CogView4

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.

Tip

This pipeline was contributed by zRzRzRzRzRzRzR. The original codebase can be found here. The original weights can be found under hf.co/THUDM.

mindone.diffusers.CogView4Pipeline

Bases: DiffusionPipeline

Pipeline for text-to-image generation using CogView4.

This model inherits from [DiffusionPipeline]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

PARAMETER	DESCRIPTION
vae	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. TYPE: [`AutoencoderKL`]
text_encoder	Frozen text-encoder. CogView4 uses glm-4-9b-hf. TYPE: [`GLMModel`]
tokenizer	Tokenizer of class PreTrainedTokenizer. TYPE: `PreTrainedTokenizer`
transformer	A text conditioned CogView4Transformer2DModel to denoise the encoded image latents. TYPE: [`CogView4Transformer2DModel`]
scheduler	A scheduler to be used in combination with transformer to denoise the encoded image latents. TYPE: [`SchedulerMixin`]

Source code in mindone/diffusers/pipelines/cogview4/pipeline_cogview4.py

class CogView4Pipeline(DiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using CogView4.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`GLMModel`]):
            Frozen text-encoder. CogView4 uses [glm-4-9b-hf](https://huggingface.co/THUDM/glm-4-9b-hf).
        tokenizer (`PreTrainedTokenizer`):
            Tokenizer of class
            [PreTrainedTokenizer](https://huggingface.co/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer).
        transformer ([`CogView4Transformer2DModel`]):
            A text conditioned `CogView4Transformer2DModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
    """

    _optional_components = []
    model_cpu_offload_seq = "text_encoder->transformer->vae"
    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

    def __init__(
        self,
        tokenizer: AutoTokenizer,
        text_encoder: GlmModel,
        vae: AutoencoderKL,
        transformer: CogView4Transformer2DModel,
        scheduler: FlowMatchEulerDiscreteScheduler,
    ):
        super().__init__()

        self.register_modules(
            tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    def _get_glm_embeds(
        self,
        prompt: Union[str, List[str]] = None,
        max_sequence_length: int = 1024,
        dtype: Optional[ms.Type] = None,
    ):
        dtype = dtype or self.text_encoder.dtype

        prompt = [prompt] if isinstance(prompt, str) else prompt

        text_inputs = self.tokenizer(
            prompt,
            padding="longest",  # not use max length
            max_length=max_sequence_length,
            truncation=True,
            add_special_tokens=True,
            return_tensors="np",
        )
        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="np").input_ids
        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not np.array_equal(
            text_input_ids, untruncated_ids
        ):
            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
            logger.warning(
                "The following part of your input was truncated because `max_sequence_length` is set to "
                f" {max_sequence_length} tokens: {removed_text}"
            )
        text_input_ids = ms.tensor(text_input_ids)
        untruncated_ids = ms.tensor(untruncated_ids)
        current_length = text_input_ids.shape[1]
        pad_length = (16 - (current_length % 16)) % 16
        if pad_length > 0:
            pad_ids = ops.full(
                (text_input_ids.shape[0], pad_length),
                fill_value=self.tokenizer.pad_token_id,
                dtype=text_input_ids.dtype,
            )
            text_input_ids = ops.cat([pad_ids, text_input_ids], axis=1)
        prompt_embeds = self.text_encoder(text_input_ids, output_hidden_states=True)[1][-2]

        prompt_embeds = prompt_embeds.to(dtype=dtype)
        return prompt_embeds

    def encode_prompt(
        self,
        prompt: Union[str, List[str]],
        negative_prompt: Optional[Union[str, List[str]]] = None,
        do_classifier_free_guidance: bool = True,
        num_images_per_prompt: int = 1,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        dtype: Optional[ms.Type] = None,
        max_sequence_length: int = 1024,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            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`).
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                Whether to use classifier free guidance or not.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                Number of images that should be generated per prompt.
            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.
            dtype: (`ms.Type`, *optional*):
                mindspore dtype
            max_sequence_length (`int`, defaults to `1024`):
                Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.
        """
        prompt = [prompt] if isinstance(prompt, str) else prompt
        if prompt is not None:
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if prompt_embeds is None:
            prompt_embeds = self._get_glm_embeds(prompt, max_sequence_length, dtype)

