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/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video | .md | frame_ids = [0] + list(range(ch_start, ch_end))
# Fix the seed for the temporal consistency
generator.manual_seed(seed)
output = pipe(prompt=prompt, video_length=len(frame_ids), generator=generator, frame_ids=frame_ids)
result.append(output.images[1:]) | 159_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video | .md | # Concatenate chunks and save
result = np.concatenate(result)
result = [(r * 255).astype("uint8") for r in result]
imageio.mimsave("video.mp4", result, fps=4)
```
- #### SDXL Support
In order to use the SDXL model when generating a video from prompt, use the `TextToVideoZeroSDXLPipeline` pipeline:
```python
import ... | 159_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video | .md | model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = TextToVideoZeroSDXLPipeline.from_pretrained(
model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
``` | 159_2_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | To generate a video from prompt with additional pose control
1. Download a demo video
```python
from huggingface_hub import hf_hub_download
filename = "__assets__/poses_skeleton_gifs/dance1_corr.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename... | 159_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
```
To extract pose from actual video, read [ControlNet documentation](controlnet).
3. Run `StableDiffusionControlNetPipeline` with our custom attention processor
```pyth... | 159_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | ```python
import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor | 159_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id, controlnet=controlnet, torch_dtype=torch.float16
).to("cuda")
# Set the attention pro... | 159_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | # fix latents for all frames
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1) | 159_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | prompt = "Darth Vader dancing in a desert"
result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images
imageio.mimsave("video.mp4", result, fps=4)
```
- #### SDXL Support
Since our attention processor also works with SDXL, it can be utilized to generate a video from prompt using Contr... | 159_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | ```python
import torch
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor | 159_3_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | controlnet_model_id = 'thibaud/controlnet-openpose-sdxl-1.0'
model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
controlnet = ControlNetModel.from_pretrained(controlnet_model_id, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id, controlnet=controlnet, torch_dtype=torch.fl... | 159_3_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-pose-control | .md | # Set the attention processor
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))
# fix latents for all frames
latents = torch.randn((1, 4, 128, 128), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)
prompt ... | 159_3_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#text-to-video-with-edge-control | .md | To generate a video from prompt with additional Canny edge control, follow the same steps described above for pose-guided generation using [Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny). | 159_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#video-instruct-pix2pix | .md | To perform text-guided video editing (with [InstructPix2Pix](pix2pix)):
1. Download a demo video
```python
from huggingface_hub import hf_hub_download
filename = "__assets__/pix2pix video/camel.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)... | 159_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#video-instruct-pix2pix | .md | reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
```
3. Run `StableDiffusionInstructPix2PixPipeline` with our custom attention processor
```python
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline
from d... | 159_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#video-instruct-pix2pix | .md | model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=3))
prompt = "make it Van Gogh Starry Night style"
result = pipe(prompt=[prompt] * len(video), image=vide... | 159_5_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#dreambooth-specialization | .md | Methods **Text-To-Video**, **Text-To-Video with Pose Control** and **Text-To-Video with Edge Control**
can run with custom [DreamBooth](../../training/dreambooth) models, as shown below for
[Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny) and
[Avatar style DreamBooth](https://hugging... | 159_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#dreambooth-specialization | .md | filename = "__assets__/canny_videos_mp4/girl_turning.mp4"
repo_id = "PAIR/Text2Video-Zero"
video_path = hf_hub_download(repo_type="space", repo_id=repo_id, filename=filename)
```
2. Read video from path
```python
from PIL import Image
import imageio | 159_6_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#dreambooth-specialization | .md | reader = imageio.get_reader(video_path, "ffmpeg")
frame_count = 8
canny_edges = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]
```
3. Run `StableDiffusionControlNetPipeline` with custom trained DreamBooth model
```python
import torch
from diffusers import StableDiffusionControlNetPipeline, ControlN... | 159_6_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#dreambooth-specialization | .md | # set model id to custom model
model_id = "PAIR/text2video-zero-controlnet-canny-avatar"
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id, controlnet=controlnet, torch_dtype=torch.float16
).to("cu... | 159_6_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#dreambooth-specialization | .md | # fix latents for all frames
latents = torch.randn((1, 4, 64, 64), device="cuda", dtype=torch.float16).repeat(len(canny_edges), 1, 1, 1) | 159_6_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#dreambooth-specialization | .md | prompt = "oil painting of a beautiful girl avatar style"
result = pipe(prompt=[prompt] * len(canny_edges), image=canny_edges, latents=latents).images
imageio.mimsave("video.mp4", result, fps=4)
```
You can filter out some available DreamBooth-trained models with [this link](https://huggingface.co/models?search=dreamb... | 159_6_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#dreambooth-specialization | .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... | 159_6_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozeropipeline | .md | TextToVideoZeroPipeline
Pipeline for zero-shot text-to-video generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
vae ([`Autoenc... | 159_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozeropipeline | .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 ([`UNet2DConditionModel`]):
A [`UNet3DConditionModel`] to denoise the encoded video latents.
