source stringclasses 273
values | url stringlengths 47 172 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusionxlcontrolnetpagimg2imgpipeline | .md | watermarker will be used.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
- all
- __call__ | 145_14_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagpipeline | .md | StableDiffusion3PAGPipeline
[PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for text-to-image generation
using Stable Diffusion 3.
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatc... | 145_15_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagpipeline | .md | A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/transformers/mod... | 145_15_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagpipeline | .md | specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
as its dimension.
text_encoder_2 ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/tran... | 145_15_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagpipeline | .md | specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
text_encoder_3 ([`T5EncoderModel`]):
Frozen text-encoder. Stable Diffusion 3 uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically th... | 145_15_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagpipeline | .md | [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.c... | 145_15_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagpipeline | .md | tokenizer_3 (`T5TokenizerFast`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
- all
- __call__ | 145_15_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagimg2imgpipeline | .md | StableDiffusion3PAGImg2ImgPipeline
[PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for image-to-image generation
using Stable Diffusion 3.
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`... | 145_16_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagimg2imgpipeline | .md | A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/transformers/mod... | 145_16_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagimg2imgpipeline | .md | specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
as its dimension.
text_encoder_2 ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/tran... | 145_16_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagimg2imgpipeline | .md | specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
text_encoder_3 ([`T5EncoderModel`]):
Frozen text-encoder. Stable Diffusion 3 uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically th... | 145_16_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagimg2imgpipeline | .md | [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.c... | 145_16_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#stablediffusion3pagimg2imgpipeline | .md | tokenizer_3 (`T5TokenizerFast`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
- all
- __call__ | 145_16_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pag.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pag/#pixartsigmapagpipeline | .md | PixArtSigmaPAGPipeline
[PAG pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/pag) for text-to-image generation
using PixArt-Sigma.
- all
- __call__ | 145_17_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/ | .md | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 146_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 146_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#self-attention-guidance | .md | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://huggingface.co/papers/2210.00939) is by Susung Hong et al.
The abstract from the paper is: | 146_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#self-attention-guidance | .md | *Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-conditional diffusion guidance methods, such as classifier and classifier-free guidance. In this paper, we present a more comprehensive pers... | 146_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#self-attention-guidance | .md | this generalized perspective, we introduce novel condition- and training-free strategies to enhance the quality of generated images. As a simple solution, blur guidance improves the suitability of intermediate samples for their fine-scale information and structures, enabling diffusion models to generate higher quality ... | 146_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#self-attention-guidance | .md | Self-Attention Guidance (SAG) uses the intermediate self-attention maps of diffusion models to enhance their stability and efficacy. Specifically, SAG adversarially blurs only the regions that diffusion models attend to at each iteration and guides them accordingly. Our experimental results show that our SAG improves t... | 146_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#self-attention-guidance | .md | You can find additional information about Self-Attention Guidance on the [project page](https://ku-cvlab.github.io/Self-Attention-Guidance), [original codebase](https://github.com/KU-CVLAB/Self-Attention-Guidance), and try it out in a [demo](https://huggingface.co/spaces/susunghong/Self-Attention-Guidance) or [notebook... | 146_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#self-attention-guidance | .md | <Tip>
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 i... | 146_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#stablediffusionsagpipeline | .md | StableDiffusionSAGPipeline
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits ... | 146_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#stablediffusionsagpipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_enco... | 146_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#stablediffusionsagpipeline | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMSch... | 146_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#stablediffusionsagpipeline | .md | [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-... | 146_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#stablediffusionsagpipeline | .md | more details about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
- __call__
- all | 146_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/self_attention_guidance.md | https://huggingface.co/docs/diffusers/en/api/pipelines/self_attention_guidance/#stablediffusionoutput | .md | StableDiffusionPipelineOutput
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
nsfw_content_detected (`List[bool]`)
List indicating whether the c... | 146_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/ | .md | <!--Copyright 2024 Marigold authors and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by ... | 147_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/ | .md | Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 147_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | .md |  | 147_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | .md | Marigold was proposed in [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://huggingface.co/papers/2312.02145), a CVPR 2024 Oral paper by [Bingxin Ke](http://www.kebingxin.com/), [Anton Obukhov](https://www.obukhov.ai/), [Shengyu Huang](https://shengyuh.github.io/), [Nando Metzger](htt... | 147_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | .md | The idea is to repurpose the rich generative prior of Text-to-Image Latent Diffusion Models (LDMs) for traditional computer vision tasks.
