thảo luận Hội tạo ảnh AI

Em vừa search thử giúp bác thì thấy trên trang comfyui có sẵn cái cơ bản nè bác

Screenshot-251.png
Để em dùng thử workflow của bác , cái này hôm qua em test kẹp với qwen image thì báo lỗi suốt nên vứt đấy
 
Cái này miễn phí à bác. Có hướng dẫn sử dụng ko ạ. Tiện cho em hỏi có con nào làm video miễn phí ko a

via theNEXTvoz for iPhone
Miễn phí nha bác, tại chạy bằng vga của máy mình, nếu không có bác thuê mấy trang như ZenComfy,Comfy Cloud, hoặc tự cài workflow, Vast.ai,RunDiffusion,

Còn hướng dẫn thì bác làm theo video bên dưới là được, video càng dài thì yêu cầu vga với vram càng nhiều, con này cũng làm video luôn, bác chỉ cần đồi đuổi mp4.webp,gif... là đc, còn không bác cứ cài comfyui xong, vào phần template search video là nó ra nhiều lắm
 
Sau khi tạo ảnh xong mà ram cứ ở mức 80% thì có bình thường không các bác . Search mãi không ra lỗi gì .(ram 32gb)
 
Đồng bộ nhân vật và đổi góc quay

Đây là một mẹo rất nhiều người đang dùng để đồng bộ nhân vật.
Nhược điểm của mọi AI hiện giờ là sự không đồng bộ. Cùng 1 câu lệnh nhưng mỗi lần tạo là sẽ ra 1 kiểu khác nhau. Mặt mũi nhân vật cũng biến đổi.
Muốn nhân vật đồng nhất thì hãy tối ưu prompt. Thay vì 1 câu prompt chỉ tạo 1 ảnh thì hãy ra lệnh cho nó tạo nhiều ảnh trong 1 lần. Ta có thể tạo 4 ảnh, 6 ảnh hoặc 9 ảnh, sắp xếp dạng lưới.

Ảnh do 1 prompt tạo ra thì nhân vật sẽ đồng nhất. Rồi chúng ta cắt ra 9 ảnh nhỏ để dùng.
Chất lượng ảnh không tệ đâu. Rồi cắt nhỏ. Mỗi ảnh cũng đủ dùng.

Xem tệp đính kèm 3472080

Generate a professional 3x3 photo grid (9 panels) featuring the same female character in a consistent character sheet.
Character Consistency: * Appearance: Maintain identical face, hairstyle, and features from the reference image in every panel.
Pose & Expression: Constant neutral expression, minimal pose variation to ensure identity lock.
Background: Solid minimal gray background, clean and distraction-free.
Photography: Photorealistic, high-end commercial photography, shot on 35mm lens, clean studio lighting, sharp focus, 8k resolution. No illustrations.
Grid Layout & Camera Angles:
[Panel 1] Close-Up (CU): Tight framing on the face, front view.
[Panel 2] Medium Close-Up (MCU): Head and shoulders, facing camera.
[Panel 3] Medium Shot (MS): Waist up, centered, arms at sides.
[Panel 4] Full Body Shot: Head to toe visible, centered.
[Panel 5] High Angle: Full body, camera tilted 45-degrees looking down.
[Panel 6] Low Angle: Camera looking up from a low point, heroic perspective.
[Panel 7] Side Profile: 90-degree angle view, looking straight ahead.
[Panel 8] Three-Quarter View: Body/face turned 45 degrees, cinematic depth.
[Panel 9] Back View: From behind, showing back of head and outfit details.

Nếu không muốn tạo nhiều ảnh, chỉ muốn xoay thì copy câu lệnh góc quay mong muốn trong prompt trên ra là được.
cái này hay nè, vụ giữ ảnh khuôn mặt hơi bị khó tìm, chưa kể tốn nhiều tiền để mua tài khoản
 
Làm một bộ ảnh cho mẫu mới.

Xem tệp đính kèm 3483756
Xem tệp đính kèm 3483757
Xem tệp đính kèm 3483758
Xem tệp đính kèm 3483759
Xem tệp đính kèm 3483760
Xem tệp đính kèm 3483761

Sử dụng ảnh của tôi làm ảnh tham khảo. Ảnh DSLR 4K UHD siêu thực về một người phụ nữ xinh đẹp.
Khuôn mặt người phụ nữ phải giống hệt khuôn mặt gốc trong ảnh tham khảo, không thay đổi bất kỳ chi tiết nào.

Trang điểm: trang điểm nhẹ nhàng và đôi môi bóng mượt màu hồng chuyển sắc. Ngón tay dài, thon gọn với móng tay đẹp và sơn gel.

Trang phục: Cô ấy mặc bộ đồ giống ảnh tham khảo thứ 2.