sched... | 159_7_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozeropipeline | .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... | 159_7_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozeropipeline | .md | more details about a model's potential harms.
feature_extractor ([`CLIPImageProcessor`]):
A [`CLIPImageProcessor`] to extract features from generated images; used as inputs to the `safety_checker`.
- all
- __call__ | 159_7_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozerosdxlpipeline | .md | TextToVideoZeroSDXLPipeline
Pipeline for zero-shot text-to-video generation using Stable Diffusion XL.
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 ([`... | 159_8_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozerosdxlpipeline | .md | text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
text_encoder_2 ([` CLIP... | 159_8_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozerosdxlpipeline | .md | Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant... | 159_8_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozerosdxlpipeline | .md | [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Condit... | 159_8_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideozerosdxlpipeline | .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`].
- all
- __call__ | 159_8_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/text_to_video_zero.md | https://huggingface.co/docs/diffusers/en/api/pipelines/text_to_video_zero/#texttovideopipelineoutput | .md | TextToVideoPipelineOutput
Output class for zero-shot text-to-video pipeline.
Args:
images (`[List[PIL.Image.Image]`, `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... | 159_9_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/ | .md | <!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan 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... | 160_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/ | .md | Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 160_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#controlnet-with-hunyuan-dit | .md | HunyuanDiTControlNetPipeline is an implementation of ControlNet for [Hunyuan-DiT](https://arxiv.org/abs/2405.08748).
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. | 160_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#controlnet-with-hunyuan-dit | .md | With a ControlNet model, you can provide an additional control image to condition and control Hunyuan-DiT 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 image ... | 160_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#controlnet-with-hunyuan-dit | .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 ... | 160_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#controlnet-with-hunyuan-dit | .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 ... | 160_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#controlnet-with-hunyuan-dit | .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.* | 160_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#controlnet-with-hunyuan-dit | .md | This code is implemented by Tencent Hunyuan Team. You can find pre-trained checkpoints for Hunyuan-DiT ControlNets on [Tencent Hunyuan](https://huggingface.co/Tencent-Hunyuan).
<Tip> | 160_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#controlnet-with-hunyuan-dit | .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... | 160_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#hunyuanditcontrolnetpipeline | .md | HunyuanDiTControlNetPipeline
Pipeline for English/Chinese-to-image generation using HunyuanDiT.
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.... | 160_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#hunyuanditcontrolnetpipeline | .md | ourselves)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. We use
`sdxl-vae-fp16-fix`.
text_encoder (Optional[`~transformers.BertModel`, `~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingfac... | 160_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#hunyuanditcontrolnetpipeline | .md | tokenizer (Optional[`~transformers.BertTokenizer`, `~transformers.CLIPTokenizer`]):
A `BertTokenizer` or `CLIPTokenizer` to tokenize text.
transformer ([`HunyuanDiT2DModel`]):
The HunyuanDiT model designed by Tencent Hunyuan.
text_encoder_2 (`T5EncoderModel`):
The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.
tokeni... | 160_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_hunyuandit/#hunyuanditcontrolnetpipeline | .md | scheduler ([`DDPMScheduler`]):
A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.
controlnet ([`HunyuanDiT2DControlNetModel`] or `List[HunyuanDiT2DControlNetModel]` or [`HunyuanDiT2DControlNetModel`]):
Provides additional conditioning to the `unet` during the denoising process. ... | 160_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/ | .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 agreed to... | 161_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/ | .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.
