Historica is working to employ AI to process and manage vast amounts of data from various scientific fields. We are sharing our technological diary about our experience with using AI to create a digital map of human history.

An abstract, steampunk-inspired mechanism

May 2024 - Roman Chepenko - Using Large Language Models for Feature Engineering and Annotating Historical Data.


The development of large language models (LLMs) offers significant opportunities for automating the analysis of vast datasets for various purposes. At Historica, our research laboratory, we have delved into the field of feature engineering using LLMs to explore their potential in historical data annotation.

Modern AI technologies are opening new horizons for analyzing historical data. In paper "The Semantics of History" by the University of Barcelona, the authors formulated the approach of annotating and storing data for analyzing textual data from different fields of study. The primary goal of our experiment is to test the hypothesis that LLMs can be effectively utilized for feature engineering of historical data based on suggested approach.

Review of the University of Barcelona's Study

  • Unit of Topography (UT): Evidence of an action or situation located in space and time, regardless of the specificity of the information source and its attributes. Each UT has a specific location and date, which can be expressed as a UTM coordinate or an administrative delimitation that might have changed over time.
  • Unit of Stratigraphy (US): Material evidence of a past action, representing an archaeological aspect of the cycle of time. Essential attributes of these units include graphic and cartographic representations.
  • Actor: An individual or corporate entity involved in an action identified as a UT, with attributes like name, gender, religion, citizenship, date of birth and death, etc. Multiple individuals gathered for a specific period with a particular purpose can act as corporate actors

Automatic Annotation of Historical Data

Automatic annotation is a crucial step in the analysis of any textual data, especially for historical data, where the accuracy of annotations is critical. Traditional methods include manual annotation, which is time-consuming and labor-intensive. Besides manual labor, alternative approaches for annotating historical textual data include spaCy, NLTK, TextBlob, and StanfordNLP. With advancements in the field of ML/NLP transformers, modern technologies like LLMs enable automation of this process, significantly speeding up and simplifying the work with large datasets.

Capabilities of LLMs for Automatic Annotation

LLMs offer powerful tools for processing and analyzing textual data. They can generate annotations, classify texts, and identify hidden connections in the data. LLMs are also suitable for feature engineering. One of the critical questions at this stage is choosing between open-source models and proprietary models (APIs from OpenAI, Google, etc.).

When selecting tools for data analysis, it is essential to consider various aspects such as accessibility, cost, and flexibility. Open-source solutions provide a high level of flexibility and customization for specific tasks. Proprietary solutions, on the other hand, may offer more stable and ready-to-use tools but are often limited in customization and require significant resources.

Experiment with LLMs

For our experiment, we aimed to compare several models from OpenAI and selected a few open-source models for a comprehensive evaluation. The criteria for selection included text generation quality, speed, and customization capability. We relied on the LLM Arena on Hugging Face to guide our choice of models.

Data Collection for the Experiment

Data for the experiment were gathered from open historical sources. We randomly selected texts related to Estonia stored on Wikipedia. The data were standardized, resulting in five texts of varying lengths. Three texts had token counts (the smallest meaningful unit for LLMs) not exceeding 400, while the remaining two texts consisted of 2955 and 12488 tokens.

Overview of the Experiment

The experiment involved annotating historical texts using the selected models. The primary task was to verify the hypothesis derived from the University of Barcelona's article. An example task included analyzing historical text using the following prompt:

“As a specialized NLP model, your task is to process historical text data for ontology-based storage. Your task is to analyze the historical text below and extract important information relevant to the ontology database. Specifically, identify entities and relationships based on the following categories:

- UT (Unit of Topography): The evidence of an action or situation that can be located in space and time. It should include both a location and a date.

- US (Unit of Stratigraphy): The material evidence of a past action, typically archaeological in nature. Graphic and cartographic representations are key attributes.

- AC (Actor): Individuals or organizations involved in an action linked to a UT. Attributes include name, gender, citizenship, and other personal details.