Initially, this idea was explored to fine-tune Stable Diffusion for Monocular Depth Estimation, as shown in the teaser above.
Later,
- [Tianfu Wang](https://tianfwang.github.io/) tra... | 147_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | .md | - [Kevin Qu](https://www.linkedin.com/in/kevin-qu-b3417621b/?locale=en_US) extended the approach to Surface Normals Estimation;
- [Anton Obukhov](https://www.obukhov.ai/) contributed the pipelines and documentation into diffusers (enabled and supported by [YiYi Xu](https://yiyixuxu.github.io/) and [Sayak Paul](https://... | 147_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | .md | *Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the... | 147_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | .md | large Transformer architectures. Still, monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout, since their knowledge of the visual world is restricted by the data seen during training, and challenged by zero-shot generalization to new domains. This motivates us to exp... | 147_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | .md | diffusion models can enable better, more generalizable depth estimation. We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic tr... | 147_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigold-pipelines-for-computer-vision-tasks | .md | performance across a wide range of datasets, including over 20% performance gains in specific cases. Project page: https://marigoldmonodepth.github.io.* | 147_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-pipelines | .md | Each pipeline supports one Computer Vision task, which takes an input RGB image as input and produces a *prediction* of the modality of interest, such as a depth map of the input image.
Currently, the following tasks are implemented: | 147_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-pipelines | .md | Currently, the following tasks are implemented:
| Pipeline | Predicted Modalities | ... | 147_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-pipelines | .md | |---------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------... | 147_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-pipelines | .md | | [MarigoldDepthPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py) | [Depth](https://en.wikipedia.org/wiki/Depth_map), [Disparity](https://en.wikipedia.org/wiki/Binocular_disparity) | [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marig... | 147_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-pipelines | .md | | [MarigoldNormalsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_normals.py) | [Surface normals](https://en.wikipedia.org/wiki/Normal_mapping) | [Fast Demo (LCM)](https://h... | 147_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-checkpoints | .md | The original checkpoints can be found under the [PRS-ETH](https://huggingface.co/prs-eth/) Hugging Face organization.
<Tip> | 147_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-checkpoints | .md | 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 mult... | 147_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-checkpoints | .md | </Tip>
<Tip warning={true}>
Marigold pipelines were designed and tested only with `DDIMScheduler` and `LCMScheduler`.
Depending on the scheduler, the number of inference steps required to get reliable predictions varies, and there is no universal value that works best across schedulers.
Because of that, the default... | 147_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#available-checkpoints | .md | Unless set explicitly, its value will be taken from the checkpoint configuration `model_index.json`.
This is done to ensure high-quality predictions when calling the pipeline with just the `image` argument.
</Tip>
See also Marigold [usage examples](marigold_usage). | 147_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthpipeline | .md | MarigoldDepthPipeline
Pipeline for monocular depth estimation using the Marigold method: https://marigoldmonodepth.github.io.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, runnin... | 147_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthpipeline | .md | Conditional U-Net to denoise the depth latent, conditioned on image latent.
vae (`AutoencoderKL`):
Variational Auto-Encoder (VAE) Model to encode and decode images and predictions to and from latent
representations.
scheduler (`DDIMScheduler` or `LCMScheduler`):
A scheduler to be used in combination with `unet` to deno... | 147_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthpipeline | .md | Text-encoder, for empty text embedding.
tokenizer (`CLIPTokenizer`):
CLIP tokenizer.
