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[docs] Model cards (#11112) 

* initial

* update

* hunyuanvideo

* ltx

* fix

* wan

* gen guide

* feedback

* feedback

* pipeline-level quant config

* feedback

* ltx
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docs/source/en/_toctree.yml
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title: API Reference
title: Hybrid Inference
- sections:
- local: using-diffusers/cogvideox
title: CogVideoX
- local: using-diffusers/consisid
title: ConsisID
- local: using-diffusers/sdxl
......
docs/source/en/api/loaders/lora.md
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## LoraBaseMixin
[[autodoc]] loaders.lora_base.LoraBaseMixin
\ No newline at end of file
[[autodoc]] loaders.lora_base.LoraBaseMixin
## WanLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
\ No newline at end of file
docs/source/en/api/pipelines/cogvideox.md
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# limitations under the License.
-->
# CogVideoX
<div class="flex flex-wrap space-x-1">
<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</a>
</div>
</div>
[CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://huggingface.co/papers/2408.06072) from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang.
The abstract from the paper is:
*We introduce CogVideoX, a large-scale diffusion transformer model designed for generating videos based on text prompts. To efficently model video data, we propose to levearge a 3D Variational Autoencoder (VAE) to compresses videos along both spatial and temporal dimensions. To improve the text-video alignment, we propose an expert transformer with the expert adaptive LayerNorm to facilitate the deep fusion between the two modalities. By employing a progressive training technique, CogVideoX is adept at producing coherent, long-duration videos characterized by significant motion. In addition, we develop an effectively text-video data processing pipeline that includes various data preprocessing strategies and a video captioning method. It significantly helps enhance the performance of CogVideoX, improving both generation quality and semantic alignment. Results show that CogVideoX demonstrates state-of-the-art performance across both multiple machine metrics and human evaluations. The model weight of CogVideoX-2B is publicly available at https://github.com/THUDM/CogVideo.*
# CogVideoX
<Tip>
[CogVideoX](https://huggingface.co/papers/2408.06072) is a large diffusion transformer model - available in 2B and 5B parameters - designed to generate longer and more consistent videos from text. This model uses a 3D causal variational autoencoder to more efficiently process video data by reducing sequence length (and associated training compute) and preventing flickering in generated videos. An "expert" transformer with adaptive LayerNorm improves alignment between text and video, and 3D full attention helps accurately capture motion and time in generated videos.
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
You can find all the original CogVideoX checkpoints under the [CogVideoX](https://huggingface.co/collections/THUDM/cogvideo-66c08e62f1685a3ade464cce) collection.
</Tip>
> [!TIP]
> Click on the CogVideoX models in the right sidebar for more examples of other video generation tasks.
This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
The example below demonstrates how to generate a video optimized for memory or inference speed.
There are three official CogVideoX checkpoints for text-to-video and video-to-video.
<hfoptions id="usage">
<hfoption id="memory">
| checkpoints | recommended inference dtype |
|:---:|:---:|
| [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b) | torch.float16 |
| [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b) | torch.bfloat16 |
| [`THUDM/CogVideoX1.5-5b`](https://huggingface.co/THUDM/CogVideoX1.5-5b) | torch.bfloat16 |
Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
There are two official CogVideoX checkpoints available for image-to-video.
The quantized CogVideoX 5B model below requires ~16GB of VRAM.
| checkpoints | recommended inference dtype |
|:---:|:---:|
| [`THUDM/CogVideoX-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-5b-I2V) | torch.bfloat16 |
| [`THUDM/CogVideoX-1.5-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-1.5-5b-I2V) | torch.bfloat16 |
```py
import torch
from diffusers import CogVideoXPipeline, AutoModel
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers.hooks import apply_group_offloading
from diffusers.utils import export_to_video
For the CogVideoX 1.5 series:
- Text-to-video (T2V) works best at a resolution of 1360x768 because it was trained with that specific resolution.
- Image-to-video (I2V) works for multiple resolutions. The width can vary from 768 to 1360, but the height must be 768. The height/width must be divisible by 16.
- Both T2V and I2V models support generation with 81 and 161 frames and work best at this value. Exporting videos at 16 FPS is recommended.
