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HiDreamImage

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HiDreamImage

HiDream-I1 by HiDream.ai

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.

Available models

The following models are available for the HiDreamImagePipeline pipeline:

Model name Description
HiDream-ai/HiDream-I1-Full -
HiDream-ai/HiDream-I1-Dev -
HiDream-ai/HiDream-I1-Fast -

HiDreamImagePipeline

class diffusers.HiDreamImagePipeline

< >

( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer text_encoder_2: CLIPTextModelWithProjection tokenizer_2: CLIPTokenizer text_encoder_3: T5EncoderModel tokenizer_3: T5Tokenizer text_encoder_4: LlamaForCausalLM tokenizer_4: PreTrainedTokenizerFast transformer: HiDreamImageTransformer2DModel )

__call__

< >

( prompt: typing.Union[str, typing.List[str]] = None prompt_2: typing.Union[str, typing.List[str], NoneType] = None prompt_3: typing.Union[str, typing.List[str], NoneType] = None prompt_4: typing.Union[str, typing.List[str], NoneType] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 5.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_2: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_3: typing.Union[str, typing.List[str], NoneType] = None negative_prompt_4: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds_t5: typing.Optional[torch.FloatTensor] = None prompt_embeds_llama3: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds_t5: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds_llama3: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 128 **kwargs ) β†’ ~pipelines.hidream_image.HiDreamImagePipelineOutput or tuple

Parameters

  • prompt (str or List[str], optional) β€” The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. instead.
  • prompt_2 (str or List[str], optional) β€” The prompt or prompts to be sent to tokenizer_2 and text_encoder_2. If not defined, prompt is will be used instead.
  • prompt_3 (str or List[str], optional) β€” The prompt or prompts to be sent to tokenizer_3 and text_encoder_3. If not defined, prompt is will be used instead.
  • prompt_4 (str or List[str], optional) β€” The prompt or prompts to be sent to tokenizer_4 and text_encoder_4. If not defined, prompt is will be used instead.
  • height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€” The height in pixels of the generated image. This is set to 1024 by default for the best results.
  • width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€” The width in pixels of the generated image. This is set to 1024 by default for the best results.
  • num_inference_steps (int, optional, defaults to 50) β€” The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
  • sigmas (List[float], optional) β€” 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 3.5) β€” 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.
  • negative_prompt (str or List[str], optional) β€” 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 true_cfg_scale is not greater than 1).
  • negative_prompt_2 (str or List[str], optional) β€” The prompt or prompts not to guide the image generation to be sent to tokenizer_2 and text_encoder_2. If not defined, negative_prompt is used in all the text-encoders.
  • negative_prompt_3 (str or List[str], optional) β€” The prompt or prompts not to guide the image generation to be sent to tokenizer_3 and text_encoder_3. If not defined, negative_prompt is used in all the text-encoders.
  • negative_prompt_4 (str or List[str], optional) β€” The prompt or prompts not to guide the image generation to be sent to tokenizer_4 and text_encoder_4. If not defined, negative_prompt is used in all the text-encoders.
  • num_images_per_prompt (int, optional, defaults to 1) β€” The number of images to generate per prompt.
  • generator (torch.Generator or List[torch.Generator], optional) β€” One or a list of torch generator(s) to make generation deterministic.
  • latents (torch.FloatTensor, optional) β€” 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 (torch.FloatTensor, optional) β€” 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.FloatTensor, optional) β€” 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.
  • pooled_prompt_embeds (torch.FloatTensor, optional) β€” Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled text embeddings will be generated from prompt input argument.
  • negative_pooled_prompt_embeds (torch.FloatTensor, optional) β€” Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_prompt input argument.
  • output_type (str, optional, defaults to "pil") β€” The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array.
  • return_dict (bool, optional, defaults to True) β€” Whether or not to return a ~pipelines.flux.FluxPipelineOutput instead of a plain tuple.
  • attention_kwargs (dict, optional) β€” A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.
  • callback_on_step_end (Callable, optional) β€” 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) β€” 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 128) β€” Maximum sequence length to use with the prompt.

Returns

~pipelines.hidream_image.HiDreamImagePipelineOutput or tuple

~pipelines.hidream_image.HiDreamImagePipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated. images.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from transformers import PreTrainedTokenizerFast, LlamaForCausalLM
>>> from diffusers import UniPCMultistepScheduler, HiDreamImagePipeline


>>> tokenizer_4 = PreTrainedTokenizerFast.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
>>> text_encoder_4 = LlamaForCausalLM.from_pretrained(
...     "meta-llama/Meta-Llama-3.1-8B-Instruct",
...     output_hidden_states=True,
...     output_attentions=True,
...     torch_dtype=torch.bfloat16,
... )

>>> pipe = HiDreamImagePipeline.from_pretrained(
...     "HiDream-ai/HiDream-I1-Full",
...     tokenizer_4=tokenizer_4,
...     text_encoder_4=text_encoder_4,
...     torch_dtype=torch.bfloat16,
... )
>>> pipe.enable_model_cpu_offload()

>>> image = pipe(
...     'A cat holding a sign that says "Hi-Dreams.ai".',
...     height=1024,
...     width=1024,
...     guidance_scale=5.0,
...     num_inference_steps=50,
...     generator=torch.Generator("cuda").manual_seed(0),
... ).images[0]
>>> image.save("output.png")

disable_vae_slicing

< >

( )

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

disable_vae_tiling

< >

( )

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

enable_vae_slicing

< >

( )

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

enable_vae_tiling

< >

( )

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.

HiDreamImagePipelineOutput

class diffusers.pipelines.hidream_image.pipeline_output.HiDreamImagePipelineOutput

< >

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] )

Parameters

  • images (List[PIL.Image.Image] or np.ndarray) β€” 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.

Output class for HiDreamImage pipelines.

< > Update on GitHub

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