work" (nsfw) content.
    """
    # 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,
        gligen_phrases,
        gligen_boxes,
        negative_prompt,
        prompt_embeds,
        negative_prompt_embeds,
    )

    # 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]

    # 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

    # 3. Encode input prompt
    prompt_embeds, negative_prompt_embeds = self.encode_prompt(
        prompt,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        clip_skip=clip_skip,
    )
    # 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 do_classifier_free_guidance:
        prompt_embeds = ops.cat([negative_prompt_embeds, prompt_embeds])

    # 4. Prepare timesteps
    self.scheduler.set_timesteps(num_inference_steps)
    timesteps = self.scheduler.timesteps

    # 5. Prepare latent variables
    num_channels_latents = self.unet.config.in_channels
    latents = self.prepare_latents(
        batch_size * num_images_per_prompt,
        num_channels_latents,
        height,
        width,
        prompt_embeds.dtype,
        generator,
        latents,
    )

    # 5.1 Prepare GLIGEN variables
    max_objs = 30
    if len(gligen_boxes) > max_objs:
        warnings.warn(
            f"More that {max_objs} objects found. Only first {max_objs} objects will be processed.",
            FutureWarning,
        )
        gligen_phrases = gligen_phrases[:max_objs]
        gligen_boxes = gligen_boxes[:max_objs]
    # prepare batched input to the GLIGENTextBoundingboxProjection (boxes, phrases, mask)
    # Get tokens for phrases from pre-trained CLIPTokenizer
    tokenizer_inputs = self.tokenizer(gligen_phrases, padding=True, return_tensors="np")
    for k, v in tokenizer_inputs.items():
        tokenizer_inputs[k] = ms.Tensor.from_numpy(v)
    # For the token, we use the same pre-trained text encoder
    # to obtain its text feature
    _text_embeddings = self.text_encoder(**tokenizer_inputs)[1]
    n_objs = len(gligen_boxes)
    # For each entity, described in phrases, is denoted with a bounding box,
    # we represent the location information as (xmin,ymin,xmax,ymax)
    boxes = ops.zeros((max_objs, 4), dtype=self.text_encoder.dtype)
    boxes[:n_objs] = ms.Tensor(gligen_boxes)
    text_embeddings = ops.zeros((max_objs, self.unet.config.cross_attention_dim), dtype=self.text_encoder.dtype)
    text_embeddings[:n_objs] = _text_embeddings
    # Generate a mask for each object that is entity described by phrases
    masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
    masks[:n_objs] = 1

    repeat_batch = batch_size * num_images_per_prompt
    boxes = boxes.unsqueeze(0).broadcast_to((repeat_batch, -1, -1)).copy()
    text_embeddings = text_embeddings.unsqueeze(0).broadcast_to((repeat_batch, -1, -1)).copy()
    masks = masks.unsqueeze(0).broadcast_to((repeat_batch, -1)).copy()
    if do_classifier_free_guidance:
        repeat_batch = repeat_batch * 2
        boxes = ops.cat([boxes] * 2)
        text_embeddings = ops.cat([text_embeddings] * 2)
        masks = ops.cat([masks] * 2)
        masks[: repeat_batch // 2] = 0
    if cross_attention_kwargs is None:
        cross_attention_kwargs = {}
    cross_attention_kwargs["gligen"] = {"boxes": boxes, "positive_embeddings": text_embeddings, "masks": masks}

    # Prepare latent variables for GLIGEN inpainting
    if gligen_inpaint_image is not None:
        # if the given input image is not of the same size as expected by VAE
        # center crop and resize the input image to expected shape
        if gligen_inpaint_image.size != (self.vae.sample_size, self.vae.sample_size):
            gligen_inpaint_image = self.target_size_center_crop(gligen_inpaint_image, self.vae.sample_size)
        # Convert a single image into a batch of images with a batch size of 1
        # The resulting shape becomes (1, C, H, W), where C is the number of channels,
        # and H and W are the height and width of the image.
        # scales the pixel values to a range [-1, 1]
        gligen_inpaint_image = self.image_processor.preprocess(gligen_inpaint_image)
        gligen_inpaint_image = gligen_inpaint_image.to(dtype=self.vae.dtype)
        # Run AutoEncoder to get corresponding latents
        gligen_inpaint_latent = self.vae.encode(gligen_inpaint_image)[0]
        gligen_inpaint_latent = self.vae.diag_gauss_dist.sample(gligen_inpaint_latent)
        gligen_inpaint_latent = self.vae.config.scaling_factor * gligen_inpaint_latent
        # Generate an inpainting mask
        # pixel value = 0, where the object is present (defined by bounding boxes above)
        #               1, everywhere else
        gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(gligen_boxes, gligen_inpaint_latent.shape[2:])
        gligen_inpaint_mask = gligen_inpaint_mask.to(dtype=gligen_inpaint_latent.dtype)
        gligen_inpaint_mask = gligen_inpaint_mask[None, None]
        gligen_inpaint_mask_addition = ops.cat(
            (gligen_inpaint_latent * gligen_inpaint_mask, gligen_inpaint_mask), axis=1
        )
        # Convert a single mask into a batch of masks with a batch size of 1
        gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.broadcast_to((repeat_batch, -1, -1, -1)).copy()

    num_grounding_steps = int(gligen_scheduled_sampling_beta * len(timesteps))
    self.enable_fuser(True)

    # 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)

    # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
    # to the unet and will raise RuntimeError.
    lora_scale = cross_attention_kwargs.pop("scale", None) if cross_attention_kwargs is not None else None
    if lora_scale is not None:
        # weight the lora layers by setting `lora_scale` for each PEFT layer
        scale_lora_layers(self.unet, lora_scale)

    # 7. Denoising loop
    num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
    with self.progress_bar(total=num_inference_steps) as progress_bar:
        for i, t in enumerate(timesteps):
            # Scheduled sampling
            if i == num_grounding_steps:
                self.enable_fuser(False)

            if latents.shape[1] != 4:
                latents = ops.randn_like(latents[:, :4], dtype=latents.dtype)

            if gligen_inpaint_image is not None:
                gligen_inpaint_latent_with_noise = (
                    self.scheduler.add_noise(
                        gligen_inpaint_latent,
                        ops.randn_like(gligen_inpaint_latent, dtype=gligen_inpaint_latent.dtype),
                        t[None],
                    )
                    .broadcast_to((latents.shape[0], -1, -1, -1))
                    .copy()
                )
                latents = gligen_inpaint_latent_with_noise * gligen_inpaint_mask + latents * (
                    1 - gligen_inpaint_mask
                )

            # expand the latents if we are doing classifier free guidance
            latent_model_input = ops.cat([latents] * 2) if do_classifier_free_guidance else latents
            # TODO: method of scheduler should not change the dtype of input.
            #  Remove the casting after cuiyushi confirm that.
            tmp_dtype = latent_model_input.dtype
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
            latent_model_input = latent_model_input.to(tmp_dtype)

            if gligen_inpaint_image is not None:
                latent_model_input = ops.cat((latent_model_input, gligen_inpaint_mask_addition), axis=1)

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input,
                t,
                encoder_hidden_states=prompt_embeds,
                cross_attention_kwargs=ms.mutable(cross_attention_kwargs),
            )[0]

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            # TODO: method of scheduler should not change the dtype of input.
            #  Remove the casting after cuiyushi confirm that.
            tmp_dtype = latents.dtype
            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
            latents = latents.to(tmp_dtype)

            # 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 lora_scale is not None:
        # remove `lora_scale` from each PEFT layer
        unscale_lora_layers(self.unet, lora_scale)

    if not output_type == "latent":
        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
        image, has_nsfw_concept = self.run_safety_checker(image, prompt_embeds.dtype)
    else:
        image = latents
        has_nsfw_concept = None

    if has_nsfw_concept is None:
        do_denormalize = [True] * image.shape[0]
    else:
        do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

    image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

    if not return_dict:
        return (image, has_nsfw_concept)

    return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

mindone.diffusers.StableDiffusionGLIGENPipeline.encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, lora_scale=None, clip_skip=None)

Encodes the prompt into text encoder hidden states.

