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/controlnetxs_sdxl.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnetxs_sdxl/#stablediffusionxlcontrolnetxspipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
Args:
vae ([`AutoencoderKL`]):
Variational A... | 130_2_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/#stablediffusionxlcontrolnetxspipeline | .md | text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
Second frozen text-encoder
([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-V... | 130_2_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/#stablediffusionxlcontrolnetxspipeline | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
tokenizer_2 ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A [`UNet2DConditionModel`] used to create a UNetControlNetXSModel to denoise the encoded image latents.
controlnet ([`Contr... | 130_2_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/#stablediffusionxlcontrolnetxspipeline | .md | scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings should ... | 130_2_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/#stablediffusionxlcontrolnetxspipeline | .md | `stabilityai/stable-diffusion-xl-base-1-0`.
add_watermarker (`bool`, *optional*):
Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to
watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
watermarker is used.
- all
-... | 130_2_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/#stablediffusionpipelineoutput | .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... | 130_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/ | .md | <!-- Copyright 2024 The HuggingFace Team. All rights reserved. | 131_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | [HunyuanVideo](https://www.arxiv.org/abs/2412.03603) by Tencent. | 131_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | *Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce Hun... | 131_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an effic... | 131_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced... | 131_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will emp... | 131_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at [this https URL](https://github.com/tencent/HunyuanVideo).* | 131_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .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... | 131_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | Recommendations for inference:
- Both text encoders should be in `torch.float16`.
- Transformer should be in `torch.bfloat16`.
- VAE should be in `torch.float16`.
- `num_frames` should be of the form `4 * k + 1`, for example `49` or `129`. | 131_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | - VAE should be in `torch.float16`.
- `num_frames` should be of the form `4 * k + 1`, for example `49` or `129`.
- For smaller resolution videos, try lower values of `shift` (between `2.0` to `5.0`) in the [Scheduler](https://huggingface.co/docs/diffusers/main/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowM... | 131_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideo | .md | - For more information about supported resolutions and other details, please refer to the original repository [here](https://github.com/Tencent/HunyuanVideo/). | 131_1_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_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. | 131_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#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 [`HunyuanVideoPipeline`] for inference with bitsandbytes.
```py
import torch
fr... | 131_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#quantization | .md | quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = HunyuanVideoTransformer3DModel.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.bfloat16,
)
pipeline = HunyuanVideoPipeline.from_pretrained(
"hunyuanvideo... | 131_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#quantization | .md | prompt = "A cat walks on the grass, realistic style."
video = pipeline(prompt=prompt, num_frames=61, num_inference_steps=30).frames[0]
export_to_video(video, "cat.mp4", fps=15)
``` | 131_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideopipeline | .md | HunyuanVideoPipeline
Pipeline for text-to-video generation using HunyuanVideo.
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:
text_encoder ([`LlamaModel`]):
... | 131_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideopipeline | .md | [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
tokenizer (`LlamaTokenizer`):
Tokenizer from [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
transformer ([`HunyuanVideoTransformer3DModel`]):
Conditional Transformer to denoise the encoded image la... | 131_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideopipeline | .md | vae ([`AutoencoderKLHunyuanVideo`]):
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
text_encoder_2 ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://hugg... | 131_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideopipeline | .md | tokenizer_2 (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
- all
- __call__ | 131_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/hunyuan_video/#hunyuanvideopipelineoutput | .md | HunyuanVideoPipelineOutput
Output class for HunyuanVideo 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... | 131_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/ | .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... | 132_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/ | .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.
-->
<Tip warning={true}>
🧪 This pipeline is for research purposes only.
</Tip> | 132_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video | .md | [ModelScope Text-to-Video Technical Report](https://arxiv.org/abs/2308.06571) is by Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang.
