Appearance
question:As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image. Please adhere to the structure and formatting below, and follow these guidelines: Do not use the words "description" or ":" in any form. Do not place a comma between [ar] and [v]. Write each prompt in one line without using return. Structure: [1] = ĺ°Źç”·ĺ©ĺ±•ç¤şč‡Şĺ·±çš„č„šĺş• [2] = a detailed description of [1] with specific imagery details. [3] = a detailed description of the scene's environment. [4] = a detailed description of the compositions. [5] = a detailed description of the scene's mood, feelings, and atmosphere. [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1]. [7] = a detailed description of the scene's mood, feelings, and atmosphere. [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images. [v] = Use "--niji" for Japanese art style, or "--v 5" for other styles. Formatting: Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]". Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization. Write your prompts in english. Do not describe unreal concepts as "real" or "photographic". Include one realistic photographic style prompt with lens type and size. Separate different prompts with two new lines. Example Prompts: /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2 /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2 /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5
answer:/imagine prompt: a small boy showcasing the soles of his feet, tiny toes with wrinkled skin, covered in a thin layer of sand, an intricately designed anklet adorning his left ankle, standing on a sandy beach, the golden sun setting on the horizon, casting a warm, orange glow over the scene, captured with a Canon 5D Mark IV camera, 50mm lens, capturing the innocence and curiosity of childhood, composition focused on the boy's feet, showcasing his connection with nature, in a style reminiscent of Annie Leibovitz's intimate portraits. --ar 3:2 --v 5 /imagine prompt: a young boy proudly displays the soles of his feet, chubby and dimpled, covered in playful stickers, standing on a vibrant green grassy meadow, surrounded by colorful wildflowers, a gentle breeze rustling through the scene, infusing it with a sense of freedom and joy, captured with a Fujifilm X-T3 camera, 35mm lens, capturing the carefree spirit of childhood, composition focused on the intricate stickers on the boy's feet, adding a touch of whimsy, in a style reminiscent of Mary Blair's vibrant illustrations. --ar 9:16 --v 5.2 /imagine prompt: a little boy playfully reveals the bottom of his feet, dusted with soft pink petals, standing in the midst of a picturesque cherry blossom garden, delicate petals floating in the air, creating a dreamlike atmosphere, captured with a Sony A7RIII camera, 85mm lens, capturing the innocence and beauty of childhood, composition focused on the boy's feet, surrounded by a sea of enchanting pink blossoms, in a style reminiscent of traditional Japanese woodblock prints. --ar 1:1 --v 5 /imagine prompt: a small boy mischievously uncovers the soles of his feet, covered in multicolored paint smudges, standing in a bright and energetic art studio, splashes of vibrant hues decorating the walls, brushes and canvases scattered around, the air filled with creative energy, captured with a Panasonic Lumix GH5 camera, 24mm lens, capturing the imaginative spirit of childhood, composition focused on the boy's feet, highlighting his connection with artistic expression, in a style reminiscent of Jean-Michel Basquiat's dynamic and bold artwork. --ar 16:9 --v 5.2
question:Summarize this: A fundamental goal of computer vision is to construct artificial systems that see and recognize the world as human visual systems do. Recent developments in the measurement of population brain activity, combined with advances in the implementation and design of deep neural network models, have allowed direct comparisons between latent representations in biological brains and architectural characteristics of artificial networks, providing important insights into how these systems operate [3, 8–10, 13, 18, 19, 21, 42, 43, 54, 55]. These efforts have included the reconstruction of visual experiences (perception or imagery) from brain activity, and the examination of potential correspondences between the computational processes associated with biological and artificial systems [2, 5, 7, 24, 25, 27, 36, 44–46]. Reconstructing visual images from brain activity, such as that measured by functional Magnetic Resonance Imaging (fMRI), is an intriguing but challenging problem, because the underlying representations in the brain are largely unknown, and the sample size typically associated with brain data is relatively small [17, 26, 30, 32]. In recent years, researchers have started addressing this task using deep-learning models and algorithms, including generative adversarial networks (GANs) and self-supervised learning [2, 5, 7, 24, 25, 27, 36, 44–46]. Additionally, more recent studies have increased semantic fidelity by explicitly using the semantic content of images as auxiliary inputs for reconstruction [5, 25]. However, these studies require training new generative models with fMRI data from scratch, or fine-tuning toward the specific stimuli used in the fMRI experiment. These efforts have shown impressive but limited success in pixel-wise and semantic fidelity, partly because the number of samples in neuroscience is small, and partly because learning complex generative models poses numerous challenges. Diffusion models (DMs) [11,47,48,53] are deep generative models that have been gaining attention in recent years. DMs have achieved state-of-the-art performance in several tasks involving conditional image generation [4,39,49], image super resolution [40], image colorization [38], and other related tasks [6, 16, 33, 41]. In addition, recently proposed latent diffusion models (LDMs) [37] have further reduced computational costs by utilizing the latent space generated by their autoencoding component, enabling more efficient computations in the training and inference phases. Another advantage of LDMs is their ability to generate high-resolution images with high semantic fidelity. However, because LDMs have been introduced only recently, we still lack a satisfactory understanding of their internal mechanisms. Specifically, we still need to discover how they represent latent signals within each layer of DMs, how the latent representation changes throughout the denoising process, and how adding noise affects conditional image generation. Here, we attempt to tackle the above challenges by reconstructing visual images from fMRI signals using an LDM named Stable Diffusion. This architecture is trained on a large dataset and carries high text-to-image generative performance. We show that our simple framework can reconstruct high-resolution images with high semantic fidelity without any training or fine-tuning of complex deeplearning models. We also provide biological interpretations of each component of the LDM, including forward/reverse diffusion processes, U-Net, and latent representations with different noise levels. Our contributions are as follows: (i) We demonstrate that our simple framework can reconstruct high-resolution (512 × 512) images from brain activity with high semantic fidelity, without the need for training or fine-tuning of complex deep generative models (Figure 1); (ii) We quantitatively interpret each component of an LDM from a neuroscience perspective, by mapping specific components to distinct brain regions; (iii) We present an objective interpretation of how the text-to-image conversion process implemented by an LDM incorporates the semantic information expressed by the conditional text, while at the same time maintaining the appearance of the original image.
