Paper bags will soon be available for customers again at the LCBO, after the premier ordered the Crown corporation to reverse its reusable bag policy.

Premier Doug Ford calls for LCBO to bring back paper bags | CBC News

Toilet paper wipes out thousands of acres of Canadian forest each year, and demand for it is growing.

Eco-friendly startups are turning to treeless toilet paper

Paper bags could soon return to the LCBO following a request from Ontario’s premier.

Doug Ford directs LCBO to go back to using single-use paper bags

Ontario’s liquor monopoly says it will comply with Ford’s directive to abandon policy it said would save the equivalent of 188,000 trees a year

Premier Doug Ford requests LCBO reinstate free paper bags - The Globe and Mail

The LCBO had first announced the phasing out of the paper bags last spring.

Doug Ford forces LCBO to reinstate paper bags for customers

The LCBO phased out paper bags in September 2023, encouraging customers to bring a reusable bag or purchase one.

Ford wants LCBO to bring back paper bags

JEE Mains 2024 Exam LIVE: The National Testing Agency (NTA) is conducting JEE Mains 2024 session 2 exam from today. As per the schedule, NTA JEE Paper 1 exam will be conducted in two shifts- 9 am to 12 noon and 3 pm to 6 pm. 12.57 lakh students have registered for JEEE Mains 2024 session 2. ...

JEE Mains 2024 LIVE Exam Analysis: Session 2 Slot 1 Ends, What Students Say | Times Now

Students of Class 10 students appeared for the Hindi and Commerce subjects while the students of Class 12 took Military Sciences and Hindi papers.

UP Boards 2024: Majority Students Were Satisfied With The Hindi Paper, Says Expert

Opaper

opaper

Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility 5 Existing Solutions This section sets out the existing perturbation solutions, taken from ZINSS. There is a natural focus on the first order approximation, although I am able to generalize salient results to non-linear solutions. I set out familiar problems with their predictions concerning persistence and optimal policy. Next, I set out two alternative models of nominal rigidity the Lucas and Rotemberg Phillips curves. Up to parametric conditions, I connect these three models in a pseudo-observational equivalence, which I later disprove. This allows me to link this wrong claim back to a particular argument in Lucas Jr [1976]. Finally, I review all aspects of this famous paper. Author:
(1) David Staines. This paper is available on arxiv under CC 4.0 license. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility 5 Existing Solutions This section sets out the existing perturbation solutions, taken from ZINSS. There is a natural focus on the first order approximation, although I am able to generalize salient results to non-linear solutions. I set out familiar problems with their predictions concerning persistence and optimal policy. Next, I set out two alternative models of nominal rigidity the Lucas and Rotemberg Phillips curves. Up to parametric conditions, I connect these three models in a pseudo-observational equivalence, which I later disprove. This allows me to link this wrong claim back to a particular argument in Lucas Jr [1976]. Finally, I review all aspects of this famous paper. Author: (1) David Staines. Author: Author: (1) David Staines. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

