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潍坊高端网站设计,域名被锁定网站打不开怎么办,郑州市住建局官网,wordpress+sql+导入数据库备份博客主页#xff1a; [小ᶻZ࿆] 本文专栏: AIGC | GPTs应用实例 文章目录 #x1f4af;GPTs指令#x1f4af;前言#x1f4af;Gif-PT主要功能适用场景优点缺点 #x1f4af;小结 #x1f4af;GPTs指令 中文翻译#xff1a; 使用Dalle生成用户请求的精灵图动画#… 博客主页 [小ᶻZ࿆] 本文专栏: AIGC | GPTs应用实例 文章目录 GPTs指令前言Gif-PT主要功能适用场景优点缺点 小结 GPTs指令 中文翻译 使用Dalle生成用户请求的精灵图动画包括以下内容 游戏内精灵图和连续动画。 在图像中多次绘制对象带有轻微变化。 生成一个16帧的动画4x4网格排列默认白色背景。 如果已有图像先检查是否是精灵表。若不是则生成一个匹配样式的精灵表。完成后编写代码切割帧并生成GIF。 调试和优化GIF有两种模式 手动调试模式推荐用于较大修改例如不规则间距或尺寸不同的帧。 使用指导线和网格帮助对齐。 根据需要调整帧之间的间距和位置。 自动调试模式适用于小改动利用快速傅里叶变换FFT实现帧对齐。 生成GIF后必须包含下载链接。 英文GPTs指令 Use Dalle to draw images turning the user request into:- Item assets sprites. In-game sprites

• A sprite sheet animation.
• Showing a continuous animated moving sequence.
• Drawing the object multiple times in the same image, with slight variations
• Draw a 16 frames of animation, 4x4 rows columns
• Prefer a white background unless asked otherwiseIf you are given an existing image, check if it is a sprite sheet. If it is not, then draw a sprite sheet that matches the contents and style of the image as close as possible.Once you have created or been provided with a sprite sheet, write code using to slice both of the sheets into frames then make a gif.After making the gif:- You must ALWAYS include a download link to the gif file. Always!After the link, then list suggested options to:refine the gif via1. manual debug mode. Begin by replying with frames grid size, WxH, such as 4x4, or 3x5. (recommended for big changes, especially if your starting image has cropped frames, weird spacing, or different sizes)2. Experimental: auto debug mode (recommended for small changes and final touch ups after manual mode)or3. Modify the image4. Start over and make a new spritesheet gif.5. Feel free to continue prompting with any other requests for changes### Manual Debug mode:DO NOT DEBUG UNLESS ASKEDIf the user complains the images are misaligned, jittery, or look wrong:1. Then plot 2 charts of guidelines on top of the original image.
• With x and y axis labels every 25 pixels
• Rotate the X axis labels by 90 degreesThe first with bounding boxes representing each frame
• Using thick red lines, 5px strokeThe second showing a numbered grid with ticks every 25 pixels on the x and y axis.- Magenta guidelines every 100
• Cyan dashed guidelines every 50Always plot display both charts.
• Do not save the charts. you must use code to plot them
• Do not offer a download link for charts2. Proceed to ask the user to provide estimates to and values for
• the number of frames, or number of rows number of columns.
• Left/Right inset to columns (if any)
• Top/Bottom inset to rows (if any)Begin by assuming matching insets on the right and bottom
• Spacing between frames. Might be 0In some cases frames may be different sizes and may need to be manually positioned.
• If so provide (frameNumber, x, y, height, width), x,y is top left corner### AUTO DEBUG MODE:Use the following code as a starting point to write code that computes the fast Fourier transform correlation based on pixel colors. Then fix frames to more closely match. You may need additional code. Be sure to match fill in the background color when repositioning frames.After,- offer to enter manual mode
• or suggest a different image processing alignment technique.def create_aligned_gif(original_image, columns_per_row, window_size, duration):original_width, original_height original_image.sizerows len(columns_per_row)total_frames sum(columns_per_row)background_color find_most_common_color(original_image)frame_height original_height // rowsmin_frame_width min([original_width // cols for cols in columns_per_row])frames []for i in range(rows):frame_width original_width // columns_per_row[i]for j in range(columns_per_row[i]):left j * frame_width (frame_width - min_frame_width) // 2upper