Diffusers
Rank:Average Model Cost: $0.0000
Number of Runs: 5,754
Models by this creator
ddpm_dummy
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4.9K
Huggingface
tiny-stable-diffusion-torch
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697
Huggingface
stable-diffusion-2-1-unclip-i2i-l
stable-diffusion-2-1-unclip-i2i-l
Platform did not provide a description for this model.
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131
Huggingface
stable-diffusion-variants
stable-diffusion-variants
Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 🤗's Stable Diffusion blog. The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. You can use this both with the 🧨Diffusers library and the RunwayML GitHub repository. Diffusers For more detailed instructions, use-cases and examples in JAX follow the instructions here Original GitHub Repository Download the weights v1-5-pruned-emaonly.ckpt - 4.27GB, ema-only weight. uses less VRAM - suitable for inference v1-5-pruned.ckpt - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning Follow instructions here. Model Details Developed by: Robin Rombach, Patrick Esser Model type: Diffusion-based text-to-image generation model Language(s): English License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based. 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 that uses a fixed, pretrained text encoder (CLIP ViT-L/14) as suggested in the Imagen paper. Resources for more information: GitHub Repository, Paper. 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, 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. 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. 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 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/ 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 trained on subsets of LAION-2B(en), which consists of images that are primarily 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. Safety Module The intended use of this model is with the Safety Checker in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the CLIPTextModel after generation of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. Training Training Data The model developers used the following dataset for training the model: LAION-2B (en) and subsets thereof (see next section) Training Procedure Stable Diffusion v1-5 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. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. stable-diffusion-v1-1: 237,000 steps at resolution 256x256 on laion2B-en. 194,000 steps at resolution 512x512 on laion-high-resolution (170M examples from LAION-5B with resolution >= 1024x1024). stable-diffusion-v1-2: Resumed from stable-diffusion-v1-1. 515,000 steps at resolution 512x512 on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size >= 512x512, estimated aesthetics score > 5.0, and an estimated watermark probability < 0.5. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an improved aesthetics estimator). stable-diffusion-v1-3: Resumed from stable-diffusion-v1-2 - 195,000 steps at resolution 512x512 on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve classifier-free guidance sampling. stable-diffusion-v1-4 Resumed from stable-diffusion-v1-2 - 225,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve classifier-free guidance sampling. stable-diffusion-v1-5 Resumed from stable-diffusion-v1-2 - 595,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve classifier-free guidance sampling. stable-diffusion-inpainting Resumed from stable-diffusion-v1-5 - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. 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 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: 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 presented in Lacoste et al. (2019). 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 This model card was written by: Robin Rombach and Patrick Esser and is based on the DALL-E Mini model card.
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40
Huggingface
ddpm-cifar10-32-demo
ddpm-cifar10-32-demo
Denoising Diffusion Probabilistic Models (DDPM) Paper: Denoising Diffusion Probabilistic Models Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel Abstract: We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Inference DDPM models can use discrete noise schedulers such as: scheduling_ddpm scheduling_ddim scheduling_pndm for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the ddim or pndm schedulers instead. See the following code: For more in-detail information, please have a look at the official inference example Training If you want to train your own model, please have a look at the official training example Samples
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14
Huggingface
stable-diffusion-2-1-unclip-i2i-h
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1
Huggingface
stable-diffusion-2-1-unclip-t2i-h
stable-diffusion-2-1-unclip-t2i-h
Platform did not provide a description for this model.
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0
Huggingface
pix2pix-sd
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0
Huggingface
tools
tools
Diffusers Tools This is a collection of scripts that can be useful for various tasks related to the diffusers library 1. Test against original checkpoints It's very important to have visually the exact same results as the original code bases.! E.g. to make use diffusers is identical to the original CompVis codebase, you can run the following script in the original CompVis codebase: Download the original SD-1-4 checkpoint and put it in the correct folder following the instructions on: https://github.com/CompVis/stable-diffusion Run the following command and compare this to the same command in diffusers: Both commands should give the following image on a V100: 2. Test against k-diffusion: You can run the following script to compare against k-diffusion. See results here
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0
Huggingface
ddpm-dummy
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0
Huggingface