Tư thế: Đứng tựa nhẹ lưng vào bức tường bê tông xám, một chân hơi co lên đặt vào tường. Hai tay buông thõng tự nhiên hoặc đan nhẹ vào nhau phía trước. Biểu cảm ""soft smile"", ánh mắt nhìn thẳng vào ống kính đầy cuốn hút.

Hậu cảnh: Những bụi cây xanh đậm và cây thông bao quanh, tạo khung tự nhiên cho người mẫu. Bộ trang phục màu trắng nổi bật giữa màu xanh của lá cây và màu xám của bê tông.

Ống kính 35mm, khẩu độ f/8 để lấy nét sâu. Ánh sáng tự nhiên điện ảnh, không chỉnh sửa, nhiếp ảnh chuyên nghiệp. Ảnh chụp toàn thân.

Prompt cũ từ trang 9. Sửa lại Tư thế và hậu cảnh mà dùng. Không có gì mới.
này chơi món gì mà k thấy logo gemini thím :ops:
 
Sau khi tạo ảnh xong mà ram cứ ở mức 80% thì có bình thường không các bác . Search mãi không ra lỗi gì .(ram 32gb)

tuỳ model và node nhiều hay ít mà nó sẽ ăn ram nhiều hay ít đó bác, em 32 gb, nhưng vẫn phải set thêm ssd lên 64, nhưng nhiều lúc vẫn lỗi, phải set low lại hết

Nếu bác bị disconnecting thì hết ram, còn không bác cứ chụp cái cmd lại cho chatgpt là nó bắt bệnh cho bác, chứ cái comfyui này nhiều lỗi lắm :D
 
này chơi món gì mà k thấy logo gemini thím :ops:
gemini cài script này là tải ảnh k watermark

Mã:
// ==UserScript==
// @name         Gemini NanoBanana Watermark Remover
// @name:zh-CN   Gemini NanoBanana 图片水印移除
// @namespace    https://github.com/journey-ad
// @version      0.1.5
// @description  Automatically removes watermarks from Gemini AI generated images
// @description:zh-CN 自动移除 Gemini AI 生成图像中的水印
// @icon         https://www.google.com/s2/favicons?domain=gemini.google.com
// @author       journey-ad
// @license      MIT
// @match        https://gemini.google.com/*
// @grant        GM_xmlhttpRequest
// @run-at       document-end
// ==/UserScript==

(() => {
  // src/core/alphaMap.js
  function calculateAlphaMap(bgCaptureImageData) {
    const { width, height, data } = bgCaptureImageData;
    const alphaMap = new Float32Array(width * height);
    for (let i = 0; i < alphaMap.length; i++) {
      const idx = i * 4;
      const r = data[idx];
      const g = data[idx + 1];
      const b = data[idx + 2];
      const maxChannel = Math.max(r, g, b);
      alphaMap[i] = maxChannel / 255;
    }
    return alphaMap;
  }

  // src/core/blendModes.js
  var ALPHA_THRESHOLD = 2e-3;
  var MAX_ALPHA = 0.99;
  var LOGO_VALUE = 255;
  function removeWatermark(imageData, alphaMap, position) {
    const { x, y, width, height } = position;
    for (let row = 0; row < height; row++) {
      for (let col = 0; col < width; col++) {
        const imgIdx = ((y + row) * imageData.width + (x + col)) * 4;
        const alphaIdx = row * width + col;
        let alpha = alphaMap[alphaIdx];
        if (alpha < ALPHA_THRESHOLD) {
          continue;
        }
        alpha = Math.min(alpha, MAX_ALPHA);
        const oneMinusAlpha = 1 - alpha;
        for (let c = 0; c < 3; c++) {
          const watermarked = imageData.data[imgIdx + c];
          const original = (watermarked - alpha * LOGO_VALUE) / oneMinusAlpha;
          imageData.data[imgIdx + c] = Math.max(0, Math.min(255, Math.round(original)));
        }
      }
    }
  }

  // src/assets/bg_48.png
  var bg_48_default = "data:image/png;base64,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";

  // src/assets/bg_96.png
  var bg_96_default = "data:image/png;base64,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";

