---
library_name: diffusers
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Script for creating dummy random model:


```python
import torch
from diffusers import HunyuanVideoTransformer3DModel, AutoencoderKLHunyuanVideo, FlowMatchEulerDiscreteScheduler, HunyuanVideoPipeline
from transformers import LlamaModel, LlamaTokenizerFast, CLIPTextModel, CLIPTokenizer, LlamaConfig, CLIPTextConfig

torch.manual_seed(0)
transformer = HunyuanVideoTransformer3DModel(
    in_channels=4,
    out_channels=4,
    num_attention_heads=2,
    attention_head_dim=10,
    num_layers=1,
    num_single_layers=1,
    num_refiner_layers=1,
    patch_size=1,
    patch_size_t=1,
    guidance_embeds=True,
    text_embed_dim=16,
    pooled_projection_dim=8,
    rope_axes_dim=(2, 4, 4),
)

torch.manual_seed(0)
vae = AutoencoderKLHunyuanVideo(
    in_channels=3,
    out_channels=3,
    latent_channels=4,
    down_block_types=(
        "HunyuanVideoDownBlock3D",
        "HunyuanVideoDownBlock3D",
        "HunyuanVideoDownBlock3D",
        "HunyuanVideoDownBlock3D",
    ),
    up_block_types=(
        "HunyuanVideoUpBlock3D",
        "HunyuanVideoUpBlock3D",
        "HunyuanVideoUpBlock3D",
        "HunyuanVideoUpBlock3D",
    ),
    block_out_channels=(8, 8, 8, 8),
    layers_per_block=1,
    act_fn="silu",
    norm_num_groups=4,
    scaling_factor=0.476986,
    spatial_compression_ratio=8,
    temporal_compression_ratio=4,
    mid_block_add_attention=True,
)

torch.manual_seed(0)
scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)

llama_text_encoder_config = LlamaConfig(
    bos_token_id=0,
    eos_token_id=2,
    hidden_size=16,
    intermediate_size=37,
    layer_norm_eps=1e-05,
    num_attention_heads=4,
    num_hidden_layers=2,
    pad_token_id=1,
    vocab_size=1000,
    hidden_act="gelu",
    projection_dim=32,
)
clip_text_encoder_config = CLIPTextConfig(
    bos_token_id=0,
    eos_token_id=2,
    hidden_size=8,
    intermediate_size=37,
    layer_norm_eps=1e-05,
    num_attention_heads=4,
    num_hidden_layers=2,
    pad_token_id=1,
    vocab_size=1000,
    hidden_act="gelu",
    projection_dim=32,
)

text_encoder = LlamaModel(llama_text_encoder_config)
tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")

torch.manual_seed(0)
text_encoder_2 = CLIPTextModel(clip_text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

pipe = HunyuanVideoPipeline(
    transformer=transformer,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    text_encoder_2=text_encoder_2,
    tokenizer_2=tokenizer_2,
    vae=vae,
    scheduler=scheduler,
)

pipe.push_to_hub("hf-internal-testing/tiny-random-hunyuanvideo")
```


## Model Details

### Model Description

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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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