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‎docs/source/en/api/pipelines/cogvideox.md‎

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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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<a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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</a>
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</div>
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</div>
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</hfoption>
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<hfoption id="inference speed">
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Compilation is slow the first time but subsequent calls to the pipeline are faster.
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[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
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The average inference time with torch.compile on a 80GB A100 is 76.27 seconds compared to 96.89 seconds for an uncompiled model.
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- CogVideoX supports LoRAs with [`~loaders.CogVideoXLoraLoaderMixin.load_lora_weights`].
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<details>
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<summary>Show example code</summary>
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```py
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import torch
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from diffusers import CogVideoXPipeline
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export_to_video(video, "output.mp4", fps=16)
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```
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</details>
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- The text-to-video (T2V) checkpoints work best with a resolution of 1360x768 because that was the resolution it was pretrained on.
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- 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.

‎docs/source/en/api/pipelines/hunyuan_video.md‎

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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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<a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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</a>
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</div>
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</div>
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# quantize weights to int4 with bitsandbytes
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pipeline_quant_config = PipelineQuantizationConfig(
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quant_backend="bitsandbytes_4bit",
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quant_kwargs={
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"load_in_4bit": True,
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_compute_dtype": torch.bfloat16
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},
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components_to_quantize=["transformer"]
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quant_backend="bitsandbytes_4bit",
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quant_kwargs={
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"load_in_4bit": True,
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_compute_dtype": torch.bfloat16
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},
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components_to_quantize=["transformer"]
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)
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pipeline = HunyuanVideoPipeline.from_pretrained(
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</hfoption>
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<hfoption id="inference speed">
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Compilation is slow the first time but subsequent calls to the pipeline are faster.
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[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
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```py
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import torch
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# quantize weights to int4 with bitsandbytes
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pipeline_quant_config = PipelineQuantizationConfig(
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quant_backend="bitsandbytes_4bit",
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quant_kwargs={
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"load_in_4bit": True,
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_compute_dtype": torch.bfloat16
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},
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components_to_quantize=["transformer"]
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quant_backend="bitsandbytes_4bit",
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quant_kwargs={
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"load_in_4bit": True,
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_compute_dtype": torch.bfloat16
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},
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components_to_quantize=["transformer"]
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)
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pipeline = HunyuanVideoPipeline.from_pretrained(
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- HunyuanVideo supports LoRAs with [`~loaders.HunyuanVideoLoraLoaderMixin.load_lora_weights`].
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<details>
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<summary>Show example code</summary>
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```py
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import torch
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from diffusers import AutoModel, HunyuanVideoPipeline
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# quantize weights to int4 with bitsandbytes
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pipeline_quant_config = PipelineQuantizationConfig(
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quant_backend="bitsandbytes_4bit",
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quant_kwargs={
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"load_in_4bit": True,
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_compute_dtype": torch.bfloat16
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},
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components_to_quantize=["transformer"]
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quant_backend="bitsandbytes_4bit",
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quant_kwargs={
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"load_in_4bit": True,
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_compute_dtype": torch.bfloat16
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},
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components_to_quantize=["transformer"]
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pipeline = HunyuanVideoPipeline.from_pretrained(
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export_to_video(video, "output.mp4", fps=15)
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```
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</details>
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- Refer to the table below for recommended inference values.
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| parameter | recommended value |

‎docs/source/en/api/pipelines/ltx_video.md‎

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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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<a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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</a>
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</div>
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</div>
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Compilation is slow the first time but subsequent calls to the pipeline are faster.
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[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
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```py
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- LTX-Video supports LoRAs with [`~loaders.LTXVideoLoraLoaderMixin.load_lora_weights`].
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<details>
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<summary>Show example code</summary>
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```py
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</details>
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- LTX-Video supports loading from single files, such as [GGUF checkpoints](../../quantization/gguf), with [`loaders.FromOriginalModelMixin.from_single_file`] or [`loaders.FromSingleFileMixin.from_single_file`].
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<details>
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<summary>Show example code</summary>
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```py
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</details>
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## LTXPipeline
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[[autodoc]] LTXPipeline

‎docs/source/en/api/pipelines/wan.md‎

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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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<a href="https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference" target="_blank" rel="noopener">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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</a>
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Compilation is slow the first time but subsequent calls to the pipeline are faster.
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[Compilation](../../optimization/fp16#torchcompile) is slow the first time but subsequent calls to the pipeline are faster.
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```py
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- Wan2.1 supports LoRAs with [`~loaders.WanLoraLoaderMixin.load_lora_weights`].
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<details>
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<summary>Show example code</summary>
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```py
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</details>
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- [`WanTransformer3DModel`] and [`AutoencoderKLWan`] supports loading from single files with [`~loaders.FromSingleFileMixin.from_single_file`].
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<details>
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<summary>Show example code</summary>
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```py
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</details>
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- Set the [`AutoencoderKLWan`] dtype to `torch.float32` for better decoding quality.
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- The number of frames per second (fps) or `k` should be calculated by `4 * k + 1`.

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