Provide the extracted data in JSON format with the following structure:


    "UT": [


            "location": "<location>",

            "date": "<date>",

            "attributes": ["<additional attributes>"]




    "US": [


            "evidence": "<description>",

            "attributes": ["<additional attributes>"]




    "AC": [


            "name": "<name>",

            "attributes": ["<additional attributes>"]





Text: {text}

Comparative Analysis of Models

We have embarked on an exploration of feature engineering using LLMs, particularly focusing on the automatic annotation of historical data. This report provides a comprehensive evaluation of several LLMs, assessing their performance based on multiple criteria:

  • Аccuracy and completeness,
  • Сonsistency and relevance,
  • Latency and cost-effectiveness,
  • Absence of hallucinations
  • Subjective evaluation of model output.

Our goal is to determine the most effective models for historical data annotation and to provide insights into their practical applications.

Model Performance Summary


GPT-3.5-turbo-instruct demonstrated high efficiency and consistent results across all analysis categories. With high accuracy, consistency, completeness, and relevance, the model only failed to meet the requirements once, indicating its reliability and capability to produce high-quality annotations. Its moderate latency and high cost-effectiveness further highlight its suitability for extensive historical data annotation tasks. The model also exhibited excellent performance in avoiding hallucinations, ensuring the integrity of the generated annotations.


This variant proved to be the weakest in our set, consistently showing below-average results compared to other models. With low accuracy, consistency, completeness, and relevance, it struggled to meet the necessary standards. Despite its high latency and moderate cost-effectiveness, its persistent low performance makes it less suitable for automatic historical data annotation tasks. Additionally, the model often produced hallucinations, further reducing its reliability.


The model exhibited variable performance depending on the task, demonstrating moderate accuracy, consistency, completeness, and relevance. In some cases, it significantly outperformed other models, showing potential for specific types of tasks. However, its moderate latency and high cost-effectiveness are offset by its inconsistent performance, making it less reliable for general application. The model performed moderately in avoiding hallucinations, indicating some potential for improvement.


While this model occasionally delivered good results, it was unstable and did not always meet evaluation criteria. It showed moderate accuracy, completeness, and relevance but low consistency. Its high latency and moderate cost-effectiveness reduce its overall effectiveness for automatic annotation, particularly where consistency is critical. The model's moderate performance in avoiding hallucinations indicates a need for further refinement.


This model operates relatively quickly and maintains the data structure in its outputs, demonstrating moderate accuracy, consistency, and relevance. However, its responses are sometimes superficial, resulting in low completeness. The model's high latency and low cost-effectiveness further impact its overall performance, making it less suitable for comprehensive historical data annotation. Its moderate ability to avoid hallucinations is insufficient to compensate for its other weaknesses.


The model demonstrates good adherence to annotation structure and effective memory utilization for attribute filling, showing high accuracy, consistency, and relevance. However, its responses are not always comprehensive, which limits its completeness. With moderate latency and high cost-effectiveness, this model excels in maintaining high accuracy and consistency while effectively avoiding hallucinations.


This model consistently delivers good results, with high accuracy, consistency, completeness, and relevance. Although it is not the fastest, its low latency and large context window allow it to handle complex tasks effectively. Its moderate cost-effectiveness is balanced by its ability to maintain high performance in avoiding hallucinations, making it a good option for similar research.


The model provides good and fast responses, showing high accuracy, consistency, completeness, and relevance. Its low latency and high cost-effectiveness make it one of the better options for similar tasks. Additionally, its high performance in avoiding hallucinations ensures the reliability of its outputs.


This model offers very fast and high-quality responses, with a large context window, demonstrating high accuracy, consistency, completeness, and relevance. Its very low latency and high performance in avoiding hallucinations make it an excellent choice for tasks involving automatic annotation of historical data, especially where speed and accuracy are paramount. While its cost-effectiveness is moderate, the overall benefits significantly outweigh this factor.


In summary, the analysis highlights GPT-4o and GPT-3.5-turbo-instruct as the most effective models for historical data annotation, offering an optimal balance of speed, accuracy, and reliability. Llama-3 and Phi-3 also demonstrate strong performance and are reliable choices. The remaining models, although showing potential in specific areas, require further refinement to meet the high standards necessary for comprehensive and dependable historical data annotation.