A Critical Review of Lucas Jr's Famous Paper on Nominal Rigidity

Author:
(1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 4 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract
The Benefits of Simple Writing
The Current Work
Study 1a: Method
Study 1a: Results
Study 1b: Method
Study 1b: Results
Study 2: Method
Study 2: Results
General Discussion, Acknowledgements, and References Study 1a: Method Data Collection To first evaluate if lay summaries had a simpler linguistic style than scientific summaries, significance statements and academic abstracts were respectively extracted from the journal Proceedings of the National Academy of Sciences (PNAS). This journal was selected because it is a widely read, high-impact general science journal that was one of the first outlets to require authors to provide traditional scientific summaries (e.g., abstracts) and lay summaries that appeal to average readers. PNAS also has topical breadth, scale, and longevity relative to other journals that may require lay summaries in that significance statements began in 2012 (31). A total of 42,022 publications were extracted from PNAS between January 2010 and March 2024 to capture possible papers that included both academic abstracts and significance statements. Only those with both summary types were included in this paper to create a yoked comparison within the same article. The final dataset included 34,584 papers (34,584 significance statements and 34,584 abstracts), totaling 10,799,256 words. Automated Text Analysis All texts were evaluated with Linguistic Inquiry and Word Count (LIWC), an automated text analysis tool that counts words as a percentage of the total word count per text (32). LIWC contains a validated internal dictionary of social (e.g., words related to family), psychological (e.g., words related to cognition, emotion), and part of speech dimensions (e.g., pronouns, articles, prepositions), and the tool measures the degree to which each text contains words from its respective dictionary categories. For example, the phrase “This science aims to improve society” contains 6 words and counts the following LIWC categories, including but not limited to: impersonal pronouns (this; 16.67% of the total word count) and positive tone words (improve; 16.67% of the total word count). All texts were run through LIWC-22 unless otherwise stated. Measures To evaluate how lay versus scientific summaries compared in terms of verbal simplicity, three measures were used from prior work to approximate simple language patterns (23): common words (e.g., the degree to which people use common and simple terms like job instead of uncommon and more complex terms like occupation), one’s analytic writing style (e.g., the degree to which people are formal and complex in their writing style compared to informal and their writing reflects a story), and readability (e.g., the number of words per sentence and big words in a person’s communication output). Consistent with prior work (23, 33–35), common words were operationalized with the LIWC dictionary category. LIWC’s dictionary represents a collection everyday words in English (36, 37). Therefore, texts that use more words from this dictionary are simpler than texts that use fewer words from this dictionary. One’s analytic writing style was operationalized with the LIWC analytic thinking index, which is a composite variable of seven style word categories. Style words represent how one is communicating rather than what they are communicating about (24, 38). This index contains high rates of articles and prepositions, but low rates of conjunctions, adverbs, auxiliary verbs, negations, and pronouns (26, 39, 40). [1] Finally, readability was operationalized with the Flesch Reading Ease metric (29) and calculated using the quanteda.textstats package in R (41). High scores on the Flesch Reading Ease metric suggest more readable and simpler writing (e.g., texts with smaller words and shorter sentences) compared to low scores. These language dimensions were evaluated as an index by first standardizing (z-scoring) each variable and then applying the following formula: Common Words + Readability – Analytic Writing. High scores are linguistically simpler than low scores. Analytic Plan Since each article contained one lay summary and one scientific summary from the same article, independent samples t-tests were conducted for the simplicity index and each individual dimension of the index. All data across studies are located on the Open Science Framework: https://osf.io/64am3/?view_only=883926733e6e494fa2f2011334b24796. This paper is available on arxiv under CC BY 4.0 DEED license. [1] Analytic writing = [articles + prepositions - pronouns - auxiliary verbs - adverb - conjunctions - negations] from LIWC scores (40). Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Author: Author: (1) David M. Markowitz, Department of Communication, Michigan State University, East Lansing, MI 48824. Editor's note: This is part 4 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 4 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Editor's note: This is part 4 of 10 of a paper evaluating the effectiveness of using generative AI to simplify science communication and enhance public trust in science. The rest of the paper can be accessed via the table of links below. Table of Links Abstract The Benefits of Simple Writing The Current Work Study 1a: Method Study 1a: Results Study 1b: Method Study 1b: Results Study 2: Method Study 2: Results General Discussion, Acknowledgements, and References Abstract Abstract The Benefits of Simple Writing The Benefits of Simple Writing The Current Work The Current Work Study 1a: Method Study 1a: Method Study 1a: Results Study 1a: Results Study 1b: Method Study 1b: Method Study 1b: Results Study 1b: Results Study 2: Method Study 2: Method Study 2: Results Study 2: Results General Discussion, Acknowledgements, and References General Discussion, Acknowledgements, and References Study 1a: Method Data Collection Data Collection To first evaluate if lay summaries had a simpler linguistic style than scientific summaries, significance statements and academic abstracts were respectively extracted from the journal Proceedings of the National Academy of Sciences (PNAS). This journal was selected because it is a widely read, high-impact general science journal that was one of the first outlets to require authors to provide traditional scientific summaries (e.g., abstracts) and lay summaries that appeal to average readers. PNAS also has topical breadth, scale, and longevity relative to other journals that may require lay summaries in that significance statements began in 2012 (31). A total of 42,022 publications were extracted from PNAS between January 2010 and March 2024 to capture possible papers that included both academic abstracts and significance statements. Only those with both summary types were included in this paper to create a yoked comparison within the same article. The final dataset included 34,584 papers (34,584 significance statements and 34,584 abstracts), totaling 10,799,256 words. Automated Text Analysis Automated Text Analysis All texts were evaluated with Linguistic Inquiry and Word Count (LIWC), an automated text analysis tool that counts words as a percentage of the total word count per text (32). LIWC contains a validated internal dictionary of social (e.g., words related to family), psychological (e.g., words related to cognition, emotion), and part of speech dimensions (e.g., pronouns, articles, prepositions), and the tool measures the degree to which each text contains words from its respective dictionary categories. For example, the phrase “This science aims to improve society” contains 6 words and counts the following LIWC categories, including but not limited to: impersonal pronouns (this; 16.67% of the total word count) and positive tone words (improve; 16.67% of the total word count). All texts were run through LIWC-22 unless otherwise stated. Measures Measures To evaluate how lay versus scientific summaries compared in terms of verbal simplicity, three measures were used from prior work to approximate simple language patterns (23): common words (e.g., the degree to which people use common and simple terms like job instead of uncommon and more complex terms like occupation), one’s analytic writing style (e.g., the degree to which people are formal and complex in their writing style compared to informal and their writing reflects a story), and readability (e.g., the number of words per sentence and big words in a person’s communication output). Consistent with prior work (23, 33–35), common words were operationalized with the LIWC dictionary category. LIWC’s dictionary represents a collection everyday words in English (36, 37). Therefore, texts that use more words from this dictionary are simpler than texts that use fewer words from this dictionary. One’s analytic writing style was operationalized with the LIWC analytic thinking index, which is a composite variable of seven style word categories. Style words represent how one is communicating rather than what they are communicating about (24, 38). This index contains high rates of articles and prepositions, but low rates of conjunctions, adverbs, auxiliary verbs, negations, and pronouns (26, 39, 40). [1] Finally, readability was operationalized with the Flesch Reading Ease metric (29) and calculated using the quanteda.textstats package in R (41). High scores on the Flesch Reading Ease metric suggest more readable and simpler writing (e.g., texts with smaller words and shorter sentences) compared to low scores. These language dimensions were evaluated as an index by first standardizing (z-scoring) each variable and then applying the following formula: Common Words + Readability – Analytic Writing. High scores are linguistically simpler than low scores. Analytic Plan Analytic Plan Since each article contained one lay summary and one scientific summary from the same article, independent samples t-tests were conducted for the simplicity index and each individual dimension of the index. All data across studies are located on the Open Science Framework: https://osf.io/64am3/?view_only=883926733e6e494fa2f2011334b24796. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] Analytic writing = [articles + prepositions - pronouns - auxiliary verbs - adverb - conjunctions - negations] from LIWC scores (40).

