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/audioldm2.md | https://huggingface.co/docs/diffusers/en/api/pipelines/audioldm2/#audiopipelineoutput | .md | AudioPipelineOutput
Output class for audio pipelines.
Args:
audios (`np.ndarray`)
List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`. | 125_9_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/ | .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... | 126_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/ | .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.
--> | 126_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md |  | 126_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by [the Kuaishou Kolors team](https://github.com/Kwai-Kolors/Kolors). Trained on billions of text-image pairs, Kolors exhibits significant advantages over both open-source and closed-source models in visual quality, complex sema... | 126_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | characters. Furthermore, Kolors supports both Chinese and English inputs, demonstrating strong performance in understanding and generating Chinese-specific content. For more details, please refer to this [technical report](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf). | 126_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | The abstract from the technical report is: | 126_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | *We present Kolors, a latent diffusion model for text-to-image synthesis, characterized by its profound understanding of both English and Chinese, as well as an impressive degree of photorealism. There are three key insights contributing to the development of Kolors. Firstly, unlike large language model T5 used in Imag... | 126_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | Model (GLM), which enhances its comprehension capabilities in both English and Chinese. Moreover, we employ a multimodal large language model to recaption the extensive training dataset for fine-grained text understanding. These strategies significantly improve Kolors’ ability to comprehend intricate semantics, particu... | 126_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | text rendering capabilities. Secondly, we divide the training of Kolors into two phases: the concept learning phase with broad knowledge and the quality improvement phase with specifically curated high-aesthetic data. Furthermore, we investigate the critical role of the noise schedule and introduce a novel schedule to ... | 126_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | enhance the visual appeal of the generated high-resolution images. Lastly, we propose a category-balanced benchmark KolorsPrompts, which serves as a guide for the training and evaluation of Kolors. Consequently, even when employing the commonly used U-Net backbone, Kolors has demonstrated remarkable performance in huma... | 126_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolors-effective-training-of-diffusion-model-for-photorealistic-text-to-image-synthesis | .md | achieving Midjourney-v6 level performance, especially in terms of visual appeal. We will release the code and weights of Kolors at <https://github.com/Kwai-Kolors/Kolors>, and hope that it will benefit future research and applications in the visual generation community.* | 126_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#usage-example | .md | ```python
import torch
from diffusers import DPMSolverMultistepScheduler, KolorsPipeline
pipe = KolorsPipeline.from_pretrained("Kwai-Kolors/Kolors-diffusers", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=Tr... | 126_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#ip-adapter | .md | Kolors needs a different IP Adapter to work, and it uses [Openai-CLIP-336](https://huggingface.co/openai/clip-vit-large-patch14-336) as an image encoder.
<Tip>
Using an IP Adapter with Kolors requires more than 24GB of VRAM. To use it, we recommend using [`~DiffusionPipeline.enable_model_cpu_offload`] on consumer G... | 126_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#ip-adapter | .md | </Tip>
<Tip>
While Kolors is integrated in Diffusers, you need to load the image encoder from a revision to use the safetensor files. You can still use the main branch of the original repository if you're comfortable loading pickle checkpoints.
</Tip>
```python
import torch
from transformers import CLIPVisionMo... | 126_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#ip-adapter | .md | from diffusers import DPMSolverMultistepScheduler, KolorsPipeline
from diffusers.utils import load_image
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"Kwai-Kolors/Kolors-IP-Adapter-Plus",
subfolder="image_encoder",
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
revision="refs/pr/4",
) | 126_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#ip-adapter | .md | pipe = KolorsPipeline.from_pretrained(
"Kwai-Kolors/Kolors-diffusers", image_encoder=image_encoder, torch_dtype=torch.float16, variant="fp16"
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
pipe.load_ip_adapter(
"Kwai-Kolors/Kolors-IP-Adapter-Plus",
subfolder="... | 126_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#ip-adapter | .md | ipa_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/kolors/cat_square.png")
image = pipe(
prompt="best quality, high quality",
negative_prompt="",
guidance_scale=6.5,
num_inference_steps=25,
ip_adapter_image=ipa_image,
).images[0]
image.save("kolors_ipa_sample.png")
`... | 126_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolorspipeline | .md | KolorsPipeline
- all
- __call__ | 126_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kolors.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kolors/#kolorsimg2imgpipeline | .md | KolorsImg2ImgPipeline
- all
- __call__ | 126_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/ | .md | <!-- Copyright 2024 The HuggingFace Team. All rights reserved. | 127_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltx-video | .md | [LTX Video](https://huggingface.co/Lightricks/LTX-Video) is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-r... | 127_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltx-video | .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... | 127_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltx-video | .md | </Tip>
Available models:
| Model name | Recommended dtype |
|:-------------:|:-----------------:|
| [`LTX Video 0.9.0`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.safetensors) | `torch.bfloat16` |
| [`LTX Video 0.9.1`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2... | 127_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltx-video | .md | Note: The recommended dtype is for the transformer component. The VAE and text encoders can be either `torch.float32`, `torch.bfloat16` or `torch.float16` but the recommended dtype is `torch.bfloat16` as used in the original repository. | 127_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | Loading the original LTX Video checkpoints is also possible with [`~ModelMixin.from_single_file`]. We recommend using `from_single_file` for the Lightricks series of models, as they plan to release multiple models in the future in the single file format.