        seq_len = prompt_embeds.shape[1]
        prompt_embeds = prompt_embeds.tile((1, num_images_per_prompt, 1))
        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        if do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt

            if prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )

            negative_prompt_embeds = self._get_glm_embeds(negative_prompt, max_sequence_length, dtype)

            seq_len = negative_prompt_embeds.shape[1]
            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)

        return prompt_embeds, negative_prompt_embeds

    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
        shape = (
            batch_size,
            num_channels_latents,
            int(height) // self.vae_scale_factor,
            int(width) // self.vae_scale_factor,
        )
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )
        latents = randn_tensor(shape, generator=generator, dtype=dtype)
        return latents

    def check_inputs(
        self,
        prompt,
        height,
        width,
        negative_prompt,
        callback_on_step_end_tensor_inputs,
        prompt_embeds=None,
        negative_prompt_embeds=None,
    ):
        if height % 16 != 0 or width % 16 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")

        if callback_on_step_end_tensor_inputs is not None and not all(
            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"  # noqa: E501
            )
        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

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

        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[0] != negative_prompt_embeds.shape[0]:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same batch size when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} and `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )
            if prompt_embeds.shape[-1] != negative_prompt_embeds.shape[-1]:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same dimension when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} and `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

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

    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
    # corresponds to doing no classifier free guidance.
    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1

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

    @property
    def interrupt(self):
        return self._interrupt

    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        timesteps: Optional[List[int]] = None,
        sigmas: Optional[List[float]] = None,
        guidance_scale: float = 5.0,
        num_images_per_prompt: int = 1,
        generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
        latents: Optional[ms.Tensor] = None,
        prompt_embeds: Optional[ms.Tensor] = None,
        negative_prompt_embeds: Optional[ms.Tensor] = None,
        original_size: Optional[Tuple[int, int]] = None,
        crops_coords_top_left: Tuple[int, int] = (0, 0),
        output_type: str = "pil",
        return_dict: bool = False,
        callback_on_step_end: Optional[
            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
        ] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        max_sequence_length: int = 1024,
    ) -> Union[CogView4PipelineOutput, Tuple]:
        """
        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`.
            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`).
            height (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image. If not provided, it is set to 1024.
            width (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image. If not provided it is set to 1024.
            num_inference_steps (`int`, *optional*, defaults to `50`):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
                passed will be used. Must be in descending order.
            sigmas (`List[float]`, *optional*):
                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
                will be used.
            guidance_scale (`float`, *optional*, defaults to `5.0`):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            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.
            latents (`ms.Tensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`ms.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *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.
            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
                explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            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_xl.StableDiffusionXLPipelineOutput`] instead
                of a plain tuple.
            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).
            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.
            max_sequence_length (`int`, defaults to `224`):
                Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.

        Examples:

        Returns:
            [`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput`] or `tuple`:
            [`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput`] if `return_dict` is True, otherwise a
            `tuple`. When returning a tuple, the first element is a list with the generated images.
        """

        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs

        height = height or self.transformer.config.sample_size * self.vae_scale_factor
        width = width or self.transformer.config.sample_size * self.vae_scale_factor

        original_size = original_size or (height, width)
        target_size = (height, width)

        # Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            height,
            width,
            negative_prompt,
            callback_on_step_end_tensor_inputs,
            prompt_embeds,
            negative_prompt_embeds,
        )
        self._guidance_scale = guidance_scale
        self._interrupt = False

        # Default call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

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

        # Prepare latents
        latent_channels = self.transformer.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            latent_channels,
            height,
            width,
            ms.float32,
            generator,
            latents,
        )

