CompVis/stable-diffusion-v1-4

Request: DOI

#243 opened 5 days ago by

awdfawaw

I am new to developement i want to know where to find vae_decoder.ckpt

#242 opened 29 days ago by

AnilMnr

how to train train new model with different channel number in unet

#241 opened 2 months ago by

jonathan0227

Adding `safetensors` variant of this model

#240 opened 4 months ago by

LIqipei

Adding `safetensors` variant of this model

#239 opened 4 months ago by

LIqipei

OSError: CompVis/stable-diffusion-v1-4 does not appear to have a file named config.json. Checkout 'https://huggingface.co/CompVis/stable-diffusion-v1-4/None' for available files.

8

#238 opened 4 months ago by

Lily0512

OSError: /CompVis/stable-diffusion-v1-4/ does not appear to have a file named config.json.

9

#237 opened 5 months ago by

Kayell

RuntimeError

#236 opened 6 months ago by

kangxi1818

AttributeError: 'AutoencoderKLOutput' object has no attribute 'sample'

#235 opened 6 months ago by

Nobparad

abc

#234 opened 6 months ago by

ywt212

Update README.md

#232 opened 8 months ago by

Cheet0s

Is this normal?

#229 opened 9 months ago by

DadOfWinter

Femdom

3

#228 opened 9 months ago by

Wowa23478fak

Femdom

#227 opened 9 months ago by

Wowa23478fak

Upload stable-diffusion-v1-4.ckpt

#224 opened 9 months ago by

hipsterweeds

where to download the "sd-v1-4.ckpt" it's not in the "files and versions" where did it go??

3

#223 opened 9 months ago by

StarektTheDEv

Train an unconditional LDM on different classes, is it ok?

#222 opened 10 months ago by

Vincent171

# Stable Diffusion v1 Model Card This model card focuses on the model associated with the Stable Diffusion model, available [here](https://github.com/CompVis/stable-diffusion). ## Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [Proprietary](LICENSE) - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - Resources for more information: [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - Cite as: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v1 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training Training Data The model developers used the following dataset for training the model: - LAION-5B and subsets thereof (see next section) Training Procedure Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide the following checkpoints: - `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. 515k steps at resolution `512x512` on [laion-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - Hardware: 32 x 8 x A100 GPUs - Optimizer: AdamW - Gradient Accumulations: 2 - Batch: 32 x 8 x 2 x 4 = 2048 - Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](assets/v1-variants-scores.jpg) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact Stable Diffusion v1 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 150000 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 11250 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).

#221 opened 10 months ago by

Juno360219

RuntimeError: CUDA out of memory in stable diffusion

2

#217 opened 10 months ago by

Ahmed960

Connection refused on localhost (127.0.0.1:7860)

1

#215 opened 10 months ago by

fubidou

How to set optional parameters? How to set the size of an image?

#214 opened 10 months ago by

blansj

fg

#213 opened 11 months ago by

Juno360219

fg

#212 opened 11 months ago by

Juno360219

f

#211 opened 11 months ago by

Volsu

Update README.md

#210 opened 11 months ago by

CoprolaliacPress

How to set negative prompt in pipe?

3

#208 opened 12 months ago by

renshengchangzui

Create .ckpt

#207 opened 12 months ago by

aye14223

RuntimeError: "upsample_nearest2d_channels_last" not implemented for 'Half'

3

#206 opened about 1 year ago by

jin941013

If you don't know how to download it

#204 opened about 1 year ago by

Hektop

Select and Load Model Error

#202 opened about 1 year ago by

avigra

🚩 Report

#201 opened about 1 year ago by

141ADAKDHIH

Create ada

#200 opened about 1 year ago by

Valkig

RuntimeError

1

#199 opened about 1 year ago by

thomaszaap

Error: got an unexpected keyword argument 'init_image'

1

#198 opened about 1 year ago by

zennee

розовый фон

#197 opened about 1 year ago by

darpax

🚩 Report : Ethical issue(s)

#195 opened about 1 year ago by

ziyuh

error in colab while running from diffusers import StableDiffusionPipeline

#191 opened about 1 year ago by

supriyo

Update README.md

#190 opened about 1 year ago by

GalaticGod66

GPU based StableDiffusion in python is not working in MAC

2

#188 opened about 1 year ago by

muralimanohar

Update README.md

#187 opened about 1 year ago by

Stirling89

how do i download

3

#182 opened over 1 year ago by

GamerX

Experiencing same issue here!

11

#181 opened over 1 year ago by

Agorium

Tutorial series for how to use Stable Diffusion both on Google Colab and on your PC with Web UI interface - Install, Run, DreamBooth, Train, Custom Models, Yaml Files, Merge, Inject, Overtraining

#178 opened over 1 year ago by

MonsterMMORPG

OSError: CompVis/stable-diffusion-v1-4 does not appear to have a file named config.json.

5

#177 opened over 1 year ago by

nazianafis

Create Dc

#176 opened over 1 year ago by

Fightclub50

Onnxruntime inferencing using openvino execution provider based on stable diffusion onnx model

1

#170 opened over 1 year ago by

sk2893

Download Issue??

4

#169 opened over 1 year ago by

Ushguyer

Where are the weights on this page: Stable Diffusion v1-4 Model Card

4

#168 opened over 1 year ago by

prompterminal

Update README.md

#166 opened over 1 year ago by

Dixonbuttsdrivemenuts

You are using a model of type clip_text_model to instantiate a model of type .

#165 opened over 1 year ago by

prompterminal