# quantize weights to int8 with torchao
pipeline_quant_config = PipelineQuantizationConfig(
quant_backend="torchao",
quant_kwargs={"quant_type": "int8wo"},
components_to_quantize=["transformer"]
)
There are two official CogVideoX checkpoints that support pose controllable generation (by the [Alibaba-PAI](https://huggingface.co/alibaba-pai) team).
# fp8 layerwise weight-casting
transformer = AutoModel.from_pretrained(
"THUDM/CogVideoX-5b",
subfolder="transformer",
torch_dtype=torch.bfloat16
)
transformer.enable_layerwise_casting(
storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16
)
| checkpoints | recommended inference dtype |
|:---:|:---:|
| [`alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose) | torch.bfloat16 |
| [`alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose) | torch.bfloat16 |
pipeline = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-5b",
transformer=transformer,
quantization_config=pipeline_quant_config,
torch_dtype=torch.bfloat16
)
pipeline.to("cuda")
# model-offloading
pipeline.enable_model_cpu_offload()
prompt = """
A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea.
The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse.
Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood,
with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.
"""
video = pipeline(
prompt=prompt,
guidance_scale=6,
num_inference_steps=50
).frames[0]
export_to_video(video, "output.mp4", fps=8)
```
## Inference
</hfoption>
<hfoption id="inference speed">
Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
First, load the pipeline:
The average inference time with torch.compile on a 80GB A100 is 76.27 seconds compared to 96.89 seconds for an uncompiled model.
```python
```py
import torch
from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
from diffusers.utils import export_to_video,load_image
pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b").to("cuda") # or "THUDM/CogVideoX-2b"
```
If you are using the image-to-video pipeline, load it as follows:
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video
```python
pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V").to("cuda")
```
pipeline = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-2b",
torch_dtype=torch.float16
).to("cuda")
Then change the memory layout of the pipelines `transformer` component to `torch.channels_last`:
# torch.compile
pipeline.transformer.to(memory_format=torch.channels_last)
pipeline.transformer = torch.compile(
pipeline.transformer, mode="max-autotune", fullgraph=True
)
```python
pipe.transformer.to(memory_format=torch.channels_last)
prompt = """
A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea.
The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse.
Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood,
with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.
"""
video = pipeline(
prompt=prompt,
guidance_scale=6,
num_inference_steps=50
).frames[0]
export_to_video(video, "output.mp4", fps=8)
```
Compile the components and run inference:
</hfoption>
</hfoptions>
```python
pipe.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
## Notes
# CogVideoX works well with long and well-described prompts
prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
```
- CogVideoX supports LoRAs with [`~loaders.CogVideoXLoraLoaderMixin.load_lora_weights`].
The [T2V benchmark](https://gist.github.com/a-r-r-o-w/5183d75e452a368fd17448fcc810bd3f) results on an 80GB A100 machine are:
<details>
<summary>Show example code</summary>
```
Without torch.compile(): Average inference time: 96.89 seconds.
With torch.compile(): Average inference time: 76.27 seconds.
```
```py
import torch
from diffusers import CogVideoXPipeline
from diffusers.hooks import apply_group_offloading
from diffusers.utils import export_to_video
### Memory optimization
pipeline = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-5b",
torch_dtype=torch.bfloat16
)
pipeline.to("cuda")
CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to [this](https://gist.github.com/a-r-r-o-w/3959a03f15be5c9bd1fe545b09dfcc93) script.
# load LoRA weights
pipeline.load_lora_weights("finetrainers/CogVideoX-1.5-crush-smol-v0", adapter_name="crush-lora")
pipeline.set_adapters("crush-lora", 0.9)
- `pipe.enable_model_cpu_offload()`:
- Without enabling cpu offloading, memory usage is `33 GB`
- With enabling cpu offloading, memory usage is `19 GB`
- `pipe.enable_sequential_cpu_offload()`:
- Similar to `enable_model_cpu_offload` but can significantly reduce memory usage at the cost of slow inference
- When enabled, memory usage is under `4 GB`
- `pipe.vae.enable_tiling()`:
- With enabling cpu offloading and tiling, memory usage is `11 GB`
- `pipe.vae.enable_slicing()`
# model-offloading
pipeline.enable_model_cpu_offload()
## Quantization
prompt = """
PIKA_CRUSH A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.
"""
negative_prompt = "inconsistent motion, blurry motion, worse quality, degenerate outputs, deformed outputs"
Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
video = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_frames=81,
height=480,
width=768,
num_inference_steps=50
).frames[0]
export_to_video(video, "output.mp4", fps=16)
```
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`CogVideoXPipeline`] for inference with bitsandbytes.
</details>
```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, CogVideoXTransformer3DModel, CogVideoXPipeline
from diffusers.utils import export_to_video
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel
- The text-to-video (T2V) checkpoints work best with a resolution of 1360x768 because that was the resolution it was pretrained on.
quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
"THUDM/CogVideoX-2b",
subfolder="text_encoder",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
- The image-to-video (I2V) checkpoints work with multiple resolutions. The width can vary from 768 to 1360, but the height must be 758. Both height and width must be divisible by 16.
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = CogVideoXTransformer3DModel.from_pretrained(
"THUDM/CogVideoX-2b",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
- Both T2V and I2V checkpoints work best with 81 and 161 frames. It is recommended to export the generated video at 16fps.
pipeline = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-2b",
text_encoder=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)
prompt = "A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting."
video = pipeline(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
export_to_video(video, "ship.mp4", fps=8)
```
- Refer to the table below to view memory usage when various memory-saving techniques are enabled.
| method | memory usage (enabled) | memory usage (disabled) |
|---|---|---|
| enable_model_cpu_offload | 19GB | 33GB |
| enable_sequential_cpu_offload | <4GB | ~33GB (very slow inference speed) |
| enable_tiling | 11GB (with enable_model_cpu_offload) | --- |
## CogVideoXPipeline
[[autodoc]] CogVideoXPipeline
......
docs/source/en/api/pipelines/hunyuan_video.md
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# See the License for the specific language governing permissions and
# limitations under the License. -->
# HunyuanVideo
<div class="flex flex-wrap space-x-1">
<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</a>
</div>
</div>
[HunyuanVideo](https://www.arxiv.org/abs/2412.03603) by Tencent.
# HunyuanVideo
*Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at [this https URL](https://github.com/tencent/HunyuanVideo).*
[HunyuanVideo](https://huggingface.co/papers/2412.03603) is a 13B parameter diffusion transformer model designed to be competitive with closed-source video foundation models and enable wider community access. This model uses a "dual-stream to single-stream" architecture to separately process the video and text tokens first, before concatenating and feeding them to the transformer to fuse the multimodal information. A pretrained multimodal large language model (MLLM) is used as the encoder because it has better image-text alignment, better image detail description and reasoning, and it can be used as a zero-shot learner if system instructions are added to user prompts. Finally, HunyuanVideo uses a 3D causal variational autoencoder to more efficiently process video data at the original resolution and frame rate.
<Tip>
You can find all the original HunyuanVideo checkpoints under the [Tencent](https://huggingface.co/tencent) organization.
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
> [!TIP]
> Click on the HunyuanVideo models in the right sidebar for more examples of video generation tasks.
>
> The examples below use a checkpoint from [hunyuanvideo-community](https://huggingface.co/hunyuanvideo-community) because the weights are stored in a layout compatible with Diffusers.
</Tip>
The example below demonstrates how to generate a video optimized for memory or inference speed.
Recommendations for inference:
- Both text encoders should be in `torch.float16`.
- Transformer should be in `torch.bfloat16`.
- VAE should be in `torch.float16`.
- `num_frames` should be of the form `4 * k + 1`, for example `49` or `129`.
- For smaller resolution videos, try lower values of `shift` (between `2.0` to `5.0`) in the [Scheduler](https://huggingface.co/docs/diffusers/main/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler.shift). For larger resolution images, try higher values (between `7.0` and `12.0`). The default value is `7.0` for HunyuanVideo.
- For more information about supported resolutions and other details, please refer to the original repository [here](https://github.com/Tencent/HunyuanVideo/).
<hfoptions id="usage">
<hfoption id="memory">
## Available models
Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
The following models are available for the [`HunyuanVideoPipeline`](text-to-video) pipeline:
The quantized HunyuanVideo model below requires ~14GB of VRAM.
| Model name | Description |
|:---|:---|
| [`hunyuanvideo-community/HunyuanVideo`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo) | Official HunyuanVideo (guidance-distilled). Performs best at multiple resolutions and frames. Performs best with `guidance_scale=6.0`, `true_cfg_scale=1.0` and without a negative prompt. |
| [`https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-T2V`](https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-T2V) | Skywork's custom finetune of HunyuanVideo (de-distilled). Performs best with `97x544x960` resolution, `guidance_scale=1.0`, `true_cfg_scale=6.0` and a negative prompt. |
```py
import torch
from diffusers import AutoModel, HunyuanVideoPipeline
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers.utils import export_to_video
The following models are available for the image-to-video pipeline:
# quantize weights to int4 with bitsandbytes
pipeline_quant_config = PipelineQuantizationConfig(
quant_backend="bitsandbytes_4bit",
quant_kwargs={
"load_in_4bit": True,
"bnb_4bit_quant_type": "nf4",
"bnb_4bit_compute_dtype": torch.bfloat16
},
components_to_quantize=["transformer"]
)
| Model name | Description |
|:---|:---|
| [`Skywork/SkyReels-V1-Hunyuan-I2V`](https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V) | Skywork's custom finetune of HunyuanVideo (de-distilled). Performs best with `97x544x960` resolution. Performs best at `97x544x960` resolution, `guidance_scale=1.0`, `true_cfg_scale=6.0` and a negative prompt. |
| [`hunyuanvideo-community/HunyuanVideo-I2V-33ch`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V) | Tecent's official HunyuanVideo 33-channel I2V model. Performs best at resolutions of 480, 720, 960, 1280. A higher `shift` value when initializing the scheduler is recommended (good values are between 7 and 20). |
| [`hunyuanvideo-community/HunyuanVideo-I2V`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V) | Tecent's official HunyuanVideo 16-channel I2V model. Performs best at resolutions of 480, 720, 960, 1280. A higher `shift` value when initializing the scheduler is recommended (good values are between 7 and 20) |
pipeline = HunyuanVideoPipeline.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
quantization_config=pipeline_quant_config,
torch_dtype=torch.bfloat16,
)
## Quantization
# model-offloading and tiling
pipeline.enable_model_cpu_offload()
pipeline.vae.enable_tiling()
Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.
prompt = "A fluffy teddy bear sits on a bed of soft pillows surrounded by children's toys."
video = pipeline(prompt=prompt, num_frames=61, num_inference_steps=30).frames[0]
export_to_video(video, "output.mp4", fps=15)
```
</hfoption>
<hfoption id="inference speed">
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`HunyuanVideoPipeline`] for inference with bitsandbytes.
[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, HunyuanVideoTransformer3DModel, HunyuanVideoPipeline
from diffusers import AutoModel, HunyuanVideoPipeline
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers.utils import export_to_video
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = HunyuanVideoTransformer3DModel.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.bfloat16,
# quantize weights to int4 with bitsandbytes
pipeline_quant_config = PipelineQuantizationConfig(
quant_backend="bitsandbytes_4bit",
quant_kwargs={
"load_in_4bit": True,
"bnb_4bit_quant_type": "nf4",
"bnb_4bit_compute_dtype": torch.bfloat16
},
components_to_quantize=["transformer"]
)
pipeline = HunyuanVideoPipeline.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
quantization_config=pipeline_quant_config,
torch_dtype=torch.bfloat16,
)
prompt = "A cat walks on the grass, realistic style."
# model-offloading and tiling
pipeline.enable_model_cpu_offload()
pipeline.vae.enable_tiling()
# torch.compile
pipeline.transformer.to(memory_format=torch.channels_last)
pipeline.transformer = torch.compile(
pipeline.transformer, mode="max-autotune", fullgraph=True
)
prompt = "A fluffy teddy bear sits on a bed of soft pillows surrounded by children's toys."
video = pipeline(prompt=prompt, num_frames=61, num_inference_steps=30).frames[0]
export_to_video(video, "cat.mp4", fps=15)
export_to_video(video, "output.mp4", fps=15)
```
</hfoption>
</hfoptions>
## Notes
- HunyuanVideo supports LoRAs with [`~loaders.HunyuanVideoLoraLoaderMixin.load_lora_weights`].
<details>
<summary>Show example code</summary>
```py
import torch
from diffusers import AutoModel, HunyuanVideoPipeline
from diffusers.quantizers import PipelineQuantizationConfig
from diffusers.utils import export_to_video
# quantize weights to int4 with bitsandbytes
pipeline_quant_config = PipelineQuantizationConfig(
quant_backend="bitsandbytes_4bit",
quant_kwargs={
"load_in_4bit": True,
"bnb_4bit_quant_type": "nf4",
"bnb_4bit_compute_dtype": torch.bfloat16
},
components_to_quantize=["transformer"]
)