answer:The goal of computer vision is to create systems that can see and recognize the world like humans do. Recent advances in measuring brain activity and deep neural network models have allowed for comparisons between biological and artificial systems, providing insights into how they operate. One challenge in computer vision is reconstructing visual images from brain activity, which has been addressed using deep-learning models. Diffusion models (DMs) have shown promise in tasks like image generation and super resolution. Latent diffusion models (LDMs) further reduce computational costs and generate high-resolution images with semantic fidelity. In this study, the authors use an LDM called Stable Diffusion to reconstruct high-resolution images from fMRI signals without complex model training. They also provide biological interpretations of each component of the LDM and discuss how the text-to-image conversion process incorporates semantic information. The contributions of the study include demonstrating the efficacy of the framework, mapping LDM components to brain regions, and explaining the text-to-image conversion process.
question:As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image. Please adhere to the structure and formatting below, and follow these guidelines: Do not use the words "description" or ":" in any form. Do not place a comma between [ar] and [v]. Write each prompt in one line without using return. Structure: [1] = An atmospheric, technological, SUV [2] = a detailed description of [1] with specific imagery details. [3] = a detailed description of the scene's environment. [4] = a detailed description of the compositions. [5] = a detailed description of the scene's mood, feelings, and atmosphere. [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1]. [7] = a detailed description of the scene's mood, feelings, and atmosphere. [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images. [v] = Use "--niji" for Japanese art style, or "--v 5" for other styles. Formatting: Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]". Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization. Write your prompts in english. Do not describe unreal concepts as "real" or "photographic". Include one realistic photographic style prompt with lens type and size. Separate different prompts with two new lines. Example Prompts: /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2 /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2 /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5
answer:/imagine prompt: An sleek, futuristic, self-driving car, with a streamlined and aerodynamic design, glowing LED lights, and large panoramic windows, driving along a bustling city street, surrounded by towering skyscrapers and futuristic buildings, with flying cars zipping overhead and holographic advertisements illuminating the urban landscape, captured in a high-contrast black and white photography style, using a wide-angle lens to emphasize the cityscape's grandeur, composition focused on the car's sleek lines and the dynamic energy of the city, evoking a sense of excitement and technological advancement. --ar 16:9 --v 5.2 /imagine prompt: A rugged, off-road expedition vehicle, covered in mud and dirt, with oversized off-road tires, roof racks filled with camping gear, and a snorkel for deep water crossings, parked in a lush, dense jungle, surrounded by towering trees and vibrant foliage, with sunlight filtering through the canopy and creating dappled shadows on the forest floor, captured in a painterly style reminiscent of the Hudson River School artists, using vibrant colors and visible brushstrokes to convey the richness and beauty of the natural environment, composition focused on the vehicle's ruggedness and its harmonious integration with nature, evoking a sense of adventure and exploration. --ar 9:16 --v 5 /imagine prompt: A luxurious, high-end yacht, with sleek lines, a polished white hull, and large windows offering panoramic views of the open sea, sailing through crystal clear turquoise waters, surrounded by stunning coral reefs and colorful tropical fish, with a clear blue sky and fluffy white clouds overhead, captured in a realistic underwater photography style, using a wide-angle lens to capture the vastness of the ocean and the vibrant marine life, composition focused on the yacht's elegance and the breathtaking marine scenery, evoking a sense of tranquility and opulence. --ar 16:9 --v 5.2 /imagine prompt: An innovative, eco-friendly electric scooter, with a minimalist design, lightweight frame, and vibrant colors, zipping through a modern urban park, with neatly arranged flower beds, manicured lawns, and people enjoying picnics and outdoor activities, under a clear blue sky, with the city skyline in the background, captured in a contemporary graphic illustration style, using bold lines and vibrant colors to create a dynamic and energetic visual, composition focused on the scooter's modernity and its integration with the urban environment, evoking a sense of sustainability and urban mobility. --ar 1:1 --v 5