Inside the Numbers: How 34,584 Science Papers Reveal the Secrets of Simpler Writing

Why Mamba Could Be the Future of Big Data Processing

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 2 Related Work Early text-to-image generative models, such as DALL-E (Ramesh et al., 2021) and CogView (Ding et al., 2021), or later Parti (Yu et al., 2022) employed autoregressive approaches but often suffered from significant content-level artifacts. This led to the development of a new breed of models that utilized the diffusion process to enhance image quality. Diffusion-based models, such as DALLE 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), and Stable Diffusion[6], have since become cornerstones in this domain. These models are typically divided into pixel-level (Ramesh et al., 2022; Saharia et al., 2022b) and latent-level (Rombach et al., 2022) approaches. This surge of interest has led to the design of innovative approaches and architectures, paving the way for numerous applications based on opensource generative models, such as DreamBooth (Ruiz et al., 2023) and DreamPose (Karras et al., 2023). These applications exploit image generation techniques to offer remarkable features, further fueling the popularity and the rapid development of diffusion-based image generation approaches. This enabled a wide array of applications like 3D object synthesis (Poole et al., 2023; Tang et al., 2023; Lin et al., 2022; Chen et al., 2023), video generation (Ho et al., 2022b; Luo et al., 2023; Ho et al., 2022a; Singer et al., 2023; Blattmann et al., 2023; Esser et al., 2023), controllable image editing (Hertz et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al., 2023; Lu et al., 2023), and more which are now at the forefront of this domain. Diffusion models achieve state-of-the-art results in image generation task both unconditional (Ho et al., 2020; Nichol and Dhariwal, 2021) and conditional (Peebles and Xie, 2022). They beat GANs (Goodfellow et al., 2014) by generating images with better scores of fidelity and diversity without adversarial training (Dhariwal and Nichol, 2021). Diffusion models also show the best performance in various image processing tasks like inpainting, outpainting, and super-resolution (Batzolis et al., 2021; Saharia et al., 2022a). Text-to-image diffusion models have become a popular research direction due to the high performance of diffusion models and the ability to simply integrate text conditions with the classifierfree guidance algorithm (Ho and Salimans, 2022). Early models like GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022b), DALL-E 2 (Ramesh et al., 2022) and eDiff-I (Balaji et al., 2022) generate low-resolution image in pixel space and then upsample it with another super-resolution diffusion models. They are also using different text encoders, large language model T5 (Raffel et al., 2020) in Imagen, CLIP (Radford et al., 2021) in GLIDE and DALL-E 2. This paper is available on arxiv under CC BY 4.0 DEED license. [6] https://github.com/CompVis/stable-diffusion Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 2 Related Work Early text-to-image generative models, such as DALL-E (Ramesh et al., 2021) and CogView (Ding et al., 2021), or later Parti (Yu et al., 2022) employed autoregressive approaches but often suffered from significant content-level artifacts. This led to the development of a new breed of models that utilized the diffusion process to enhance image quality. Diffusion-based models, such as DALLE 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), and Stable Diffusion[6], have since become cornerstones in this domain. These models are typically divided into pixel-level (Ramesh et al., 2022; Saharia et al., 2022b) and latent-level (Rombach et al., 2022) approaches. This surge of interest has led to the design of innovative approaches and architectures, paving the way for numerous applications based on opensource generative models, such as DreamBooth (Ruiz et al., 2023) and DreamPose (Karras et al., 2023). These applications exploit image generation techniques to offer remarkable features, further fueling the popularity and the rapid development of diffusion-based image generation approaches. This enabled a wide array of applications like 3D object synthesis (Poole et al., 2023; Tang et al., 2023; Lin et al., 2022; Chen et al., 2023), video generation (Ho et al., 2022b; Luo et al., 2023; Ho et al., 2022a; Singer et al., 2023; Blattmann et al., 2023; Esser et al., 2023), controllable image editing (Hertz et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al., 2023; Lu et al., 2023), and more which are now at the forefront of this domain. Diffusion models achieve state-of-the-art results in image generation task both unconditional (Ho et al., 2020; Nichol and Dhariwal, 2021) and conditional (Peebles and Xie, 2022). They beat GANs (Goodfellow et al., 2014) by generating images with better scores of fidelity and diversity without adversarial training (Dhariwal and Nichol, 2021). Diffusion models also show the best performance in various image processing tasks like inpainting, outpainting, and super-resolution (Batzolis et al., 2021; Saharia et al., 2022a). Text-to-image diffusion models have become a popular research direction due to the high performance of diffusion models and the ability to simply integrate text conditions with the classifierfree guidance algorithm (Ho and Salimans, 2022). Early models like GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022b), DALL-E 2 (Ramesh et al., 2022) and eDiff-I (Balaji et al., 2022) generate low-resolution image in pixel space and then upsample it with another super-resolution diffusion models. They are also using different text encoders, large language model T5 (Raffel et al., 2020) in Imagen, CLIP (Radford et al., 2021) in GLIDE and DALL-E 2. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [6] https://github.com/CompVis/stable-diffusion

