# HiDreamImageTransformer2DModel

A Transformer model for image-like data from [HiDream-I1](https://huggingface.co/HiDream-ai).

The model can be loaded with the following code snippet.

```python
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`

```python
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]]

- **hidden_states** (`torch.Tensor` of shape `(batch_size, in_channels, height, width)` or `(batch_size, patch_height * patch_width, patch_size * patch_size * channels)`) --
  Input `hidden_states`.
- **timesteps** (`torch.LongTensor`) --
  Used to indicate denoising step.
- **encoder_hidden_states_t5** (`torch.Tensor`) --
  Conditional embeddings computed from the T5 text encoder.
- **encoder_hidden_states_llama3** (`torch.Tensor`) --
  Conditional embeddings computed from the Llama3 text encoder.
- **pooled_embeds** (`torch.Tensor`) --
  Pooled text embeddings used for additional conditioning.
- **img_ids** (`torch.Tensor`, *optional*) --
  Image position ids for the patched hidden states.
- **img_sizes** (`list` of `tuple` of `int`, *optional*) --
  Per-sample patch grid sizes used to unpatchify the output.
- **hidden_states_masks** (`torch.Tensor`, *optional*) --
  Mask over patched `hidden_states`.
- **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).
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain
  tuple.If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

The [HiDreamImageTransformer2DModel](/docs/diffusers/main/en/api/models/hidream_image_transformer#diffusers.HiDreamImageTransformer2DModel) forward method.

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

- **sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel) is discrete) --
  The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
  distributions for the unnoised latent pixels.

The output of [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).