source stringclasses 273
values | url stringlengths 47 172 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen-v2-comes-to-diffusers | .md | - Higher resolution (1024x1024 up to 2048x2048)
- Faster inference
- Multi Aspect Resolution Sampling
- Better quality
We are releasing 3 checkpoints for the text-conditional image generation model (Stage C). Those are:
- v2-base
- v2-aesthetic
- **(default)** v2-interpolated (50% interpolation between v2-base and ... | 107_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#würstchen-v2-comes-to-diffusers | .md | - v2-base
- v2-aesthetic
- **(default)** v2-interpolated (50% interpolation between v2-base and v2-aesthetic)
We recommend using v2-interpolated, as it has a nice touch of both photorealism and aesthetics. Use v2-base for finetunings as it does not have a style bias and use v2-aesthetic for very artistic generations.... | 107_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#text-to-image-generation | .md | For the sake of usability, Würstchen can be used with a single pipeline. This pipeline can be used as follows:
```python
import torch
from diffusers import AutoPipelineForText2Image
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
pipe = AutoPipelineForText2Image.from_pretrained("warp-ai/wuerstch... | 107_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#text-to-image-generation | .md | caption = "Anthropomorphic cat dressed as a fire fighter"
images = pipe(
caption,
width=1024,
height=1536,
prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
prior_guidance_scale=4.0,
num_images_per_prompt=2,
).images
``` | 107_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#text-to-image-generation | .md | For explanation purposes, we can also initialize the two main pipelines of Würstchen individually. Würstchen consists of 3 stages: Stage C, Stage B, Stage A. They all have different jobs and work only together. When generating text-conditional images, Stage C will first generate the latents in a very compressed latent ... | 107_4_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#text-to-image-generation | .md | the generated latents will be passed to Stage B, which decompresses the latents into a bigger latent space of a VQGAN. These latents can then be decoded by Stage A, which is a VQGAN, into the pixel-space. Stage B & Stage A are both encapsulated in the `decoder_pipeline`. For more details, take a look at the [paper](htt... | 107_4_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#text-to-image-generation | .md | ```python
import torch
from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS | 107_4_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#text-to-image-generation | .md | device = "cuda"
dtype = torch.float16
num_images_per_prompt = 2
prior_pipeline = WuerstchenPriorPipeline.from_pretrained(
"warp-ai/wuerstchen-prior", torch_dtype=dtype
).to(device)
decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained(
"warp-ai/wuerstchen", torch_dtype=dtype
).to(device)
caption = "Anthropomor... | 107_4_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#text-to-image-generation | .md | caption = "Anthropomorphic cat dressed as a fire fighter"
negative_prompt = ""
prior_output = prior_pipeline(
prompt=caption,
height=1024,
width=1536,
timesteps=DEFAULT_STAGE_C_TIMESTEPS,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=num_images_per_prompt,
)
decoder_output = decoder_pipeli... | 107_4_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#speed-up-inference | .md | You can make use of `torch.compile` function and gain a speed-up of about 2-3x:
```python
prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True)
decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="reduce-overhead", fullgraph=True)
``` | 107_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#limitations | .md | - Due to the high compression employed by Würstchen, generations can lack a good amount
of detail. To our human eye, this is especially noticeable in faces, hands etc.
- **Images can only be generated in 128-pixel steps**, e.g. the next higher resolution
after 1024x1024 is 1152x1152
- The model lacks the ability to ren... | 107_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#limitations | .md | - The model often does not achieve photorealism
- Difficult compositional prompts are hard for the model
The original codebase, as well as experimental ideas, can be found at [dome272/Wuerstchen](https://github.com/dome272/Wuerstchen). | 107_6_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchencombinedpipeline | .md | WuerstchenCombinedPipeline
Combined Pipeline for text-to-image generation using Wuerstchen
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.)