```python
import torch
from diffusers import AutoencoderKLLTXVi... | 127_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | # `single_file_url` could also be https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.1.safetensors
single_file_url = "https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors"
transformer = LTXVideoTransformer3DModel.from_single_file(
single_file_url, torch_dtype=torch.bfloat16
)
vae = Autoe... | 127_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | pipe = LTXImageToVideoPipeline.from_pretrained(
"Lightricks/LTX-Video", transformer=transformer, vae=vae, torch_dtype=torch.bfloat16
) | 127_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | # ... inference code ...
```
Alternatively, the pipeline can be used to load the weights with [`~FromSingleFileMixin.from_single_file`].
```python
import torch
from diffusers import LTXImageToVideoPipeline
from transformers import T5EncoderModel, T5Tokenizer | 127_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | single_file_url = "https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors"
text_encoder = T5EncoderModel.from_pretrained(
"Lightricks/LTX-Video", subfolder="text_encoder", torch_dtype=torch.bfloat16
)
tokenizer = T5Tokenizer.from_pretrained(
"Lightricks/LTX-Video", subfolder="tokenizer", torch_dtype=... | 127_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | single_file_url, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=torch.bfloat16
)
```
Loading [LTX GGUF checkpoints](https://huggingface.co/city96/LTX-Video-gguf) are also supported:
```py
import torch
from diffusers.utils import export_to_video
from diffusers import LTXPipeline, LTXVideoTransformer3DMo... | 127_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | ckpt_path = (
"https://huggingface.co/city96/LTX-Video-gguf/blob/main/ltx-video-2b-v0.9-Q3_K_S.gguf"
)
transformer = LTXVideoTransformer3DModel.from_single_file(
ckpt_path,
quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
torch_dtype=torch.bfloat16,
)
pipe = LTXPipeline.from_pretrained(
"Lightr... | 127_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, l... | 127_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | video = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=704,
height=480,
num_frames=161,
num_inference_steps=50,
).frames[0]
export_to_video(video, "output_gguf_ltx.mp4", fps=24)
```
Make sure to read the [documentation on GGUF](../../quantization/gguf) to learn more about our GGUF support.
<!-- TODO(ar... | 127_2_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | <!-- TODO(aryan): Update this when official weights are supported -->
Loading and running inference with [LTX Video 0.9.1](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors) weights.
```python
import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_vi... | 127_2_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | pipe = LTXPipeline.from_pretrained("a-r-r-o-w/LTX-Video-0.9.1-diffusers", torch_dtype=torch.bfloat16)
pipe.to("cuda") | 127_2_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, l... | 127_2_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#loading-single-files | .md | video = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=768,
height=512,
num_frames=161,
decode_timestep=0.03,
decode_noise_scale=0.025,
num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)
```
Refer to [this section](https://huggingface.co/docs/diffusers/main/en/api/pipelines/... | 127_2_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#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.
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported q... | 127_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#quantization | .md | ```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, LTXVideoTransformer3DModel, LTXPipeline
from diffusers.utils import export_to_video
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel | 127_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#quantization | .md | quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
"Lightricks/LTX-Video",
subfolder="text_encoder",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = LTXVideoTransformer3... | 127_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#quantization | .md | pipeline = LTXPipeline.from_pretrained(
"Lightricks/LTX-Video",
text_encoder=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
) | 127_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#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... | 127_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#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." | 127_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#quantization | .md | video = pipeline(prompt=prompt, num_frames=161, num_inference_steps=50).frames[0]
export_to_video(video, "ship.mp4", fps=24)
``` | 127_3_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltxpipeline | .md | LTXPipeline
Pipeline for text-to-video generation.
