Kandinsky 3

Kandinsky 3 is created by Vladimir Arkhipkin,Anastasia Maltseva,Igor Pavlov,Andrei Filatov,Arseniy Shakhmatov,Andrey Kuznetsov,Denis Dimitrov, Zein Shaheen

The description from it's GitHub page:

Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.

Its architecture includes 3 main components: 1. FLAN-UL2, which is an encoder decoder model based on the T5 architecture. 2. New U-Net architecture featuring BigGAN-deep blocks doubles depth while maintaining the same number of parameters. 3. Sber-MoVQGAN is a decoder proven to have superior results in image restoration.

The original codebase can be found at ai-forever/Kandinsky-3.

Tip

Check out the Kandinsky Community organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.

Tip

Make sure to check out the schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

mindone.diffusers.Kandinsky3Pipeline

Bases: DiffusionPipeline, LoraLoaderMixin

Source code in mindone/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py

class Kandinsky3Pipeline(DiffusionPipeline, LoraLoaderMixin):
    model_cpu_offload_seq = "text_encoder->unet->movq"
    _callback_tensor_inputs = [
        "latents",
        "prompt_embeds",
        "negative_prompt_embeds",
        "negative_attention_mask",
        "attention_mask",
    ]

    def __init__(
        self,
        tokenizer: T5Tokenizer,
        text_encoder: T5EncoderModel,
        unet: Kandinsky3UNet,
        scheduler: DDPMScheduler,
        movq: VQModel,
    ):
        super().__init__()

        self.register_modules(tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, movq=movq)

    def process_embeds(self, embeddings, attention_mask, cut_context):
        if cut_context:
            embeddings[attention_mask == 0] = ops.zeros_like(embeddings[attention_mask == 0])
            max_seq_length = attention_mask.sum(axis=-1).max() + 1
            embeddings = embeddings[:, :max_seq_length]
            attention_mask = attention_mask[:, :max_seq_length]
        return embeddings, attention_mask

    def encode_prompt(
        self,
        prompt,
        do_classifier_free_guidance=True,
        num_images_per_prompt=1,
        negative_prompt=None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        _cut_context=False,
        attention_mask: Optional[ms.Tensor] = None,
        negative_attention_mask: Optional[ms.Tensor] = 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`, *optional*, defaults to 1):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                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. 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.
            attention_mask (`ms.Tensor`, *optional*):
                Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
            negative_attention_mask (`ms.Tensor`, *optional*):
                Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
        """
        if prompt is not None and negative_prompt is not None:
            if 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)}."
                )

        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]

        max_length = 128

        if prompt_embeds is None:
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="np",
            )
            text_input_ids = ms.tensor(text_inputs.input_ids)
            attention_mask = ms.tensor(text_inputs.attention_mask)
            prompt_embeds = self.text_encoder(
                text_input_ids,
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]
            prompt_embeds, attention_mask = self.process_embeds(prompt_embeds, attention_mask, _cut_context)
            prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2)

        if self.text_encoder is not None:
            dtype = self.text_encoder.dtype
        else:
            dtype = None

        prompt_embeds = prompt_embeds.to(dtype=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)
        attention_mask = attention_mask.tile((num_images_per_prompt, 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 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
            if negative_prompt is not None:
                uncond_input = self.tokenizer(
                    uncond_tokens,
                    padding="max_length",
                    max_length=128,
                    truncation=True,
                    return_attention_mask=True,
                    return_tensors="np",
                )
                text_input_ids = ms.tensor(uncond_input.input_ids)
                negative_attention_mask = ms.tensor(uncond_input.attention_mask)

                negative_prompt_embeds = self.text_encoder(
                    text_input_ids,
                    attention_mask=negative_attention_mask,
                )
                negative_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]]
                negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]]
                negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2)

            else:
                negative_prompt_embeds = ops.zeros_like(prompt_embeds)
                negative_attention_mask = ops.zeros_like(attention_mask)

        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=dtype)
            if negative_prompt_embeds.shape != prompt_embeds.shape:
                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)
                negative_attention_mask = negative_attention_mask.tile((num_images_per_prompt, 1))

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
        else:
            negative_prompt_embeds = None
            negative_attention_mask = None
        return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask

    def prepare_latents(self, shape, dtype, generator, latents, scheduler):
        if latents is None:
            latents = randn_tensor(shape, generator=generator, dtype=dtype)
        else:
            if latents.shape != shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")

        latents = (latents * scheduler.init_noise_sigma).to(dtype)
        return latents

    def check_inputs(
        self,
        prompt,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
        attention_mask=None,
        negative_attention_mask=None,
    ):
        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}, "
                f"but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt 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 negative_prompt_embeds is not None and negative_attention_mask is None:
            raise ValueError("Please provide `negative_attention_mask` along with `negative_prompt_embeds`")

        if negative_prompt_embeds is not None and negative_attention_mask is not None:
            if negative_prompt_embeds.shape[:2] != negative_attention_mask.shape:
                raise ValueError(
                    "`negative_prompt_embeds` and `negative_attention_mask` must have the same batch_size and token length when passed directly, but"
                    f" got: `negative_prompt_embeds` {negative_prompt_embeds.shape[:2]} != `negative_attention_mask`"
                    f" {negative_attention_mask.shape}."
                )

        if prompt_embeds is not None and attention_mask is None:
            raise ValueError("Please provide `attention_mask` along with `prompt_embeds`")

        if prompt_embeds is not None and attention_mask is not None:
            if prompt_embeds.shape[:2] != attention_mask.shape:
                raise ValueError(
                    "`prompt_embeds` and `attention_mask` must have the same batch_size and token length when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape[:2]} != `attention_mask`"
                    f" {attention_mask.shape}."
                )

