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ControlNet with Hunyuan-DiT

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ControlNet with Hunyuan-DiT

HunyuanDiTControlNetPipeline is an implementation of ControlNet for Hunyuan-DiT.

ControlNet was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.

With a ControlNet model, you can provide an additional control image to condition and control Hunyuan-DiT generation. For example, if you provide a depth map, the ControlNet model generates an image that’ll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.

The abstract from the paper is:

We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with “zero convolutions” (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.

This code is implemented by Tencent Hunyuan Team. You can find pre-trained checkpoints for Hunyuan-DiT ControlNets on Tencent Hunyuan.

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.

HunyuanDiTControlNetPipeline

class diffusers.HunyuanDiTControlNetPipeline

< >

( vae: AutoencoderKL text_encoder: BertModel tokenizer: BertTokenizer transformer: HunyuanDiT2DModel scheduler: DDPMScheduler safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor controlnet: typing.Union[diffusers.models.controlnets.controlnet_hunyuan.HunyuanDiT2DControlNetModel, typing.List[diffusers.models.controlnets.controlnet_hunyuan.HunyuanDiT2DControlNetModel], typing.Tuple[diffusers.models.controlnets.controlnet_hunyuan.HunyuanDiT2DControlNetModel], diffusers.models.controlnets.controlnet_hunyuan.HunyuanDiT2DMultiControlNetModel] text_encoder_2 = <class 'transformers.models.t5.modeling_t5.T5EncoderModel'> tokenizer_2 = <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'> requires_safety_checker: bool = True )

Parameters

  • vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. We use sdxl-vae-fp16-fix.
  • text_encoder (Optional[~transformers.BertModel, ~transformers.CLIPTextModel]) — Frozen text-encoder (clip-vit-large-patch14). HunyuanDiT uses a fine-tuned [bilingual CLIP].
  • tokenizer (Optional[~transformers.BertTokenizer, ~transformers.CLIPTokenizer]) — A BertTokenizer or CLIPTokenizer to tokenize text.
  • transformer (HunyuanDiT2DModel) — The HunyuanDiT model designed by Tencent Hunyuan.
  • text_encoder_2 (T5EncoderModel) — The mT5 embedder. Specifically, it is ‘t5-v1_1-xxl’.
  • tokenizer_2 (MT5Tokenizer) — The tokenizer for the mT5 embedder.
  • scheduler (DDPMScheduler) — A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
  • controlnet (HunyuanDiT2DControlNetModel or List[HunyuanDiT2DControlNetModel] or HunyuanDiT2DControlNetModel) — Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets as a list, the outputs from each ControlNet are added together to create one combined additional conditioning.

Pipeline for English/Chinese-to-image generation using HunyuanDiT.

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.)

HunyuanDiT uses two text encoders: mT5 and [bilingual CLIP](fine-tuned by ourselves)

__call__

< >

( prompt: typing.Union[str, typing.List[str]] = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: typing.Optional[int] = 50 guidance_scale: typing.Optional[float] = 5.0 control_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None controlnet_conditioning_scale: typing.Union[float, typing.List[float]] = 1.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: typing.Optional[float] = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None prompt_embeds_2: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds_2: typing.Optional[torch.Tensor] = None prompt_attention_mask: typing.Optional[torch.Tensor] = None prompt_attention_mask_2: typing.Optional[torch.Tensor] = None negative_prompt_attention_mask: typing.Optional[torch.Tensor] = None negative_prompt_attention_mask_2: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback_on_step_end: typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] guidance_rescale: float = 0.0 original_size: typing.Optional[typing.Tuple[int, int]] = (1024, 1024) target_size: typing.Optional[typing.Tuple[int, int]] = None crops_coords_top_left: typing.Tuple[int, int] = (0, 0) use_resolution_binning: bool = True ) → StableDiffusionPipelineOutput or tuple