The abstract from the paper is: | 132_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video | .md | *This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during... | 132_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video | .md | and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across... | 132_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video | .md | You can find additional information about Text-to-Video on the [project page](https://modelscope.cn/models/damo/text-to-video-synthesis/summary), [original codebase](https://github.com/modelscope/modelscope/), and try it out in a [demo](https://huggingface.co/spaces/damo-vilab/modelscope-text-to-video-synthesis). Offic... | 132_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | Let's start by generating a short video with the default length of 16 frames (2s at 8 fps):
```python
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video
pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16"... | 132_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | prompt = "Spiderman is surfing"
video_frames = pipe(prompt).frames[0]
video_path = export_to_video(video_frames)
video_path
```
Diffusers supports different optimization techniques to improve the latency
and memory footprint of a pipeline. Since videos are often more memory-heavy than images,
we can enable CPU offloa... | 132_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | Let's generate a video of 8 seconds (64 frames) on the same GPU using CPU offloading and VAE slicing:
```python
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video | 132_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
pipe.enable_model_cpu_offload()
# memory optimization
pipe.enable_vae_slicing() | 132_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | prompt = "Darth Vader surfing a wave"
video_frames = pipe(prompt, num_frames=64).frames[0]
video_path = export_to_video(video_frames)
video_path
```
It just takes **7 GBs of GPU memory** to generate the 64 video frames using PyTorch 2.0, "fp16" precision and the techniques mentioned above.
We can also use a differe... | 132_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | ```python
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video | 132_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload() | 132_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | prompt = "Spiderman is surfing"
video_frames = pipe(prompt, num_inference_steps=25).frames[0]
video_path = export_to_video(video_frames)
video_path
```
Here are some sample outputs:
<table>
<tr>
<td><center>
An astronaut riding a horse.
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images... | 132_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#text-to-video-ms-17b | .md | alt="An astronaut riding a horse."
style="width: 300px;" />
</center></td>
<td ><center>
Darth vader surfing in waves.
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vader.gif"
alt="Darth vader surfing in waves."
style="width: 300px;" />
</center></td>
</tr>
</tab... | 132_2_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#cerspensezeroscopev2576w--cerspensezeroscopev2xl | .md | Zeroscope are watermark-free model and have been trained on specific sizes such as `576x320` and `1024x576`.
One should first generate a video using the lower resolution checkpoint [`cerspense/zeroscope_v2_576w`](https://huggingface.co/cerspense/zeroscope_v2_576w) with [`TextToVideoSDPipeline`],
which can then be upsca... | 132_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#cerspensezeroscopev2576w--cerspensezeroscopev2xl | .md | ```py
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
from PIL import Image | 132_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#cerspensezeroscopev2576w--cerspensezeroscopev2xl | .md | pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
# memory optimization
pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.enable_vae_slicing() | 132_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#cerspensezeroscopev2576w--cerspensezeroscopev2xl | .md | # memory optimization
pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.enable_vae_slicing()
prompt = "Darth Vader surfing a wave"
video_frames = pipe(prompt, num_frames=24).frames[0]
video_path = export_to_video(video_frames)
video_path
```
Now the video can be upscaled:
```py
pipe = DiffusionPipeline.f... | 132_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#cerspensezeroscopev2576w--cerspensezeroscopev2xl | .md | # memory optimization
pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.enable_vae_slicing()
video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames] | 132_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#cerspensezeroscopev2576w--cerspensezeroscopev2xl | .md | video_frames = pipe(prompt, video=video, strength=0.6).frames[0]
video_path = export_to_video(video_frames)
video_path
```
Here are some sample outputs:
<table>
<tr>
<td ><center>
Darth vader surfing in waves.
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/dar... | 132_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#tips | .md | Video generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient.
Check out the [Text or image-to-video](text-img2vid) guide for more det... | 132_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#tips | .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... | 132_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#texttovideosdpipeline | .md | TextToVideoSDPipeline
Pipeline for text-to-video generation.
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 the following loading method... | 132_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#texttovideosdpipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`]):
Var... | 132_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#texttovideosdpipeline | .md | text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`UNet3DConditionModel`]):
A [`UNet3DConditionModel`] to denoise the encoded video latents.
sched... | 132_5_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#texttovideosdpipeline | .md | A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- all
- __call__ | 132_5_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#videotovideosdpipeline | .md | VideoToVideoSDPipeline
Pipeline for text-guided video-to-video generation.
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 the following ... | 132_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#videotovideosdpipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
vae ([`AutoencoderKL`]):
Var... | 132_6_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#videotovideosdpipeline | .md | text_encoder ([`CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer (`CLIPTokenizer`):
A [`~transformers.CLIPTokenizer`] to tokenize text.
unet ([`UNet3DConditionModel`]):
A [`UNet3DConditionModel`] to denoise the encoded video latents.
sched... | 132_6_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#videotovideosdpipeline | .md | A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- all
- __call__ | 132_6_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video/#texttovideosdpipelineoutput | .md | TextToVideoSDPipelineOutput
Output class for text-to-video 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... | 132_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/ | .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... | 133_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/ | .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.