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1

    @property
    def num_timesteps(self):
        return self._num_timesteps

    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        num_inference_steps: int = 25,
        guidance_scale: float = 3.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        height: Optional[int] = 1024,
        width: Optional[int] = 1024,
        generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        attention_mask: Optional[ms.Tensor] = None,
        negative_attention_mask: Optional[ms.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = False,
        latents=None,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        **kwargs,
    ):
        """
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            num_inference_steps (`int`, *optional*, defaults to 25):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
                timesteps are used. Must be in descending order.
            guidance_scale (`float`, *optional*, defaults to 3.0):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            height (`int`, *optional*, defaults to self.unet.config.sample_size):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size):
                The width in pixels of the generated image.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
                One or a list of [np.random.Generator(s)](https://numpy.org/doc/stable/reference/random/generator.html)
                to make generation deterministic.
            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.
            attention_mask (`ms.Tensor`, *optional*):
                Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
            negative_attention_mask (`ms.Tensor`, *optional*):
                Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: ms.Tensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            clean_caption (`bool`, *optional*, defaults to `True`):
                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
                be installed. If the dependencies are not installed, the embeddings will be created from the raw
                prompt.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).

        Examples:

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`

        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )

        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}, "
                f"but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        cut_context = True

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            callback_on_step_end_tensor_inputs,
            attention_mask,
            negative_attention_mask,
        )

        self._guidance_scale = guidance_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]

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(
            prompt,
            self.do_classifier_free_guidance,
            num_images_per_prompt=num_images_per_prompt,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            _cut_context=cut_context,
            attention_mask=attention_mask,
            negative_attention_mask=negative_attention_mask,
        )

        if self.do_classifier_free_guidance:
            prompt_embeds = ops.cat([negative_prompt_embeds, prompt_embeds])
            attention_mask = ops.cat([negative_attention_mask, attention_mask])
        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latents
        height, width = downscale_height_and_width(height, width, 8)

        latents = self.prepare_latents(
            (batch_size * num_images_per_prompt, 4, height, width),
            prompt_embeds.dtype,
            generator,
            latents,
            self.scheduler,
        )

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        self._num_timesteps = len(timesteps)
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                latent_model_input = ops.cat([latents] * 2) if self.do_classifier_free_guidance else latents

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    encoder_attention_mask=attention_mask,
                    return_dict=False,
                )[0]

                if self.do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)

                    noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond
                    # noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(
                    noise_pred,
                    t,
                    latents,
                    generator=generator,
                )[0]

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
                    attention_mask = callback_outputs.pop("attention_mask", attention_mask)
                    negative_attention_mask = callback_outputs.pop("negative_attention_mask", negative_attention_mask)

                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)

            # post-processing
            if output_type not in ["ms", "np", "pil", "latent"]:
                raise ValueError(
                    f"Only the output types `ms`, `pil`, `np` and `latent` are supported not output_type={output_type}"
                )

            if not output_type == "latent":
                image = self.movq.decode(latents, force_not_quantize=True)[0]

                if output_type in ["np", "pil"]:
                    image = image * 0.5 + 0.5
                    image = image.clamp(0, 1)
                    image = image.permute(0, 2, 3, 1).float().numpy()

                if output_type == "pil":
                    image = self.numpy_to_pil(image)
            else:
                image = latents

            if not return_dict:
                return (image,)

            return ImagePipelineOutput(images=image)

mindone.diffusers.Kandinsky3Pipeline.__call__(prompt=None, num_inference_steps=25, guidance_scale=3.0, negative_prompt=None, num_images_per_prompt=1, height=1024, width=1024, generator=None, prompt_embeds=None, negative_prompt_embeds=None, attention_mask=None, negative_attention_mask=None, output_type='pil', return_dict=False, latents=None, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], **kwargs)

Function invoked when calling the pipeline for generation.