pipeline = HunyuanVideoPipeline.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
quantization_config=pipeline_quant_config,
torch_dtype=torch.bfloat16,
)
# load LoRA weights
pipeline.load_lora_weights("https://huggingface.co/lucataco/hunyuan-steamboat-willie-10", adapter_name="steamboat-willie")
pipeline.set_adapters("steamboat-willie", 0.9)
# model-offloading and tiling
pipeline.enable_model_cpu_offload()
pipeline.vae.enable_tiling()
# use "In the style of SWR" to trigger the LoRA
prompt = """
In the style of SWR. A black and white animated scene featuring a fluffy teddy bear sits on a bed of soft pillows surrounded by children's toys.
"""
video = pipeline(prompt=prompt, num_frames=61, num_inference_steps=30).frames[0]
export_to_video(video, "output.mp4", fps=15)
```
</details>
- Refer to the table below for recommended inference values.
| parameter | recommended value |
|---|---|
| text encoder dtype | `torch.float16` |
| transformer dtype | `torch.bfloat16` |
| vae dtype | `torch.float16` |
| `num_frames (k)` | 4 * `k` + 1 |
- Try lower `shift` values (`2.0` to `5.0`) for lower resolution videos and higher `shift` values (`7.0` to `12.0`) for higher resolution images.
## HunyuanVideoPipeline
[[autodoc]] HunyuanVideoPipeline
......
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# CogVideoX
CogVideoX is a text-to-video generation model focused on creating more coherent videos aligned with a prompt. It achieves this using several methods.
- a 3D variational autoencoder that compresses videos spatially and temporally, improving compression rate and video accuracy.
- an expert transformer block to help align text and video, and a 3D full attention module for capturing and creating spatially and temporally accurate videos.
## Load model checkpoints
Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~DiffusionPipeline.from_pretrained`] method.
```py
from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
pipe = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-2b",
torch_dtype=torch.float16
)
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
"THUDM/CogVideoX-5b-I2V",
torch_dtype=torch.bfloat16
)
```
## Text-to-Video
For text-to-video, pass a text prompt. By default, CogVideoX generates a 720x480 video for the best results.
```py
import torch
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video
prompt = "An elderly gentleman, with a serene expression, sits at the water's edge, a steaming cup of tea by his side. He is engrossed in his artwork, brush in hand, as he renders an oil painting on a canvas that's propped up against a small, weathered table. The sea breeze whispers through his silver hair, gently billowing his loose-fitting white shirt, while the salty air adds an intangible element to his masterpiece in progress. The scene is one of tranquility and inspiration, with the artist's canvas capturing the vibrant hues of the setting sun reflecting off the tranquil sea."
pipe = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-5b",
torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
video = pipe(
prompt=prompt,
num_videos_per_prompt=1,
num_inference_steps=50,
num_frames=49,
guidance_scale=6,
generator=torch.Generator(device="cuda").manual_seed(42),
).frames[0]
export_to_video(video, "output.mp4", fps=8)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_out.gif" alt="generated image of an astronaut in a jungle"/>
</div>
## Image-to-Video
You'll use the [THUDM/CogVideoX-5b-I2V](https://huggingface.co/THUDM/CogVideoX-5b-I2V) checkpoint for this guide.
```py
import torch
from diffusers import CogVideoXImageToVideoPipeline
from diffusers.utils import export_to_video, load_image
prompt = "A vast, shimmering ocean flows gracefully under a twilight sky, its waves undulating in a mesmerizing dance of blues and greens. The surface glints with the last rays of the setting sun, casting golden highlights that ripple across the water. Seagulls soar above, their cries blending with the gentle roar of the waves. The horizon stretches infinitely, where the ocean meets the sky in a seamless blend of hues. Close-ups reveal the intricate patterns of the waves, capturing the fluidity and dynamic beauty of the sea in motion."
image = load_image(image="cogvideox_rocket.png")
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
"THUDM/CogVideoX-5b-I2V",
torch_dtype=torch.bfloat16
)
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()
video = pipe(
prompt=prompt,
image=image,
num_videos_per_prompt=1,
num_inference_steps=50,
num_frames=49,
guidance_scale=6,
generator=torch.Generator(device="cuda").manual_seed(42),
).frames[0]
export_to_video(video, "output.mp4", fps=8)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_rocket.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">initial image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cogvideox/cogvideox_outrocket.gif"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated video</figcaption>
</div>
</div>
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