Russian Scientists Identify Why Older Text-to-Image Models Couldn’t Create Realistic Images

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 5 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 5 Experiments We sought to evaluate and refine the performance of our proposed latent diffusion architecture in our experimental analysis. To this end, we employed automatic metrics, specifically FID-CLIP curves on the COCO-30K dataset, to obtain the optimal guidance-scale value and compare Kandinsky with competitors (cf. Figure 4). Furthermore, we conducted investigations with various image prior setups, exploring the impact of different configurations on the performance. These setups included: no prior, utilizing text embeddings directly; linear prior, implementing one linear layer; ResNet prior, consisting of 18 residual MLP blocks; and transformer diffusion prior. An essential aspect of our experiments was the exploration of the effect of latent quantization within the MoVQ autoencoder. We examined the outputs with latent quantization, both enabled and disabled, to better comprehend its influence on image generation quality. To ensure a comprehensive evaluation, we also included an assessment of the IF model [12], which is the closest open-source competitor to our proposed model. For this purpose, we computed FID scores for the IF model [13] (Table 1). However, we acknowledged the limitations of automatic metrics that become obvious when it comes to capturing user experience nuances. Hence, in addition to the FID-CLIP curves, we conducted a blind human evaluation to obtain insightful feed-back and validate the quality of the generated images from the perspective of human perception based on the DrawBench dataset (Saharia et al., 2022b). The combination of automatic metrics and human evaluation provides a comprehensive assessment of Kandinsky performance, enabling us to make informed decisions about the effectiveness and usability of our proposed image prior to design. This paper is available on arxiv under CC BY 4.0 DEED license. [12] https://github.com/deep-floyd/IF [13] https://github.com/mseitzer/pytorch-fid Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 5 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 5 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 5 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 5 Experiments We sought to evaluate and refine the performance of our proposed latent diffusion architecture in our experimental analysis. To this end, we employed automatic metrics, specifically FID-CLIP curves on the COCO-30K dataset, to obtain the optimal guidance-scale value and compare Kandinsky with competitors (cf. Figure 4). Furthermore, we conducted investigations with various image prior setups, exploring the impact of different configurations on the performance. These setups included: no prior, utilizing text embeddings directly; linear prior, implementing one linear layer; ResNet prior, consisting of 18 residual MLP blocks; and transformer diffusion prior. An essential aspect of our experiments was the exploration of the effect of latent quantization within the MoVQ autoencoder. We examined the outputs with latent quantization, both enabled and disabled, to better comprehend its influence on image generation quality. To ensure a comprehensive evaluation, we also included an assessment of the IF model [12], which is the closest open-source competitor to our proposed model. For this purpose, we computed FID scores for the IF model [13] (Table 1). However, we acknowledged the limitations of automatic metrics that become obvious when it comes to capturing user experience nuances. Hence, in addition to the FID-CLIP curves, we conducted a blind human evaluation to obtain insightful feed-back and validate the quality of the generated images from the perspective of human perception based on the DrawBench dataset (Saharia et al., 2022b). The combination of automatic metrics and human evaluation provides a comprehensive assessment of Kandinsky performance, enabling us to make informed decisions about the effectiveness and usability of our proposed image prior to design. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [12] https://github.com/deep-floyd/IF [13] https://github.com/mseitzer/pytorch-fid