prediction_type (`str`, *optional*):
Type of predictions made by the model.
scale_invariant (`bool`, *optional*):
A model property specifying whether the predicted depth maps are scale-invariant. This value must be set in
the model con... | 147_4_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthpipeline | .md | "affine-invariant". NB: overriding this value is not supported.
shift_invariant (`bool`, *optional*):
A model property specifying whether the predicted depth maps are shift-invariant. This value must be set in
the model config. When used together with the `scale_invariant=True` flag, the model is also called
"affine-in... | 147_4_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthpipeline | .md | "affine-invariant". NB: overriding this value is not supported.
default_denoising_steps (`int`, *optional*):
The minimum number of denoising diffusion steps that are required to produce a prediction of reasonable
quality with the given model. This value must be set in the model config. When the pipeline is called
witho... | 147_4_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthpipeline | .md | without explicitly setting `num_inference_steps`, the default value is used. This is required to ensure
reasonable results with various model flavors compatible with the pipeline, such as those relying on very
short denoising schedules (`LCMScheduler`) and those with full diffusion schedules (`DDIMScheduler`).
default_... | 147_4_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthpipeline | .md | The recommended value of the `processing_resolution` parameter of the pipeline. This value must be set in
the model config. When the pipeline is called without explicitly setting `processing_resolution`, the
default value is used. This is required to ensure reasonable results with various model flavors trained
with var... | 147_4_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalspipeline | .md | MarigoldNormalsPipeline
Pipeline for monocular normals estimation using the Marigold method: https://marigoldmonodepth.github.io.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, ru... | 147_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalspipeline | .md | Conditional U-Net to denoise the normals latent, conditioned on image latent.
vae (`AutoencoderKL`):
Variational Auto-Encoder (VAE) Model to encode and decode images and predictions to and from latent
representations.
scheduler (`DDIMScheduler` or `LCMScheduler`):
A scheduler to be used in combination with `unet` to de... | 147_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalspipeline | .md | Text-encoder, for empty text embedding.
tokenizer (`CLIPTokenizer`):
CLIP tokenizer.
prediction_type (`str`, *optional*):
Type of predictions made by the model.
use_full_z_range (`bool`, *optional*):
Whether the normals predicted by this model utilize the full range of the Z dimension, or only its positive
half.
defaul... | 147_5_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalspipeline | .md | The minimum number of denoising diffusion steps that are required to produce a prediction of reasonable
quality with the given model. This value must be set in the model config. When the pipeline is called
without explicitly setting `num_inference_steps`, the default value is used. This is required to ensure
reasonable... | 147_5_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalspipeline | .md | reasonable results with various model flavors compatible with the pipeline, such as those relying on very
short denoising schedules (`LCMScheduler`) and those with full diffusion schedules (`DDIMScheduler`).
default_processing_resolution (`int`, *optional*):
The recommended value of the `processing_resolution` paramete... | 147_5_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalspipeline | .md | the model config. When the pipeline is called without explicitly setting `processing_resolution`, the
default value is used. This is required to ensure reasonable results with various model flavors trained
with varying optimal processing resolution values.
- all
- __call__ | 147_5_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthoutput | .md | MarigoldDepthOutput
Output class for Marigold monocular depth prediction pipeline.
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted depth maps with values in the range [0, 1]. The shape is always $numimages imes 1 imes height
imes width$, regardless of whether the images were passed as a 4D array or a lis... | 147_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigolddepthoutput | .md | imes 1 imes height imes width$.
latent (`None`, `torch.Tensor`):
Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
The shape is $numimages * numensemble imes 4 imes latentheight imes latentwidth$. | 147_6_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalsoutput | .md | MarigoldNormalsOutput
Output class for Marigold monocular normals prediction pipeline.