PARAMETER	DESCRIPTION
prompt	The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead. TYPE: `str` or `List[str]`, *optional* 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 25 DEFAULT: 25
timesteps	Custom timesteps to use for the denoising process. If not defined, equal spaced num_inference_steps timesteps are used. Must be in descending order. TYPE: `List[int]`, *optional*
guidance_scale	Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality. TYPE: `float`, *optional*, defaults to 3.0 DEFAULT: 3.0
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
num_images_per_prompt	The number of images to generate per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
height	The height in pixels of the generated image. TYPE: `int`, *optional*, defaults to self.unet.config.sample_size DEFAULT: 1024
width	The width in pixels of the generated image. TYPE: `int`, *optional*, defaults to self.unet.config.sample_size DEFAULT: 1024
eta	Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [schedulers.DDIMScheduler], will be ignored for others. TYPE: `float`, *optional*, defaults to 0.0
generator	One or a list of np.random.Generator(s) to make generation deterministic. TYPE: `np.random.Generator` or `List[np.random.Generator]`, *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
attention_mask	Pre-generated attention mask. Must provide if passing prompt_embeds directly. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_attention_mask	Pre-generated negative attention mask. Must provide if passing negative_prompt_embeds directly. TYPE: `ms.Tensor`, *optional* DEFAULT: None
output_type	The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array. TYPE: `str`, *optional*, defaults to `"pil"` DEFAULT: 'pil'
return_dict	Whether or not to return a [~pipelines.stable_diffusion.IFPipelineOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
callback	A function that will be called every callback_steps steps during inference. The function will be called with the following arguments: callback(step: int, timestep: int, latents: ms.Tensor). TYPE: `Callable`, *optional*
callback_steps	The frequency at which the callback function will be called. If not specified, the callback will be called at every step. TYPE: `int`, *optional*, defaults to 1
clean_caption	Whether or not to clean the caption before creating embeddings. Requires beautifulsoup4 and ftfy to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. TYPE: `bool`, *optional*, defaults to `True`
cross_attention_kwargs	A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor. TYPE: `dict`, *optional*

RETURNS	DESCRIPTION
	[~pipelines.ImagePipelineOutput] or tuple

Source code in mindone/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py

def __call__(
    self,
    prompt: Union[str, List[str]] = None,
    num_inference_steps: int = 25,
    guidance_scale: float = 3.0,
    negative_prompt: Optional[Union[str, List[str]]] = None,
    num_images_per_prompt: Optional[int] = 1,
    height: Optional[int] = 1024,
    width: Optional[int] = 1024,
    generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    attention_mask: Optional[ms.Tensor] = None,
    negative_attention_mask: Optional[ms.Tensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = False,
    latents=None,
    callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
    callback_on_step_end_tensor_inputs: List[str] = ["latents"],
    **kwargs,
):
    """
    Function invoked when calling the pipeline for generation.

    Args:
        prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
            instead.
        num_inference_steps (`int`, *optional*, defaults to 25):
            The number of denoising steps. More denoising steps usually lead to a higher quality image at the
            expense of slower inference.
        timesteps (`List[int]`, *optional*):
            Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
            timesteps are used. Must be in descending order.
        guidance_scale (`float`, *optional*, defaults to 3.0):
            Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
            `guidance_scale` is defined as `w` of equation 2. of [Imagen
            Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
            1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
            usually at the expense of lower image quality.
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts not to guide the image generation. If not defined, one has to pass
            `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
            less than `1`).
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        height (`int`, *optional*, defaults to self.unet.config.sample_size):
            The height in pixels of the generated image.
        width (`int`, *optional*, defaults to self.unet.config.sample_size):
            The width in pixels of the generated image.
        eta (`float`, *optional*, defaults to 0.0):
            Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
            [`schedulers.DDIMScheduler`], will be ignored for others.
        generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
            One or a list of [np.random.Generator(s)](https://numpy.org/doc/stable/reference/random/generator.html)
            to make generation deterministic.
        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.
        attention_mask (`ms.Tensor`, *optional*):
            Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
        negative_attention_mask (`ms.Tensor`, *optional*):
            Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generate image. Choose between
            [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `False`):
            Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
        callback (`Callable`, *optional*):
            A function that will be called every `callback_steps` steps during inference. The function will be
            called with the following arguments: `callback(step: int, timestep: int, latents: ms.Tensor)`.
        callback_steps (`int`, *optional*, defaults to 1):
            The frequency at which the `callback` function will be called. If not specified, the callback will be
            called at every step.
        clean_caption (`bool`, *optional*, defaults to `True`):
            Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
            be installed. If the dependencies are not installed, the embeddings will be created from the raw
            prompt.
        cross_attention_kwargs (`dict`, *optional*):
            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
            `self.processor` in
            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).

    Examples:

    Returns:
        [`~pipelines.ImagePipelineOutput`] or `tuple`

    """

    callback = kwargs.pop("callback", None)
    callback_steps = kwargs.pop("callback_steps", None)

    if callback is not None:
        deprecate(
            "callback",
            "1.0.0",
            "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
        )
    if callback_steps is not None:
        deprecate(
            "callback_steps",
            "1.0.0",
            "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
        )

    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}, "
            f"but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
        )

    cut_context = True

    # 1. Check inputs. Raise error if not correct
    self.check_inputs(
        prompt,
        callback_steps,
        negative_prompt,
        prompt_embeds,
        negative_prompt_embeds,
        callback_on_step_end_tensor_inputs,
        attention_mask,
        negative_attention_mask,
    )

    self._guidance_scale = guidance_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]

