Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. Image Models SD1.x, SD2.x, SDXL,SDXL Turbo Stable Cascade SD3 and SD3.5 Pixart Alpha and Sigma AuraFlow HunyuanDiT ...
第一步先c出Ksampler,然后最上端model通过chekpoint lodersimpe链接load chekpoint,postive,negative通过clip text encode链接正反向提示词,latent image这里记忆为空白的潜在空间,所以当然选择一张白纸只设定尺寸的empty latent image,最右侧latent理解为已经采样完完成的潜空间到像素空间的入口,但是必须的有VAE decode解码...
Create Nested Rectangles PNG MaskCreate a Nested Rectangles PNG image with Color Mask stack for regional conditioning mask. Ideas from Watermark RemovalInputsDescription Width Height Image size, could be difference with cavan size, but recommended to connect them together. X, Y Center point (X,Y)...
// key 为 node id 的 map 对象typeTProgressmap[string]TProgressNodetypeTProgressNodestruct{Max int`json:"max"`// 进度的最大值Value int`json:"value"`// 当前进度Start int64`json:"start"`// 开始时间LastUpdated int64`json:"last_updated"`// 最后一次更新时间Images[]TProgressNodeImage`json:"i...
"Load Image" } }, "15": { "inputs": { "threshold_r": 0.15, "threshold_g": 0.15, "threshold_b": 0.15, "remove_isolated_pixels": 0, "fill_holes": false, "image": [ "13", 0 ] }, "class_type": "MaskFromRGBCMYBW+", "_meta": { "title": "🔧 Mask From RGB/CMY/BW...
},"class_type":"MaskFromRGBCMYBW+","_meta": {"title":"🔧 Mask From RGB/CMY/BW"} },"21": {"inputs": {"image_weight":0.8,"prompt_weight":1,"weight_type":"linear","start_at":0,"end_at":1,"image": ["10",0],"mask": ["15",0],"positive": ["24",0],"negative"...
from api_server.routes.internal.internal_routes import InternalRoutes class BinaryEventTypes: PREVIEW_IMAGE = 1 UNENCODED_PREVIEW_IMAGE = 2 async def send_socket_catch_exception(function, message): try: await function(message) except (aiohttp.ClientError, aiohttp.ClientPayloadError, Connection...
Create No-Code Workflow In short, for thetxt2mask: LoadImage,imageoutput to... CLIPSeg,maskoutput to... VAEEncodeForInpaint also addPreviewImageand connect the input to either theCLIPSegheatmap mask or BW mask output for visualization
🟨mask_optional: attention masks to apply to controlnets; basically, decides what part of the image the controlnet to apply to (and the relative strength, if the mask is not binary). Same as image input, if you provide more than one mask, each can apply to a different latent. ...
mask_optional: attention masks to apply to controlnets; basically, decides what part of the image the controlnet to apply to (and the relative strength, if the mask is not binary). Same as image input, if you provide more than one mask, each can apply to a different latent. ...