Our experiment also revealed several pitfalls in comparing models, one of the most significant being the limitations of context windows. Only two models were able to fully process the text containing 12,488 tokens. For the other models, it was necessary to segment the text into smaller parts to fit within their context window limits.

This issue raises a critical debate: Is it more effective to generate multiple responses from a model for different parts of a single text and then integrate these responses into a cohesive final answer (a process that demands additional computational and integration resources)? Or is it preferable to obtain a single, comprehensive response from a model capable of analyzing the entire text in one go, despite the risk of potentially omitting some information?

This trade-off between resource allocation and the risk of data loss requires further investigation to identify the optimal strategy for large-scale historical data annotation tasks. Such exploration will be crucial in determining whether the consolidation of multiple outputs or the use of models with larger context windows better serves the goals of accuracy and efficiency in historical data processing.

Comparative Analysis of LLM
Total Scores


The use of LLMs for historical data annotation is a promising direction, capable of significantly accelerating and improving the quality of analysis. Our research has confirmed the hypothesis that LLMs can be effectively utilized for feature engineering of historical data, based on the annotation data format formulated in the results of the University of Barcelona's article. Modern models provide researchers with powerful tools for automating the annotation and processing of textual data, ensuring high accuracy, consistency, and completeness of results. The choice of a specific model depends on priorities such as accuracy, speed, cost-effectiveness, and reliability.

The integration of advanced AI technologies into historical analysis opens new pathways for interdisciplinary research, fostering more accurate and efficient methods of data processing and interpretation.

In the future, our laboratory plans to experiment with the use of LLM agents for the automatic population of historical ontology, further expanding the capabilities and applications of these advanced models in the field of digital humanities.

For further inquiries or detailed information on our findings, please feel free to contact us.


  • University of Barcelona. The Semantics of History. Read
  • HuggingFace LLM Arena. Read

April 2023 - Anadea - How we use the Perceptual similarity metric (LPIPS).

In the ongoing series of experiments surrounding the generation of historical maps, this article introduces a crucial tool for evaluating the fidelity of generated images: the Perceptual Similarity Metric, or LPIPS. Rather than relying on mere pixel-by-pixel comparisons, LPIPS leverages the power of neural networks to provide a more nuanced understanding of image similarity.

This document describes how similarity between generated and original images can be evaluated with a perceptual similarity metric (LPIPS), and how we can use it to compare “quality” of generated images during training.

Reminder about data

A map of Europe in a year 1400
A map of Europe in a year 1400.

Metric description

LPIPS is a metric that compares similarity between two images.

Instead of comparing two images by pixels, it uses features that can be extracted from a pre-trained neural network - meaning, we feed a network an image, and get some information from hidden layers of the network as an output.

Perceptual image similarity metric has two properties:

  • It is large when human observes large difference between images
  • It is small when observers consider images similar

We used this library to compare LPIPS between our images. To evaluate generation results we resized generated and original images to 1080x1080 (original images were cropped, generated images were downscaled to 3072x3072). Additionally, we compared LPIPS values for the same images downsized to 512x512.

How LPIPS can be used?

  • Validation metric during training (to check if generation improved?)
  • Comparing different models with one another
  • Selecting best frame from multiple generated samples
  • Selecting best model version after training
  • Having multiple maps of the same style but in different periods, LPIPS can answer which map is “the closest in time” to some 3rd map - due to a general rule that the more years between the two maps - the more border changes there are on a map. This way, LPIPS can be used to cluster maps by period.
  • Identify specific years or periods where generated images are of lower quality - and work on parts of the dataset related to it.

Let’s say we have an original image of a map of Europe in the year 1400.

A map of Europe in a year 1400.

We want to compare it with a generated image

A map of Europe in a year 1400.

LPIPS(original_1400, generated_1400) = 0.13760228

LPIPS(original_1400, generated_1400) = 0.13760228

A map of Europe in a year 1600

LPIPS(original_1400, generated_1600) = 0.4240757

LPIPS(original_1400, generated_1600) = 0.4240757

A map of Europe in a year 1500

LPIPS(original_1400, generated_1500) = 0.3257943

Comparing LPIPS for similar images

LPIPS differences are smaller if generated images are very similar.