  // src/core/watermarkEngine.js
  function detectWatermarkConfig(imageWidth, imageHeight) {
    if (imageWidth > 1024 && imageHeight > 1024) {
      return {
        logoSize: 96,
        marginRight: 64,
        marginBottom: 64
      };
    } else {
      return {
        logoSize: 48,
        marginRight: 32,
        marginBottom: 32
      };
    }
  }
  function calculateWatermarkPosition(imageWidth, imageHeight, config) {
    const { logoSize, marginRight, marginBottom } = config;
    return {
      x: imageWidth - marginRight - logoSize,
      y: imageHeight - marginBottom - logoSize,
      width: logoSize,
      height: logoSize
    };
  }
  var WatermarkEngine = class _WatermarkEngine {
    constructor(bgCaptures) {
      this.bgCaptures = bgCaptures;
      this.alphaMaps = {};
    }
    static async create() {
      const bg48 = new Image();
      const bg96 = new Image();
      await Promise.all([
        new Promise((resolve, reject) => {
          bg48.onload = resolve;
          bg48.onerror = reject;
          bg48.src = bg_48_default;
        }),
        new Promise((resolve, reject) => {
          bg96.onload = resolve;
          bg96.onerror = reject;
          bg96.src = bg_96_default;
        })
      ]);
      return new _WatermarkEngine({ bg48, bg96 });
    }
    /**
     * Get alpha map from background captured image based on watermark size
     * @param {number} size - Watermark size (48 or 96)
     * @returns {Promise<Float32Array>} Alpha map
     */
    async getAlphaMap(size) {
      if (this.alphaMaps[size]) {
        return this.alphaMaps[size];
      }
      const bgImage = size === 48 ? this.bgCaptures.bg48 : this.bgCaptures.bg96;
      const canvas = document.createElement("canvas");
      canvas.width = size;
      canvas.height = size;
      const ctx = canvas.getContext("2d");
      ctx.drawImage(bgImage, 0, 0);
      const imageData = ctx.getImageData(0, 0, size, size);
      const alphaMap = calculateAlphaMap(imageData);
      this.alphaMaps[size] = alphaMap;
      return alphaMap;
    }
    /**
     * Remove watermark from image based on watermark size
     * @param {HTMLImageElement|HTMLCanvasElement} image - Input image
     * @returns {Promise<HTMLCanvasElement>} Processed canvas
     */
    async removeWatermarkFromImage(image) {
      const canvas = document.createElement("canvas");
      canvas.width = image.width;
      canvas.height = image.height;
      const ctx = canvas.getContext("2d");
      ctx.drawImage(image, 0, 0);
      const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
      const config = detectWatermarkConfig(canvas.width, canvas.height);
      const position = calculateWatermarkPosition(canvas.width, canvas.height, config);
      const alphaMap = await this.getAlphaMap(config.logoSize);
      removeWatermark(imageData, alphaMap, position);
      ctx.putImageData(imageData, 0, 0);
      return canvas;
    }
    /**
     * Get watermark information (for display)
     * @param {number} imageWidth - Image width
     * @param {number} imageHeight - Image height
     * @returns {Object} Watermark information {size, position, config}
     */
    getWatermarkInfo(imageWidth, imageHeight) {
      const config = detectWatermarkConfig(imageWidth, imageHeight);
      const position = calculateWatermarkPosition(imageWidth, imageHeight, config);
      return {
        size: config.logoSize,
        position,
        config
      };
    }
  };