    # 3. Encode input prompt
    prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(
        prompt,
        self.do_classifier_free_guidance,
        num_images_per_prompt=num_images_per_prompt,
        negative_prompt=negative_prompt,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        _cut_context=cut_context,
        attention_mask=attention_mask,
        negative_attention_mask=negative_attention_mask,
    )

    if self.do_classifier_free_guidance:
        prompt_embeds = ops.cat([negative_prompt_embeds, prompt_embeds])
        attention_mask = ops.cat([negative_attention_mask, attention_mask])
    # 4. Prepare timesteps
    self.scheduler.set_timesteps(num_inference_steps)
    timesteps = self.scheduler.timesteps

    # 5. Prepare latents
    height, width = downscale_height_and_width(height, width, 8)

    latents = self.prepare_latents(
        (batch_size * num_images_per_prompt, 4, height, width),
        prompt_embeds.dtype,
        generator,
        latents,
        self.scheduler,
    )

    # 7. Denoising loop
    num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
    self._num_timesteps = len(timesteps)
    with self.progress_bar(total=num_inference_steps) as progress_bar:
        for i, t in enumerate(timesteps):
            latent_model_input = ops.cat([latents] * 2) if self.do_classifier_free_guidance else latents

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input,
                t,
                encoder_hidden_states=prompt_embeds,
                encoder_attention_mask=attention_mask,
                return_dict=False,
            )[0]

            if self.do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)

                noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond
                # noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(
                noise_pred,
                t,
                latents,
                generator=generator,
            )[0]

            if callback_on_step_end is not None:
                callback_kwargs = {}
                for k in callback_on_step_end_tensor_inputs:
                    callback_kwargs[k] = locals()[k]
                callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                latents = callback_outputs.pop("latents", latents)
                prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
                attention_mask = callback_outputs.pop("attention_mask", attention_mask)
                negative_attention_mask = callback_outputs.pop("negative_attention_mask", negative_attention_mask)

            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)

        # post-processing
        if output_type not in ["ms", "np", "pil", "latent"]:
            raise ValueError(
                f"Only the output types `ms`, `pil`, `np` and `latent` are supported not output_type={output_type}"
            )

        if not output_type == "latent":
            image = self.movq.decode(latents, force_not_quantize=True)[0]

            if output_type in ["np", "pil"]:
                image = image * 0.5 + 0.5
                image = image.clamp(0, 1)
                image = image.permute(0, 2, 3, 1).float().numpy()

            if output_type == "pil":
                image = self.numpy_to_pil(image)
        else:
            image = latents

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)

mindone.diffusers.Kandinsky3Pipeline.encode_prompt(prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, _cut_context=False, attention_mask=None, negative_attention_mask=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`, *optional*, defaults to 1 DEFAULT: 1
do_classifier_free_guidance	whether to use classifier free guidance or not TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True
negative_prompt	The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds. instead. 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
attention_mask	Pre-generated attention mask. Must provide if passing prompt_embeds directly. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_attention_mask	Pre-generated negative attention mask. Must provide if passing negative_prompt_embeds directly. TYPE: `ms.Tensor`, *optional* DEFAULT: None

Source code in mindone/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py

def encode_prompt(
    self,
    prompt,
    do_classifier_free_guidance=True,
    num_images_per_prompt=1,
    negative_prompt=None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    _cut_context=False,
    attention_mask: Optional[ms.Tensor] = None,
    negative_attention_mask: Optional[ms.Tensor] = 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`, *optional*, defaults to 1):
            number of images that should be generated per prompt
        do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
            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. 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.
        attention_mask (`ms.Tensor`, *optional*):
            Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
        negative_attention_mask (`ms.Tensor`, *optional*):
            Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
    """
    if prompt is not None and negative_prompt is not None:
        if 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)}."
            )

    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]

    max_length = 128

    if prompt_embeds is None:
        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=max_length,
            truncation=True,
            return_tensors="np",
        )
        text_input_ids = ms.tensor(text_inputs.input_ids)
        attention_mask = ms.tensor(text_inputs.attention_mask)
        prompt_embeds = self.text_encoder(
            text_input_ids,
            attention_mask=attention_mask,
        )
        prompt_embeds = prompt_embeds[0]
        prompt_embeds, attention_mask = self.process_embeds(prompt_embeds, attention_mask, _cut_context)
        prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2)

    if self.text_encoder is not None:
        dtype = self.text_encoder.dtype
    else:
        dtype = None

    prompt_embeds = prompt_embeds.to(dtype=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)
    attention_mask = attention_mask.tile((num_images_per_prompt, 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 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
        if negative_prompt is not None:
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=128,
                truncation=True,
                return_attention_mask=True,
                return_tensors="np",
            )
            text_input_ids = ms.tensor(uncond_input.input_ids)
            negative_attention_mask = ms.tensor(uncond_input.attention_mask)

            negative_prompt_embeds = self.text_encoder(
                text_input_ids,
                attention_mask=negative_attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]
            negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]]
            negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]]
            negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2)

        else:
            negative_prompt_embeds = ops.zeros_like(prompt_embeds)
            negative_attention_mask = ops.zeros_like(attention_mask)