A... | 107_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchencombinedpipeline | .md | text_encoder (`CLIPTextModel`):
The decoder text encoder to be used for text inputs.
decoder (`WuerstchenDiffNeXt`):
The decoder model to be used for decoder image generation pipeline.
scheduler (`DDPMWuerstchenScheduler`):
The scheduler to be used for decoder image generation pipeline.
vqgan (`PaellaVQModel`):
The VQG... | 107_7_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchencombinedpipeline | .md | prior_tokenizer (`CLIPTokenizer`):
The prior tokenizer to be used for text inputs.
prior_text_encoder (`CLIPTextModel`):
The prior text encoder to be used for text inputs.
prior_prior (`WuerstchenPrior`):
The prior model to be used for prior pipeline.
prior_scheduler (`DDPMWuerstchenScheduler`):
The scheduler to be use... | 107_7_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchenpriorpipeline | .md | WuerstchenPriorPipeline
Pipeline for generating image prior for Wuerstchen.
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.)
The pipeline als... | 107_8_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchenpriorpipeline | .md | - [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
Args:
prior ([`Prior`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
text_encoder ([`CLIPTextModel... | 107_8_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchenpriorpipeline | .md | text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
scheduler ([`DDPMWuerstchenScheduler`]):
A scheduler to be used in combination with `prior`... | 107_8_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchenpriorpipeline | .md | Mean value for latent diffusers.
latent_std ('float', *optional*, defaults to 1.0):
Standard value for latent diffusers.
resolution_multiple ('float', *optional*, defaults to 42.67):
Default resolution for multiple images generated.
- all
- __call__ | 107_8_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchenpriorpipelineoutput | .md | WuerstchenPriorPipelineOutput
Output class for WuerstchenPriorPipeline.
Args:
image_embeddings (`torch.Tensor` or `np.ndarray`)
Prior image embeddings for text prompt | 107_9_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchendecoderpipeline | .md | WuerstchenDecoderPipeline
Pipeline for generating images from the Wuerstchen model.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
to... | 107_10_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchendecoderpipeline | .md | text_encoder (`CLIPTextModel`):
The CLIP text encoder.
decoder ([`WuerstchenDiffNeXt`]):
The WuerstchenDiffNeXt unet decoder.
vqgan ([`PaellaVQModel`]):
The VQGAN model.
scheduler ([`DDPMWuerstchenScheduler`]):
A scheduler to be used in combination with `prior` to generate image embedding.
latent_dim_scale (float, `opt... | 107_10_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#wuerstchendecoderpipeline | .md | Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are
height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and
width=int(24*10.67)=256 in order to match the training conditions.
- all
- __call__ | 107_10_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md | https://huggingface.co/docs/diffusers/en/api/pipelines/wuerstchen/#citation | .md | ```bibtex
@misc{pernias2023wuerstchen,
title={Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models},
author={Pablo Pernias and Dominic Rampas and Mats L. Richter and Christopher J. Pal and Marc Aubreville},
year={2023},
eprint={2306.00637},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
`... | 107_11_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/ | .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... | 108_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/ | .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.
--> | 108_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | 
[PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://huggingface.co/papers/2310.00426) is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie... | 108_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | *The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PIXART-α, a Transformer-based T2I diffusion model whose image generation quality is ... | 108_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | SDXL, and even Midjourney), reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost, as shown in Figure 1 and 2. To achieve this goal, three core designs are proposed: (1) Training strategy decomposition: We devise three di... | 108_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | text-image alignment, and image aesthetic quality; (2) Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; (3) High-informative data: We emphasize the significance of concept density... | 108_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | to auto-label dense pseudo-captions to assist text-image alignment learning. As a result, PIXART-α's training speed markedly surpasses existing large-scale T2I models, e.g., PIXART-α only takes 10.8% of Stable Diffusion v1.5's training time (675 vs. 6,250 A100 GPU days), saving nearly $300,000 ($26,000 vs. $320,000) an... | 108_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | SOTA model, RAPHAEL, our training cost is merely 1%. Extensive experiments demonstrate that PIXART-α excels in image quality, artistry, and semantic control. We hope PIXART-α will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from sc... | 108_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | You can find the original codebase at [PixArt-alpha/PixArt-alpha](https://github.com/PixArt-alpha/PixArt-alpha) and all the available checkpoints at [PixArt-alpha](https://huggingface.co/PixArt-alpha).