--> | 161_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/#kandinsky-3 | .md | Kandinsky 3 is created by [Vladimir Arkhipkin](https://github.com/oriBetelgeuse),[Anastasia Maltseva](https://github.com/NastyaMittseva),[Igor Pavlov](https://github.com/boomb0om),[Andrei Filatov](https://github.com/anvilarth),[Arseniy Shakhmatov](https://github.com/cene555),[Andrey Kuznetsov](https://github.com/kuznet... | 161_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/#kandinsky-3 | .md | The description from it's GitHub page:
*Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder ... | 161_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/#kandinsky-3 | .md | Its architecture includes 3 main components:
1. [FLAN-UL2](https://huggingface.co/google/flan-ul2), which is an encoder decoder model based on the T5 architecture.
2. New U-Net architecture featuring BigGAN-deep blocks doubles depth while maintaining the same number of parameters.
3. Sber-MoVQGAN is a decoder proven to... | 161_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/#kandinsky-3 | .md | The original codebase can be found at [ai-forever/Kandinsky-3](https://github.com/ai-forever/Kandinsky-3).
<Tip>
Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.
... | 161_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/#kandinsky-3 | .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... | 161_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/#kandinsky3pipeline | .md | Kandinsky3Pipeline
- all
- __call__ | 161_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/kandinsky3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/kandinsky3/#kandinsky3img2imgpipeline | .md | Kandinsky3Img2ImgPipeline
- all
- __call__ | 161_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/ | .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... | 162_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/ | .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.
--> | 162_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .md | StableDiffusion3ControlNetPipeline is an implementation of ControlNet for Stable Diffusion 3.
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. | 162_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .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... | 162_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .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 ... | 162_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .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 ... | 162_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .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.* | 162_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .md | This controlnet code is mainly implemented by [The InstantX Team](https://huggingface.co/InstantX). The inpainting-related code was developed by [The Alimama Creative Team](https://huggingface.co/alimama-creative). You can find pre-trained checkpoints for SD3-ControlNet in the table below:
| ControlNet type | Develop... | 162_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .md | | Canny | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Canny) |
| Depth | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Depth) |
| Pose | [The InstantX Team](https://huggingface.co/InstantX) | ... | 162_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .md | | Tile | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/SD3-Controlnet-Tile) |
| Inpainting | [The AlimamaCreative Team](https://huggingface.co/alimama-creative) | [link](https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting) |
<Tip> | 162_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#controlnet-with-stable-diffusion-3 | .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... | 162_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetpipeline | .md | StableDiffusion3ControlNetPipeline
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`Au... | 162_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetpipeline | .md | text_encoder ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
with an additional added projection layer that is initialized... | 162_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetpipeline | .md | as its dimension.
text_encoder_2 ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
text_encoder... | 162_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetpipeline | .md | [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
[t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIP... | 162_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetpipeline | .md | [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_3 (`T5TokenizerFast`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
controlnet ([`SD3ControlNetModel`] or `List[SD3ControlNetMode... | 162_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetpipeline | .md | 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 together to create one combined
additional conditioning.
image_encoder (`PreTrainedModel`, *optional*):
Pre-trained Vision Model for IP Adapter.
feature_extr... | 162_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetinpaintingpipeline | .md | StableDiffusion3ControlNetInpaintingPipeline
Args:
transformer ([`SD3Transformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.... | 162_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetinpaintingpipeline | .md | text_encoder ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
with an additional added projection layer that is initialized... | 162_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetinpaintingpipeline | .md | as its dimension.
text_encoder_2 ([`CLIPTextModelWithProjection`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
specifically the
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
variant.
text_encoder... | 162_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetinpaintingpipeline | .md | [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
[t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIP... | 162_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetinpaintingpipeline | .md | [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_3 (`T5TokenizerFast`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
controlnet ([`SD3ControlNetModel`] or `List[SD3ControlNetMode... | 162_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3controlnetinpaintingpipeline | .md | Provides additional conditioning to the `transformer` during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added together to create one combined
additional conditioning.
image_encoder (`PreTrainedModel`, *optional*):
Pre-trained Vision Model for IP Adapter.
featu... | 162_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_sd3/#stablediffusion3pipelineoutput | .md | StableDiffusion3PipelineOutput
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)`. PIL images or numpy array present the denoised images of the diff... | 162_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/ | .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... | 163_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/ | .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.