    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=dtype)
        if negative_prompt_embeds.shape != prompt_embeds.shape:
            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)
            negative_attention_mask = negative_attention_mask.tile((num_images_per_prompt, 1))

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
    else:
        negative_prompt_embeds = None
        negative_attention_mask = None
    return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask

mindone.diffusers.Kandinsky3Img2ImgPipeline

Bases: DiffusionPipeline, LoraLoaderMixin

Source code in mindone/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py

class Kandinsky3Img2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):
    model_cpu_offload_seq = "text_encoder->movq->unet->movq"
    _callback_tensor_inputs = [
        "latents",
        "prompt_embeds",
        "negative_prompt_embeds",
        "negative_attention_mask",
        "attention_mask",
    ]

    def __init__(
        self,
        tokenizer: T5Tokenizer,
        text_encoder: T5EncoderModel,
        unet: Kandinsky3UNet,
        scheduler: DDPMScheduler,
        movq: VQModel,
    ):
        super().__init__()

        self.register_modules(tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, movq=movq)

    def get_timesteps(self, num_inference_steps, strength):
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

    def _process_embeds(self, embeddings, attention_mask, cut_context):
        # return embeddings, attention_mask
        if cut_context:
            embeddings[attention_mask == 0] = ops.zeros_like(embeddings[attention_mask == 0])
            max_seq_length = attention_mask.sum(axis=-1).max() + 1
            embeddings = embeddings[:, :max_seq_length]
            attention_mask = attention_mask[:, :max_seq_length]
        return embeddings, attention_mask

    def encode_prompt(
        self,
        prompt,
        do_classifier_free_guidance=True,
        num_images_per_prompt=1,
        negative_prompt=None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        _cut_context=False,
        attention_mask: Optional[ms.Tensor] = None,
        negative_attention_mask: Optional[ms.Tensor] = 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`, *optional*, defaults to 1):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                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. 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.
            attention_mask (`ms.Tensor`, *optional*):
                Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
            negative_attention_mask (`ms.Tensor`, *optional*):
                Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
        """
        if prompt is not None and negative_prompt is not None:
            if 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)}."
                )

        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]

        max_length = 128

        if prompt_embeds is None:
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="np",
            )
            text_input_ids = ms.tensor(text_inputs.input_ids)
            attention_mask = ms.tensor(text_inputs.attention_mask)
            prompt_embeds = self.text_encoder(
                text_input_ids,
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]
            prompt_embeds, attention_mask = self._process_embeds(prompt_embeds, attention_mask, _cut_context)
            prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2)

        if self.text_encoder is not None:
            dtype = self.text_encoder.dtype
        else:
            dtype = None

        prompt_embeds = prompt_embeds.to(dtype=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)
        attention_mask = attention_mask.tile((num_images_per_prompt, 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 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
            if negative_prompt is not None:
                uncond_input = self.tokenizer(
                    uncond_tokens,
                    padding="max_length",
                    max_length=128,
                    truncation=True,
                    return_attention_mask=True,
                    return_tensors="np",
                )
                text_input_ids = ms.tensor(uncond_input.input_ids)
                negative_attention_mask = ms.tensor(uncond_input.attention_mask)

                negative_prompt_embeds = self.text_encoder(
                    text_input_ids,
                    attention_mask=negative_attention_mask,
                )
                negative_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]]
                negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]]
                negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2)

            else:
                negative_prompt_embeds = ops.zeros_like(prompt_embeds)
                negative_attention_mask = ops.zeros_like(attention_mask)

        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=dtype)
            if negative_prompt_embeds.shape != prompt_embeds.shape:
                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)
                negative_attention_mask = negative_attention_mask.tile((num_images_per_prompt, 1))

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
        else:
            negative_prompt_embeds = None
            negative_attention_mask = None
        return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask

    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, generator=None):
        if not isinstance(image, (ms.Tensor, PIL.Image.Image, list)):
            raise ValueError(f"`image` has to be of type `ms.Tensor`, `PIL.Image.Image` or list but is {type(image)}")

        image = image.to(dtype=dtype)

        batch_size = batch_size * num_images_per_prompt

        if image.shape[1] == 4:
            init_latents = image

        else:
            if isinstance(generator, list) and len(generator) != batch_size:
                raise ValueError(
                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
                )

            elif isinstance(generator, list):
                init_latents = [
                    self.movq.diag_gauss_dist.sample(self.movq.encode(image[i : i + 1])[0]) for i in range(batch_size)
                ]
                init_latents = ops.cat(init_latents, axis=0)
            else:
                init_latents = self.movq.diag_gauss_dist.sample(self.movq.encode(image)[0])

            init_latents = self.movq.config.scaling_factor * init_latents

        init_latents = ops.cat([init_latents], axis=0)

        shape = init_latents.shape
        noise = randn_tensor(shape, generator=generator, dtype=dtype)