Some notes about this pipeline:
* It uses a Transformer backbone (instead of a UNet) for denoising. As such it has... | 108_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | * It uses a Transformer backbone (instead of a UNet) for denoising. As such it has a similar architecture as [DiT](./dit).
* It was trained using text conditions computed from T5. This aspect makes the pipeline better at following complex text prompts with intricate details. | 108_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .md | * It is good at producing high-resolution images at different aspect ratios. To get the best results, the authors recommend some size brackets which can be found [here](https://github.com/PixArt-alpha/PixArt-alpha/blob/08fbbd281ec96866109bdd2cdb75f2f58fb17610/diffusion/data/datasets/utils.py).
* It rivals the quality o... | 108_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixart-α | .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... | 108_1_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | .md | Run the [`PixArtAlphaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.
First, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library:
```bash
pip install -U bitsandbytes
```
Then load the text encoder in 8-bit:
... | 108_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | .md | text_encoder = T5EncoderModel.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS",
subfolder="text_encoder",
load_in_8bit=True,
device_map="auto", | 108_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | .md | )
pipe = PixArtAlphaPipeline.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS",
text_encoder=text_encoder,
transformer=None,
device_map="auto"
)
```
Now, use the `pipe` to encode a prompt:
```python
with torch.no_grad():
prompt = "cute cat"
prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_at... | 108_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | .md | ```python
import gc | 108_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | .md | def flush():
gc.collect()
torch.cuda.empty_cache()
del text_encoder
del pipe
flush()
```
Then compute the latents with the prompt embeddings as inputs:
```python
pipe = PixArtAlphaPipeline.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS",
text_encoder=None,
torch_dtype=torch.float16,
).to("cuda") | 108_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | .md | latents = pipe(
negative_prompt=None,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
prompt_attention_mask=prompt_attention_mask,
negative_prompt_attention_mask=negative_prompt_attention_mask,
num_images_per_prompt=1,
output_type="latent",
).images | 108_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | .md | del pipe.transformer
flush()
```
<Tip>
Notice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded.
</Tip>
Once the latents are computed, pass it off to the VAE to decode into a real image:
```python
with torch.no_grad():
image = pipe.vae.decode(latents / pipe.vae.co... | 108_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#inference-with-under-8gb-gpu-vram | .md | image = pipe.image_processor.postprocess(image, output_type="pil")[0]
image.save("cat.png")
```
By deleting components you aren't using and flushing the GPU VRAM, you should be able to run [`PixArtAlphaPipeline`] with under 8GB GPU VRAM.
Args:
vae ([`A... | 108_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixartalphapipeline | .md | text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. PixArt-Alpha uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
[t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
tokenizer (`T5Tokenizer`):
Tokenizer of class... | 108_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/pixart.md | https://huggingface.co/docs/diffusers/en/api/pipelines/pixart/#pixartalphapipeline | .md | transformer ([`PixArtTransformer2DModel`]):
A text conditioned `PixArtTransformer2DModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
- all
- __call__ | 108_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/ | .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... | 109_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/ | .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.
--> | 109_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#latent-diffusion | .md | Latent Diffusion was 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.
The abstract from the paper is: | 109_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#latent-diffusion | .md | *By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. Howe... | 109_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#latent-diffusion | .md | optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previou... | 109_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#latent-diffusion | .md | allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text o... | 109_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#latent-diffusion | .md | in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compare... | 109_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#latent-diffusion | .md | The original codebase can be found at [CompVis/latent-diffusion](https://github.com/CompVis/latent-diffusion).
<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 pipeline... | 109_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#ldmtexttoimagepipeline | .md | LDMTextToImagePipeline
Pipeline for text-to-image generation using latent 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.).
Parameters:
vqvae ([`VQModel`])... | 109_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#ldmtexttoimagepipeline | .md | bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combinat... | 109_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#ldmsuperresolutionpipeline | .md | LDMSuperResolutionPipeline
A pipeline for image super-resolution using latent 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.).