--> | 133_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#controlnet | .md | ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. | 133_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#controlnet | .md | With a 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 control the i... | 133_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#controlnet | .md | *We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn ... | 133_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#controlnet | .md | connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple ... | 133_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#controlnet | .md | ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.* | 133_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#controlnet | .md | This model was contributed by [takuma104](https://huggingface.co/takuma104). ❤️
The original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet), and you can find official ControlNet checkpoints on [lllyasviel's](https://huggingface.co/lllyasviel) Hub profile.
<Tip> | 133_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#controlnet | .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... | 133_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetpipeline | .md | StableDiffusionControlNetPipeline
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
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.... | 133_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetpipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.fro... | 133_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetpipeline | .md | - [`~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_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co... | 133_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetpipeline | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the `unet` during the denoising process. If yo... | 133_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetpipeline | .md | additional conditioning.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whethe... | 133_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetpipeline | .md | Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
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`.
- a... | 133_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetpipeline | .md | - disable_attention_slicing
- enable_vae_slicing
- disable_vae_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
- load_textual_inversion | 133_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetimg2imgpipeline | .md | StableDiffusionControlNetImg2ImgPipeline
Pipeline for image-to-image generation using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular devi... | 133_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetimg2imgpipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.fro... | 133_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetimg2imgpipeline | .md | - [`~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_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co... | 133_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetimg2imgpipeline | .md | tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the `unet` during the denoising process. If yo... | 133_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetimg2imgpipeline | .md | additional conditioning.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whethe... | 133_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetimg2imgpipeline | .md | Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
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`.
- a... | 133_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetimg2imgpipeline | .md | - disable_attention_slicing
- enable_vae_slicing
- disable_vae_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
- load_textual_inversion | 133_3_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | .md | StableDiffusionControlNetInpaintPipeline
Pipeline for image inpainting using Stable Diffusion with ControlNet guidance.
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.)... | 133_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | .md | - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.fro... | 133_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | .md | - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
<Tip>
This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting
([stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting))
as well as defa... | 133_4_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | .md | ([stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)).
Default text-to-image Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on
those, such as [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel... | 133_4_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | .md | vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):... | 133_4_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | .md | unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the `unet` during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added ... | 133_4_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | .md | A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmf... | 133_4_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusioncontrolnetinpaintpipeline | .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`.
- all
- __call__
- enable_attention_slicing
- disable_attention_slicing
- enable_vae_slicing
- disable_vae_s... | 133_4_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#stablediffusionpipelineoutput | .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... | 133_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#flaxstablediffusioncontrolnetpipeline | .md | FlaxStableDiffusionControlNetPipeline
- all
- __call__ | 133_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet/#flaxstablediffusioncontrolnetpipelineoutput | .md | [[autodoc]] FlaxStableDiffusionPipelineOutput: module diffusers.pipelines.stable_diffusion has no attribute FlaxStableDiffusionPipelineOutput | 133_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/ | .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... | 134_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/ | .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.
--> | 134_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#diffedit | .md | [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://huggingface.co/papers/2210.11427) is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.
The abstract from the paper is: | 134_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#diffedit | .md | *Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to ... | 134_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#diffedit | .md | an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to provide a mask, making the task much easier by treating it as a conditional inpainting task. In contrast... | 134_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#diffedit | .md | highlighting regions of the input image that need to be edited, by contrasting predictions of a diffusion model conditioned on different text prompts. Moreover, we rely on latent inference to preserve content in those regions of interest and show excellent synergies with mask-based diffusion. DiffEdit achieves state-of... | 134_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#diffedit | .md | semantic image editing in more challenging settings, using images from the COCO dataset as well as text-based generated images.* | 134_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/diffedit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/diffedit/#diffedit | .md | The original codebase can be found at [Xiang-cd/DiffEdit-stable-diffusion](https://github.com/Xiang-cd/DiffEdit-stable-diffusion), and you can try it out in this [demo](https://blog.problemsolversguild.com/technical/research/2022/11/02/DiffEdit-Implementation.html).
This pipeline was contributed by [clarencechen](htt... | 134_1_5 |
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