  // src/userscript/index.js
  var engine = null;
  var processingQueue = /* @__PURE__ */ new Set();
  var debounce = (func, wait) => {
    let timeout;
    return (...args) => {
      clearTimeout(timeout);
      timeout = setTimeout(() => func(...args), wait);
    };
  };
  var loadImage = (src) => new Promise((resolve, reject) => {
    const img = new Image();
    img.onload = () => resolve(img);
    img.onerror = reject;
    img.src = src;
  });
  var canvasToBlob = (canvas, type = "image/png") => new Promise((resolve) => canvas.toBlob(resolve, type));
  var isValidGeminiImage = (img) => img.closest("generated-image,.generated-image-container") !== null;
  var findGeminiImages = () => [...document.querySelectorAll('img[src*="googleusercontent.com"]')].filter(isValidGeminiImage);
  var fetchBlob = (url) => new Promise((resolve, reject) => {
    GM_xmlhttpRequest({
      method: "GET",
      url,
      responseType: "blob",
      onload: (response) => resolve(response.response),
      onerror: reject
    });
  });
  var replaceWithNormalSize = (src) => {
    return src.replace(/=s\d+(?=[-?#]|$)/, "=s0");
  };
  async function processImage(imgElement) {
    if (!engine || processingQueue.has(imgElement)) return;
    processingQueue.add(imgElement);
    imgElement.dataset.watermarkProcessed = "processing";
    const originalSrc = imgElement.src;
    try {
      imgElement.src = "";
      const normalSizeBlob = await fetchBlob(replaceWithNormalSize(originalSrc));
      const normalSizeBlobUrl = URL.createObjectURL(normalSizeBlob);
      const normalSizeImg = await loadImage(normalSizeBlobUrl);
      const processedCanvas = await engine.removeWatermarkFromImage(normalSizeImg);
      const processedBlob = await canvasToBlob(processedCanvas);
      URL.revokeObjectURL(normalSizeBlobUrl);
      imgElement.src = URL.createObjectURL(processedBlob);
      imgElement.dataset.watermarkProcessed = "true";
      console.log("[Gemini Watermark Remover] Processed image");
    } catch (error) {
      console.warn("[Gemini Watermark Remover] Failed to process image:", error);
      imgElement.dataset.watermarkProcessed = "failed";
      imgElement.src = originalSrc;
    } finally {
      processingQueue.delete(imgElement);
    }
  }
  var processAllImages = () => {
    const images = findGeminiImages();
    if (images.length === 0) return;
    console.log(`[Gemini Watermark Remover] Found ${images.length} images to process`);
    images.forEach(processImage);
  };
  var setupMutationObserver = () => {
    new MutationObserver(debounce(processAllImages, 100)).observe(document.body, { childList: true, subtree: true });
    console.log("[Gemini Watermark Remover] MutationObserver active");
  };
  async function processImageBlob(blob) {
    const blobUrl = URL.createObjectURL(blob);
    const img = await loadImage(blobUrl);
    const canvas = await engine.removeWatermarkFromImage(img);
    URL.revokeObjectURL(blobUrl);
    return canvasToBlob(canvas);
  }
  var GEMINI_URL_PATTERN = /^https:\/\/lh3\.googleusercontent\.com\/rd-gg(?:-dl)?\/.+=s(?!0-d\?).*/;
  var { fetch: origFetch } = unsafeWindow;
  unsafeWindow.fetch = async (...args) => {
    const url = typeof args[0] === "string" ? args[0] : args[0]?.url;
    if (GEMINI_URL_PATTERN.test(url)) {
      console.log("[Gemini Watermark Remover] Intercepting:", url);
      const origUrl = replaceWithNormalSize(url);
      if (typeof args[0] === "string") args[0] = origUrl;
      else if (args[0]?.url) args[0].url = origUrl;
      const response = await origFetch(...args);
      if (!engine || !response.ok) return response;
      try {
        const processedBlob = await processImageBlob(await response.blob());
        return new Response(processedBlob, {
          status: response.status,
          statusText: response.statusText,
          headers: response.headers
        });
      } catch (error) {
        console.warn("[Gemini Watermark Remover] Processing failed:", error);
        return response;
      }
    }
    return origFetch(...args);
  };
  (async function init() {
    try {
      console.log("[Gemini Watermark Remover] Initializing...");
      engine = await WatermarkEngine.create();
      processAllImages();
      setupMutationObserver();
      console.log("[Gemini Watermark Remover] Ready");
    } catch (error) {
      console.error("[Gemini Watermark Remover] Initialization failed:", error);
    }
  })();
})();
 
gemini cài script này là tải ảnh k watermark

Mã:
// ==UserScript==
// @name         Gemini NanoBanana Watermark Remover
// @name:zh-CN   Gemini NanoBanana 图片水印移除
// @namespace    https://github.com/journey-ad
// @version      0.1.5
// @description  Automatically removes watermarks from Gemini AI generated images
// @description:zh-CN 自动移除 Gemini AI 生成图像中的水印
// @icon         https://www.google.com/s2/favicons?domain=gemini.google.com
// @author       journey-ad
// @license      MIT
// @match        https://gemini.google.com/*
// @grant        GM_xmlhttpRequest
// @run-at       document-end
// ==/UserScript==

(() => {
  // src/core/alphaMap.js
  function calculateAlphaMap(bgCaptureImageData) {
    const { width, height, data } = bgCaptureImageData;
    const alphaMap = new Float32Array(width * height);
    for (let i = 0; i < alphaMap.length; i++) {
      const idx = i * 4;
      const r = data[idx];
      const g = data[idx + 1];
      const b = data[idx + 2];
      const maxChannel = Math.max(r, g, b);
      alphaMap[i] = maxChannel / 255;
    }
    return alphaMap;
  }

  // src/core/blendModes.js
  var ALPHA_THRESHOLD = 2e-3;
  var MAX_ALPHA = 0.99;
  var LOGO_VALUE = 255;
  function removeWatermark(imageData, alphaMap, position) {
    const { x, y, width, height } = position;
    for (let row = 0; row < height; row++) {
      for (let col = 0; col < width; col++) {
        const imgIdx = ((y + row) * imageData.width + (x + col)) * 4;
        const alphaIdx = row * width + col;
        let alpha = alphaMap[alphaIdx];
        if (alpha < ALPHA_THRESHOLD) {
          continue;
        }
        alpha = Math.min(alpha, MAX_ALPHA);
        const oneMinusAlpha = 1 - alpha;
        for (let c = 0; c < 3; c++) {
          const watermarked = imageData.data[imgIdx + c];
          const original = (watermarked - alpha * LOGO_VALUE) / oneMinusAlpha;
          imageData.data[imgIdx + c] = Math.max(0, Math.min(255, Math.round(original)));
        }
      }
    }
  }