For instance, let’s compare original image for a year 1400 and three generated images for this year

LPIPS equals to 0.14 (left), 0.17(center) and 0.15 (right)
LPIPS equals to 0.14 (left), 0.17(center) and 0.15 (right)

Image with higher LPIPS in the center has different colors for two countries in the middle of a map (Lithuania and Moldova). While comparing all three images, we can see that only some parts of the map are visually different. After taking a closer look,  you may notice that only Ottoman and Polish-Lithuanian borders are different, and on other parts of the map only few artifacts are different. 

Relation of age differences between maps

Table values are MeanLPIPS between original image and 10 generated images
Table values are MeanLPIPS between original image and 10 generated images

*note that years 950 & 1800 were not present in a training data, generated images are purely fictional

It may be observed that the bigger the age gap between maps, the bigger LPIPS becomes

A note on resolution

We compared LPIPS on resolution 512x512

Table values

Comparing results between two tables (1080 x 1080 vs 512x512 resolution), it may be seen that when LPIPS@1080 is larger (i.e., 0.45), LPIPS@512 becomes roughly the same or slightly smaller (roughly -3% difference). 

However, smaller LPIPS@1080 values (i.e., 0.15) leads to a bigger difference with LPIPS@512 (~25% difference). This implies that we should upscale our images to detect smaller differences on the map.


LPIPS widely extends our capabilities to understand generated maps quality.

It can be used in validation, selecting the best generated image and evaluation of model predictive power in general.

Moreover, LPIPS approach can be extended, and be potentially used to compare original and generated maps and show regions where model makes mistakes.

February, 2023 - Anadea- Experiments on Generating Historical Maps Using the StableDiffusion Model on Real Data.

In the evolving realm of digital cartography, the role of advanced models in generating detailed and accurate historical maps has become paramount. This article delves into the recent experiments conducted with the StableDiffusion model, focusing on its application to real-world maps. We explore the challenges and nuances of training this model using a diverse dataset comprising maps and historical texts.

Training StableDiffusion model on real maps

Second part of our work was dedicated to building a foundation model for various maps and texts. Our plan was to fine-tune Stable Diffusion on a large dataset of pairs of maps and historical texts relevant to them.

As for the data, we decided to proceed with WiT dataset, as it already includes both texts and maps of high quality - and is a great tool for building a foundation model. WiT consists of images and related text fields - we used  a combination of page title, abstract and image caption as a text illustrating a map together with a map itself. To train our model only with relevant information we built two supplementary models, one for filtering images, and another one for texts. We worked with an `en`-only subset of WiT (5.4 mln entries).

We trained Stable Diffusion 2.1 on this data to see what it would generate from free-form historical textual description. Despite the fact that training took only 100k steps (single GPU, 5 days of training), it achieved significant results in generating maps from historical data.

  • Ability of the model to generate maps from prompt
  • Map quality - lack of artifacts, map details, etc.
  • Ability to understand time period and region described in prompts

Ability to understand time period and region described in prompts

King Alexander III  and map
When Wallace was growing up, King Alexander III ruled Scotland. His reign had seen a period of peace and economic stability. On 19 March 1286, however, Alexander died after falling from his horse.[18][19] The heir to the throne was Alexander\'s granddaughter, Margaret, Maid of Norway. As she was still a child and in Norway, the Scottish lords set up a government of guardians. Margaret fell ill on the voyage to Scotland and died in Orkney in late September 1290.[20] The lack of a clear heir led to a period known as the "Great Cause", with a total of thirteen contenders laying claim to the throne…

With supply difficulties hampering both sides, neither the Japanese nor the combined Ming and Joseon forces were able to mount a successful offensive or gain any additional territory, resulting in a military stalemate in the areas between Hanseong and Kaesong. The war continued in this manner for five years, and was followed by a brief interlude between 1596 and 1597 during which Japan and the

As you can see, the model trained on our data is able to identify historical region where events take place and draw its map. The prompt used for inference was not in the training dataset (and is of a different format).Maps are very diverse.