--> | 163_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/#autopipeline | .md | The `AutoPipeline` is designed to make it easy to load a checkpoint for a task without needing to know the specific pipeline class. Based on the task, the `AutoPipeline` automatically retrieves the correct pipeline class from the checkpoint `model_index.json` file.
> [!TIP]
> Check out the [AutoPipeline](../../tutori... | 163_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/#autopipelinefortext2image | .md | AutoPipelineForText2Image
[`AutoPipelineForText2Image`] is a generic pipeline class that instantiates a text-to-image pipeline class. The
specific underlying pipeline class is automatically selected from either the
[`~AutoPipelineForText2Image.from_pretrained`] or [`~AutoPipelineForText2Image.from_pipe`] methods.
T... | 163_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/#autopipelinefortext2image | .md | This class cannot be instantiated using `__init__()` (throws an error).
Class attributes:
- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
diffusion pipeline's components.
- all
- from_pretrained
- from_pipe | 163_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/#autopipelineforimage2image | .md | AutoPipelineForImage2Image
[`AutoPipelineForImage2Image`] is a generic pipeline class that instantiates an image-to-image pipeline class. The
specific underlying pipeline class is automatically selected from either the
[`~AutoPipelineForImage2Image.from_pretrained`] or [`~AutoPipelineForImage2Image.from_pipe`] method... | 163_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/#autopipelineforimage2image | .md | This class cannot be instantiated using `__init__()` (throws an error).
Class attributes:
- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
diffusion pipeline's components.
- all
- from_pretrained
- from_pipe | 163_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/#autopipelineforinpainting | .md | AutoPipelineForInpainting
[`AutoPipelineForInpainting`] is a generic pipeline class that instantiates an inpainting pipeline class. The
specific underlying pipeline class is automatically selected from either the
[`~AutoPipelineForInpainting.from_pretrained`] or [`~AutoPipelineForInpainting.from_pipe`] methods.
Thi... | 163_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/auto_pipeline.md | https://huggingface.co/docs/diffusers/en/api/pipelines/auto_pipeline/#autopipelineforinpainting | .md | This class cannot be instantiated using `__init__()` (throws an error).
Class attributes:
- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
diffusion pipeline's components.
- all
- from_pretrained
- from_pipe | 163_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/ | .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... | 164_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/ | .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.
--> | 164_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and ... | 164_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. | 164_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs. | 164_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI [announcement](https://stability.ai/blog/stable-diffusion-announcement) and our own [blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) for more... | 164_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official ch... | 164_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | tasks can be found on the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case! | 164_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:
<div class="flex justify-center">
<div class="rounded-xl border border-gray-200">
<table class="min-w-full divide-y-2 divide-gray-200 bg-white text-sm">
<thead>
<tr>
<th class="px-4 py-2 font-medium te... | 164_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | Supported tasks
</th>
<th class="px-4 py-2 font-medium text-gray-900 text-left">
🤗 Space
</th>
</tr>
</thead>
<tbody class="divide-y divide-gray-200">
<tr>
<td class="px-4 py-2 text-gray-700">
<a href="./text2img">StableDiffusion</a>
</td>
<td class="px-4 py-2 text-gray-700">text-to-image</td>
<td class="px-4 py-2"><a... | 164_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | </td>
</tr>
<tr>
<td class="px-4 py-2 text-gray-700">
<a href="./img2img">StableDiffusionImg2Img</a>
</td>
<td class="px-4 py-2 text-gray-700">image-to-image</td>
<td class="px-4 py-2"><a href="https://huggingface.co/spaces/huggingface/diffuse-the-rest"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Fa... | 164_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md | https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview/#stable-diffusion-pipelines | .md | <a href="./inpaint">StableDiffusionInpaint</a>
</td>
<td class="px-4 py-2 text-gray-700">inpainting</td>
<td class="px-4 py-2"><a href="https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
</td>
</tr>
<tr>
<td class="... | 164_1_9 |
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