Parameters:
vqvae ([`VQMode... | 109_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#ldmsuperresolutionpipeline | .md | unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],
[`EulerAncestralDiscreteScheduler`], [`DPMSo... | 109_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md | https://huggingface.co/docs/diffusers/en/api/pipelines/latent_diffusion/#imagepipelineoutput | .md | ImagePipelineOutput
Output class for image 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)`. | 109_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/ | .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... | 110_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/ | .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.
--> | 110_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | 
[Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT](https://github.com/Alpha-VLLM/Lumina-T2X/blob/main/assets/lumina-next.pdf) from Alpha-VLLM, OpenGVLab, Shanghai AI Laboratory.
The abstract f... | 110_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | *Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers (Flag-DiT) that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions. Despite its promising capabilities, Lumina-T2X still encounters challenges including tra... | 110_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | In this paper, we present Lumina-Next, an improved version of Lumina-T2X, showcasing stronger generation performance with increased training and inference efficiency. We begin with a comprehensive analysis of the Flag-DiT architecture and identify several suboptimal components, which we address by introducing the Next-... | 110_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | enable better resolution extrapolation, we thoroughly compare different context extrapolation methods applied to text-to-image generation with 3D RoPE, and propose Frequency- and Time-Aware Scaled RoPE tailored for diffusion transformers. Additionally, we introduce a sigmoid time discretization schedule to reduce sampl... | 110_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | merge redundant visual tokens for faster network evaluation, effectively boosting the overall sampling speed. Thanks to these improvements, Lumina-Next not only improves the quality and efficiency of basic text-to-image generation but also demonstrates superior resolution extrapolation capabilities and multilingual gen... | 110_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | in a zero-shot manner. To further validate Lumina-Next as a versatile generative framework, we instantiate it on diverse tasks including visual recognition, multi-view, audio, music, and point cloud generation, showcasing strong performance across these domains. By releasing all codes and model weights at https://githu... | 110_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | **Highlights**: Lumina-Next is a next-generation Diffusion Transformer that significantly enhances text-to-image generation, multilingual generation, and multitask performance by introducing the Next-DiT architecture, 3D RoPE, and frequency- and time-aware RoPE, among other improvements.
Lumina-Next has the following... | 110_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | * It uses a Next-DiT as a transformer backbone with Sandwichnorm 3D RoPE, and Grouped-Query Attention.
* It uses a Frequency- and Time-Aware Scaled RoPE.
---
[Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers](https://arxiv.org/abs/2405.05945) from... | 110_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | *Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details. In this technical report, we introduce the Lumina-T2X family - a series of Flow-based Large Diffusion... | 110_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | as a unified framework designed to transform noise into images, videos, multi-view 3D objects, and audio clips conditioned on text instructions. By tokenizing the latent spatial-temporal space and incorporating learnable placeholders such as [nextline] and [nextframe] tokens, Lumina-T2X seamlessly unifies the represent... | 110_1_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | resolutions. This unified approach enables training within a single framework for different modalities and allows for flexible generation of multimodal data at any resolution, aspect ratio, and length during inference. Advanced techniques like RoPE, RMSNorm, and flow matching enhance the stability, flexibility, and sca... | 110_1_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | to scale up to 7 billion parameters and extend the context window to 128K tokens. This is particularly beneficial for creating ultra-high-definition images with our Lumina-T2I model and long 720p videos with our Lumina-T2V model. Remarkably, Lumina-T2I, powered by a 5-billion-parameter Flag-DiT, requires only 35% of th... | 110_1_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | naive DiT. Our further comprehensive analysis underscores Lumina-T2X's preliminary capability in resolution extrapolation, high-resolution editing, generating consistent 3D views, and synthesizing videos with seamless transitions. We expect that the open-sourcing of Lumina-T2X will further foster creativity, transparen... | 110_1_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | You can find the original codebase at [Alpha-VLLM](https://github.com/Alpha-VLLM/Lumina-T2X) and all the available checkpoints at [Alpha-VLLM Lumina Family](https://huggingface.co/collections/Alpha-VLLM/lumina-family-66423205bedb81171fd0644b).