        # Prepare additional timestep conditions
        original_size = ms.tensor([original_size], dtype=prompt_embeds.dtype)
        target_size = ms.tensor([target_size], dtype=prompt_embeds.dtype)
        crops_coords_top_left = ms.tensor([crops_coords_top_left], dtype=prompt_embeds.dtype)

        original_size = original_size.tile((batch_size * num_images_per_prompt, 1))
        target_size = target_size.tile((batch_size * num_images_per_prompt, 1))
        crops_coords_top_left = crops_coords_top_left.tile((batch_size * num_images_per_prompt, 1))

        # Prepare timesteps
        image_seq_len = ((height // self.vae_scale_factor) * (width // self.vae_scale_factor)) // (
            self.transformer.config.patch_size**2
        )
        timesteps = (
            np.linspace(self.scheduler.config.num_train_timesteps, 1.0, num_inference_steps)
            if timesteps is None
            else np.array(timesteps)
        )
        timesteps = timesteps.astype(np.int64).astype(np.float32)
        sigmas = timesteps / self.scheduler.config.num_train_timesteps if sigmas is None else sigmas
        mu = calculate_shift(
            image_seq_len,
            self.scheduler.config.get("base_image_seq_len", 256),
            self.scheduler.config.get("base_shift", 0.25),
            self.scheduler.config.get("max_shift", 0.75),
        )
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler, num_inference_steps, timesteps, sigmas, mu=mu
        )
        self._num_timesteps = len(timesteps)

        # Denoising loop
        transformer_dtype = self.transformer.dtype
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue

                latent_model_input = latents.to(transformer_dtype)

                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                timestep = t.broadcast_to((latents.shape[0],))

                noise_pred_cond = self.transformer(
                    hidden_states=latent_model_input,
                    encoder_hidden_states=prompt_embeds,
                    timestep=timestep,
                    original_size=original_size,
                    target_size=target_size,
                    crop_coords=crops_coords_top_left,
                    return_dict=False,
                )[0]

                # perform guidance
                if self.do_classifier_free_guidance:
                    noise_pred_uncond = self.transformer(
                        hidden_states=latent_model_input,
                        encoder_hidden_states=negative_prompt_embeds,
                        timestep=timestep,
                        original_size=original_size,
                        target_size=target_size,
                        crop_coords=crops_coords_top_left,
                        return_dict=False,
                    )[0]

                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
                else:
                    noise_pred = noise_pred_cond

                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]

                # call the callback, if provided
                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, self.scheduler.sigmas[i], 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)

                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()

        if not output_type == "latent":
            latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
            image = self.vae.decode(latents, return_dict=False, generator=generator)[0]
        else:
            image = latents

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

        if not return_dict:
            return (image,)

        return CogView4PipelineOutput(images=image)

mindone.diffusers.CogView4Pipeline.__call__(prompt=None, negative_prompt=None, height=None, width=None, num_inference_steps=50, timesteps=None, sigmas=None, guidance_scale=5.0, num_images_per_prompt=1, generator=None, latents=None, prompt_embeds=None, negative_prompt_embeds=None, original_size=None, crops_coords_top_left=(0, 0), output_type='pil', return_dict=False, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], max_sequence_length=1024)