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted normals with values in the range [-1, 1]. The shape is always $numimages imes 3 imes height
imes width$, regardless of whether the images were passed as a 4D array or a l... | 147_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md | https://huggingface.co/docs/diffusers/en/api/pipelines/marigold/#marigoldnormalsoutput | .md | imes 1 imes height imes width$.
latent (`None`, `torch.Tensor`):
Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
The shape is $numimages * numensemble imes 4 imes latentheight imes latentwidth$. | 147_7_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/ | .md | <!--Copyright 2024 The HuggingFace Team. All rights reserved. | 148_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#limitations-under-the-license | .md | --> | 148_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://arxiv.org/abs/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... | 148_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | *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 prop... | 148_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | 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... | 148_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | 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.* | 148_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | <Tip>
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 i... | 148_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | </Tip>
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).
There are three official CogVideoX checkpoints for text-to-video ... | 148_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | |:---:|:---:|
| [`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 |
There are two official CogVideoX check... | 148_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | | 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 |
For the CogVideoX 1.5 series:
- Text-to-video (T2V) works be... | 148_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | - 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 generatio... | 148_2_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideox | .md | There are two official CogVideoX checkpoints that support pose controllable generation (by the [Alibaba-PAI](https://huggingface.co/alibaba-pai) team).
| checkpoints | recommended inference dtype |
|:---:|:---:|
| [`alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose... | 148_2_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#inference | .md | Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
First, load the pipeline:
```python
import torch
from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
from diffusers.utils import export_to_video,load_image
p... | 148_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#inference | .md | ```
If you are using the image-to-video pipeline, load it as follows:
```python
pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V").to("cuda")
```
Then change the memory layout of the pipelines `transformer` component to `torch.channels_last`:
```python
pipe.transformer.to(memory_form... | 148_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#inference | .md | # CogVideoX works well with long and well-described prompts | 148_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#inference | .md | 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 ta... | 148_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#inference | .md | 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." | 148_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#inference | .md | video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
```
The [T2V benchmark](https://gist.github.com/a-r-r-o-w/5183d75e452a368fd17448fcc810bd3f) results on an 80GB A100 machine are:
```
Without torch.compile(): Average inference time: 96.89 seconds.
With torch.compile(): Average inference... | 148_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#memory-optimization | .md | 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 r... | 148_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#memory-optimization | .md | - `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... | 148_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | .md | 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. | 148_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | .md | 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.
```py
import torch
from ... | 148_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | .md | from diffusers.utils import export_to_video
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel | 148_5_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | .md | 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,
)
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = CogVideoXTransformer3D... | 148_5_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | .md | pipeline = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-2b",
text_encoder=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
) | 148_5_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | .md | 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. Surroun... | 148_5_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | .md | 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." | 148_5_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#quantization | .md | video = pipeline(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
export_to_video(video, "ship.mp4", fps=8)
``` | 148_5_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxpipeline | .md | CogVideoXPipeline
Pipeline for text-to-video generation using CogVideoX.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`Autoen... | 148_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxpipeline | .md | text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. CogVideoX uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
[t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
tokenizer (`T5Tokenizer`):
Tokenizer of class
[T... | 148_6_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxpipeline | .md | transformer ([`CogVideoXTransformer3DModel`]):
A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
- all
- __call__ | 148_6_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoximagetovideopipeline | .md | CogVideoXImageToVideoPipeline
Pipeline for image-to-video generation using CogVideoX.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
... | 148_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoximagetovideopipeline | .md | text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. CogVideoX uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
[t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
tokenizer (`T5Tokenizer`):
Tokenizer of class
[T... | 148_7_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoximagetovideopipeline | .md | transformer ([`CogVideoXTransformer3DModel`]):
A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
- all
- __call__ | 148_7_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxvideotovideopipeline | .md | CogVideoXVideoToVideoPipeline
Pipeline for video-to-video generation using CogVideoX.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
... | 148_8_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md | https://huggingface.co/docs/diffusers/en/api/pipelines/cogvideox/#cogvideoxvideotovideopipeline | .md | text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. CogVideoX uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
[t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
tokenizer (`T5Tokenizer`):
Tokenizer of class
[T... | 148_8_1 |
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