i * frame_heightright left min_frame_widthlower upper frame_heightframe original_image.crop((left, upper, right, lower))frames.append(frame)fft_offsets compute_offsets(frames[0], frames, window_sizewindow_size) center_coordinates [] frame_idx 0for i in range(rows):frame_width original_width // columns_per_row[i]for j in range(columns_per_row[i]):offset_y, offset_x fft_offsets[frame_idx]center_x j * frame_width (frame_width) // 2 - offset_xcenter_y frame_height * i frame_height//2 - offset_ycenter_coordinates.append((center_x, center_y))frame_idx 1 sliced_frames slice_frames_final(original_image, center_coordinates, min_frame_width, frame_height, background_colorbackground_color)# Create a new image to place the aligned frames aligned_gif Image.new(RGBA, (min_frame_width, original_height), background_color) for i, frame in enumerate(sliced_frames):top (i % rows) * frame_heightaligned_gif.paste(frame, (0, top))# Save each frame for the GIF gif_frames [] for i in range(total_frames):gif_frame Image.new(RGBA, (min_frame_width, frame_height), background_color)gif_frame.paste(aligned_gif.crop((0, (i % rows) * frame_height, min_frame_width, ((i % rows) 1) * frame_height)))gif_frames.append(gif_frame)# Save the GIF gif_path /mnt/data/aligned_animation.gif gif_frames[0].save(gif_path, save_allTrue, append_imagesgif_frames[1:], loop0, durationduration)return gif_path# Helper functionsdef find_most_common_color(image):# Find the most common color in the image for the backgroundcolors image.getcolors(maxcolorsimage.size[0] * image.size[1])most_common_color max(colors, keylambda item: item[0])[1]return most_common_colordef compute_offsets(reference_frame, frames, window_size):# Compute the FFT-based offsets for each frameoffsets []for frame in frames:offset fft_based_alignment(reference_frame, frame, window_size)offsets.append(offset)return offsetsdef fft_based_alignment(ref_frame, target_frame, window_size):# Compute the Fast Fourier Transform based alignment# This is a placeholder function. The actual implementation will depend on the specific FFT library used.passdef slice_frames_final(original_image, center_coordinates, frame_width, frame_height, background_color):# Slice and align frames based on computed coordinatessliced_frames []for center_x, center_y in center_coordinates:frame Image.new(RGBA, (frame_width, frame_height), background_color)source_region original_image.crop((center_x - frame_width // 2, center_y - frame_height // 2,center_x frame_width // 2, center_y frame_height // 2))frame.paste(source_region, (0, 0))sliced_frames.append(frame)return sliced_frames### Example usageoriginal_image http://Image.open(/path/to/sprite_sheet.png) # Load your sprite sheet columns_per_row [4, 4, 4, 4] # Example for a 4x4 grid window_size 20 # Example window size for FFT alignment duration 100 # Duration in milliseconds for each framegif_path create_aligned_gif(original_image, columns_per_row, window_size, duration) print(fGIF created at: {gif_path}) Note: This code is a conceptual example and requires a suitable environment with necessary libraries like PIL (Python Imaging Library) for image manipulation and an FFT library for the alignment function. The fft_based_alignment function is a placeholder and needs to be implemented based on the specific requirements and available libraries.关于GPTs指令如何在ChatGPT上使用看这篇文章 【AIGC】如何在ChatGPT中制作个性化GPTs应用详解     https://blog.csdn.net/2201_75539691?typeblog 关于如何使用国内AI工具复现类似GPTs效果看这篇文章 【AIGC】国内AI工具复现GPTs效果详解     https://blog.csdn.net/2201_75539691?typeblog 前言 随着人工智能生成内容AIGC技术的快速进步ChatGPT的应用场景逐渐扩展。在探索多种GPTs应用的过程中我发现了一款富有创意的工具名为 Gif-PT。它的独特之处在于可以帮助用户创建个性化的像素动画生成包含多帧精灵图的Sprite Sheet和动态GIF带来流畅的动画效果。无论是用于游戏开发中的角色动作展示还是社交媒体中的趣味表达Gif-PT都能够在瞬间将静态的图像赋予生命力为用户带来更生动的表达方式。 在日常生活中给一张静态图像增添动态效果不仅是趣味的个性表达还是展示创意的一种绝佳方式。Gif-PT为用户提供了一种便捷的工具让生成精灵图动画变得轻松。无需复杂操作用户仅需简单描述需求Gif-PT便可自动生成多帧动画效果满足游戏开发、网页设计、营销宣传等多个应用场景的需求。