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. TYPE: `str` or `List[str]`, *optional* DEFAULT: None
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
height	The height in pixels of the generated image. If not provided, it is set to 1024. TYPE: `int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor DEFAULT: None
width	The width in pixels of the generated image. If not provided it is set to 1024. TYPE: `int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor DEFAULT: None
num_inference_steps	The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. TYPE: `int`, *optional*, defaults to `50` DEFAULT: 50
timesteps	Custom timesteps to use for the denoising process with schedulers which support a timesteps argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used. Must be in descending order. TYPE: `List[int]`, *optional* DEFAULT: None
sigmas	Custom sigmas to use for the denoising process with schedulers which support a sigmas argument in their set_timesteps method. If not defined, the default behavior when num_inference_steps is passed will be used. TYPE: `List[float]`, *optional* DEFAULT: None
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 `5.0` DEFAULT: 5.0
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
latents	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random generator. TYPE: `ms.Tensor`, *optional* DEFAULT: None
prompt_embeds	Pre-generated text embeddings. Can be used to easily tweak text inputs, 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
original_size	If original_size is not the same as target_size the image will appear to be down- or upsampled. original_size defaults to (height, width) if not specified. Part of SDXL's micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. TYPE: `Tuple[int]`, *optional*, defaults to (1024, 1024 DEFAULT: None
crops_coords_top_left	crops_coords_top_left can be used to generate an image that appears to be "cropped" from the position crops_coords_top_left downwards. Favorable, well-centered images are usually achieved by setting crops_coords_top_left to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of https://huggingface.co/papers/2307.01952. TYPE: `Tuple[int]`, *optional*, defaults to (0, 0 DEFAULT: (0, 0)
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_xl.StableDiffusionXLPipelineOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
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*
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']
max_sequence_length	Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results. TYPE: `int`, defaults to `224` DEFAULT: 1024

RETURNS	DESCRIPTION
Union[CogView4PipelineOutput, Tuple]	[~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput] or tuple:
Union[CogView4PipelineOutput, Tuple]	[~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput] if return_dict is True, otherwise a
Union[CogView4PipelineOutput, Tuple]	tuple. When returning a tuple, the first element is a list with the generated images.

Source code in mindone/diffusers/pipelines/cogview4/pipeline_cogview4.py

def __call__(
    self,
    prompt: Optional[Union[str, List[str]]] = None,
    negative_prompt: Optional[Union[str, List[str]]] = None,
    height: Optional[int] = None,
    width: Optional[int] = None,
    num_inference_steps: int = 50,
    timesteps: Optional[List[int]] = None,
    sigmas: Optional[List[float]] = None,
    guidance_scale: float = 5.0,
    num_images_per_prompt: int = 1,
    generator: Optional[Union[np.random.Generator, List[np.random.Generator]]] = None,
    latents: Optional[ms.Tensor] = None,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    original_size: Optional[Tuple[int, int]] = None,
    crops_coords_top_left: Tuple[int, int] = (0, 0),
    output_type: str = "pil",
    return_dict: bool = False,
    callback_on_step_end: Optional[
        Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
    ] = None,
    callback_on_step_end_tensor_inputs: List[str] = ["latents"],
    max_sequence_length: int = 1024,
) -> Union[CogView4PipelineOutput, Tuple]:
    """
    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`.
        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`).
        height (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
            The height in pixels of the generated image. If not provided, it is set to 1024.
        width (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
            The width in pixels of the generated image. If not provided it is set to 1024.
        num_inference_steps (`int`, *optional*, defaults to `50`):
            The number of denoising steps. More denoising steps usually lead to a higher quality image at the
            expense of slower inference.
        timesteps (`List[int]`, *optional*):
            Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
            in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
            passed will be used. Must be in descending order.
        sigmas (`List[float]`, *optional*):
            Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
            their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
            will be used.
        guidance_scale (`float`, *optional*, defaults to `5.0`):
            Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
            `guidance_scale` is defined as `w` of equation 2. of [Imagen
            Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
            1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
            usually at the expense of lower image quality.
        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.
        latents (`ms.Tensor`, *optional*):
            Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
            generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
            tensor will ge generated by sampling using the supplied random `generator`.
        prompt_embeds (`ms.Tensor`, *optional*):
            Pre-generated text embeddings. Can be used to easily tweak text inputs, *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.
        original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
            If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
            `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
            explained in section 2.2 of
            [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
        crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
            `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
            `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
            `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
            [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
        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_xl.StableDiffusionXLPipelineOutput`] instead
            of a plain tuple.
        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).
        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.
        max_sequence_length (`int`, defaults to `224`):
            Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.