Russian Scientists Create AI That Generates Images People Actually Love

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 1 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References Abstract Text-to-image generation is a significant domain in modern computer vision and has achieved substantial improvements through the evolution of generative architectures. Among these, there are diffusion-based models that have demonstrated essential quality enhancements. These models are generally split into two categories: pixel-level and latent-level approaches. We present Kandinsky[1], a novel exploration of latent diffusion architecture, combining the principles of the image prior models with latent diffusion techniques. The image prior model is trained separately to map text embeddings to image embeddings of CLIP. Another distinct feature of the proposed model is the modified MoVQ implementation, which serves as the image autoencoder component. Overall, the designed model contains 3.3B parameters. We also deployed a user-friendly demo system that supports diverse generative modes such as text-to-image generation, image fusion, text and image fusion, image variations generation, and text-guided inpainting/outpainting. Additionally, we released the source code and checkpoints for the Kandinsky models. Experimental evaluations demonstrate a FID score of 8.03 on the COCO-30K dataset, marking our model as the top open-source performer in terms of measurable image generation quality. 1 Introduction In quite a short period of time, generative abilities of text-to-image models have improved substantially, providing users with photorealistic quality, near real-time inference speed, a great number of applications and features, including simple easy-to-use web-based platforms and sophisticated AI graphics editors. This paper presents our unique investigation of latent diffusion architecture design, offering a fresh and innovative perspective on this dynamic field of study. First, we describe the new architecture of Kandinsky and its details. The demo system with implemented features of the model is also described. Second, we show the experiments, carried out in terms of image generation quality and come up with the highest FID score among existing open-source models. Additionally, we present the rigorous ablation study of prior setups that we conducted, enabling us to carefully analyze and evaluate various configurations to arrive at the most effective and refined model design. Our contributions are as follows: • We present the first text-to-image architecture designed using a combination of image prior and latent diffusion. • We demonstrate experimental results comparable to the state-of-the-art (SotA) models such as Stable Diffusion, IF, and DALL-E 2, in terms of FID metric and achieve the SotA score among all existing open source models. • We provide a software implementation of the proposed state-of-the-art method for textto-image generation, and release pre-trained models, which is unique among the topperforming methods. Apache 2.0 license makes it possible to use the model for both non-commercial and commercial purposes.2 3 • We create a web image editor application that can be used for interactive generation of images by text prompts (English and Russian languages are supported) on the basis of the proposed method, and provides inpainting/outpainting functionality.4 The video demonstration is available on YouTube.5 This paper is available on arxiv under CC BY 4.0 DEED license. [1] The system is named after Wassily Kandinsky, a famous painter and an art theorist. [2] https://github.com/ai-forever/Kandinsky-2 [3] https://huggingface.co/kandinsky-community [4] https://fusionbrain.ai/en/editor [5] https://www.youtube.com/watch?v=c7zHPc59cWU Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 1 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 1 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 1 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References Abstract Text-to-image generation is a significant domain in modern computer vision and has achieved substantial improvements through the evolution of generative architectures. Among these, there are diffusion-based models that have demonstrated essential quality enhancements. These models are generally split into two categories: pixel-level and latent-level approaches. We present Kandinsky[1], a novel exploration of latent diffusion architecture, combining the principles of the image prior models with latent diffusion techniques. The image prior model is trained separately to map text embeddings to image embeddings of CLIP. Another distinct feature of the proposed model is the modified MoVQ implementation, which serves as the image autoencoder component. Overall, the designed model contains 3.3B parameters. We also deployed a user-friendly demo system that supports diverse generative modes such as text-to-image generation, image fusion, text and image fusion, image variations generation, and text-guided inpainting/outpainting. Additionally, we released the source code and checkpoints for the Kandinsky models. Experimental evaluations demonstrate a FID score of 8.03 on the COCO-30K dataset, marking our model as the top open-source performer in terms of measurable image generation quality. 1 Introduction In quite a short period of time, generative abilities of text-to-image models have improved substantially, providing users with photorealistic quality, near real-time inference speed, a great number of applications and features, including simple easy-to-use web-based platforms and sophisticated AI graphics editors. This paper presents our unique investigation of latent diffusion architecture design, offering a fresh and innovative perspective on this dynamic field of study. First, we describe the new architecture of Kandinsky and its details. The demo system with implemented features of the model is also described. Second, we show the experiments, carried out in terms of image generation quality and come up with the highest FID score among existing open-source models. Additionally, we present the rigorous ablation study of prior setups that we conducted, enabling us to carefully analyze and evaluate various configurations to arrive at the most effective and refined model design. Our contributions are as follows: contributions • We present the first text-to-image architecture designed using a combination of image prior and latent diffusion. • We demonstrate experimental results comparable to the state-of-the-art (SotA) models such as Stable Diffusion, IF, and DALL-E 2, in terms of FID metric and achieve the SotA score among all existing open source models. • We provide a software implementation of the proposed state-of-the-art method for textto-image generation, and release pre-trained models, which is unique among the topperforming methods. Apache 2.0 license makes it possible to use the model for both non-commercial and commercial purposes.2 3 • We create a web image editor application that can be used for interactive generation of images by text prompts (English and Russian languages are supported) on the basis of the proposed method, and provides inpainting/outpainting functionality.4 The video demonstration is available on YouTube.5 This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] The system is named after Wassily Kandinsky, a famous painter and an art theorist. [2] https://github.com/ai-forever/Kandinsky-2 [3] https://huggingface.co/kandinsky-community [4] https://fusionbrain.ai/en/editor [5] https://www.youtube.com/watch?v=c7zHPc59cWU