**Highlights**: Lumina-T2X supports Any Modality, Resolution, and Duration... | 110_1_13 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .md | Lumina-T2X has the following components:
* It uses a Flow-based Large Diffusion Transformer as the backbone
* It supports different any modalities with one backbone and corresponding encoder, decoder.
This pipeline was contributed by [PommesPeter](https://github.com/PommesPeter). The original codebase can be found [h... | 110_1_14 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#lumina-t2x | .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... | 110_1_15 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#inference-text-to-image | .md | Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
First, load the pipeline:
```python
from diffusers import LuminaText2ImgPipeline
import torch | 110_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#inference-text-to-image | .md | pipeline = LuminaText2ImgPipeline.from_pretrained(
"Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16
).to("cuda")
```
Then change the memory layout of the pipelines `transformer` and `vae` components to `torch.channels-last`:
```python
pipeline.transformer.to(memory_format=torch.channels_last)
pipe... | 110_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#inference-text-to-image | .md | pipeline.vae.to(memory_format=torch.channels_last)
```
Finally, compile the components and run inference:
```python
pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)
pipeline.vae.decode = torch.compile(pipeline.vae.decode, mode="max-autotune", fullgraph=True) | 110_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#inference-text-to-image | .md | image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit with brass goggles and leather straps. Background shows an industrial revolution cityscape with smoky skies and tall, metal structures").images[0]
``` | 110_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#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. | 110_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#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 [`LuminaText2ImgPipeline`] for inference with bitsandbytes.
```py
import torch
... | 110_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#quantization | .md | from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, Transformer2DModel, LuminaText2ImgPipeline
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel | 110_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#quantization | .md | quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
"Alpha-VLLM/Lumina-Next-SFT-diffusers",
subfolder="text_encoder",
quantization_config=quant_config,
torch_dtype=torch.float16,
)
quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = Tran... | 110_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#quantization | .md | pipeline = LuminaText2ImgPipeline.from_pretrained(
"Alpha-VLLM/Lumina-Next-SFT-diffusers",
text_encoder=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)
prompt = "a tiny astronaut hatching from an egg on the moon"
image = pipeline(prompt).images[0]
image.save("lumina... | 110_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#luminatext2imgpipeline | .md | LuminaText2ImgPipeline
Pipeline for text-to-image generation using Lumina-T2I.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`... | 110_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#luminatext2imgpipeline | .md | text_encoder ([`AutoModel`]):
Frozen text-encoder. Lumina-T2I uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel), specifically the
[t5-v1_1-xxl](https://huggingface.co/Alpha-VLLM/tree/main/t5-v1_1-xxl) variant.
tokenizer (`AutoModel`):
Tokenizer of class
[AutoModel](https://huggingf... | 110_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/lumina.md | https://huggingface.co/docs/diffusers/en/api/pipelines/lumina/#luminatext2imgpipeline | .md | transformer ([`Transformer2DModel`]):
A text conditioned `Transformer2DModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
- all
- __call__ | 110_4_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/ | .md | <!--Copyright 2024 The HuggingFace Team, The InstantX Team, and the XLabs 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
U... | 111_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/ | .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.
--> | 111_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#controlnet-with-flux1 | .md | FluxControlNetPipeline is an implementation of ControlNet for Flux.1.
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. | 111_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#controlnet-with-flux1 | .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... | 111_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#controlnet-with-flux1 | .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 ... | 111_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#controlnet-with-flux1 | .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 ... | 111_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#controlnet-with-flux1 | .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.* | 111_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#controlnet-with-flux1 | .md | This controlnet code is implemented by [The InstantX Team](https://huggingface.co/InstantX). You can find pre-trained checkpoints for Flux-ControlNet in the table below:
| ControlNet type | Developer | Link |
| -------- | ---------- | ---- |
| Canny | [The InstantX Team](https://huggingface.co/InstantX) | [Link](http... | 111_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/pipelines/controlnet_flux/#controlnet-with-flux1 | .md | | Depth | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth) |
| Union | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union) |
XLabs ControlNets are also supported, which ... | 111_1_6 |
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