    Examples:

    Returns:
        [`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput`] or `tuple`:
        [`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput`] if `return_dict` is True, otherwise a
        `tuple`. When returning a tuple, the first element is a list with the generated images.
    """

    if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
        callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs

    height = height or self.transformer.config.sample_size * self.vae_scale_factor
    width = width or self.transformer.config.sample_size * self.vae_scale_factor

    original_size = original_size or (height, width)
    target_size = (height, width)

    # Check inputs. Raise error if not correct
    self.check_inputs(
        prompt,
        height,
        width,
        negative_prompt,
        callback_on_step_end_tensor_inputs,
        prompt_embeds,
        negative_prompt_embeds,
    )
    self._guidance_scale = guidance_scale
    self._interrupt = False

    # Default call parameters
    if prompt is not None and isinstance(prompt, str):
        batch_size = 1
    elif prompt is not None and isinstance(prompt, list):
        batch_size = len(prompt)
    else:
        batch_size = prompt_embeds.shape[0]

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

    # Prepare latents
    latent_channels = self.transformer.config.in_channels
    latents = self.prepare_latents(
        batch_size * num_images_per_prompt,
        latent_channels,
        height,
        width,
        ms.float32,
        generator,
        latents,
    )

    # Prepare additional timestep conditions
    original_size = ms.tensor([original_size], dtype=prompt_embeds.dtype)
    target_size = ms.tensor([target_size], dtype=prompt_embeds.dtype)
    crops_coords_top_left = ms.tensor([crops_coords_top_left], dtype=prompt_embeds.dtype)

    original_size = original_size.tile((batch_size * num_images_per_prompt, 1))
    target_size = target_size.tile((batch_size * num_images_per_prompt, 1))
    crops_coords_top_left = crops_coords_top_left.tile((batch_size * num_images_per_prompt, 1))

    # Prepare timesteps
    image_seq_len = ((height // self.vae_scale_factor) * (width // self.vae_scale_factor)) // (
        self.transformer.config.patch_size**2
    )
    timesteps = (
        np.linspace(self.scheduler.config.num_train_timesteps, 1.0, num_inference_steps)
        if timesteps is None
        else np.array(timesteps)
    )
    timesteps = timesteps.astype(np.int64).astype(np.float32)
    sigmas = timesteps / self.scheduler.config.num_train_timesteps if sigmas is None else sigmas
    mu = calculate_shift(
        image_seq_len,
        self.scheduler.config.get("base_image_seq_len", 256),
        self.scheduler.config.get("base_shift", 0.25),
        self.scheduler.config.get("max_shift", 0.75),
    )
    timesteps, num_inference_steps = retrieve_timesteps(
        self.scheduler, num_inference_steps, timesteps, sigmas, mu=mu
    )
    self._num_timesteps = len(timesteps)

    # Denoising loop
    transformer_dtype = self.transformer.dtype
    num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

    with self.progress_bar(total=num_inference_steps) as progress_bar:
        for i, t in enumerate(timesteps):
            if self.interrupt:
                continue

            latent_model_input = latents.to(transformer_dtype)

            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            timestep = t.broadcast_to((latents.shape[0],))

            noise_pred_cond = self.transformer(
                hidden_states=latent_model_input,
                encoder_hidden_states=prompt_embeds,
                timestep=timestep,
                original_size=original_size,
                target_size=target_size,
                crop_coords=crops_coords_top_left,
                return_dict=False,
            )[0]

            # perform guidance
            if self.do_classifier_free_guidance:
                noise_pred_uncond = self.transformer(
                    hidden_states=latent_model_input,
                    encoder_hidden_states=negative_prompt_embeds,
                    timestep=timestep,
                    original_size=original_size,
                    target_size=target_size,
                    crop_coords=crops_coords_top_left,
                    return_dict=False,
                )[0]

                noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
            else:
                noise_pred = noise_pred_cond