Russian Scientists Develop First Text-to-Image Architecture Using Image Prior, Latent Diffusion

Rusia markan cientificonakax Nayrïr Arquitectura Texto-Imagen ukar uñstayapxi, Image Prior, Difusión Latente ukamp apnaqasa

Rus alimləri Şəkildən əvvəl, gizli diffuziyadan istifadə edərək ilk mətndən şəkilə arxitektura hazırlayıblar

Ruski naučnici razvili prvu arhitekturu teksta u sliku koristeći prethodnu, latentnu difuziju slike

Científicos rusos desarrollan la primera arquitectura de texto a imagen utilizando difusión latente previa a la imagen

دانشمندان روسی اولین معماری متن به تصویر را با استفاده از تصویر قبلی، انتشار پنهان توسعه دادند.

Ilmuwan Rusia Kembangkan Arsitektur Teks-ke-Gambar Pertama Menggunakan Prioritas Gambar, Difusi Laten

ロシアの科学者が、画像の事前分布と潜在的拡散を利用した初のテキストから画像へのアーキテクチャを開発

Орус илимпоздору сүрөттүн алдындагы, жашыруун диффузияны колдонуу менен биринчи тексттен сүрөткө архитектурасын иштеп чыгышты

Saintis Rusia Membangunkan Seni Bina Teks-ke-Imej Pertama Menggunakan Imej Terdahulu, Resapan Terpendam

रूसी वैज्ञानिकहरूले छवि पूर्व, अव्यक्त प्रसार प्रयोग गरेर पहिलो पाठ-देखि-छवि वास्तुकला विकास गर्छन्

Abashakashatsi b'Abarusiya Batezimbere Umwandiko-Kuri-Ishusho Ubwubatsi Ukoresheje Ishusho Mbere, Gutandukana Bitinze

Ruskí vedci vyvinuli prvú architektúru textu na obrázok pomocou latentnej difúzie obrazu

This Open-Source Image Generator From Russian Scientists Uses A Novel Approach to Diffusion

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 7 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 7 Conclusion We presented Kandinsky, a system for various image generation and processing tasks based on a novel latent diffusion model. Our model yielded the SotA results among open-sourced systems. Additionally, we provided an extensive ablation study of an image prior to design choices. Our system is equipped with free-to-use interfaces in the form of Web application and Telegram messenger bot. The pre-trained models are available on Hugging Face, and the source code is released under a permissive license enabling various, including commercial, applications of the developed technology. In future research, our goal is to investigate the potential of the latest image encoders. We plan to explore the development of more efficient UNet architectures for text-to-image tasks and focus on improving the understanding of textual prompts. Additionally, we aim to experiment with generating images at higher resolutions and to investigate new features extending the model: local image editing by a text prompt, attention reweighting, physics-based generation control, etc. The robustness against generating abusive content remains a crucial concern, warranting the exploration of real-time moderation layers or robust classifiers to mitigate undesirable, e.g. toxic or abusive, outputs. 8 Limitations The current system produces images that appear natural, however, additional research can be conducted to (1) enhance the semantic coherence between the input text and the generated image, and (2) to improve the absolute values of FID and image quality based on human evaluations. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 7 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 7 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 7 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 7 Conclusion We presented Kandinsky, a system for various image generation and processing tasks based on a novel latent diffusion model. Our model yielded the SotA results among open-sourced systems. Additionally, we provided an extensive ablation study of an image prior to design choices. Our system is equipped with free-to-use interfaces in the form of Web application and Telegram messenger bot. The pre-trained models are available on Hugging Face, and the source code is released under a permissive license enabling various, including commercial, applications of the developed technology. In future research, our goal is to investigate the potential of the latest image encoders. We plan to explore the development of more efficient UNet architectures for text-to-image tasks and focus on improving the understanding of textual prompts. Additionally, we aim to experiment with generating images at higher resolutions and to investigate new features extending the model: local image editing by a text prompt, attention reweighting, physics-based generation control, etc. The robustness against generating abusive content remains a crucial concern, warranting the exploration of real-time moderation layers or robust classifiers to mitigate undesirable, e.g. toxic or abusive, outputs. 8 Limitations The current system produces images that appear natural, however, additional research can be conducted to (1) enhance the semantic coherence between the input text and the generated image, and (2) to improve the absolute values of FID and image quality based on human evaluations. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Russian Scientists Unveil Open-Source Image Generator With a Groundbreaking Diffusion Method