  // src/assets/bg_48.png
  var bg_48_default = "data:image/png;base64,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";

  // src/assets/bg_96.png
  var bg_96_default = "data:image/png;base64,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";

  // src/core/watermarkEngine.js
  function detectWatermarkConfig(imageWidth, imageHeight) {
    if (imageWidth > 1024 && imageHeight > 1024) {
      return {
        logoSize: 96,
        marginRight: 64,
        marginBottom: 64
      };
    } else {
      return {
        logoSize: 48,
        marginRight: 32,
        marginBottom: 32
      };
    }
  }
  function calculateWatermarkPosition(imageWidth, imageHeight, config) {
    const { logoSize, marginRight, marginBottom } = config;
    return {
      x: imageWidth - marginRight - logoSize,
      y: imageHeight - marginBottom - logoSize,
      width: logoSize,
      height: logoSize
    };
  }
  var WatermarkEngine = class _WatermarkEngine {
    constructor(bgCaptures) {
      this.bgCaptures = bgCaptures;
      this.alphaMaps = {};
    }
    static async create() {
      const bg48 = new Image();
      const bg96 = new Image();
      await Promise.all([
        new Promise((resolve, reject) => {
          bg48.onload = resolve;
          bg48.onerror = reject;
          bg48.src = bg_48_default;
        }),
        new Promise((resolve, reject) => {
          bg96.onload = resolve;
          bg96.onerror = reject;
          bg96.src = bg_96_default;
        })
      ]);
      return new _WatermarkEngine({ bg48, bg96 });
    }
    /**
     * Get alpha map from background captured image based on watermark size
     * @param {number} size - Watermark size (48 or 96)
     * @returns {Promise<Float32Array>} Alpha map
     */
    async getAlphaMap(size) {
      if (this.alphaMaps[size]) {
        return this.alphaMaps[size];
      }
      const bgImage = size === 48 ? this.bgCaptures.bg48 : this.bgCaptures.bg96;
      const canvas = document.createElement("canvas");
      canvas.width = size;
      canvas.height = size;
      const ctx = canvas.getContext("2d");
      ctx.drawImage(bgImage, 0, 0);
      const imageData = ctx.getImageData(0, 0, size, size);
      const alphaMap = calculateAlphaMap(imageData);
      this.alphaMaps[size] = alphaMap;
      return alphaMap;
    }
    /**
     * Remove watermark from image based on watermark size
     * @param {HTMLImageElement|HTMLCanvasElement} image - Input image
     * @returns {Promise<HTMLCanvasElement>} Processed canvas
     */
    async removeWatermarkFromImage(image) {
      const canvas = document.createElement("canvas");
      canvas.width = image.width;
      canvas.height = image.height;
      const ctx = canvas.getContext("2d");
      ctx.drawImage(image, 0, 0);
      const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
      const config = detectWatermarkConfig(canvas.width, canvas.height);
      const position = calculateWatermarkPosition(canvas.width, canvas.height, config);
      const alphaMap = await this.getAlphaMap(config.logoSize);
      removeWatermark(imageData, alphaMap, position);
      ctx.putImageData(imageData, 0, 0);
      return canvas;
    }
    /**
     * Get watermark information (for display)
     * @param {number} imageWidth - Image width
     * @param {number} imageHeight - Image height
     * @returns {Object} Watermark information {size, position, config}
     */
    getWatermarkInfo(imageWidth, imageHeight) {
      const config = detectWatermarkConfig(imageWidth, imageHeight);
      const position = calculateWatermarkPosition(imageWidth, imageHeight, config);
      return {
        size: config.logoSize,
        position,
        config
      };
    }
  };