Let’s take a look at some more examples:

El Cid fought against the Moorish stronghold of Zaragoza, making its emir al-Muqtadir a vassal of Sancho. In the spring of 1063, El Cid fought in the Battle of Graus, where Ferdinand\'s half-brother, Ramiro I of Aragon, was laying siege to the Moorish town of Graus, which was fought on Zaragozan lands in the valley of the river Cinca. Al-Muqtadir, accompanied by Castilian troops including El Cid, fought against the Aragonese.
The sons of Hacı I Giray contended against each other to succeed him. The Ottomans intervened and installed one of the sons, Meñli I Giray, on the throne. Menli I Giray, took the imperial title "Sovereign of Two Continents and Khan of Khans of Two Seas.

Despite the fact that the maps in this variation do not suffice goals of the project, one may argue that increasing dataset scale, applying better filters and using more tricks during preprocessing step combined with much longer pre-train will give accurate results of desired quality. Note that this version of the model was trained with less than 0.001% compute used during the training process of a proper StableDiffusion.

Additionally, we’d like to demonstrate how the quality of generated maps improves with longer training. Results after 40000 training steps are to the left, results after 100000 training steps are to the right

Constantine III (died 411) was a common Roman soldier who was declared emperor in Roman Britain in 407.  He moved to Gaul (modern France), taking all of the mobile troops from Britain, with their commander Gerontius, to confront bands of Germanic invaders who had crossed the Rhine the previous winter. With a mixture of fighting and diplomacy Constantine stabilized the situation and established control over Gaul and Hispania (modern Spain and Portugal), establishing his capital at Arles

The sons of Hacı I Giray contended against each other to succeed him. The Ottomans intervened and installed one of the sons, Meñli I Giray, on the throne. Menli I Giray, took the imperial title "Sovereign of Two Continents and Khan of Khans of Two Seas.
In 1475 the Ottoman forces, under the command of Gedik Ahmet Pasha, conquered the Greek Principality of Theodoro and the Genoese colonies at Cembalo, Soldaia, and Caffa (modern Feodosiya). Thenceforth the khanate was a protectorate of the Ottoman Empire. The Ottoman sultan enjoyed veto power over the selection of new Crimean khans. The Empire annexed the Crimean coast but recognized the legitimacy of the khanate rule of the steppes, as the khans were descendants of Genghis Khan


During our research we demonstrated that diffusion models can be used to generate maps.

We showed that, depending on the prompt, diffusion models are capable of generating parts of the map (region, province or even smaller scale), accurately re-draw borders according to a historical period, and editing already-existing maps based on new context.

We’ve shown that using generalized map dataset we can create a maps-only version of diffusion network, and with power of unsupervised pre-training at scale it will be able to achieve high generalization capabilities.

We’ve shown that using generalized map dataset we can create a maps-only version of diffusion network, and with power of unsupervised pre-training at scale it will be able to achieve high generalization capabilities.

Additional steps that may be taken into consideration

  1. Train a model for longer and on a larger dataset
  2. Explore different text2image networks
  3. Tune the model on “downstream tasks” - i.e., editing maps based on prompt, generating map given year and region, etc.
  4. Improve understanding of textual part of the network by training on text only

Next steps with regard to overall project development

To verify the hypothesis in full and make generated maps usable on physical maps, the following tasks need to be addressed:

  1. Learn how to translate generated maps into external format (i.e., OpenStreetMap). It would be necessary to “read” generated maps. In our vision, this problem has to be split into two parts: identification of countries and borders and matching them with textual information (i.e., country names). It is possible to integrate identification of countries and borders into a diffusion network.
  2. Maps should be generated  in different styles. For that, one may assign various labels to maps in the dataset, based on content (i.e., political map, religious map) and style (lithography map, globe). Once labeled, labels would be inserted in prompt and be later used for generation.
  3. Finally, we could add some input-output layers to the model, and get different outputs from it (i.e., provide each map with coordinates of its boundaries, map type and any other information)

December 2022 - Anadea -A “Toy” Dataset for the Initial Learning.