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 4 Kandinsky Architecture In our work, we opted to deliver state-of-the-art text-to-image synthesis. In the initial stages of our research, we experimented with multilingual text encoders, such as mT5 (Xue et al., 2021), XLMR (Conneau et al., 2020), XLMR-CLIP[7], to facilitate robust multilingual text-to-image generation. However, we discovered that using the CLIP-image embeddings instead of standalone text encoders resulted in improved image quality. As a result, we adopted an image prior approach, utilizing diffusion and linear mappings between text and image embedding spaces of CLIP, while keeping additional conditioning with XLMR text embeddings. That is why Kandinsky uses two text encoders: CLIP-text with image prior mapping and XLMR. We have set these encoders to be frozen during the training phase. The significant factor that influenced our design choice was the efficiency of training latent diffusion models, as compared to pixel-level diffusion models (Rombach et al., 2022). This led us to focus our efforts on the latent diffusion architecture. Our model essentially comprises three stages: text encoding, embedding mapping (image prior), and latent diffusion. The construction of our model involves three primary steps: text encoding, embedding mapping (image prior), and latent diffusion. At the embedding mapping step, which we also refer to as the image prior, we use the transformer-encoder model. This model was trained from scratch with a diffusion process on text and image embeddings provided by the CLIP-ViT-L14 model. A noteworthy feature in our training process is the use of elementwise normalization of visual embeddings. This normalization is based on full-dataset statistics and leads to faster convergence of the diffusion process. We implemented inverse normalization to revert to the original CLIP-image embedding space in the inference stage. The image prior model is trained on text and image embeddings, provided by the CLIP models. We conducted a series of experiments and ablation studies on the specific architecture design of the image prior model (Table 3, Figure 6). The model with the best human evaluation score is based on a 1D-diffusion and standard transformer-encoder with the following parameters: num_layers=20, num_heads=32, and hidden_size=2048. The latent diffusion part employs a UNet model along with a custom pre-trained autoencoder. Our diffusion model uses a combination of multiple condition signals: CLIP-image embeddings, CLIPtext embeddings, and XLMR-CLIP text embeddings. CLIP-image and XLMR-CLIP embeddings are merged and utilized as an input to the latent diffusion process. Also, we conditioned the diffusion process on these embeddings by adding all of them to the time-embedding. Notably, we did not skip the quantization step of the autoencoder during diffusion inference as it leads to an increase in the diversity and the quality of generated images (cf. Figure 4). In total, our model comprises 3.3 B parameters (Table 2). We observed that the image decoding was our main bottleneck in terms of generated image quality; hence, we developed a Sber-MoVQGAN, our custom implementation of MoVQGAN (Zheng et al., 2022) with minor modifications. We trained this autoencoder on the LAION HighRes dataset (Schuhmann et al., 2022), obtaining the SotA results in image reconstruction. We released the weights and code for these models under an open source licence[11]. The comparison of our autoencoder with competitors can be found in Table 4. This paper is available on arxiv under CC BY 4.0 DEED license. [7] https://github.com/FreddeFrallan/ Multilingual-CLIP [8] https://github.com/Stability-AI/ stablediffusion [9] https://github.com/ai-forever/ru-dalle [10] https://github.com/gligen/GLIGEN [11] https://github.com/ai-forever/MoVQGAN Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 4 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 4 Kandinsky Architecture In our work, we opted to deliver state-of-the-art text-to-image synthesis. In the initial stages of our research, we experimented with multilingual text encoders, such as mT5 (Xue et al., 2021), XLMR (Conneau et al., 2020), XLMR-CLIP[7], to facilitate robust multilingual text-to-image generation. However, we discovered that using the CLIP-image embeddings instead of standalone text encoders resulted in improved image quality. As a result, we adopted an image prior approach, utilizing diffusion and linear mappings between text and image embedding spaces of CLIP, while keeping additional conditioning with XLMR text embeddings. That is why Kandinsky uses two text encoders: CLIP-text with image prior mapping and XLMR. We have set these encoders to be frozen during the training phase. The significant factor that influenced our design choice was the efficiency of training latent diffusion models, as compared to pixel-level diffusion models (Rombach et al., 2022). This led us to focus our efforts on the latent diffusion architecture. Our model essentially comprises three stages: text encoding, embedding mapping (image prior), and latent diffusion. The construction of our model involves three primary steps: text encoding, embedding mapping (image prior), and latent diffusion. At the embedding mapping step, which we also refer to as the image prior, we use the transformer-encoder model. This model was trained from scratch with a diffusion process on text and image embeddings provided by the CLIP-ViT-L14 model. A noteworthy feature in our training process is the use of elementwise normalization of visual embeddings. This normalization is based on full-dataset statistics and leads to faster convergence of the diffusion process. We implemented inverse normalization to revert to the original CLIP-image embedding space in the inference stage. The image prior model is trained on text and image embeddings, provided by the CLIP models. We conducted a series of experiments and ablation studies on the specific architecture design of the image prior model (Table 3, Figure 6). The model with the best human evaluation score is based on a 1D-diffusion and standard transformer-encoder with the following parameters: num_layers=20, num_heads=32, and hidden_size=2048. The latent diffusion part employs a UNet model along with a custom pre-trained autoencoder. Our diffusion model uses a combination of multiple condition signals: CLIP-image embeddings, CLIPtext embeddings, and XLMR-CLIP text embeddings. CLIP-image and XLMR-CLIP embeddings are merged and utilized as an input to the latent diffusion process. Also, we conditioned the diffusion process on these embeddings by adding all of them to the time-embedding. Notably, we did not skip the quantization step of the autoencoder during diffusion inference as it leads to an increase in the diversity and the quality of generated images (cf. Figure 4). In total, our model comprises 3.3 B parameters (Table 2). We observed that the image decoding was our main bottleneck in terms of generated image quality; hence, we developed a Sber-MoVQGAN, our custom implementation of MoVQGAN (Zheng et al., 2022) with minor modifications. We trained this autoencoder on the LAION HighRes dataset (Schuhmann et al., 2022), obtaining the SotA results in image reconstruction. We released the weights and code for these models under an open source licence[11]. The comparison of our autoencoder with competitors can be found in Table 4. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [7] https://github.com/FreddeFrallan/ Multilingual-CLIP [8] https://github.com/Stability-AI/ stablediffusion [9] https://github.com/ai-forever/ru-dalle [10] https://github.com/gligen/GLIGEN [11] https://github.com/ai-forever/MoVQGAN