  // src/userscript/index.js
  var engine = null;
  var processingQueue = /* @__PURE__ */ new Set();
  var debounce = (func, wait) => {
    let timeout;
    return (...args) => {
      clearTimeout(timeout);
      timeout = setTimeout(() => func(...args), wait);
    };
  };
  var loadImage = (src) => new Promise((resolve, reject) => {
    const img = new Image();
    img.onload = () => resolve(img);
    img.onerror = reject;
    img.src = src;
  });
  var canvasToBlob = (canvas, type = "image/png") => new Promise((resolve) => canvas.toBlob(resolve, type));
  var isValidGeminiImage = (img) => img.closest("generated-image,.generated-image-container") !== null;
  var findGeminiImages = () => [...document.querySelectorAll('img[src*="googleusercontent.com"]')].filter(isValidGeminiImage);
  var fetchBlob = (url) => new Promise((resolve, reject) => {
    GM_xmlhttpRequest({
      method: "GET",
      url,
      responseType: "blob",
      onload: (response) => resolve(response.response),
      onerror: reject
    });
  });
  var replaceWithNormalSize = (src) => {
    return src.replace(/=s\d+(?=[-?#]|$)/, "=s0");
  };
  async function processImage(imgElement) {
    if (!engine || processingQueue.has(imgElement)) return;
    processingQueue.add(imgElement);
    imgElement.dataset.watermarkProcessed = "processing";
    const originalSrc = imgElement.src;
    try {
      imgElement.src = "";
      const normalSizeBlob = await fetchBlob(replaceWithNormalSize(originalSrc));
      const normalSizeBlobUrl = URL.createObjectURL(normalSizeBlob);
      const normalSizeImg = await loadImage(normalSizeBlobUrl);
      const processedCanvas = await engine.removeWatermarkFromImage(normalSizeImg);
      const processedBlob = await canvasToBlob(processedCanvas);
      URL.revokeObjectURL(normalSizeBlobUrl);
      imgElement.src = URL.createObjectURL(processedBlob);
      imgElement.dataset.watermarkProcessed = "true";
      console.log("[Gemini Watermark Remover] Processed image");
    } catch (error) {
      console.warn("[Gemini Watermark Remover] Failed to process image:", error);
      imgElement.dataset.watermarkProcessed = "failed";
      imgElement.src = originalSrc;
    } finally {
      processingQueue.delete(imgElement);
    }
  }
  var processAllImages = () => {
    const images = findGeminiImages();
    if (images.length === 0) return;
    console.log(`[Gemini Watermark Remover] Found ${images.length} images to process`);
    images.forEach(processImage);
  };
  var setupMutationObserver = () => {
    new MutationObserver(debounce(processAllImages, 100)).observe(document.body, { childList: true, subtree: true });
    console.log("[Gemini Watermark Remover] MutationObserver active");
  };
  async function processImageBlob(blob) {
    const blobUrl = URL.createObjectURL(blob);
    const img = await loadImage(blobUrl);
    const canvas = await engine.removeWatermarkFromImage(img);
    URL.revokeObjectURL(blobUrl);
    return canvasToBlob(canvas);
  }
  var GEMINI_URL_PATTERN = /^https:\/\/lh3\.googleusercontent\.com\/rd-gg(?:-dl)?\/.+=s(?!0-d\?).*/;
  var { fetch: origFetch } = unsafeWindow;
  unsafeWindow.fetch = async (...args) => {
    const url = typeof args[0] === "string" ? args[0] : args[0]?.url;
    if (GEMINI_URL_PATTERN.test(url)) {
      console.log("[Gemini Watermark Remover] Intercepting:", url);
      const origUrl = replaceWithNormalSize(url);
      if (typeof args[0] === "string") args[0] = origUrl;
      else if (args[0]?.url) args[0].url = origUrl;
      const response = await origFetch(...args);
      if (!engine || !response.ok) return response;
      try {
        const processedBlob = await processImageBlob(await response.blob());
        return new Response(processedBlob, {
          status: response.status,
          statusText: response.statusText,
          headers: response.headers
        });
      } catch (error) {
        console.warn("[Gemini Watermark Remover] Processing failed:", error);
        return response;
      }
    }
    return origFetch(...args);
  };
  (async function init() {
    try {
      console.log("[Gemini Watermark Remover] Initializing...");
      engine = await WatermarkEngine.create();
      processAllImages();
      setupMutationObserver();
      console.log("[Gemini Watermark Remover] Ready");
    } catch (error) {
      console.error("[Gemini Watermark Remover] Initialization failed:", error);
    }
  })();
})();
Đù hay vậy ! Khuyên mọi người nên dùng
CjN9SbE.gif

Đức !!!
 
Sau khi tạo ảnh xong mà ram cứ ở mức 80% thì có bình thường không các bác . Search mãi không ra lỗi gì .(ram 32gb)
Trong quá trình chạy thì model được cache vào RAM. Sau khi tắt ComfyUI (Ctrl-C) thì RAM mới được giải phóng đó bác.
 
Prompt đồ nữ nhiều rồi. Giờ đổi qua prompt phối đồ cho nam.
Chủ đề đồ thường nhật, màu chủ đạo xanh rêu, phối ngẫu nhiên.

Vừa dụ được kép mới trẻ đẹp làm mẫu phối đồ nam.
Gemini_Generated_Image_c2c9ksc2c9ksc2c9.webp


Prompt dưới từng ảnh.
Using_my_photo_202602212026.webp


Using my photo as a reference. Ultra-realistic full-length portrait of an adult man in a cozy bedroom, 4K resolution, captured with a 35mm lens at f/2.2. He wearing an oversized forest green sweatshirt, gray cargo pants, silver chains, and black rings. He is looking down at a smartphone with a calm expression. Background features a clothing rack, dark gray bedding, and a "NO ONE LEFT BEHIND" poster. Warm ambient lighting, soft shadows, extreme detail on skin pores, fabric texture, and metallic reflections. Photorealistic, cinematic composition, high visual fidelity.