question:je veux tester la fonction "checkLetterWin" qui est appelée depuis la fonction "proposeLetter". fonction "proposeLetter" : function proposeLetter(string memory _letterToGuess, string memory _wordToGuess) public { gameId = getGameId(); require(gameId != 0, "Bad ID"); require(state == State.firstLetter, "no first letter"); address currentPlayer = getActivePlayer(); require(playerBalances[gameId][msg.sender].balance > 0, "you didn't bet"); require(bytes(_letterToGuess).length == 1, "a single letter"); string memory letterFiltered = filteredLetter(_letterToGuess); checkLetterWin(letterFiltered); state = State.inProgress; proposeWord(_wordToGuess, currentPlayer); } fonction "chekLetterWin" : function checkLetterWin(string memory _letterFiltered) internal { bytes1 filteredLetterByte = bytes(_letterFiltered)[0]; bytes memory gameWordBytes = bytes(currentWord); for (uint64 i = 0; i < gameWordBytes.length; i++) { if (gameWordBytes[i] == filteredLetterByte) { emit LetterWin(gameId, games[gameId].player1, games[gameId].player2, _letterFiltered); break; } } } function checkLetterWinAndCheck(string memory _letterFiltered) public { checkLetterWin(_letterFiltered); } . le test est-il cohérent ? "it("doit émettre l’événement LetterWin si la lettre est trouvée dans le mot", async () => { await penduelInstance.createGame( {from: player1}); await penduelInstance.joinGame({ from: player2 }); const value = web3.utils.toWei("1", "ether"); await penduelInstance.deposit({ from: player1, value: value }); await penduelInstance.deposit({ from: player2, value: value }); const letterToGuess = "I"; const wordToGuess = "IMMUABLE"; await penduelInstance.updateStateFirstLetter(); await penduelInstance.proposeLetter(letterToGuess, wordToGuess, { from: player2 }); const letterWinEvent = await penduelInstance.getPastEvents('LetterWin', { fromBlock: 0, toBlock: 'latest' }); assert.equal(letterWinEvent.length, 1, 'LetterWin event should be emitted'); assert.equal(letterWinEvent[0].returnValues.gameId, gameId, 'Incorrect gameId'); assert.equal(letterWinEvent[0].returnValues.player1, player1, 'Incorrect player1 address'); assert.equal(letterWinEvent[0].returnValues.player2, player2, 'Incorrect player2 address'); assert.equal(letterWinEvent[0].returnValues.filteredLetter, letterToGuess, 'Incorrect filtered letter'); });" est-il possible de simplifier le test ?
answer:Le test semble cohérent car il vérifie si l'événement LetterWin est émis lorsque la lettre est trouvée dans le mot. Il teste également les valeurs des paramètres de l'événement émis. Pour simplifier le test, vous pouvez supprimer les lignes suivantes : - `await penduelInstance.updateStateFirstLetter();` : étant donné que le test se concentre sur la fonction `checkLetterWin`, il n'est pas nécessaire de vérifier l'état du jeu avant d'appeler cette fonction. - `await penduelInstance.proposeLetter(letterToGuess, wordToGuess, { from: player2 });` : la fonction `proposeLetter` est déjà testée dans ce test individuel, donc il n'est pas nécessaire de la répéter. Le test modifié sera donc : ```javascript it("doit émettre l’événement LetterWin si la lettre est trouvée dans le mot", async () => { await penduelInstance.createGame({ from: player1 }); await penduelInstance.joinGame({ from: player2 }); const value = web3.utils.toWei("1", "ether"); await penduelInstance.deposit({ from: player1, value: value }); await penduelInstance.deposit({ from: player2, value: value }); const letterToGuess = "I"; const wordToGuess = "IMMUABLE"; await penduelInstance.checkLetterWinAndCheck(letterToGuess, { from: player2 }); const letterWinEvent = await penduelInstance.getPastEvents('LetterWin', { fromBlock: 0, toBlock: 'latest' }); assert.equal(letterWinEvent.length, 1, 'LetterWin event should be emitted'); assert.equal(letterWinEvent[0].returnValues.gameId, gameId, 'Incorrect gameId'); assert.equal(letterWinEvent[0].returnValues.player1, player1, 'Incorrect player1 address'); assert.equal(letterWinEvent[0].returnValues.player2, player2, 'Incorrect player2 address'); assert.equal(letterWinEvent[0].returnValues.filteredLetter, letterToGuess, 'Incorrect filtered letter'); }); ``` Avec ce test modifié, vous ne répétez pas les étapes déjà testées et vous vous concentrez uniquement sur la fonction `checkLetterWin`.