This article covers the first stage of our research into generating historical maps using neural networks. Our initial work focuses on a simple "toy" dataset for preliminary learning. Here, we examine how the StableDiffusion model responds to textual prompts and what results can be expected at this early stage.

Project Goals and our ideas

Map generation project was focused on the idea that accurate historical maps can be generated using neural networks based on textual information

Our research aimed to test a hypothesis that historical maps of high quality can be generated with diffusion models from prompts with textual description of requested region and historical period. Our task was to check that specifically StableDiffusion can be used, and to understand its possibilities and limitations with regard to how it understands prompts.

We decided to start with the following ideas in mind:

  • Use StableDiffusion as our base network
  • Explore how to generate maps from different prompts - starting from easiest to hardest.
  • Conduct experiments with one prompt at a time.
  • Each experiment has its own training data.
  • During the experiment network is additionally trained to generate maps from textual prompts like in training data.
  • After the first phase of experiments is done, collect a large corpora of historical texts and maps – and train StableDiffusion on various maps and real historical texts.

Used data

To speed up our research, we used a “toy” dataset of historical maps for experiment purposes. It was based on this video and consisted of yearly maps of Europe in a single style. This dataset was not historically accurate, yet it let us understand the challenges of real-world data.

Time period of maps that we used for experiments was limited to 1000 A.D. - 1800 A.D.

Original map 1400 year
Original map. Prompts for different tasks are different

Each image in a dataset had a corresponding prompt where region, year and list of countries could have been mentioned.

We used StableDiffusion (mostly, v.2.1) and trained the U-Net part of the model using <image, prompt> pairs, and then evaluated using prompts in and out of the given time period. A typical dataset for such an experiment consisted of 700 to 8000 image-text pairs, which was enough for experimenting purposes.

Experiments and results

We’ve done five major experiments, each time feeding the model with more complex prompts:

1. Generate a map given prompt with a year and an image of a map.

A map o Europe in a year 1400

The goal was to check the possibility of generating maps that would change based on a year in a prompt and see how SD catches map style.

We got good results:

  • Generated maps borders are highly accurate
  • There were almost no artifacts, generation quality is good
  • We even got readable country names, which was not expected

Generated examples: (click to open larger images)

“A map of Europe in a year 1400” | “A map of Europe in a year 1600”
Prompt: “A map of Europe in a year 1400” | “A map of Europe in a year 1600”

These results can conclude that the model can change countries and their borders using a given year.

2. Generate a map given prompt with a year and list of countries on a part of a map.

A map o Europe in a year 1561

The goal was to check if the model can understand connections between country and region, and generate smaller maps where countries from the prompt would be displayed.

Results for this experiment were great, the model was drawing accurate crops of different regions. By scaling the dataset with different crops we could potentially train a model to generate very small parts of the map or manipulate the scale of the map through the prompt.

Map year 1800

3. Generate a map given prompt with a year and a message like “country A conquered country B”.

A map of Europe in a year 1022

In our next experiment we wanted to check if parts of the generated map can be manipulated through the prompt. Experiment results were mixed - final “accuracy” of the model is ~40% (based on the number of correct re-drawings of country B into colors of country A).

Although the results were poor, the experiment was still useful:

  • We discovered LoRA - a method to quickly learn new “concepts”, such as borders of specific countries - and to learn it in literally seconds.
  • We learned that not all the prompts fit diffusion models - partially because most prompts in datasets used for pretraining only describe an image - and do not give any instructions on how to change it. We concluded that manipulating a generated map is a different task.
  • We found a special version of SD trained specifically to edit images -Instruct Pix2Pix. Training InstructPix2Pix to edit maps became our next experiment.

4. Edit a map given prompt with a year and a message like “country A conquered country B”.


In this experiment we wanted to check if map can be edited with a prompt that describes certain changes (i.e., year is the same, but a certain country conquered another country). We trained InstructPix2Pix to edit the original map with a certain edit instruction.