        # get latents
        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)

        latents = init_latents

        return latents

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_inputs(
        self,
        prompt,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
        attention_mask=None,
        negative_attention_mask=None,
    ):
        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}, "
                f"but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt 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 negative_prompt_embeds is not None and negative_attention_mask is None:
            raise ValueError("Please provide `negative_attention_mask` along with `negative_prompt_embeds`")

        if negative_prompt_embeds is not None and negative_attention_mask is not None:
            if negative_prompt_embeds.shape[:2] != negative_attention_mask.shape:
                raise ValueError(
                    "`negative_prompt_embeds` and `negative_attention_mask` must have the same batch_size and token length when passed directly, but"
                    f" got: `negative_prompt_embeds` {negative_prompt_embeds.shape[:2]} != `negative_attention_mask`"
                    f" {negative_attention_mask.shape}."
                )

        if prompt_embeds is not None and attention_mask is None:
            raise ValueError("Please provide `attention_mask` along with `prompt_embeds`")

        if prompt_embeds is not None and attention_mask is not None:
            if prompt_embeds.shape[:2] != attention_mask.shape:
                raise ValueError(
                    "`prompt_embeds` and `attention_mask` must have the same batch_size and token length when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape[:2]} != `attention_mask`"
                    f" {attention_mask.shape}."
                )

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1

    @property
    def num_timesteps(self):
        return self._num_timesteps

    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        image: Union[ms.Tensor, PIL.Image.Image, List[ms.Tensor], List[PIL.Image.Image]] = None,
        strength: float = 0.3,
        num_inference_steps: int = 25,
        guidance_scale: float = 3.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        attention_mask: Optional[ms.Tensor] = None,
        negative_attention_mask: Optional[ms.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = False,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        **kwargs,
    ):
        """
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            image (`ms.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[ms.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
                `Image`, or tensor representing an image batch, that will be used as the starting point for the
                process.
            strength (`float`, *optional*, defaults to 0.8):
                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
                essentially ignores `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 3.0):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
                One or a list of [np.random.Generator(s)](https://numpy.org/doc/stable/reference/random/generator.html)
                to make generation deterministic.
            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.
            attention_mask (`ms.Tensor`, *optional*):
                Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
            negative_attention_mask (`ms.Tensor`, *optional*):
                Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.

        Examples:

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`

        """
        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )

        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}, "
                f"but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        cut_context = True
        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            callback_on_step_end_tensor_inputs,
            attention_mask,
            negative_attention_mask,
        )

        self._guidance_scale = guidance_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]

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(
            prompt,
            self.do_classifier_free_guidance,
            num_images_per_prompt=num_images_per_prompt,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            _cut_context=cut_context,
            attention_mask=attention_mask,
            negative_attention_mask=negative_attention_mask,
        )

        if self.do_classifier_free_guidance:
            prompt_embeds = ops.cat([negative_prompt_embeds, prompt_embeds])
            attention_mask = ops.cat([negative_attention_mask, attention_mask])
        if not isinstance(image, list):
            image = [image]
        if not all(isinstance(i, (PIL.Image.Image, ms.Tensor)) for i in image):
            raise ValueError(
                f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and mindspore tensor"
            )

        image = ops.cat([prepare_image(i) for i in image], axis=0)
        image = image.to(dtype=prompt_embeds.dtype)
        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
        # 5. Prepare latents
        latents = self.movq.encode(image)[0]
        latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
        latents = self.prepare_latents(
            latents, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, generator
        )

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        self._num_timesteps = len(timesteps)
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                latent_model_input = ops.cat([latents] * 2) if self.do_classifier_free_guidance else latents

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    encoder_attention_mask=attention_mask,
                )[0]
                if self.do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)

                    noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(
                    noise_pred,
                    t,
                    latents,
                    generator=generator,
                )[0]

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
                    attention_mask = callback_outputs.pop("attention_mask", attention_mask)
                    negative_attention_mask = callback_outputs.pop("negative_attention_mask", negative_attention_mask)

                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)

            # post-processing
            if output_type not in ["ms", "np", "pil", "latent"]:
                raise ValueError(
                    f"Only the output types `ms`, `pil`, `np` and `latent` are supported not output_type={output_type}"
                )
            if not output_type == "latent":
                image = self.movq.decode(latents, force_not_quantize=True)[0]

                if output_type in ["np", "pil"]:
                    image = image * 0.5 + 0.5
                    image = image.clamp(0, 1)
                    image = image.permute(0, 2, 3, 1).float().numpy()

                if output_type == "pil":
                    image = self.numpy_to_pil(image)
            else:
                image = latents

            if not return_dict:
                return (image,)

            return ImagePipelineOutput(images=image)

mindone.diffusers.Kandinsky3Img2ImgPipeline.__call__(prompt=None, image=None, strength=0.3, num_inference_steps=25, guidance_scale=3.0, negative_prompt=None, num_images_per_prompt=1, generator=None, prompt_embeds=None, negative_prompt_embeds=None, attention_mask=None, negative_attention_mask=None, output_type='pil', return_dict=False, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], **kwargs)

Function invoked when calling the pipeline for generation.

