diffusers_src/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py

from typing import Callable

import torch
from transformers import T5EncoderModel, T5Tokenizer

from ...loaders import StableDiffusionLoraLoaderMixin
from ...models import Kandinsky3UNet, VQModel
from ...schedulers import DDPMScheduler
from ...utils import (
    deprecate,
    is_torch_xla_available,
    logging,
    replace_example_docstring,
)
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


if is_torch_xla_available():
    import torch_xla.core.xla_model as xm

    XLA_AVAILABLE = True
else:
    XLA_AVAILABLE = False

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> from diffusers import AutoPipelineForText2Image
        >>> import torch

        >>> pipe = AutoPipelineForText2Image.from_pretrained(
        ...     "kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16
        ... )
        >>> pipe.enable_model_cpu_offload()

        >>> prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background."

        >>> generator = torch.Generator(device="cpu").manual_seed(0)
        >>> image = pipe(prompt, num_inference_steps=25, generator=generator).images[0]
        ```

"""


def downscale_height_and_width(height, width, scale_factor=8):
    new_height = height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    new_width = width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor


class Kandinsky3Pipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
    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] = torch.zeros_like(embeddings[attention_mask == 0])
            max_seq_length = attention_mask.sum(-1).max() + 1
            embeddings = embeddings[:, :max_seq_length]
            attention_mask = attention_mask[:, :max_seq_length]
        return embeddings, attention_mask

    @torch.no_grad()
    def encode_prompt(
        self,
        prompt,
        do_classifier_free_guidance=True,
        num_images_per_prompt=1,
        device=None,
        negative_prompt=None,
        prompt_embeds: torch.Tensor | None = None,
        negative_prompt_embeds: torch.Tensor | None = None,
        _cut_context=False,
        attention_mask: torch.Tensor | None = None,
        negative_attention_mask: torch.Tensor | None = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `list[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`, *optional*):
                torch device to place the resulting embeddings on
            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 (`torch.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 (`torch.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 (`torch.Tensor`, *optional*):
                Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
            negative_attention_mask (`torch.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 device is None:
            device = self._execution_device

        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="pt",
            )
            text_input_ids = text_inputs.input_ids.to(device)
            attention_mask = text_inputs.attention_mask.to(device)
            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, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
        attention_mask = attention_mask.repeat(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="pt",
                )
                text_input_ids = uncond_input.input_ids.to(device)
                negative_attention_mask = uncond_input.attention_mask.to(device)

                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 = torch.zeros_like(prompt_embeds)
                negative_attention_mask = torch.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, device=device)
            if negative_prompt_embeds.shape != prompt_embeds.shape:
                negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
                negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
                negative_attention_mask = negative_attention_mask.repeat(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, device, generator, latents, scheduler):
        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            if latents.shape != shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
            latents = latents.to(device)

        latents = latents * scheduler.init_noise_sigma
        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}, 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

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: str | list[str] = None,
        num_inference_steps: int = 25,
        guidance_scale: float = 3.0,
        negative_prompt: str | list[str] | None = None,
        num_images_per_prompt: int | None = 1,
        height: int | None = 1024,
        width: int | None = 1024,
        generator: torch.Generator | list[torch.Generator] | None = None,
        prompt_embeds: torch.Tensor | None = None,
        negative_prompt_embeds: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        negative_attention_mask: torch.Tensor | None = None,
        output_type: str | None = "pil",
        return_dict: bool = True,
        latents=None,
        callback_on_step_end: Callable[[int, int], None] | 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.
            guidance_scale (`float`, *optional*, defaults to 3.0):
                Guidance scale as defined in [Classifier-Free Diffusion
                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
            generator (`torch.Generator` or `list[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            prompt_embeds (`torch.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 (`torch.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 (`torch.Tensor`, *optional*):
                Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
            negative_attention_mask (`torch.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 `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
            latents (`torch.Tensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will be generated by sampling using the supplied random `generator`.
            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}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        cut_context = True
        device = self._execution_device

        # 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,
            device=device,
            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 = torch.cat([negative_prompt_embeds, prompt_embeds])
            attention_mask = torch.cat([negative_attention_mask, attention_mask]).bool()
        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        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,
            device,
            generator,
            latents,
            self.scheduler,
        )

        if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
            self.text_encoder_offload_hook.offload()

        # 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 = torch.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,
                ).prev_sample

                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)

                if XLA_AVAILABLE:
                    xm.mark_step()

            # post-processing
            if output_type not in ["pt", "np", "pil", "latent"]:
                raise ValueError(
                    f"Only the output types `pt`, `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)["sample"]

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

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

            self.maybe_free_model_hooks()

            if not return_dict:
                return (image,)

            return ImagePipelineOutput(images=image)