Results were accurate - model did exactly what was asked by a prompt:


5. Edit and generate maps based on historical texts of arbitrary length

A fundamental limitation of the StableDiffusion model is that the max length of its text encoder (CLIP) is limited to 77 tokens.  To overcome this limitation we implemented a trick of splitting input text into chunks of 77 tokens, encoding each chunk into its own embedding, and feeding the model with average embedding. To verify that average embeddings do not negatively affect model generation capabilities we trained both vanilla SD that generates maps from long inputs and InstructPix2Pix model for editing maps based on longer texts.

For longer prompts, we decided to query DBPedia for information on historical events:

- For XVth century, we got descriptions of 286 events, 213 of them were longer than 77 tokens

- For InstructPix2Pix model we used the scheme from previous experiment - having a map and event for a year X, we can:

  • Use map for a year X as edited image
  • Use event description as prompt
  • Use map for a year X-N (N close to 10) as an original map
  • Generate samples with different N re-writing the prompts using LLM

- ~2200 pairs - enough for basic training


Maps 1442

Results demonstrated that the model understands long prompts, uses texts from various parts of the prompt and can work with very long texts.

Experiments conclusions

Aforementioned experiments were a necessary foundation to understand model behavior while training on real maps and texts.

During experiments, we discovered a few important things that allow us to speed up future development. Here are just a few of them:

  1. Found an effective way to increase generated image size and quality up to 16x using SuperResolution. This allows us to potentially generate images up to size 12288x12288. (Generated examples in the doc are 3072x3072 and are already of decent quality).
  2. Started using LPIPS as an evaluation metric - it allows us to measure how similar the generated image is to the original .
  3. Sped up training process by at least 3 times (using memory-efficient optimizers, experimental implementation of certain network layers and utilizing more aggressive training strategies that allow us reduce number of training steps 10 times)
  4. Experimented with training models on different checkpoints at different resolutions, and concluded that the highest quality of images can be achieved with SD-2.1 at 768x768, with SD2.1 being the best checkpoint available so far overall (SD-1.5 may be better at generating texts, but is generally worse and unable to work with 768x768 without proper fine-tuning on high-res images). Most of our experiments were on images of 512x512 due to time and resource constraints.


How can I contribute to or collaborate with the Historica project?
If you're interested in contributing to or collaborating with Historica, you can use the contact form on the Historica website to express your interest and detail how you would like to be involved. The Historica team will then be able to guide you through the process.
What role does Historica play in the promotion of culture?
Historica acts as a platform for promoting cultural objects and events by local communities. It presents these in great detail, from previously inaccessible perspectives, and in fresh contexts.
How does Historica support educational endeavors?
Historica serves as a powerful tool for research and education. It can be used in school curricula, scientific projects, educational software development, and the organization of educational events.
What benefits does Historica offer to local cultural entities and events?
Historica provides a global platform for local communities and cultural events to display their cultural artifacts and historical events. It offers detailed presentations from unique perspectives and in fresh contexts.
Can you give a brief overview of Historica?
Historica is an initiative that uses artificial intelligence to build a digital map of human history. It combines different data types to portray the progression of civilization from its inception to the present day.
What is the meaning of Historica's principles?
The principles of Historica represent its methodological, organizational, and technological foundations: Methodological principle of interdisciplinarity: This principle involves integrating knowledge from various fields to provide a comprehensive and scientifically grounded view of history. Organizational principle of decentralization: This principle encourages open collaboration from a global community, allowing everyone to contribute to the digital depiction of human history. Technological principle of reliance on AI: This principle focuses on extensively using AI to handle large data sets, reconcile different scientific domains, and continuously enrich the historical model.
Who are the intended users of Historica?
Historica is beneficial to a diverse range of users. In academia, it's valuable for educators, students, and policymakers. Culturally, it aids workers in museums, heritage conservation, tourism, and cultural event organization. For recreational purposes, it serves gamers, history enthusiasts, authors, and participants in historical reenactments.
How does Historica use artificial intelligence?
Historica uses AI to process and manage vast amounts of data from various scientific fields. This technology allows for the constant addition of new facts to the historical model and aids in resolving disagreements and contradictions in interpretation across different scientific fields.
Can anyone participate in the Historica project?
Yes, Historica encourages wide-ranging collaboration. Scholars, researchers, AI specialists, bloggers and all history enthusiasts are all welcome to contribute to the project.