Parameters

  • prompt (str or List[str], optional) — The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds.
  • height (int) — The height in pixels of the generated image.
  • width (int) — The width in pixels of the generated image.
  • num_inference_steps (int, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter is modulated by strength.
  • guidance_scale (float, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1.
  • control_guidance_start (float or List[float], optional, defaults to 0.0) — The percentage of total steps at which the ControlNet starts applying.
  • control_guidance_end (float or List[float], optional, defaults to 1.0) — The percentage of total steps at which the ControlNet stops applying.
  • control_image (torch.Tensor, PIL.Image.Image, np.ndarray, List[torch.Tensor], List[PIL.Image.Image], List[np.ndarray], — List[List[torch.Tensor]], List[List[np.ndarray]] or List[List[PIL.Image.Image]]): The ControlNet input condition to provide guidance to the unet for generation. If the type is specified as torch.Tensor, it is passed to ControlNet as is. PIL.Image.Image can also be accepted as an image. The dimensions of the output image defaults to image’s dimensions. If height and/or width are passed, image is resized accordingly. If multiple ControlNets are specified in init, images must be passed as a list such that each element of the list can be correctly batched for input to a single ControlNet.
  • controlnet_conditioning_scale (float or List[float], optional, defaults to 1.0) — The outputs of the ControlNet are multiplied by controlnet_conditioning_scale before they are added to the residual in the original unet. If multiple ControlNets are specified in init, you can set the corresponding scale as a list.
  • negative_prompt (str or List[str], optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1).
  • num_images_per_prompt (int, optional, defaults to 1) — The number of images to generate per prompt.
  • eta (float, optional, defaults to 0.0) — Corresponds to parameter eta (η) from the DDIM paper. Only applies to the DDIMScheduler, and is ignored in other schedulers.
  • generator (torch.Generator or List[torch.Generator], optional) — A torch.Generator to make generation deterministic.
  • prompt_embeds (torch.Tensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument.
  • prompt_embeds_2 (torch.Tensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument.
  • negative_prompt_embeds (torch.Tensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument.
  • negative_prompt_embeds_2 (torch.Tensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument.
  • prompt_attention_mask (torch.Tensor, optional) — Attention mask for the prompt. Required when prompt_embeds is passed directly.
  • prompt_attention_mask_2 (torch.Tensor, optional) — Attention mask for the prompt. Required when prompt_embeds_2 is passed directly.
  • negative_prompt_attention_mask (torch.Tensor, optional) — Attention mask for the negative prompt. Required when negative_prompt_embeds is passed directly.
  • negative_prompt_attention_mask_2 (torch.Tensor, optional) — Attention mask for the negative prompt. Required when negative_prompt_embeds_2 is passed directly.
  • output_type (str, optional, defaults to "pil") — The output format of the generated image. Choose between PIL.Image or np.array.
  • return_dict (bool, optional, defaults to True) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
  • callback_on_step_end (Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks, optional) — A callback function or a list of callback functions to be called at the end of each denoising step.
  • callback_on_step_end_tensor_inputs (List[str], optional) — A list of tensor inputs that should be passed to the callback function. If not defined, all tensor inputs will be passed.
  • guidance_rescale (float, optional, defaults to 0.0) — Rescale the noise_cfg according to guidance_rescale. Based on findings of Common Diffusion Noise Schedules and Sample Steps are Flawed. See Section 3.4
  • original_size (Tuple[int, int], optional, defaults to (1024, 1024)) — The original size of the image. Used to calculate the time ids.
  • target_size (Tuple[int, int], optional) — The target size of the image. Used to calculate the time ids.
  • crops_coords_top_left (Tuple[int, int], optional, defaults to (0, 0)) — The top left coordinates of the crop. Used to calculate the time ids.
  • use_resolution_binning (bool, optional, defaults to True) — Whether to use resolution binning or not. If True, the input resolution will be mapped to the closest standard resolution. Supported resolutions are 1024x1024, 1280x1280, 1024x768, 1152x864, 1280x960, 768x1024, 864x1152, 960x1280, 1280x768, and 768x1280. It is recommended to set this to True.

Returns

StableDiffusionPipelineOutput or tuple

If return_dict is True, StableDiffusionPipelineOutput is returned, otherwise a tuple is returned where the first element is a list with the generated images and the second element is a list of bools indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content.

The call function to the pipeline for generation with HunyuanDiT.

Examples:

from diffusers import HunyuanDiT2DControlNetModel, HunyuanDiTControlNetPipeline
import torch

controlnet = HunyuanDiT2DControlNetModel.from_pretrained(
    "Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Canny", torch_dtype=torch.float16
)

pipe = HunyuanDiTControlNetPipeline.from_pretrained(
    "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.to("cuda")

from diffusers.utils import load_image

cond_image = load_image(
    "https://huggingface.co./Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Canny/resolve/main/canny.jpg?download=true"
)

## You may also use English prompt as HunyuanDiT supports both English and Chinese
prompt = "在夜晚的酒店门前，一座古老的中国风格的狮子雕像矗立着，它的眼睛闪烁着光芒，仿佛在守护着这座建筑。背景是夜晚的酒店前，构图方式是特写，平视，居中构图。这张照片呈现了真实摄影风格，蕴含了中国雕塑文化，同时展现了神秘氛围"
# prompt="At night, an ancient Chinese-style lion statue stands in front of the hotel, its eyes gleaming as if guarding the building. The background is the hotel entrance at night, with a close-up, eye-level, and centered composition. This photo presents a realistic photographic style, embodies Chinese sculpture culture, and reveals a mysterious atmosphere."
image = pipe(
    prompt,
    height=1024,
    width=1024,
    control_image=cond_image,
    num_inference_steps=50,
).images[0]

encode_prompt

< >

( prompt: str device: device = None dtype: dtype = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: typing.Optional[str] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None prompt_attention_mask: typing.Optional[torch.Tensor] = None negative_prompt_attention_mask: typing.Optional[torch.Tensor] = None max_sequence_length: typing.Optional[int] = None text_encoder_index: int = 0 )

Parameters

  • prompt (str or List[str], optional) — prompt to be encoded
  • device — (torch.device): torch device
  • dtype (torch.dtype) — torch dtype
  • num_images_per_prompt (int) — number of images that should be generated per prompt
  • do_classifier_free_guidance (bool) — whether to use classifier free guidance or not
  • negative_prompt (str or List[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
  • prompt_embeds (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.
  • prompt_attention_mask (torch.Tensor, optional) — Attention mask for the prompt. Required when prompt_embeds is passed directly.
  • negative_prompt_attention_mask (torch.Tensor, optional) — Attention mask for the negative prompt. Required when negative_prompt_embeds is passed directly.
  • max_sequence_length (int, optional) — maximum sequence length to use for the prompt.
  • text_encoder_index (int, optional) — Index of the text encoder to use. 0 for clip and 1 for T5.

Encodes the prompt into text encoder hidden states.

< > Update on GitHub

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