Using_my_photo_202602212029.webp

Using my photo as a reference. Ultra-realistic full-body portrait of a man, 4K resolution, captured on Sony A7R V, 35mm f/1.8 lens, f/2.2. He is wearing oversized black sunglasses, a forest green varsity jacket with black leather sleeves and a beige "B" patch, a white t-shirt, and cream denim jeans. Standing on a grey stone plaza during golden hour, contemplative expression, gazing off-camera. Hyper-detailed textures on skin pores, fabric weaves, and leather. Soft background blur of a modern stone and glass building, warm sunlight highlights, cinematic lighting, 4K visual fidelity.

Using_my_photo_202602212030.webp

Using my photo as a reference. Ultra-realistic 4K portrait of a man, maintaining exact facial likeness. He wears an oversized dark forest green t-shirt, light beige cargo shorts, a silver pendant necklace, and a black leather tote bag. The subject is posed confidently with one hand near his ear and the other in his pocket, standing against a modern white building with glass reflections and green shrubs. Captured on a full-frame mirrorless camera, 35mm lens, f/2.0, featuring natural daylight, precise skin textures, fabric weave, and professional color grading.

Using_my_photo_202602212032.webp

Using my photo as a reference. A full-length ultra-realistic 4K portrait of a man standing in a sunlit greenhouse. The subject wearing a dark forest green linen button-down shirt with rolled sleeves and black athletic shorts. Accurate facial likeness and a serene expression. Details include a silver bracelet, a wristwatch, white crew socks, and grey sneakers. The setting features a gravel floor, terracotta pots with cycad plants, and stone walls covered in vines and red-orange flowers. Lighting is bright, diffused natural daylight with soft dappled shadows. Captured on 35mm lens, f/2.0, sharp focus on subject with a soft bokeh background. High-fidelity textures on fabric, skin pores, and botanical elements.

Using_my_photo_202602212036.webp

Using my photo as a reference. Ultra-realistic full-body portrait of a man, preserving exact facial likeness, standing on concrete against a minimalist beige paneled wall. Natural, relaxed pose, hands in pockets, gazing aside. Wearing an oversized forest green "JOMARU ATH DEPT" sweatshirt with yellow text and bulldog graphic, high-waisted beige corduroy trousers, and white/green leather sneakers. Shot on Sony A7R V, 35mm lens, f/2.0, ISO 125, 4K resolution. Photorealistic skin texture, detailed fabric weave, soft overcast daylight, sharp focus, cinematic color grading, high visual fidelity.

Using_my_photo_202602212038.webp

Using my photo as a reference. Ultra-realistic 4K medium-full portrait of a man in a tropical setting. He wears an olive green linen-cotton shirt with rolled sleeves and off-white trousers, holding a fabric bag. Accessories include black rectangular sunglasses, a silver watch, a ring, and a thin bracelet. The lighting is bright natural daylight (5600K), creating soft depth with a 35mm f/2.0 lens effect. High fidelity details on skin pores, fabric weave, and lush palm background. Captured on a full-frame mirrorless camera for professional, clean, low-noise quality.

Using_my_photo_202602212040.webp

Using my photo as a reference. Ultra-realistic medium-full portrait of a young adult man, wearing glasses and a dark green "NY" cap, sitting at an outdoor urban café. He is wearing a white t-shirt, a green sweatshirt draped over shoulders, black tailored trousers, and white/green leather sneakers. He holds an iced drink in one hand, sitting on a dark green metal chair by a light grey table. Captured with a full-frame mirrorless camera, 35mm lens, f/2.2, 4K resolution. High-fidelity details on skin pores, fabric textures, metal watch links, and cobblestone ground. Soft bokeh background of a modern storefront, natural bright daylight, neutral sophisticated color palette.

Using_my_photo_202602212042.webp

Using my photo as a reference. Full-length 4K ultra-realistic portrait of a young man in an urban setting. Captured on 50mm f/1.4 lens, f/2.2 aperture, professional DSLR quality. Subject wears a dark forest green button-down shirt with rolled sleeves, orange chest logo, and tailored light beige chino trousers. Accessories include dark round sunglasses, brown leather belt, and silver wristwatch. Sharp focus on subject with soft bokeh historic stone building background. Natural daylight, 5400K white balance. High-fidelity textures: skin pores, short stubble, fabric weave, and polished leather shoes. Accurate likeness, 1080x1440, impeccable detail.
 
Anh chàng thư giãn và áo thun tinh nghịch

PCtre1.webp

Using my photo as a reference. Ultra-realistic portrait of an adult man with warm skin tone, no beard or mustache, eyes closed in a calm expression. Seated on a concrete ledge, angled right, wearing a vibrant blue "MARVEL" Spider-Man graphic t-shirt and sleek black pants. Captured from mid-thigh up, slightly low angle, 50mm lens, f/4. Bright diffused natural daylight, rooftop architectural background with white structures and glass. 4K resolution, hyper-detailed skin pores, fabric weave, and crisp text rendering. No facial hair, high visual fidelity.

