Inpaint

Inpaint Phần mềm xoá chi tiết thừa trên ảnh Inpaint 9.1 mang tới cách cách xóa người trong ảnh trên máy tính, đối tượng không mong muốn nhanh chóng và đơn giản. Download Inpaint và sử dụng miễn phí. Xếp hạng: 4 34 Phiếu bầuSử dụng: Dùng thử 48.683 Tải về Inpaint Xóa chi tiết thừa trên ảnh trực tuyến miễn phí Inpaint là ứng dụng chỉnh sửa ảnh trực tuyến miễn phí với tính năng loại bỏ chi tiết thừa trên ảnh đơn giản. Với WebInpaint, bạn thoải mái xóa logo bằng một bước. Xếp hạng: 4 10 Phiếu bầuSử dụng: Miễn phí 24.366 Truy cập Inpaint cho Mac Ứng dụng xóa chi tiết thừa trên ảnh cho Mac InPaint cho Mac là phần mềm chỉnh sửa ảnh nhanh chóng và hữu ích cho máy tính Mac với khả năng xóa chi tiết thừa trên ảnh và tự động bù lại vùng bị xóa với khả năng tự tạo texture bằng vùng ảnh xung quanh cực kì thông minh. Xếp hạng: 5 4 Phiếu bầuSử dụng: Dùng thử 3.671 Tải về Inpaint Tool 1.0 Inpaint Tool là một ứng dụng nhỏ gọn cho phép bạn chỉnh sửa và loại bỏ các đối tượng không mong muốn từ hình ảnh của bạn. Xếp hạng: 5 2 Phiếu bầuSử dụng: Dùng thử 4.204 Tải về Multi-View Inpaint Loại bỏ chi tiết thừa trên ảnh thông minh Multi-View Inpaint là phần mềm xóa bỏ chi tiết thừa trên ảnh vô cùng thông minh khi giúp bạn loại bỏ các nội dung không mong muốn trên ảnh và thay thế bằng chi tiết từ 1 hình ảnh khác có chủ thể bị che. Xếp hạng: 3 1 Phiếu bầuSử dụng: Dùng thử 148 Tải về Multi-View Inpaint cho Mac Xóa bỏ chi tiết

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Threshold for foreground object extraction (default: 25.0)--pl_pos_r float Rotation radius of the point light (default: 5.0)--pl_pos_h float Height of the point light (default: 3.0)--power float Power of the point light (default: 1200.0)--inpaint bool, --noinpaint bool Inpaint the background of generated point light images (default: False)--env_map [str] Environment map for the rendering, defaults to None (white point light) (default: None)--frames int Number of frames for lighting controlled video (default: 120)--use_gpu_for_rendering bool, --nouse_gpu_for_rendering bool Use GPU for radiance hints rendering (default: True)--cache_radiance_hints bool, --nocache_radiance_hints bool Cache the radiance hints for the video (default: True)--radiance_hints_path [str] pre-rendered radiance hint path (default: None)Generation TipsForeground mask: By default we use U2Net to generate an inital mask and use SAM to further refine it. But if it doesn't work well on your image, you can provide a mask image with --mask_path. The mask image can be RGBA or grayscale, which we directly use the last channel as the mask.Background inpainting: For environment map lightings, the script will automatically inpaint the background with the background color of the environment map. For point light lightings, you can use --inpaint to inpaint the background of the generated images using the stable diffusion inpainting model, but we suggest a manual intervention to get better inpainting results.Randomness: Due to the ambiguity in the provisional image (e.g. shape, original lighting, fine-grained material properties, etc.), generated results can and should have diversity. Hence, you can try different seeds and prompts to get the wanted results, just as any diffusion model. The script will generate num_imgs_per_prompt (default=4) images for each prompt.Depth Conditioned GenerationIf you have the mesh of an object, you can use the mesh to render finer radiance hints, and use a depth-conditioned diffusion model to generate the provisional image. We provide a simple script to render radiance hints and groud-truth depth map

Professional Photo Editing As your photo is being edited, you can see the changes as they emerge, leave comments, and request modifications, whether you're working in a contest or a private order. Step 3: Choose Your Best Image Once you're happy with the edits, you can select the best version of your photo. After your approval, the edited photo will be made available for download, and an independent retoucher will be added to your team. Step 4: Work with Your Retoucher Team If you want to continue working with the Phowd retouchers you selected, you can do so via private orders. You can keep track of the work, monitor progress, and only pay for what you approve and like. Tool 3. Inpaint Tired of dealing with water damage photos that have been smudged or discolored? Don't worry, Inpaint can help you out. This powerful tool is a great way to improve your photos' overall look and feel. It can remove dust, blemishes, spots, wrinkles, and other imperfections in just minutes. Plus, it's incredibly easy to use. Steps to use the Inpaint software: Scan the damaged photo to your computer. Then, open the photo in Inpaint and select the areas that require attention. Use the Magic Wand or another selection tool to choose all the defective areas on your photo. Finally, run the Erase Process from the toolbar. Inpaint will remove the defects and restore your damaged photo to its former glory. Above we have introduced three digital tools that can help you repair water damage photos, and you don't have to worry about it anymore. Part 3: Tips For Repairing Water Damage Photos 1. Keep the pictures from drying out! If the pictures dry out too quickly, the colors can bleed, and the paper can crack. Therefore, keeping the water damage photos wet is essential until you're ready to start repairing them. You can store them in a zip-lock bag with moist towels or immerse them in clean, cold water until you're ready to start the restoration process. Remember that every second counts, so act quickly to prevent further damage.

This repository contains code and examples for DreamBooth fine-tuning the SDXL inpainting model's UNet via LoRA adaptation.DreamBooth is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject.UsageInstall the requirementsgit clone train-lora-sdxl-inpaint/diffusers && pip install -e .pip install -r train-lora-sdxl-inpaint/diffusers/examples/research_projects/requirements.txtpip install -U "huggingface_hub[cli]"Download the inpainting modelhuggingface-cli download diffusers/stable-diffusion-xl-1.0-inpainting-0.1 --local-dir ./models/sdxl-inpainting-1.0 --local-dir-use-symlinks FalsePlace your subject images in dataset/subdir and runaccelerate launch examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora_sdxl.py \ --pretrained_model_name_or_path="./models/sdxl-inpainting-1.0" \ --instance_data_dir="./dataset/your-subject-images-directory" \ --output_dir="./lora-weights/sks-your-subject-sdxl-from-inpainting" \ --instance_prompt="a photo of a sks dog" \ --mixed_precision="fp16" \ --resolution=1024 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --learning_rate=1e-4 \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --max_train_steps=500 \ --seed="42" \Make sure to replace the directory names and unique identifiers accordingly. At least 16GB of VRAM is required for training.What this script doesThis is a fork of the diffusers repository with the only difference being the addition of the train_dreambooth_inpaint_lora_sdxl.py script. You could use this script to fine-tune the SDXL inpainting model UNet via LoRA adaptation with your own subject images. This could be useful in e-commerce applications, for virtual try-on for example.ResultsAfter running a few tests, arguably, doing dreambooth finetuning on the SDXL-inpaint model gives higher quality images than the proposed alternative with SD inpainting modelSDXL InpaintingSD InpaintingWhat this script maybe doesThis script has only been tested for doing doing lora adaption for the unet of the SDXL inpainting model. Fine-tuning the text encoder(s) hasn't been tested. Feel free to try that out and provide feedback!What this script doesn't do and what you should probably never doThis script shouldn't be used for inpersonating anyone without their consent. The script also does not support any form of harmful or malicious use. It should not be used to create inappropriate or offensive content.