PARAMETER	DESCRIPTION
prompt	prompt to be encoded TYPE: `str` or `List[str]`, *optional*
num_images_per_prompt	number of images that should be generated per prompt TYPE: `int`
do_classifier_free_guidance	whether to use classifier free guidance or not TYPE: `bool`
negative_prompt	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). TYPE: `str` or `List[str]`, *optional* DEFAULT: None
prompt_embeds	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_prompt_embeds	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
lora_scale	A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. TYPE: `float`, *optional* DEFAULT: None
clip_skip	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. TYPE: `int`, *optional* DEFAULT: None

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py

def encode_prompt(
    self,
    prompt,
    num_images_per_prompt,
    do_classifier_free_guidance,
    negative_prompt=None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = 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
        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`).
        prompt_embeds (`ms.Tensor`, *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 (`ms.Tensor`, *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.
        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.
    """
    # 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, LoraLoaderMixin):
        self._lora_scale = lora_scale

        # dynamically adjust the LoRA scale
        scale_lora_layers(self.text_encoder, lora_scale)

    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]

    if prompt_embeds is None:
        # textual inversion: process multi-vector tokens if necessary
        if isinstance(self, TextualInversionLoaderMixin):
            prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="np",
        )
        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="np").input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not np.array_equal(
            text_input_ids, untruncated_ids
        ):
            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
            )

        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
            attention_mask = ms.Tensor(text_inputs.attention_mask)
        else:
            attention_mask = None

        if clip_skip is None:
            prompt_embeds = self.text_encoder(ms.Tensor(text_input_ids), attention_mask=attention_mask)
            prompt_embeds = prompt_embeds[0]
        else:
            prompt_embeds = self.text_encoder(
                ms.Tensor(text_input_ids), attention_mask=attention_mask, output_hidden_states=True
            )
            # Access the `hidden_states` first, that contains a tuple of
            # all the hidden states from the encoder layers. Then index into
            # the tuple to access the hidden states from the desired layer.
            prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
            # We also need to apply the final LayerNorm here to not mess with the
            # representations. The `last_hidden_states` that we typically use for
            # obtaining the final prompt representations passes through the LayerNorm
            # layer.
            prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)

    if self.text_encoder is not None:
        prompt_embeds_dtype = self.text_encoder.dtype
    elif self.unet is not None:
        prompt_embeds_dtype = self.unet.dtype
    else:
        prompt_embeds_dtype = prompt_embeds.dtype

    prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype)

    bs_embed, seq_len, _ = prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    prompt_embeds = prompt_embeds.tile((1, num_images_per_prompt, 1))
    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance and negative_prompt_embeds is None:
        uncond_tokens: List[str]
        if negative_prompt is None:
            uncond_tokens = [""] * batch_size
        elif 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 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

        # textual inversion: process multi-vector tokens if necessary
        if isinstance(self, TextualInversionLoaderMixin):
            uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

        max_length = prompt_embeds.shape[1]
        uncond_input = self.tokenizer(
            uncond_tokens,
            padding="max_length",
            max_length=max_length,
            truncation=True,
            return_tensors="np",
        )

        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
            attention_mask = ms.Tensor(uncond_input.attention_mask)
        else:
            attention_mask = None

        negative_prompt_embeds = self.text_encoder(
            ms.Tensor(uncond_input.input_ids),
            attention_mask=attention_mask,
        )
        negative_prompt_embeds = negative_prompt_embeds[0]

    if do_classifier_free_guidance:
        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        seq_len = negative_prompt_embeds.shape[1]

        negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype)

        negative_prompt_embeds = negative_prompt_embeds.tile((1, num_images_per_prompt, 1))
        negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

    if self.text_encoder is not None:
        if isinstance(self, LoraLoaderMixin):
            # Retrieve the original scale by scaling back the LoRA layers
            unscale_lora_layers(self.text_encoder, lora_scale)

    return prompt_embeds, negative_prompt_embeds

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline

Bases: DiffusionPipeline, StableDiffusionMixin

Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).

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.).

PARAMETER	DESCRIPTION
vae	Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. TYPE: [`AutoencoderKL`]
text_encoder	Frozen text-encoder (clip-vit-large-patch14). TYPE: [`~transformers.CLIPTextModel`]
tokenizer	A CLIPTokenizer to tokenize text. TYPE: [`~transformers.CLIPTokenizer`]
processor	A CLIPProcessor to procces reference image. TYPE: [`~transformers.CLIPProcessor`]
image_encoder	Frozen image-encoder (clip-vit-large-patch14). TYPE: [`~transformers.CLIPVisionModelWithProjection`]
image_project	A CLIPImageProjection to project image embedding into phrases embedding space. TYPE: [`CLIPImageProjection`]
unet	A UNet2DConditionModel to denoise the encoded image latents. TYPE: [`UNet2DConditionModel`]
scheduler	A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of [DDIMScheduler], [LMSDiscreteScheduler], or [PNDMScheduler]. TYPE: [`SchedulerMixin`]
safety_checker	Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the model card for more details about a model's potential harms. TYPE: [`StableDiffusionSafetyChecker`]
feature_extractor	A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker. TYPE: [`~transformers.CLIPImageProcessor`]

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

class StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline, StableDiffusionMixin):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).

    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.).

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        text_encoder ([`~transformers.CLIPTextModel`]):
            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer ([`~transformers.CLIPTokenizer`]):
            A `CLIPTokenizer` to tokenize text.
        processor ([`~transformers.CLIPProcessor`]):
            A `CLIPProcessor` to procces reference image.
        image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
            Frozen image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        image_project ([`CLIPImageProjection`]):
            A `CLIPImageProjection` to project image embedding into phrases embedding space.
        unet ([`UNet2DConditionModel`]):
            A `UNet2DConditionModel` 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`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
    """

    model_cpu_offload_seq = "text_encoder->unet->vae"
    _optional_components = ["safety_checker", "feature_extractor"]
    _exclude_from_cpu_offload = ["safety_checker"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        processor: CLIPProcessor,
        image_encoder: CLIPVisionModelWithProjection,
        image_project: CLIPImageProjection,
        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,
            image_encoder=image_encoder,
            processor=processor,
            image_project=image_project,
            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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
    def encode_prompt(
        self,
        prompt,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = 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
            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`).
            prompt_embeds (`ms.Tensor`, *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 (`ms.Tensor`, *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.
            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.
        """
        # 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, LoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            scale_lora_layers(self.text_encoder, lora_scale)

        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]

        if prompt_embeds is None:
            # textual inversion: process multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="np",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="np").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not np.array_equal(
                text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = ms.Tensor(text_inputs.attention_mask)
            else:
                attention_mask = None

            if clip_skip is None:
                prompt_embeds = self.text_encoder(ms.Tensor(text_input_ids), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                prompt_embeds = self.text_encoder(
                    ms.Tensor(text_input_ids), attention_mask=attention_mask, output_hidden_states=True
                )
                # Access the `hidden_states` first, that contains a tuple of
                # all the hidden states from the encoder layers. Then index into
                # the tuple to access the hidden states from the desired layer.
                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
                # We also need to apply the final LayerNorm here to not mess with the
                # representations. The `last_hidden_states` that we typically use for
                # obtaining the final prompt representations passes through the LayerNorm
                # layer.
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)

        if self.text_encoder is not None:
            prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None:
            prompt_embeds_dtype = self.unet.dtype
        else:
            prompt_embeds_dtype = prompt_embeds.dtype

        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.tile((1, num_images_per_prompt, 1))
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif 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 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

            # textual inversion: process multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="np",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = ms.Tensor(uncond_input.attention_mask)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                ms.Tensor(uncond_input.input_ids),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype)

            negative_prompt_embeds = negative_prompt_embeds.tile((1, num_images_per_prompt, 1))
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        if self.text_encoder is not None:
            if isinstance(self, LoraLoaderMixin):
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder, lora_scale)

        return prompt_embeds, negative_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
    def run_safety_checker(self, image, dtype):
        if self.safety_checker is None:
            has_nsfw_concept = None
        else:
            if ops.is_tensor(image):
                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
            else:
                feature_extractor_input = self.image_processor.numpy_to_pil(image)
            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="np")
            image, has_nsfw_concept = self.safety_checker(
                images=image, clip_input=ms.Tensor(safety_checker_input.pixel_values).to(dtype)
            )

            # Warning for safety checker operations here as it couldn't been done in construct()
            if ops.any(has_nsfw_concept):
                logger.warning(
                    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
                    " Try again with a different prompt and/or seed."
                )
        return image, has_nsfw_concept

    # 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_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs
    def check_inputs(
        self,
        prompt,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_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]}"  # noqa: E501
            )

        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, generator, latents=None):
        shape = (
            batch_size,
            num_channels_latents,
            int(height) // self.vae_scale_factor,
            int(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, dtype=dtype)
        else:
            latents = latents.to(dtype)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        # wtf? The above line changes the dtype of latents from fp16 to fp32, so we need a casting.
        latents = latents.to(dtype=dtype)
        return latents

    def enable_fuser(self, enabled=True):
        for sub_name, module in self.unet.cells_and_names():
            if type(module) is GatedSelfAttentionDense:
                module.enabled = enabled

    def draw_inpaint_mask_from_boxes(self, boxes, size):
        """
        Create an inpainting mask based on given boxes. This function generates an inpainting mask using the provided
        boxes to mark regions that need to be inpainted.
        """
        inpaint_mask = ops.ones((size[0], size[1]))
        for box in boxes:
            x0, x1 = box[0] * size[0], box[2] * size[0]
            y0, y1 = box[1] * size[1], box[3] * size[1]
            inpaint_mask[int(y0) : int(y1), int(x0) : int(x1)] = 0
        return inpaint_mask

    def crop(self, im, new_width, new_height):
        """
        Crop the input image to the specified dimensions.
        """
        width, height = im.size
        left = (width - new_width) / 2
        top = (height - new_height) / 2
        right = (width + new_width) / 2
        bottom = (height + new_height) / 2
        return im.crop((left, top, right, bottom))

    def target_size_center_crop(self, im, new_hw):
        """
        Crop and resize the image to the target size while keeping the center.
        """
        width, height = im.size
        if width != height:
            im = self.crop(im, min(height, width), min(height, width))
        return im.resize((new_hw, new_hw), PIL.Image.LANCZOS)

    def complete_mask(self, has_mask, max_objs):
        """
        Based on the input mask corresponding value `0 or 1` for each phrases and image, mask the features
        corresponding to phrases and images.
        """
        mask = ops.ones((1, max_objs)).to(self.text_encoder.dtype)
        if has_mask is None:
            return mask

        if isinstance(has_mask, int):
            return mask * has_mask
        else:
            for idx, value in enumerate(has_mask):
                mask[0, idx] = value
            return mask

    def get_clip_feature(self, input, normalize_constant, is_image=False):
        """
        Get image and phrases embedding by using CLIP pretrain model. The image embedding is transformed into the
        phrases embedding space through a projection.
        """
        if is_image:
            if input is None:
                return None
            inputs = self.processor(images=[input], return_tensors="np")
            for k, v in inputs.items():
                inputs[k] = ms.Tensor.from_numpy(v)
            inputs["pixel_values"] = inputs["pixel_values"].to(self.image_encoder.dtype)

            outputs = self.image_encoder(**inputs)
            feature = outputs[0]
            feature = self.image_project(feature).squeeze(0)
            feature = (feature / feature.norm()) * normalize_constant
            feature = feature.unsqueeze(0)
        else:
            if input is None:
                return None
            inputs = self.tokenizer(input, return_tensors="np", padding=True)
            for k, v in inputs.items():
                inputs[k] = ms.Tensor.from_numpy(v)
            outputs = self.text_encoder(**inputs)
            feature = outputs[1]
        return feature

    def get_cross_attention_kwargs_with_grounded(
        self,
        hidden_size,
        gligen_phrases,
        gligen_images,
        gligen_boxes,
        input_phrases_mask,
        input_images_mask,
        repeat_batch,
        normalize_constant,
        max_objs,
    ):
        """
        Prepare the cross-attention kwargs containing information about the grounded input (boxes, mask, image
        embedding, phrases embedding).
        """
        phrases, images = gligen_phrases, gligen_images
        images = [None] * len(phrases) if images is None else images
        phrases = [None] * len(images) if phrases is None else phrases

        boxes = ops.zeros((max_objs, 4), dtype=self.text_encoder.dtype)
        masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
        phrases_masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
        image_masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
        phrases_embeddings = ops.zeros((max_objs, hidden_size), dtype=self.text_encoder.dtype)
        image_embeddings = ops.zeros((max_objs, hidden_size), dtype=self.text_encoder.dtype)

        text_features = []
        image_features = []
        for phrase, image in zip(phrases, images):
            text_features.append(self.get_clip_feature(phrase, normalize_constant, is_image=False))
            image_features.append(self.get_clip_feature(image, normalize_constant, is_image=True))

        for idx, (box, text_feature, image_feature) in enumerate(zip(gligen_boxes, text_features, image_features)):
            boxes[idx] = ms.tensor(box)
            masks[idx] = 1
            if text_feature is not None:
                phrases_embeddings[idx : idx + 1] = text_feature  # unsqueeze for shape matching
                phrases_masks[idx] = 1
            if image_feature is not None:
                image_embeddings[idx : idx + 1] = image_feature  # unsqueeze for shape matching
                image_masks[idx] = 1

        input_phrases_mask = self.complete_mask(input_phrases_mask, max_objs)
        phrases_masks = phrases_masks.unsqueeze(0).tile((repeat_batch, 1)) * input_phrases_mask
        input_images_mask = self.complete_mask(input_images_mask, max_objs)
        image_masks = image_masks.unsqueeze(0).tile((repeat_batch, 1)) * input_images_mask
        boxes = boxes.unsqueeze(0).tile((repeat_batch, 1, 1))
        masks = masks.unsqueeze(0).tile((repeat_batch, 1))
        phrases_embeddings = phrases_embeddings.unsqueeze(0).tile((repeat_batch, 1, 1))
        image_embeddings = image_embeddings.unsqueeze(0).tile((repeat_batch, 1, 1))

        out = {
            "boxes": boxes,
            "masks": masks,
            "phrases_masks": phrases_masks,
            "image_masks": image_masks,
            "phrases_embeddings": phrases_embeddings,
            "image_embeddings": image_embeddings,
        }

        return out

    def get_cross_attention_kwargs_without_grounded(self, hidden_size, repeat_batch, max_objs):
        """
        Prepare the cross-attention kwargs without information about the grounded input (boxes, mask, image embedding,
        phrases embedding) (All are zero tensor).
        """
        boxes = ops.zeros((max_objs, 4), dtype=self.text_encoder.dtype)
        masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
        phrases_masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
        image_masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
        phrases_embeddings = ops.zeros((max_objs, hidden_size), dtype=self.text_encoder.dtype)
        image_embeddings = ops.zeros((max_objs, hidden_size), dtype=self.text_encoder.dtype)

        out = {
            "boxes": boxes.unsqueeze(0).tile((repeat_batch, 1, 1)),
            "masks": masks.unsqueeze(0).tile((repeat_batch, 1)),
            "phrases_masks": phrases_masks.unsqueeze(0).tile((repeat_batch, 1)),
            "image_masks": image_masks.unsqueeze(0).tile((repeat_batch, 1)),
            "phrases_embeddings": phrases_embeddings.unsqueeze(0).tile((repeat_batch, 1, 1)),
            "image_embeddings": image_embeddings.unsqueeze(0).tile((repeat_batch, 1, 1)),
        }

        return out

    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        gligen_scheduled_sampling_beta: float = 0.3,
        gligen_phrases: List[str] = None,
        gligen_images: List[PIL.Image.Image] = None,
        input_phrases_mask: Union[int, List[int]] = None,
        input_images_mask: Union[int, List[int]] = None,
        gligen_boxes: List[List[float]] = None,
        gligen_inpaint_image: Optional[PIL.Image.Image] = None,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
        latents: Optional[ms.Tensor] = None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = False,
        callback: Optional[Callable[[int, int, ms.Tensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        gligen_normalize_constant: float = 28.7,
        clip_skip: int = None,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
            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):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            gligen_phrases (`List[str]`):
                The phrases to guide what to include in each of the regions defined by the corresponding
                `gligen_boxes`. There should only be one phrase per bounding box.
            gligen_images (`List[PIL.Image.Image]`):
                The images to guide what to include in each of the regions defined by the corresponding `gligen_boxes`.
                There should only be one image per bounding box
            input_phrases_mask (`int` or `List[int]`):
                pre phrases mask input defined by the correspongding `input_phrases_mask`
            input_images_mask (`int` or `List[int]`):
                pre images mask input defined by the correspongding `input_images_mask`
            gligen_boxes (`List[List[float]]`):
                The bounding boxes that identify rectangular regions of the image that are going to be filled with the
                content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a
                `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
            gligen_inpaint_image (`PIL.Image.Image`, *optional*):
                The input image, if provided, is inpainted with objects described by the `gligen_boxes` and
                `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.
            gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
                Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
                Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
                scheduled sampling during inference for improved quality and controllability.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
                A [`np.random.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            latents (`ms.Tensor`, *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 is generated by sampling using the supplied random `generator`.
            prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            negative_prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is called with the
                following arguments: `callback(step: int, timestep: int, latents: ms.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function is called. If not specified, the callback is called at
                every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            gligen_normalize_constant (`float`, *optional*, defaults to 28.7):
                The normalize value of the image embedding.
            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.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list with the generated images and the
                second element is a list of `bool`s indicating whether the corresponding generated image contains
                "not-safe-for-work" (nsfw) content.
        """
        # 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,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        # 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]
        # 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

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            clip_skip=clip_skip,
        )

        if do_classifier_free_guidance:
            prompt_embeds = ops.cat([negative_prompt_embeds, prompt_embeds])

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            generator,
            latents,
        )

        # 5.1 Prepare GLIGEN variables
        max_objs = 30
        if len(gligen_boxes) > max_objs:
            warnings.warn(
                f"More that {max_objs} objects found. Only first {max_objs} objects will be processed.",
                FutureWarning,
            )
            gligen_phrases = gligen_phrases[:max_objs]
            gligen_boxes = gligen_boxes[:max_objs]
            gligen_images = gligen_images[:max_objs]

        repeat_batch = batch_size * num_images_per_prompt

        if do_classifier_free_guidance:
            repeat_batch = repeat_batch * 2

        if cross_attention_kwargs is None:
            cross_attention_kwargs = {}

        hidden_size = prompt_embeds.shape[2]

        cross_attention_kwargs["gligen"] = self.get_cross_attention_kwargs_with_grounded(
            hidden_size=hidden_size,
            gligen_phrases=gligen_phrases,
            gligen_images=gligen_images,
            gligen_boxes=gligen_boxes,
            input_phrases_mask=input_phrases_mask,
            input_images_mask=input_images_mask,
            repeat_batch=repeat_batch,
            normalize_constant=gligen_normalize_constant,
            max_objs=max_objs,
        )

        cross_attention_kwargs_without_grounded = {}
        cross_attention_kwargs_without_grounded["gligen"] = self.get_cross_attention_kwargs_without_grounded(
            hidden_size=hidden_size, repeat_batch=repeat_batch, max_objs=max_objs
        )

        # Prepare latent variables for GLIGEN inpainting
        if gligen_inpaint_image is not None:
            # if the given input image is not of the same size as expected by VAE
            # center crop and resize the input image to expected shape
            if gligen_inpaint_image.size != (self.vae.sample_size, self.vae.sample_size):
                gligen_inpaint_image = self.target_size_center_crop(gligen_inpaint_image, self.vae.sample_size)
            # Convert a single image into a batch of images with a batch size of 1
            # The resulting shape becomes (1, C, H, W), where C is the number of channels,
            # and H and W are the height and width of the image.
            # scales the pixel values to a range [-1, 1]
            gligen_inpaint_image = self.image_processor.preprocess(gligen_inpaint_image)
            gligen_inpaint_image = gligen_inpaint_image.to(dtype=self.vae.dtype)
            # Run AutoEncoder to get corresponding latents
            gligen_inpaint_latent = self.vae.encode(gligen_inpaint_image)[0]
            gligen_inpaint_latent = self.vae.diag_gauss_dist.sample(gligen_inpaint_latent)
            gligen_inpaint_latent = self.vae.config.scaling_factor * gligen_inpaint_latent
            # Generate an inpainting mask
            # pixel value = 0, where the object is present (defined by bounding boxes above)
            #               1, everywhere else
            gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(gligen_boxes, gligen_inpaint_latent.shape[2:])
            gligen_inpaint_mask = gligen_inpaint_mask.to(dtype=gligen_inpaint_latent.dtype)
            gligen_inpaint_mask = gligen_inpaint_mask[None, None]
            gligen_inpaint_mask_addition = ops.cat(
                (gligen_inpaint_latent * gligen_inpaint_mask, gligen_inpaint_mask), axis=1
            )
            # Convert a single mask into a batch of masks with a batch size of 1
            gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.broadcast_to((repeat_batch, -1, -1, -1)).copy()

        int(gligen_scheduled_sampling_beta * len(timesteps))
        self.enable_fuser(True)

        # 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)

        # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
        # to the unet and will raise RuntimeError.
        lora_scale = cross_attention_kwargs.pop("scale", None) if cross_attention_kwargs is not None else None
        if lora_scale is not None:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self.unet, lora_scale)

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if latents.shape[1] != 4:
                    # mindspore.ops.randn_like(x) returns tensor with dtype float32, instead of x.dtype as torch does
                    latents = ops.randn_like(latents[:, :4], dtype=latents.dtype)

                if gligen_inpaint_image is not None:
                    gligen_inpaint_latent_with_noise = (
                        self.scheduler.add_noise(
                            gligen_inpaint_latent,
                            ops.randn_like(gligen_inpaint_latent, dtype=gligen_inpaint_latent.dtype),
                            t[None],
                        )
                        .broadcast_to((latents.shape[0], -1, -1, -1))
                        .copy()
                    )
                    latents = gligen_inpaint_latent_with_noise * gligen_inpaint_mask + latents * (
                        1 - gligen_inpaint_mask
                    )

                # expand the latents if we are doing classifier free guidance
                latent_model_input = ops.cat([latents] * 2) if do_classifier_free_guidance else latents
                # TODO: method of scheduler should not change the dtype of input.
                #  Remove the casting after cuiyushi confirm that.
                tmp_dtype = latent_model_input.dtype
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                latent_model_input = latent_model_input.to(tmp_dtype)

                if gligen_inpaint_image is not None:
                    latent_model_input = ops.cat((latent_model_input, gligen_inpaint_mask_addition), axis=1)

                # predict the noise residual with grounded information
                noise_pred_with_grounding = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=ms.mutable(cross_attention_kwargs),
                )[0]

                # predict the noise residual without grounded information
                noise_pred_without_grounding = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=ms.mutable(cross_attention_kwargs_without_grounded),
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    # Using noise_pred_text from noise residual with grounded information and noise_pred_uncond from noise residual without grounded information
                    _, noise_pred_text = noise_pred_with_grounding.chunk(2)
                    noise_pred_uncond, _ = noise_pred_without_grounding.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                else:
                    noise_pred = noise_pred_with_grounding

                # compute the previous noisy sample x_t -> x_t-1
                # TODO: method of scheduler should not change the dtype of input.
                #  Remove the casting after cuiyushi confirm that.
                tmp_dtype = latents.dtype
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
                latents = latents.to(tmp_dtype)

                # 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 lora_scale is not None:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self.unet, lora_scale)

        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, prompt_embeds.dtype)
        else:
            image = latents
            has_nsfw_concept = None

        if has_nsfw_concept is None:
            do_denormalize = [True] * image.shape[0]
        else:
            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.__call__(prompt=None, height=None, width=None, num_inference_steps=50, guidance_scale=7.5, gligen_scheduled_sampling_beta=0.3, gligen_phrases=None, gligen_images=None, input_phrases_mask=None, input_images_mask=None, gligen_boxes=None, gligen_inpaint_image=None, negative_prompt=None, num_images_per_prompt=1, eta=0.0, generator=None, latents=None, prompt_embeds=None, negative_prompt_embeds=None, output_type='pil', return_dict=False, callback=None, callback_steps=1, cross_attention_kwargs=None, gligen_normalize_constant=28.7, clip_skip=None)

The call function to the pipeline for generation.

PARAMETER	DESCRIPTION
prompt	The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
height	The height in pixels of the generated image. TYPE: `int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor` DEFAULT: None
width	The width in pixels of the generated image. TYPE: `int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor` DEFAULT: None
num_inference_steps	The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. TYPE: `int`, *optional*, defaults to 50 DEFAULT: 50
guidance_scale	A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. TYPE: `float`, *optional*, defaults to 7.5 DEFAULT: 7.5
gligen_phrases	The phrases to guide what to include in each of the regions defined by the corresponding gligen_boxes. There should only be one phrase per bounding box. TYPE: `List[str]` DEFAULT: None
gligen_images	The images to guide what to include in each of the regions defined by the corresponding gligen_boxes. There should only be one image per bounding box TYPE: `List[PIL.Image.Image]` DEFAULT: None
input_phrases_mask	pre phrases mask input defined by the correspongding input_phrases_mask TYPE: `int` or `List[int]` DEFAULT: None
input_images_mask	pre images mask input defined by the correspongding input_images_mask TYPE: `int` or `List[int]` DEFAULT: None
gligen_boxes	The bounding boxes that identify rectangular regions of the image that are going to be filled with the content described by the corresponding gligen_phrases. Each rectangular box is defined as a List[float] of 4 elements [xmin, ymin, xmax, ymax] where each value is between [0,1]. TYPE: `List[List[float]]` DEFAULT: None
gligen_inpaint_image	The input image, if provided, is inpainted with objects described by the gligen_boxes and gligen_phrases. Otherwise, it is treated as a generation task on a blank input image. TYPE: `PIL.Image.Image`, *optional* DEFAULT: None
gligen_scheduled_sampling_beta	Scheduled Sampling factor from GLIGEN: Open-Set Grounded Text-to-Image Generation. Scheduled Sampling factor is only varied for scheduled sampling during inference for improved quality and controllability. TYPE: `float`, defaults to 0.3 DEFAULT: 0.3
negative_prompt	The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). TYPE: `str` or `List[str]`, *optional* DEFAULT: None
num_images_per_prompt	The number of images to generate per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
eta	Corresponds to parameter eta (η) from the DDIM paper. Only applies to the [~schedulers.DDIMScheduler], and is ignored in other schedulers. TYPE: `float`, *optional*, defaults to 0.0 DEFAULT: 0.0
generator	A np.random.Generator to make generation deterministic. TYPE: `np.random.Generator` or `List[np.random.Generator]`, *optional* DEFAULT: None
latents	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 is generated by sampling using the supplied random generator. TYPE: `ms.Tensor`, *optional* DEFAULT: None
prompt_embeds	Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_prompt_embeds	Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument. TYPE: `ms.Tensor`, *optional* DEFAULT: None
output_type	The output format of the generated image. Choose between PIL.Image or np.array. TYPE: `str`, *optional*, defaults to `"pil"` DEFAULT: 'pil'
return_dict	Whether or not to return a [~pipelines.stable_diffusion.StableDiffusionPipelineOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
callback	A function that calls every callback_steps steps during inference. The function is called with the following arguments: callback(step: int, timestep: int, latents: ms.Tensor). TYPE: `Callable`, *optional* DEFAULT: None
callback_steps	The frequency at which the callback function is called. If not specified, the callback is called at every step. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
cross_attention_kwargs	A kwargs dictionary that if specified is passed along to the [AttentionProcessor] as defined in self.processor. TYPE: `dict`, *optional* DEFAULT: None
gligen_normalize_constant	The normalize value of the image embedding. TYPE: `float`, *optional*, defaults to 28.7 DEFAULT: 28.7
clip_skip	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. TYPE: `int`, *optional* DEFAULT: None

RETURNS	DESCRIPTION
	[~pipelines.stable_diffusion.StableDiffusionPipelineOutput] or tuple: If return_dict is True, [~pipelines.stable_diffusion.StableDiffusionPipelineOutput] is returned, otherwise a tuple is returned where the first element is a list with the generated images and the second element is a list of bools indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def __call__(
    self,
    prompt: Union[str, List[str]] = None,
    height: Optional[int] = None,
    width: Optional[int] = None,
    num_inference_steps: int = 50,
    guidance_scale: float = 7.5,
    gligen_scheduled_sampling_beta: float = 0.3,
    gligen_phrases: List[str] = None,
    gligen_images: List[PIL.Image.Image] = None,
    input_phrases_mask: Union[int, List[int]] = None,
    input_images_mask: Union[int, List[int]] = None,
    gligen_boxes: List[List[float]] = None,
    gligen_inpaint_image: Optional[PIL.Image.Image] = None,
    negative_prompt: Optional[Union[str, List[str]]] = None,
    num_images_per_prompt: Optional[int] = 1,
    eta: float = 0.0,
    generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
    latents: Optional[ms.Tensor] = None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = False,
    callback: Optional[Callable[[int, int, ms.Tensor], None]] = None,
    callback_steps: int = 1,
    cross_attention_kwargs: Optional[Dict[str, Any]] = None,
    gligen_normalize_constant: float = 28.7,
    clip_skip: int = None,
):
    r"""
    The call function to the pipeline for generation.

    Args:
        prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
        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):
            A higher guidance scale value encourages the model to generate images closely linked to the text
            `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
        gligen_phrases (`List[str]`):
            The phrases to guide what to include in each of the regions defined by the corresponding
            `gligen_boxes`. There should only be one phrase per bounding box.
        gligen_images (`List[PIL.Image.Image]`):
            The images to guide what to include in each of the regions defined by the corresponding `gligen_boxes`.
            There should only be one image per bounding box
        input_phrases_mask (`int` or `List[int]`):
            pre phrases mask input defined by the correspongding `input_phrases_mask`
        input_images_mask (`int` or `List[int]`):
            pre images mask input defined by the correspongding `input_images_mask`
        gligen_boxes (`List[List[float]]`):
            The bounding boxes that identify rectangular regions of the image that are going to be filled with the
            content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a
            `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
        gligen_inpaint_image (`PIL.Image.Image`, *optional*):
            The input image, if provided, is inpainted with objects described by the `gligen_boxes` and
            `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.
        gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
            Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
            Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
            scheduled sampling during inference for improved quality and controllability.
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to guide what to not include in image generation. If not defined, you need to
            pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
            to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
        generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
            A [`np.random.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
            generation deterministic.
        latents (`ms.Tensor`, *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 is generated by sampling using the supplied random `generator`.
        prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
            provided, text embeddings are generated from the `prompt` input argument.
        negative_prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
            not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generated image. Choose between `PIL.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `False`):
            Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
            plain tuple.
        callback (`Callable`, *optional*):
            A function that calls every `callback_steps` steps during inference. The function is called with the
            following arguments: `callback(step: int, timestep: int, latents: ms.Tensor)`.
        callback_steps (`int`, *optional*, defaults to 1):
            The frequency at which the `callback` function is called. If not specified, the callback is called at
            every step.
        cross_attention_kwargs (`dict`, *optional*):
            A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
            [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
        gligen_normalize_constant (`float`, *optional*, defaults to 28.7):
            The normalize value of the image embedding.
        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.

    Examples:

    Returns:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
            otherwise a `tuple` is returned where the first element is a list with the generated images and the
            second element is a list of `bool`s indicating whether the corresponding generated image contains
            "not-safe-for-work" (nsfw) content.
    """
    # 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,
        negative_prompt,
        prompt_embeds,
        negative_prompt_embeds,
    )

    # 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]
    # 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

    # 3. Encode input prompt
    prompt_embeds, negative_prompt_embeds = self.encode_prompt(
        prompt,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        clip_skip=clip_skip,
    )

    if do_classifier_free_guidance:
        prompt_embeds = ops.cat([negative_prompt_embeds, prompt_embeds])

    # 4. Prepare timesteps
    self.scheduler.set_timesteps(num_inference_steps)
    timesteps = self.scheduler.timesteps

    # 5. Prepare latent variables
    num_channels_latents = self.unet.config.in_channels
    latents = self.prepare_latents(
        batch_size * num_images_per_prompt,
        num_channels_latents,
        height,
        width,
        prompt_embeds.dtype,
        generator,
        latents,
    )

    # 5.1 Prepare GLIGEN variables
    max_objs = 30
    if len(gligen_boxes) > max_objs:
        warnings.warn(
            f"More that {max_objs} objects found. Only first {max_objs} objects will be processed.",
            FutureWarning,
        )
        gligen_phrases = gligen_phrases[:max_objs]
        gligen_boxes = gligen_boxes[:max_objs]
        gligen_images = gligen_images[:max_objs]

    repeat_batch = batch_size * num_images_per_prompt

    if do_classifier_free_guidance:
        repeat_batch = repeat_batch * 2

    if cross_attention_kwargs is None:
        cross_attention_kwargs = {}

    hidden_size = prompt_embeds.shape[2]

    cross_attention_kwargs["gligen"] = self.get_cross_attention_kwargs_with_grounded(
        hidden_size=hidden_size,
        gligen_phrases=gligen_phrases,
        gligen_images=gligen_images,
        gligen_boxes=gligen_boxes,
        input_phrases_mask=input_phrases_mask,
        input_images_mask=input_images_mask,
        repeat_batch=repeat_batch,
        normalize_constant=gligen_normalize_constant,
        max_objs=max_objs,
    )

    cross_attention_kwargs_without_grounded = {}
    cross_attention_kwargs_without_grounded["gligen"] = self.get_cross_attention_kwargs_without_grounded(
        hidden_size=hidden_size, repeat_batch=repeat_batch, max_objs=max_objs
    )

    # Prepare latent variables for GLIGEN inpainting
    if gligen_inpaint_image is not None:
        # if the given input image is not of the same size as expected by VAE
        # center crop and resize the input image to expected shape
        if gligen_inpaint_image.size != (self.vae.sample_size, self.vae.sample_size):
            gligen_inpaint_image = self.target_size_center_crop(gligen_inpaint_image, self.vae.sample_size)
        # Convert a single image into a batch of images with a batch size of 1
        # The resulting shape becomes (1, C, H, W), where C is the number of channels,
        # and H and W are the height and width of the image.
        # scales the pixel values to a range [-1, 1]
        gligen_inpaint_image = self.image_processor.preprocess(gligen_inpaint_image)
        gligen_inpaint_image = gligen_inpaint_image.to(dtype=self.vae.dtype)
        # Run AutoEncoder to get corresponding latents
        gligen_inpaint_latent = self.vae.encode(gligen_inpaint_image)[0]
        gligen_inpaint_latent = self.vae.diag_gauss_dist.sample(gligen_inpaint_latent)
        gligen_inpaint_latent = self.vae.config.scaling_factor * gligen_inpaint_latent
        # Generate an inpainting mask
        # pixel value = 0, where the object is present (defined by bounding boxes above)
        #               1, everywhere else
        gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(gligen_boxes, gligen_inpaint_latent.shape[2:])
        gligen_inpaint_mask = gligen_inpaint_mask.to(dtype=gligen_inpaint_latent.dtype)
        gligen_inpaint_mask = gligen_inpaint_mask[None, None]
        gligen_inpaint_mask_addition = ops.cat(
            (gligen_inpaint_latent * gligen_inpaint_mask, gligen_inpaint_mask), axis=1
        )
        # Convert a single mask into a batch of masks with a batch size of 1
        gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.broadcast_to((repeat_batch, -1, -1, -1)).copy()

    int(gligen_scheduled_sampling_beta * len(timesteps))
    self.enable_fuser(True)

    # 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)

    # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
    # to the unet and will raise RuntimeError.
    lora_scale = cross_attention_kwargs.pop("scale", None) if cross_attention_kwargs is not None else None
    if lora_scale is not None:
        # weight the lora layers by setting `lora_scale` for each PEFT layer
        scale_lora_layers(self.unet, lora_scale)

    # 7. Denoising loop
    num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
    with self.progress_bar(total=num_inference_steps) as progress_bar:
        for i, t in enumerate(timesteps):
            if latents.shape[1] != 4:
                # mindspore.ops.randn_like(x) returns tensor with dtype float32, instead of x.dtype as torch does
                latents = ops.randn_like(latents[:, :4], dtype=latents.dtype)

            if gligen_inpaint_image is not None:
                gligen_inpaint_latent_with_noise = (
                    self.scheduler.add_noise(
                        gligen_inpaint_latent,
                        ops.randn_like(gligen_inpaint_latent, dtype=gligen_inpaint_latent.dtype),
                        t[None],
                    )
                    .broadcast_to((latents.shape[0], -1, -1, -1))
                    .copy()
                )
                latents = gligen_inpaint_latent_with_noise * gligen_inpaint_mask + latents * (
                    1 - gligen_inpaint_mask
                )

            # expand the latents if we are doing classifier free guidance
            latent_model_input = ops.cat([latents] * 2) if do_classifier_free_guidance else latents
            # TODO: method of scheduler should not change the dtype of input.
            #  Remove the casting after cuiyushi confirm that.
            tmp_dtype = latent_model_input.dtype
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
            latent_model_input = latent_model_input.to(tmp_dtype)

            if gligen_inpaint_image is not None:
                latent_model_input = ops.cat((latent_model_input, gligen_inpaint_mask_addition), axis=1)

            # predict the noise residual with grounded information
            noise_pred_with_grounding = self.unet(
                latent_model_input,
                t,
                encoder_hidden_states=prompt_embeds,
                cross_attention_kwargs=ms.mutable(cross_attention_kwargs),
            )[0]

            # predict the noise residual without grounded information
            noise_pred_without_grounding = self.unet(
                latent_model_input,
                t,
                encoder_hidden_states=prompt_embeds,
                cross_attention_kwargs=ms.mutable(cross_attention_kwargs_without_grounded),
            )[0]

            # perform guidance
            if do_classifier_free_guidance:
                # Using noise_pred_text from noise residual with grounded information and noise_pred_uncond from noise residual without grounded information
                _, noise_pred_text = noise_pred_with_grounding.chunk(2)
                noise_pred_uncond, _ = noise_pred_without_grounding.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            else:
                noise_pred = noise_pred_with_grounding

            # compute the previous noisy sample x_t -> x_t-1
            # TODO: method of scheduler should not change the dtype of input.
            #  Remove the casting after cuiyushi confirm that.
            tmp_dtype = latents.dtype
            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
            latents = latents.to(tmp_dtype)

            # 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 lora_scale is not None:
        # remove `lora_scale` from each PEFT layer
        unscale_lora_layers(self.unet, lora_scale)

    if not output_type == "latent":
        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
        image, has_nsfw_concept = self.run_safety_checker(image, prompt_embeds.dtype)
    else:
        image = latents
        has_nsfw_concept = None

    if has_nsfw_concept is None:
        do_denormalize = [True] * image.shape[0]
    else:
        do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

    image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

    if not return_dict:
        return (image, has_nsfw_concept)

    return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.complete_mask(has_mask, max_objs)

Based on the input mask corresponding value 0 or 1 for each phrases and image, mask the features corresponding to phrases and images.

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def complete_mask(self, has_mask, max_objs):
    """
    Based on the input mask corresponding value `0 or 1` for each phrases and image, mask the features
    corresponding to phrases and images.
    """
    mask = ops.ones((1, max_objs)).to(self.text_encoder.dtype)
    if has_mask is None:
        return mask

    if isinstance(has_mask, int):
        return mask * has_mask
    else:
        for idx, value in enumerate(has_mask):
            mask[0, idx] = value
        return mask

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.crop(im, new_width, new_height)

Crop the input image to the specified dimensions.

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def crop(self, im, new_width, new_height):
    """
    Crop the input image to the specified dimensions.
    """
    width, height = im.size
    left = (width - new_width) / 2
    top = (height - new_height) / 2
    right = (width + new_width) / 2
    bottom = (height + new_height) / 2
    return im.crop((left, top, right, bottom))

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.draw_inpaint_mask_from_boxes(boxes, size)

Create an inpainting mask based on given boxes. This function generates an inpainting mask using the provided boxes to mark regions that need to be inpainted.

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def draw_inpaint_mask_from_boxes(self, boxes, size):
    """
    Create an inpainting mask based on given boxes. This function generates an inpainting mask using the provided
    boxes to mark regions that need to be inpainted.
    """
    inpaint_mask = ops.ones((size[0], size[1]))
    for box in boxes:
        x0, x1 = box[0] * size[0], box[2] * size[0]
        y0, y1 = box[1] * size[1], box[3] * size[1]
        inpaint_mask[int(y0) : int(y1), int(x0) : int(x1)] = 0
    return inpaint_mask

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, lora_scale=None, clip_skip=None)

Encodes the prompt into text encoder hidden states.

PARAMETER	DESCRIPTION
prompt	prompt to be encoded TYPE: `str` or `List[str]`, *optional*
num_images_per_prompt	number of images that should be generated per prompt TYPE: `int`
do_classifier_free_guidance	whether to use classifier free guidance or not TYPE: `bool`
negative_prompt	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). TYPE: `str` or `List[str]`, *optional* DEFAULT: None
prompt_embeds	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_prompt_embeds	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. TYPE: `ms.Tensor`, *optional* DEFAULT: None
lora_scale	A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. TYPE: `float`, *optional* DEFAULT: None
clip_skip	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. TYPE: `int`, *optional* DEFAULT: None

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def encode_prompt(
    self,
    prompt,
    num_images_per_prompt,
    do_classifier_free_guidance,
    negative_prompt=None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = 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
        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`).
        prompt_embeds (`ms.Tensor`, *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 (`ms.Tensor`, *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.
        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.
    """
    # 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, LoraLoaderMixin):
        self._lora_scale = lora_scale

        # dynamically adjust the LoRA scale
        scale_lora_layers(self.text_encoder, lora_scale)

    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]

    if prompt_embeds is None:
        # textual inversion: process multi-vector tokens if necessary
        if isinstance(self, TextualInversionLoaderMixin):
            prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="np",
        )
        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="np").input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not np.array_equal(
            text_input_ids, untruncated_ids
        ):
            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
            )

        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
            attention_mask = ms.Tensor(text_inputs.attention_mask)
        else:
            attention_mask = None

        if clip_skip is None:
            prompt_embeds = self.text_encoder(ms.Tensor(text_input_ids), attention_mask=attention_mask)
            prompt_embeds = prompt_embeds[0]
        else:
            prompt_embeds = self.text_encoder(
                ms.Tensor(text_input_ids), attention_mask=attention_mask, output_hidden_states=True
            )
            # Access the `hidden_states` first, that contains a tuple of
            # all the hidden states from the encoder layers. Then index into
            # the tuple to access the hidden states from the desired layer.
            prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
            # We also need to apply the final LayerNorm here to not mess with the
            # representations. The `last_hidden_states` that we typically use for
            # obtaining the final prompt representations passes through the LayerNorm
            # layer.
            prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)

    if self.text_encoder is not None:
        prompt_embeds_dtype = self.text_encoder.dtype
    elif self.unet is not None:
        prompt_embeds_dtype = self.unet.dtype
    else:
        prompt_embeds_dtype = prompt_embeds.dtype

    prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype)

    bs_embed, seq_len, _ = prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    prompt_embeds = prompt_embeds.tile((1, num_images_per_prompt, 1))
    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance and negative_prompt_embeds is None:
        uncond_tokens: List[str]
        if negative_prompt is None:
            uncond_tokens = [""] * batch_size
        elif 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 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

        # textual inversion: process multi-vector tokens if necessary
        if isinstance(self, TextualInversionLoaderMixin):
            uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

        max_length = prompt_embeds.shape[1]
        uncond_input = self.tokenizer(
            uncond_tokens,
            padding="max_length",
            max_length=max_length,
            truncation=True,
            return_tensors="np",
        )