            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]

            # call the callback, if provided
            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, self.scheduler.sigmas[i], 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)

            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                progress_bar.update()

    if not output_type == "latent":
        latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
        image = self.vae.decode(latents, return_dict=False, generator=generator)[0]
    else:
        image = latents

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

    if not return_dict:
        return (image,)

    return CogView4PipelineOutput(images=image)

mindone.diffusers.CogView4Pipeline.encode_prompt(prompt, negative_prompt=None, do_classifier_free_guidance=True, num_images_per_prompt=1, prompt_embeds=None, negative_prompt_embeds=None, dtype=None, max_sequence_length=1024)

Encodes the prompt into text encoder hidden states.

PARAMETER	DESCRIPTION
prompt	prompt to be encoded TYPE: `str` or `List[str]`, *optional*
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
do_classifier_free_guidance	Whether to use classifier free guidance or not. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True
num_images_per_prompt	Number of images that should be generated per prompt. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1
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
dtype	(ms.Type, optional): mindspore dtype TYPE: Optional[Type] DEFAULT: None
max_sequence_length	Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results. TYPE: `int`, defaults to `1024` DEFAULT: 1024

Source code in mindone/diffusers/pipelines/cogview4/pipeline_cogview4.py

def encode_prompt(
    self,
    prompt: Union[str, List[str]],
    negative_prompt: Optional[Union[str, List[str]]] = None,
    do_classifier_free_guidance: bool = True,
    num_images_per_prompt: int = 1,
    prompt_embeds: Optional[ms.Tensor] = None,
    negative_prompt_embeds: Optional[ms.Tensor] = None,
    dtype: Optional[ms.Type] = None,
    max_sequence_length: int = 1024,
):
    r"""
    Encodes the prompt into text encoder hidden states.

    Args:
        prompt (`str` or `List[str]`, *optional*):
            prompt to be encoded
        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`).
        do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
            Whether to use classifier free guidance or not.
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            Number of images that should be generated per prompt.
        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.
        dtype: (`ms.Type`, *optional*):
            mindspore dtype
        max_sequence_length (`int`, defaults to `1024`):
            Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.
    """
    prompt = [prompt] if isinstance(prompt, str) else prompt
    if prompt is not None:
        batch_size = len(prompt)
    else:
        batch_size = prompt_embeds.shape[0]

    if prompt_embeds is None:
        prompt_embeds = self._get_glm_embeds(prompt, max_sequence_length, dtype)

    seq_len = prompt_embeds.shape[1]
    prompt_embeds = prompt_embeds.tile((1, num_images_per_prompt, 1))
    prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

    if do_classifier_free_guidance and negative_prompt_embeds is None:
        negative_prompt = negative_prompt or ""
        negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt

        if prompt is not None and type(prompt) is not type(negative_prompt):
            raise TypeError(
                f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                f" {type(prompt)}."
            )
        elif batch_size != len(negative_prompt):
            raise ValueError(
                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                " the batch size of `prompt`."
            )

        negative_prompt_embeds = self._get_glm_embeds(negative_prompt, max_sequence_length, dtype)

        seq_len = negative_prompt_embeds.shape[1]
        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)

    return prompt_embeds, negative_prompt_embeds

mindone.diffusers.pipelines.cogview4.pipeline_output.CogView4PipelineOutput dataclass

Bases: BaseOutput

Output class for CogView3 pipelines.

Source code in mindone/diffusers/pipelines/cogview4/pipeline_output.py

@dataclass
class CogView4PipelineOutput(BaseOutput):
    """
    Output class for CogView3 pipelines.

    Args:
        images (`List[PIL.Image.Image]` or `np.ndarray`)
            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
    """

    images: Union[List[PIL.Image.Image], np.ndarray]