Russian Scientists Say New AI Architecture Produces State-of-the-Art Text-to-Image Synthesis

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 6 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 6 Results Our experiments and evaluations have showcased the capabilities of Kandinsky architecture in text-toimage synthesis. Kandinsky achieved the FID score of 8.03 on the COCO-30K validation set at a resolution of 256×256, which puts it in close competition with the state-of-the-art models, and among the top performers within open-source systems. Our methodical ablation studies further dissected the performance of different configurations: quantization of latent codes in MoVQ slightly improves the quality of images (FID 9.86 vs 9.87). The best CLIP score and human-eval score are obtained by diffusion prior. The best FID score is achieved using Linear Prior. This configuration stands out with the best FID score of 8.03. It is an intriguing outcome: the simplest linear mapping showcased the best FID, suggesting that there might exist a linear relationship between visual and textual embedding vector spaces. To further scrutinize this hypothesis, we trained a linear mapping on a subset of 500 cat images and termed it the "cat prior". Astonishingly, this mapping displayed high proficiency (cf. Figure 5). This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 6 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 6 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 6 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 6 Results Our experiments and evaluations have showcased the capabilities of Kandinsky architecture in text-toimage synthesis. Kandinsky achieved the FID score of 8.03 on the COCO-30K validation set at a resolution of 256×256, which puts it in close competition with the state-of-the-art models, and among the top performers within open-source systems. Our methodical ablation studies further dissected the performance of different configurations: quantization of latent codes in MoVQ slightly improves the quality of images (FID 9.86 vs 9.87). The best CLIP score and human-eval score are obtained by diffusion prior. The best FID score is achieved using Linear Prior. This configuration stands out with the best FID score of 8.03. It is an intriguing outcome: the simplest linear mapping showcased the best FID, suggesting that there might exist a linear relationship between visual and textual embedding vector spaces. To further scrutinize this hypothesis, we trained a linear mapping on a subset of 500 cat images and termed it the "cat prior". Astonishingly, this mapping displayed high proficiency (cf. Figure 5). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Stories, News & Mentions about OPAPER

Opaper’s Stories on HackerNoon

Opaper's News & Mentions