diffusers/pr_11739/en/api/models/hidream_image_transformer.md

HiDreamImageTransformer2DModel

A Transformer model for image-like data from HiDream-I1.

The model can be loaded with the following code snippet.

from diffusers import HiDreamImageTransformer2DModel

transformer = HiDreamImageTransformer2DModel.from_pretrained("HiDream-ai/HiDream-I1-Full", subfolder="transformer", torch_dtype=torch.bfloat16)

Loading GGUF quantized checkpoints for HiDream-I1

GGUF checkpoints for the HiDreamImageTransformer2DModel can be loaded using ~FromOriginalModelMixin.from_single_file

import torch
from diffusers import GGUFQuantizationConfig, HiDreamImageTransformer2DModel

ckpt_path = "https://huggingface.co/city96/HiDream-I1-Dev-gguf/blob/main/hidream-i1-dev-Q2_K.gguf"
transformer = HiDreamImageTransformer2DModel.from_single_file(
    ckpt_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16
)

HiDreamImageTransformer2DModel[[diffusers.HiDreamImageTransformer2DModel]]

diffusers.HiDreamImageTransformer2DModel[[diffusers.HiDreamImageTransformer2DModel]]

Source

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

diffusers.models.modeling_outputs.Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

Source

The output of Transformer2DModel.

Parameters:

sample (torch.Tensor of shape (batch_size, num_channels, height, width) or (batch size, num_vector_embeds - 1, num_latent_pixels) if Transformer2DModel is discrete) : The hidden states output conditioned on the encoder_hidden_states input. If discrete, returns probability distributions for the unnoised latent pixels.