PCtre2.webp

Using my photo as a reference. Ultra-realistic 4K portrait of a clean-shaven male with warm skin tone, mid-torso view, gazing upward. Subject wears a black crew neck t-shirt featuring a sharp "Creation of Adam" graphic with "ROMA" and "THE SISTINE CHAPEL" text, and a teal pendant necklace. Low-angle shot, 35mm lens, f/4 aperture. Golden hour lighting. Background features a dramatic cloudy sky, green foliage, and thin utility wires. High-fidelity textures, visible skin pores, cotton fabric weave, hyper-detailed, masterpiece.

PCtre3.webp

Using my photo as a reference. Ultra-realistic portrait of an adult male, no facial hair. Subject is seated on a dark textured ledge in a low-angle shot, gazing thoughtfully off-camera. Wearing a crisp white crew neck t-shirt, dark wash denim jeans, a gold curb chain, and 1 silver rings. Lighting is soft, diffused natural daylight under a partly cloudy sky. Captured with 85mm lens, f/3.2, ISO 100, providing sharp focus on the subject with a gentle background blur. 4K resolution, hyper-detailed skin texture, visible fabric weave, and realistic metallic reflections.

PCtre4.webp

Using my photo as a reference. Ultra-realistic 4K portrait of a man, standing mid-thigh up in an urban rooftop setting. He wears an oversized white t-shirt with a complex abstract graphic (black brushstrokes, yellow skull, star, ladybug, and "UP" text), tie-dye green/blue pants, and a natural stone necklace. Hands in pockets, neutral expression, no facial hair. High-fidelity skin textures, natural daylight, 50mm lens effect, f/4.5 aperture with a soft-focus city background. Balanced composition, crisp details on fabric and hair, no anatomical distortions, 4K resolution.

PCtre5.webp

Using my photo as a reference. Ultra-realistic portrait of a clean-shaven male, 4K resolution, 35mm lens, low-angle shot. Subject wearing an oversized navy blue t-shirt with a minimalist white smiley face graphic and loose white pants. Left wrist features a black sturdy watch. Natural golden hour lighting, serene expression looking at a cerulean sky with cumulus clouds. Hyper-detailed textures on skin pores, fabric weave, and hair strands. Soft bokeh, professional photography, cinematic composition, high visual fidelity.

PCtre6.webp

Using my photo as a reference. Ultra-realistic 4K portrait of a clean-shaven male with light-medium skin, sitting on a white ledge, low-angle shot. He wears an oversized cream "The Simpsons" graphic t-shirt (accurate text) and blue textured denim jeans with a black cross print on the thigh. Hands resting in hair, looking directly at camera, neutral expression. Natural daylight, bright blue sky with clouds. 35mm lens, f/3.5, sharp focus on subject, subtle bokeh. Hyper-detailed skin pores, fabric weave, and metallic watch/jewelry. No facial hair, high visual fidelity.

PCtre7.webp

Using my photo as a reference. Ultra-realistic portrait of a clean-shaven male, light-medium warm skin, hands on hips, looking up with eyes closed, serene expression. Wearing a white "SEASON ONE" graphic t-shirt with orange icon, light wash denim, silver chain, stud earrings, and gold ring. Low-angle shot, vertical framing, 4K resolution, 50mm lens, f/4. Bright natural daylight, soft shadows, cerulean sky with fluffy clouds background. Hyper-detailed skin pores, fabric weave, and metallic textures.
 
Sẵn mod cho em hỏi, ảnh hở như trong bài này có sao không, để em biết đường sửa lại @Fioren
Từ thớt cũ mấy năm trước, hông biết giờ @Fioren đã cởi mở hơn với wibu, séc gầy,... chưa :extreme_sexy_girl:
Mod Fioren:
Mọi người chú ý về việc ảnh 18+ nếu như ảnh chỉ hở hang, thì để ô spoiler kiểu để người khác lướt thread ko bị ngại khi ở nơi công cộng. Còn ảnh lộ bộ phận kín, ghê rợn, máu me, chỉ có súng, kiếm thì phải dẫn link ra hosting image khác vd imgur và đặt link trong ô code như vậy (nếu dùng mã hóa hex to text đc nữa thì càng tốt), thường google sẽ không quét link trong ô code

Mã:
Link
Mình cũng hay xem thread này nên thấy post nào không như vậy là mình sẽ xóa kèm thông báo nhé, mọi người thông cảm vì thread bị google warn là phải xóa hết thread chứ không phải chỉ xóa post là xong.
 

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