Reference: https://github.com/Lightricks/LTX-Video
Args:
transformer ([`LTXVideoTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combinatio... | 127_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltxpipeline | .md | Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
to... | 127_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltxpipeline | .md | tokenizer (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
- all
- __call__ | 127_4_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltximagetovideopipeline | .md | LTXImageToVideoPipeline
Pipeline for image-to-video generation.
Reference: https://github.com/Lightricks/LTX-Video
Args:
transformer ([`LTXVideoTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used ... | 127_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltximagetovideopipeline | .md | A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKLLTXVideo`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`T5EncoderModel`]):
[T5](https://huggingface.co/docs/transformers/en/model_d... | 127_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltximagetovideopipeline | .md | the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingf... | 127_5_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/ltx_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/ltx_video/#ltxpipelineoutput | .md | LTXPipelineOutput
Output class for LTX pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or To... | 127_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/ | .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... | 128_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/ | .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.
--> | 128_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | MusicLDM was proposed in [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://huggingface.co/papers/2308.01546) by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
MusicLDM takes a text prompt as input and predicts the corres... | 128_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | MusicLDM takes a text prompt as input and predicts the corresponding music sample.
Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview) and [AudioLDM](https://huggingface.co/docs/diffusers/api/pipelines/audioldm),
MusicLDM is a text-to-music _latent diffusion m... | 128_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | latents.
MusicLDM is trained on a corpus of 466 hours of music data. Beat-synchronous data augmentation strategies are applied to the music samples, both in the time domain and in the latent space. Using beat-synchronous data augmentation strategies encourages the model to interpolate between the training samples, bu... | 128_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | The abstract of the paper is the following: | 128_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | *Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In t... | 128_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address ... | 128_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical trainin... | 128_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality... | 128_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldm | .md | This pipeline was contributed by [sanchit-gandhi](https://huggingface.co/sanchit-gandhi). | 128_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#tips | .md | When constructing a prompt, keep in mind:
* Descriptive prompt inputs work best; use adjectives to describe the sound (for example, "high quality" or "clear") and make the prompt context specific where possible (e.g. "melodic techno with a fast beat and synths" works better than "techno").
* Using a *negative prompt*... | 128_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#tips | .md | During inference:
* The _quality_ of the generated audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.
* Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1 to enable. Autom... | 128_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#tips | .md | * The _length_ of the generated audio sample can be controlled by varying the `audio_length_in_s` argument.
<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](... | 128_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldmpipeline | .md | MusicLDMPipeline
Pipeline for text-to-audio generation using MusicLDM.
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.).
Args:
vae ([`AutoencoderKL`]):
Variational Au... | 128_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldmpipeline | .md | text_encoder ([`~transformers.ClapModel`]):
Frozen text-audio embedding model (`ClapTextModel`), specifically the
[laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.
tokenizer ([`PreTrainedTokenizer`]):
A [`~transformers.RobertaTokenizer`] to tokenize text.
feature_extractor ([`~transfo... | 128_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/musicldm.md | https://huggingface.co/docs/diffusers/en/api/pipelines/musicldm/#musicldmpipeline | .md | Feature extractor to compute mel-spectrograms from audio waveforms.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded audio latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
[`DDIMScheduler`], [`L... | 128_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/ | .md | <!-- # Copyright 2024 The HuggingFace Team. All rights reserved. | 129_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | .md | 
[Latte: Latent Diffusion Transformer for Video Generation](https://arxiv.org/abs/2401.03048) from Monash University, Shanghai AI Lab, Nanjing University, and Nanyang Technological Univers... | 129_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | .md | *We propose a novel Latent Diffusion Transformer, namely Latte, for video generation. Latte first extracts spatio-temporal tokens from input videos and then adopts a series of Transformer blocks to model video distribution in the latent space. In order to model a substantial number of tokens extracted from videos, four... | 129_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | .md | the spatial and temporal dimensions of input videos. To improve the quality of generated videos, we determine the best practices of Latte through rigorous experimental analysis, including video clip patch embedding, model variants, timestep-class information injection, temporal positional embedding, and learning strate... | 129_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | .md | state-of-the-art performance across four standard video generation datasets, i.e., FaceForensics, SkyTimelapse, UCF101, and Taichi-HD. In addition, we extend Latte to text-to-video generation (T2V) task, where Latte achieves comparable results compared to recent T2V models. We strongly believe that Latte provides valua... | 129_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | .md | **Highlights**: Latte is a latent diffusion transformer proposed as a backbone for modeling different modalities (trained for text-to-video generation here). It achieves state-of-the-art performance across four standard video benchmarks - [FaceForensics](https://arxiv.org/abs/1803.09179), [SkyTimelapse](https://arxiv.o... | 129_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | .md | and [Taichi-HD](https://arxiv.org/abs/2003.00196). To prepare and download the datasets for evaluation, please refer to [this https URL](https://github.com/Vchitect/Latte/blob/main/docs/datasets_evaluation.md). | 129_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | .md | This pipeline was contributed by [maxin-cn](https://github.com/maxin-cn). The original codebase can be found [here](https://github.com/Vchitect/Latte). The original weights can be found under [hf.co/maxin-cn](https://huggingface.co/maxin-cn).