PARAMETER	DESCRIPTION
prompt	The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
image	Image, or tensor representing an image batch, that will be used as the starting point for the process. TYPE: `ms.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[ms.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]` DEFAULT: None
strength	Indicates extent to transform the reference image. Must be between 0 and 1. image is used as a starting point and more noise is added the higher the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in num_inference_steps. A value of 1 essentially ignores image. TYPE: `float`, *optional*, defaults to 0.8 DEFAULT: 0.3
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: 25
guidance_scale	Guidance scale as defined in Classifier-Free Diffusion Guidance. guidance_scale is defined as w of equation 2. of Imagen Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality. TYPE: `float`, *optional*, defaults to 3.0 DEFAULT: 3.0
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
num_images_per_prompt	The number of images to generate per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
generator	One or a list of np.random.Generator(s) to make generation deterministic. TYPE: `np.random.Generator` or `List[np.random.Generator]`, *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
attention_mask	Pre-generated attention mask. Must provide if passing prompt_embeds directly. TYPE: `ms.Tensor`, *optional* DEFAULT: None
negative_attention_mask	Pre-generated negative attention mask. Must provide if passing negative_prompt_embeds directly. TYPE: `ms.Tensor`, *optional* DEFAULT: None
output_type	The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array. TYPE: `str`, *optional*, defaults to `"pil"` DEFAULT: 'pil'
return_dict	Whether or not to return a [~pipelines.stable_diffusion.IFPipelineOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
callback_on_step_end	A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by callback_on_step_end_tensor_inputs. TYPE: `Callable`, *optional* DEFAULT: None
callback_on_step_end_tensor_inputs	The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list will be passed as callback_kwargs argument. You will only be able to include variables listed in the ._callback_tensor_inputs attribute of your pipeline class. TYPE: `List`, *optional* DEFAULT: ['latents']

RETURNS	DESCRIPTION
	[~pipelines.ImagePipelineOutput] or tuple

Source code in mindone/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py

def __call__(
    self,
    prompt: Union[str, List[str]] = None,
    image: Union[ms.Tensor, PIL.Image.Image, List[ms.Tensor], List[PIL.Image.Image]] = None,
    strength: float = 0.3,
    num_inference_steps: int = 25,
    guidance_scale: float = 3.0,
    negative_prompt: Optional[Union[str, List[str]]] = None,
    num_images_per_prompt: Optional[int] = 1,
    generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    attention_mask: Optional[ms.Tensor] = None,
    negative_attention_mask: Optional[ms.Tensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = False,
    callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
    callback_on_step_end_tensor_inputs: List[str] = ["latents"],
    **kwargs,
):
    """
    Function invoked when calling the pipeline for generation.

    Args:
        prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
            instead.
        image (`ms.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[ms.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
            `Image`, or tensor representing an image batch, that will be used as the starting point for the
            process.
        strength (`float`, *optional*, defaults to 0.8):
            Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
            starting point and more noise is added the higher the `strength`. The number of denoising steps depends
            on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
            process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
            essentially ignores `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 3.0):
            Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
            `guidance_scale` is defined as `w` of equation 2. of [Imagen
            Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
            1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
            usually at the expense of lower image quality.
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts not to guide the image generation. If not defined, one has to pass
            `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
            less than `1`).
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        generator (`np.random.Generator` or `List[np.random.Generator]`, *optional*):
            One or a list of [np.random.Generator(s)](https://numpy.org/doc/stable/reference/random/generator.html)
            to make generation deterministic.
        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.
        attention_mask (`ms.Tensor`, *optional*):
            Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
        negative_attention_mask (`ms.Tensor`, *optional*):
            Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generate image. Choose between
            [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `False`):
            Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
        callback_on_step_end (`Callable`, *optional*):
            A function that calls at the end of each denoising steps during the inference. The function is called
            with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
            callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
            `callback_on_step_end_tensor_inputs`.
        callback_on_step_end_tensor_inputs (`List`, *optional*):
            The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
            will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
            `._callback_tensor_inputs` attribute of your pipeline class.

    Examples:

    Returns:
        [`~pipelines.ImagePipelineOutput`] or `tuple`

    """
    callback = kwargs.pop("callback", None)
    callback_steps = kwargs.pop("callback_steps", None)

    if callback is not None:
        deprecate(
            "callback",
            "1.0.0",
            "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
        )
    if callback_steps is not None:
        deprecate(
            "callback_steps",
            "1.0.0",
            "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
        )

    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}, "
            f"but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
        )

    cut_context = True
    # 1. Check inputs. Raise error if not correct
    self.check_inputs(
        prompt,
        callback_steps,
        negative_prompt,
        prompt_embeds,
        negative_prompt_embeds,
        callback_on_step_end_tensor_inputs,
        attention_mask,
        negative_attention_mask,
    )

    self._guidance_scale = guidance_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]