        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
            attention_mask = ms.Tensor(uncond_input.attention_mask)
        else:
            attention_mask = None

        negative_prompt_embeds = self.text_encoder(
            ms.Tensor(uncond_input.input_ids),
            attention_mask=attention_mask,
        )
        negative_prompt_embeds = negative_prompt_embeds[0]

    if do_classifier_free_guidance:
        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        seq_len = negative_prompt_embeds.shape[1]

        negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype)

        negative_prompt_embeds = negative_prompt_embeds.tile((1, num_images_per_prompt, 1))
        negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

    if self.text_encoder is not None:
        if isinstance(self, LoraLoaderMixin):
            # Retrieve the original scale by scaling back the LoRA layers
            unscale_lora_layers(self.text_encoder, lora_scale)

    return prompt_embeds, negative_prompt_embeds

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.get_clip_feature(input, normalize_constant, is_image=False)

Get image and phrases embedding by using CLIP pretrain model. The image embedding is transformed into the phrases embedding space through a projection.

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def get_clip_feature(self, input, normalize_constant, is_image=False):
    """
    Get image and phrases embedding by using CLIP pretrain model. The image embedding is transformed into the
    phrases embedding space through a projection.
    """
    if is_image:
        if input is None:
            return None
        inputs = self.processor(images=[input], return_tensors="np")
        for k, v in inputs.items():
            inputs[k] = ms.Tensor.from_numpy(v)
        inputs["pixel_values"] = inputs["pixel_values"].to(self.image_encoder.dtype)

        outputs = self.image_encoder(**inputs)
        feature = outputs[0]
        feature = self.image_project(feature).squeeze(0)
        feature = (feature / feature.norm()) * normalize_constant
        feature = feature.unsqueeze(0)
    else:
        if input is None:
            return None
        inputs = self.tokenizer(input, return_tensors="np", padding=True)
        for k, v in inputs.items():
            inputs[k] = ms.Tensor.from_numpy(v)
        outputs = self.text_encoder(**inputs)
        feature = outputs[1]
    return feature

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.get_cross_attention_kwargs_with_grounded(hidden_size, gligen_phrases, gligen_images, gligen_boxes, input_phrases_mask, input_images_mask, repeat_batch, normalize_constant, max_objs)

Prepare the cross-attention kwargs containing information about the grounded input (boxes, mask, image embedding, phrases embedding).

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def get_cross_attention_kwargs_with_grounded(
    self,
    hidden_size,
    gligen_phrases,
    gligen_images,
    gligen_boxes,
    input_phrases_mask,
    input_images_mask,
    repeat_batch,
    normalize_constant,
    max_objs,
):
    """
    Prepare the cross-attention kwargs containing information about the grounded input (boxes, mask, image
    embedding, phrases embedding).
    """
    phrases, images = gligen_phrases, gligen_images
    images = [None] * len(phrases) if images is None else images
    phrases = [None] * len(images) if phrases is None else phrases

    boxes = ops.zeros((max_objs, 4), dtype=self.text_encoder.dtype)
    masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
    phrases_masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
    image_masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
    phrases_embeddings = ops.zeros((max_objs, hidden_size), dtype=self.text_encoder.dtype)
    image_embeddings = ops.zeros((max_objs, hidden_size), dtype=self.text_encoder.dtype)

    text_features = []
    image_features = []
    for phrase, image in zip(phrases, images):
        text_features.append(self.get_clip_feature(phrase, normalize_constant, is_image=False))
        image_features.append(self.get_clip_feature(image, normalize_constant, is_image=True))

    for idx, (box, text_feature, image_feature) in enumerate(zip(gligen_boxes, text_features, image_features)):
        boxes[idx] = ms.tensor(box)
        masks[idx] = 1
        if text_feature is not None:
            phrases_embeddings[idx : idx + 1] = text_feature  # unsqueeze for shape matching
            phrases_masks[idx] = 1
        if image_feature is not None:
            image_embeddings[idx : idx + 1] = image_feature  # unsqueeze for shape matching
            image_masks[idx] = 1

    input_phrases_mask = self.complete_mask(input_phrases_mask, max_objs)
    phrases_masks = phrases_masks.unsqueeze(0).tile((repeat_batch, 1)) * input_phrases_mask
    input_images_mask = self.complete_mask(input_images_mask, max_objs)
    image_masks = image_masks.unsqueeze(0).tile((repeat_batch, 1)) * input_images_mask
    boxes = boxes.unsqueeze(0).tile((repeat_batch, 1, 1))
    masks = masks.unsqueeze(0).tile((repeat_batch, 1))
    phrases_embeddings = phrases_embeddings.unsqueeze(0).tile((repeat_batch, 1, 1))
    image_embeddings = image_embeddings.unsqueeze(0).tile((repeat_batch, 1, 1))

    out = {
        "boxes": boxes,
        "masks": masks,
        "phrases_masks": phrases_masks,
        "image_masks": image_masks,
        "phrases_embeddings": phrases_embeddings,
        "image_embeddings": image_embeddings,
    }

    return out

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.get_cross_attention_kwargs_without_grounded(hidden_size, repeat_batch, max_objs)

Prepare the cross-attention kwargs without information about the grounded input (boxes, mask, image embedding, phrases embedding) (All are zero tensor).

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def get_cross_attention_kwargs_without_grounded(self, hidden_size, repeat_batch, max_objs):
    """
    Prepare the cross-attention kwargs without information about the grounded input (boxes, mask, image embedding,
    phrases embedding) (All are zero tensor).
    """
    boxes = ops.zeros((max_objs, 4), dtype=self.text_encoder.dtype)
    masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
    phrases_masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
    image_masks = ops.zeros((max_objs,), dtype=self.text_encoder.dtype)
    phrases_embeddings = ops.zeros((max_objs, hidden_size), dtype=self.text_encoder.dtype)
    image_embeddings = ops.zeros((max_objs, hidden_size), dtype=self.text_encoder.dtype)

    out = {
        "boxes": boxes.unsqueeze(0).tile((repeat_batch, 1, 1)),
        "masks": masks.unsqueeze(0).tile((repeat_batch, 1)),
        "phrases_masks": phrases_masks.unsqueeze(0).tile((repeat_batch, 1)),
        "image_masks": image_masks.unsqueeze(0).tile((repeat_batch, 1)),
        "phrases_embeddings": phrases_embeddings.unsqueeze(0).tile((repeat_batch, 1, 1)),
        "image_embeddings": image_embeddings.unsqueeze(0).tile((repeat_batch, 1, 1)),
    }

    return out

mindone.diffusers.StableDiffusionGLIGENTextImagePipeline.target_size_center_crop(im, new_hw)

Crop and resize the image to the target size while keeping the center.

Source code in mindone/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py

def target_size_center_crop(self, im, new_hw):
    """
    Crop and resize the image to the target size while keeping the center.
    """
    width, height = im.size
    if width != height:
        im = self.crop(im, min(height, width), min(height, width))
    return im.resize((new_hw, new_hw), PIL.Image.LANCZOS)

mindone.diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput dataclass

Bases: BaseOutput

Output class for Stable Diffusion pipelines.

Source code in mindone/diffusers/pipelines/stable_diffusion/pipeline_output.py

@dataclass
class StableDiffusionPipelineOutput(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)`.
        nsfw_content_detected (`List[bool]`)
            List indicating whether the corresponding generated image contains "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]]