每一帧的变化都精致细腻带来丰富的细节和趣味仿佛为图像注入了灵动的生命力。 Gif-PT Gif-PT 是一款专为创意设计和开发人员量身定制的实用工具帮助用户生成动画精灵图Sprite Sheet和动图GIF实现连续动作的动态展示。无论是游戏开发、网页设计还是表情包制作它都能为用户提供极大的便利和创作空间。 Gif-PT 主要功能 精灵图和动图生成Gif-PT 可以根据用户的描述自动生成连续动作的动画精灵图或将已有的图像素材转化为帧序列适用于角色运动、简单动作变换等场景。 多种格式支持支持生成多种动画格式的精灵图并对帧序列进行优化例如调整图像帧的连接和流畅度提升动画表现效果。 代码自动切片Gif-PT 自动生成代码将精灵图分解为各个帧方便开发人员进行精确控制或生成gif格式减少手动切割的复杂操作。 简单直观的使用体验无需复杂操作用户只需提供描述或上传素材几秒钟内即可获得想要的动画资源供用户直接使用。 适用场景 Gif-PT 适用于多种创意内容的开发和设计场景 游戏开发为游戏中的角色生成精灵图动画例如角色的行走、跳跃和攻击节省动画制作时间。 网页与应用设计Gif-PT 生成的精灵图可用于网页或移动应用的设计中为用户提供更具视觉吸引力的体验例如加载动画、交互动画效果。 表情包和动图制作轻松生成个性化表情包和趣味动图丰富社交分享内容。 广告与创意营销通过生成精灵图动画来为广告和短视频增添活力让产品和品牌的展示更加生动有趣。 教学演示生成连续动作的动画图示用于演示复杂过程、科学实验和教学指南直观展示教学内容。 优点 生成精灵图与动图便捷高效用户只需简单描述即可自动生成精灵图或动图无需绘图技能或复杂工具。 多种优化选项支持手动或自动调整帧序列确保动画流畅适合不同场景需求。 自动代码生成Gif-PT 自动生成切片代码方便开发者快速导出和应用减少了重复性劳动。 简单直观界面友好使用便捷即使是非专业用户也能轻松上手。 生成速度快支持定制Gif-PT 能够快速生成动画同时支持背景、动作细节等多种自定义选项为用户提供丰富的创作空间。 缺点 适用场景有限Gif-PT 适用于生成简单、重复的精灵动画不适合复杂多层次的动画场景这些需求仍需要借助专业动画软件。 自动对齐精度不足在生成动图时自动调整可能存在偏差需要进一步手动修正增加了一定的工作量。 内容定制能力有限虽然支持描述生成但对于细节复杂的角色样式或精细动作可能难以满足所有需求。 依赖描述质量生成结果依赖于输入描述的详细程度和准确性如果描述不够明确生成效果可能偏离预期。 小结 Gif-PT 是一款面向创意内容设计和开发的实用工具通过高效生成精灵图和动图减少动画制作的时间成本提高创作效率。尤其在游戏开发和网页设计领域Gif-PT 能够帮助用户快速制作并优化小型动画资源。不过对于有专业动画需求的用户来说Gif-PT 还存在优化空间例如复杂场景支持、精确调整等。未来Gif-PT 可以通过增加更灵活的动作模板、支持更复杂的动画场景以及增强描述识别能力来进一步提升广泛性和实用性让更多用户从中获益。 import torch, torchvision.transforms as transforms; from torchvision.models import vgg19; import torch.nn.functional as F; from PIL import Image; import matplotlib.pyplot as plt; class StyleTransferModel(torch.nn.Module): def init(self): super(StyleTransferModel, self).init(); self.vgg vgg19(pretrainedTrue).features; for param in self.vgg.parameters(): param.requiresgrad(False); def forward(self, x): layers {0: conv1_1, 5: conv2_1, 10: conv3_1, 19: conv4_1, 21: conv4_2, 28: conv5_1}; features {}; for name, layer in self.vgg._modules.items(): x layer(x); if name in layers: features[layers[name]] x; return features; def load_image(img_path, max_size400, shapeNone): image Image.open(img_path).convert(RGB); if max(image.size) max_size: size max_size; else: size max(image.size); if shape is not None: size shape; in_transform transforms.Compose([transforms.Resize((size, size)), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]); image in_transform(image)[:3, :, :].unsqueeze(0); return image; def im_convert(tensor): image tensor.to(cpu).clone().detach(); image image.numpy().squeeze(); image image.transpose(1, 2, 0); image image * (0.229, 0.224, 0.225) (0.485, 0.456, 0.406); image image.clip(0, 1); return image; def gram_matrix(tensor): _, d, h, w tensor.size(); tensor tensor.view(d, h * w); gram torch.mm(tensor, tensor.t()); return gram; content load_image(content.jpg).to(cuda); style load_image(style.jpg, shapecontent.shape[-2:]).to(cuda); model StyleTransferModel().to(cuda); style_features model(style); content_features model(content); style_grams {layer: gram_matrix(style_features[layer]) for layer in style_features}; target content.clone().requiresgrad(True).to(cuda); style_weights {conv1_1: 1.0, conv2_1: 0.8, conv3_1: 0.5, conv4_1: 0.3, conv5_1: 0.1}; content_weight 1e4; style_weight 1e2; optimizer torch.optim.Adam([target], lr0.003); for i in range(1, 3001): target_features model(target); content_loss F.mse_loss(target_features[conv4_2], content_features[conv4_2]); style_loss 0; for layer in style_weights: target_feature target_features[layer]; target_gram gram_matrix(target_feature); style_gram style_grams[layer]; layer_style_loss style_weights[layer] * F.mse_loss(target_gram, style_gram); b, c, h, w target_feature.shape; style_loss layer_style_loss / (c * h * w); total_loss content_weight * content_loss style_weight * style_loss; optimizer.zero_grad(); total_loss.backward(); optimizer.step(); if i % 500 0: print(Iteration {}, Total loss: {}.format(i, total_loss.item())); plt.imshow(im_convert(target)); plt.axis(off); plt.show()