<Tip> | 129_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#latte | .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... | 129_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#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 LattePipeline | 129_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#inference | .md | pipeline = LattePipeline.from_pretrained(
"maxin-cn/Latte-1", torch_dtype=torch.float16
).to("cuda")
```
Then change the memory layout of the pipelines `transformer` and `vae` components to `torch.channels-last`:
```python
pipeline.transformer.to(memory_format=torch.channels_last)
pipeline.vae.to(memory_format=torc... | 129_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#inference | .md | ```
Finally, compile the components and run inference:
```python
pipeline.transformer = torch.compile(pipeline.transformer)
pipeline.vae.decode = torch.compile(pipeline.vae.decode) | 129_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#inference | .md | video = pipeline(prompt="A dog wearing sunglasses floating in space, surreal, nebulae in background").frames[0]
```
The [benchmark](https://gist.github.com/a-r-r-o-w/4e1694ca46374793c0361d740a99ff19) results on an 80GB A100 machine are:
```
Without torch.compile(): Average inference time: 16.246 seconds.
With torch... | 129_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#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. | 129_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#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 [`LattePipeline`] for inference with bitsandbytes.
```py
import torch
from diff... | 129_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#quantization | .md | from diffusers.utils import export_to_gif
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel | 129_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#quantization | .md | quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
"maxin-cn/Latte-1",
subfolder="text_encoder",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = LatteTransformer3DModel.... | 129_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#quantization | .md | pipeline = LattePipeline.from_pretrained(
"maxin-cn/Latte-1",
text_encoder=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)
prompt = "A small cactus with a happy face in the Sahara desert."
video = pipeline(prompt).frames[0]
export_to_gif(video, "latte.gif")
``` | 129_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#lattepipeline | .md | LattePipeline
Pipeline for text-to-video generation using Latte.
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 ([`AutoencoderKL`... | 129_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#lattepipeline | .md | text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. Latte 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
[T5Tok... | 129_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latte.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latte/#lattepipeline | .md | transformer ([`LatteTransformer3DModel`]):
A text conditioned `LatteTransformer3DModel` 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__ | 129_4_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/ | .md | <!--Copyright 2023 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... | 130_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/ | .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.
--> | 130_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#controlnet-xs-with-stable-diffusion-xl | .md | ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results. | 130_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#controlnet-xs-with-stable-diffusion-xl | .md | Like the original ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to co... | 130_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#controlnet-xs-with-stable-diffusion-xl | .md | ControlNet-XS generates images with comparable quality to a regular ControlNet, but it is 20-25% faster ([see benchmark](https://github.com/UmerHA/controlnet-xs-benchmark/blob/main/Speed%20Benchmark.ipynb)) and uses ~45% less memory.
Here's the overview from the [project page](https://vislearn.github.io/ControlNet-XS... | 130_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#controlnet-xs-with-stable-diffusion-xl | .md | *With increasing computing capabilities, current model architectures appear to follow the trend of simply upscaling all components without validating the necessity for doing so. In this project we investigate the size and architectural design of ControlNet [Zhang et al., 2023] for controlling the image generation proce... | 130_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#controlnet-xs-with-stable-diffusion-xl | .md | with as little as 1% of the parameters of the base model achieves state-of-the art results, considerably better than ControlNet in terms of FID score. Hence we call it ControlNet-XS. We provide the code for controlling StableDiffusion-XL [Podell et al., 2023] (Model B, 48M Parameters) and StableDiffusion 2.1 [Rombach e... | 130_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#controlnet-xs-with-stable-diffusion-xl | .md | This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️
<Tip warning={true}>
🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on ho... | 130_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#controlnet-xs-with-stable-diffusion-xl | .md | </Tip>
<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 com... | 130_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#stablediffusionxlcontrolnetxspipeline | .md | StableDiffusionXLControlNetXSPipeline
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet-XS guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular de... | 130_2_0 |
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