    # 3. Encode input prompt
    prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(
        prompt,
        self.do_classifier_free_guidance,
        num_images_per_prompt=num_images_per_prompt,
        negative_prompt=negative_prompt,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        _cut_context=cut_context,
        attention_mask=attention_mask,
        negative_attention_mask=negative_attention_mask,
    )

    if self.do_classifier_free_guidance:
        prompt_embeds = ops.cat([negative_prompt_embeds, prompt_embeds])
        attention_mask = ops.cat([negative_attention_mask, attention_mask])
    if not isinstance(image, list):
        image = [image]
    if not all(isinstance(i, (PIL.Image.Image, ms.Tensor)) for i in image):
        raise ValueError(
            f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and mindspore tensor"
        )

    image = ops.cat([prepare_image(i) for i in image], axis=0)
    image = image.to(dtype=prompt_embeds.dtype)
    # 4. Prepare timesteps
    self.scheduler.set_timesteps(num_inference_steps)
    timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
    # 5. Prepare latents
    latents = self.movq.encode(image)[0]
    latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
    latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
    latents = self.prepare_latents(
        latents, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, generator
    )

    # 7. Denoising loop
    num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
    self._num_timesteps = len(timesteps)
    with self.progress_bar(total=num_inference_steps) as progress_bar:
        for i, t in enumerate(timesteps):
            latent_model_input = ops.cat([latents] * 2) if self.do_classifier_free_guidance else latents

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input,
                t,
                encoder_hidden_states=prompt_embeds,
                encoder_attention_mask=attention_mask,
            )[0]
            if self.do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)

                noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond

            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(
                noise_pred,
                t,
                latents,
                generator=generator,
            )[0]

            if callback_on_step_end is not None:
                callback_kwargs = {}
                for k in callback_on_step_end_tensor_inputs:
                    callback_kwargs[k] = locals()[k]
                callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                latents = callback_outputs.pop("latents", latents)
                prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
                attention_mask = callback_outputs.pop("attention_mask", attention_mask)
                negative_attention_mask = callback_outputs.pop("negative_attention_mask", negative_attention_mask)

            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)

        # post-processing
        if output_type not in ["ms", "np", "pil", "latent"]:
            raise ValueError(
                f"Only the output types `ms`, `pil`, `np` and `latent` are supported not output_type={output_type}"
            )
        if not output_type == "latent":
            image = self.movq.decode(latents, force_not_quantize=True)[0]

            if output_type in ["np", "pil"]:
                image = image * 0.5 + 0.5
                image = image.clamp(0, 1)
                image = image.permute(0, 2, 3, 1).float().numpy()

            if output_type == "pil":
                image = self.numpy_to_pil(image)
        else:
            image = latents

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)

mindone.diffusers.Kandinsky3Img2ImgPipeline.encode_prompt(prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, _cut_context=False, attention_mask=None, negative_attention_mask=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 (`int`, *optional*, defaults to 1):
    number of images that should be generated per prompt
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
    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. 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.
attention_mask (`ms.Tensor`, *optional*):
    Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
negative_attention_mask (`ms.Tensor`, *optional*):
    Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.

Source code in mindone/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py

def encode_prompt(
    self,
    prompt,
    do_classifier_free_guidance=True,
    num_images_per_prompt=1,
    negative_prompt=None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    _cut_context=False,
    attention_mask: Optional[ms.Tensor] = None,
    negative_attention_mask: Optional[ms.Tensor] = 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`, *optional*, defaults to 1):
            number of images that should be generated per prompt
        do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
            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. 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.
        attention_mask (`ms.Tensor`, *optional*):
            Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
        negative_attention_mask (`ms.Tensor`, *optional*):
            Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
    """
    if prompt is not None and negative_prompt is not None:
        if 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)}."
            )

    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]

    max_length = 128

    if prompt_embeds is None:
        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=max_length,
            truncation=True,
            return_tensors="np",
        )
        text_input_ids = ms.tensor(text_inputs.input_ids)
        attention_mask = ms.tensor(text_inputs.attention_mask)
        prompt_embeds = self.text_encoder(
            text_input_ids,
            attention_mask=attention_mask,
        )
        prompt_embeds = prompt_embeds[0]
        prompt_embeds, attention_mask = self._process_embeds(prompt_embeds, attention_mask, _cut_context)
        prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2)

    if self.text_encoder is not None:
        dtype = self.text_encoder.dtype
    else:
        dtype = None

    prompt_embeds = prompt_embeds.to(dtype=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)
    attention_mask = attention_mask.tile((num_images_per_prompt, 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 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
        if negative_prompt is not None:
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=128,
                truncation=True,
                return_attention_mask=True,
                return_tensors="np",
            )
            text_input_ids = ms.tensor(uncond_input.input_ids)
            negative_attention_mask = ms.tensor(uncond_input.attention_mask)

            negative_prompt_embeds = self.text_encoder(
                text_input_ids,
                attention_mask=negative_attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]
            negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]]
            negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]]
            negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2)

        else:
            negative_prompt_embeds = ops.zeros_like(prompt_embeds)
            negative_attention_mask = ops.zeros_like(attention_mask)

    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=dtype)
        if negative_prompt_embeds.shape != prompt_embeds.shape:
            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)
            negative_attention_mask = negative_attention_mask.tile((num_images_per_prompt, 1))

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
    else:
        negative_prompt_embeds = None
        negative_attention_mask = None
    return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask