DemonGatanjieuandClaude Opus 5.5
5d023f3a7c
A block node can be "like" a role (takes its settings) or "replace" it (its links, place and settings too; LanPaint's sampler for the KSampler). $save is the output node the picture reaches, past a stitch. Upscale blocks take an upscale model this ComfyUI has; control blocks a ControlNet whose base fits the canvas's model, and the picture to follow copied into the input folder. The directory is grouped by job, a few words a block, to keep begin_task short. Drop-down choices are read in both forms of ComfyUI's node list. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
275 lines
18 KiB
Python
275 lines
18 KiB
Python
"""Ready-made blocks of nodes, distilled from workflows that do one thing well (a repaint that
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crops around the marked area and stitches it back, face and hand detailing): a light model
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names one (add_nodes block=…) and Anomalous builds it on the canvas, spliced into the
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workflow there. A block is a group in ComfyUI's API form (mcp_canvas_edit.add_nodes) whose
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links may name roles the page finds on the canvas by their sockets (web/modules/canvas_roles.js):
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"$sampler" (the KSampler), "$decode" (the node decoding its latent), "$save" (the node taking
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that picture); ["$decode", 0] is that node's output, ["$sampler.positive", "*"] what feeds that
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input, a path ("$sampler.positive.clip") followed upstream. A node "like" a role takes its
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settings of the same name; one that "replaces" a role takes its place, links and settings. The
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directory (directory()) is the "reference" the AI is handed when it begins a task: one line a block,
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the rest comes with the block.
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"""
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import re
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# A redraw after enlarging: low enough to keep the picture, high enough for real detail (the source used 0.32),
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# at the size the source used (its author: more than 1.5× goes wrong; 1.5× of 832×1216 is too much for a 6 GB card).
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UPSCALE_DENOISE = 0.35
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UPSCALE_BY = 1.25
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INPAINT_IMAGE = "the picture with the area as its alpha (inpaint_image from propose_repaint_area or ask_user's answer)"
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REFERENCE_IMAGE = "the picture to follow: one the user handed you (its path, as their message gives it) or one in the input folder"
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# The words a ControlNet file's name has for what it follows; a union model follows all of them.
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CONTROLS = {"pose": ("openpose", "pose"), "depth": ("depth",)}
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ANY_CONTROL = ("union", "promax")
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# The directory's headings, what an AI wants done; under each, its blocks with a few words apiece.
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JOBS = {"repaint": "Repaint a marked area", "upscale": "Enlarge a picture", "follow": "Follow a picture the user gave",
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"detail": "Fix faces and hands"}
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BLOCKS = {
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"inpaint_crop_stitch": {
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"job": "repaint", "short": "small fixes, the rest untouched",
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"packs": {"InpaintCropImproved": "ComfyUI-Inpaint-CropAndStitch", "InpaintStitchImproved": "ComfyUI-Inpaint-CropAndStitch"},
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"instead": "inpaint_basic",
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"takes": INPAINT_IMAGE,
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"nodes": {
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"picture": {"class_type": "LoadImage", "inputs": {"image": "$image"}},
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"soft": {"class_type": "DifferentialDiffusion", "inputs": {"model": ["$sampler.model", "*"]}, "wire_to": "$sampler"},
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"crop": {"class_type": "InpaintCropImproved", "inputs": {
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"image": ["picture", 0], "mask": ["picture", 1], "mask_blend_pixels": 32, "context_from_mask_extend_factor": 2.0,
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"output_resize_to_target_size": True, "output_target_width": 1024, "output_target_height": 1024,
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"downscale_algorithm": "lanczos", "upscale_algorithm": "lanczos"}},
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"condition": {"class_type": "InpaintModelConditioning", "inputs": {
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"vae": ["$decode.vae", "*"], "pixels": ["crop", "cropped_image"], "mask": ["crop", "cropped_mask"]},
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"wire_to": "$sampler"},
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"stitch": {"class_type": "InpaintStitchImproved", "inputs": {"stitcher": ["crop", "stitcher"], "inpainted_image": ["$decode", 0]},
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"wire_to": "$save"},
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},
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"roles": {"sampler": {"denoise": 0.75}}, # at 1 a model not made for inpainting paints a whole new subject into the crop
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"after": "The sampler now repaints only the cropped area, at denoise 0.75 (lower keeps more of the old). Its "
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"prompt is still the whole picture's: set_prompt it to what belongs in the area (\"a hand, five "
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"fingers\"), or it paints the whole subject there. Then run_workflow; the saved picture is the whole "
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"one, stitched.",
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},
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"inpaint_basic": {
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"job": "repaint", "short": "built-in nodes only",
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"packs": {},
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"takes": INPAINT_IMAGE,
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"nodes": {
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"picture": {"class_type": "LoadImage", "inputs": {"image": "$image"}},
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"soft": {"class_type": "DifferentialDiffusion", "inputs": {"model": ["$sampler.model", "*"]}, "wire_to": "$sampler"},
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"encode": {"class_type": "VAEEncode", "inputs": {"pixels": ["picture", 0], "vae": ["$decode.vae", "*"]}},
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"area": {"class_type": "SetLatentNoiseMask", "inputs": {"samples": ["encode", 0], "mask": ["picture", 1]},
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"wire_to": "$sampler"},
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},
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"roles": {"sampler": {"denoise": 0.7}},
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"after": "The sampler repaints only the area, at denoise 0.7 so it keeps the picture's look (higher changes more). "
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"Then run_workflow.",
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},
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"inpaint_lanpaint": {
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"job": "repaint", "short": "best blend, slow",
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"packs": {"LanPaint_KSampler": "LanPaint", "LanPaint_ImageEncode": "LanPaint", "LanPaint_ImageDecode": "LanPaint"},
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"instead": "inpaint_crop_stitch",
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"takes": INPAINT_IMAGE,
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"nodes": {
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"picture": {"class_type": "LoadImage", "inputs": {"image": "$image"}},
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"encode": {"class_type": "LanPaint_ImageEncode", "inputs": {"image": ["picture", 0], "vae": ["$decode.vae", "*"], "mask": ["picture", 1]}},
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"sampler": {"class_type": "LanPaint_KSampler", "replaces": "$sampler", "inputs": {"latent_image": ["encode", 0], "denoise": 1.0}},
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"decode": {"class_type": "LanPaint_ImageDecode", "replaces": "$decode", "inputs": {
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"samples": ["sampler", 0], "image": ["picture", 0], "mask": ["picture", 1]}},
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},
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"after": "The KSampler is now LanPaint's (same steps, cfg and seed) and repaints only the area, blending it "
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"into the rest. Its prompt is still the whole picture's: set_prompt it to what belongs in the area "
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"(\"a hand, five fingers\"). Then run_workflow; it takes several times longer than before.",
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},
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"upscale": {
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"job": "upscale", "short": "1.25×",
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"packs": {},
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"upscaler": True,
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"nodes": {
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"upscaler": {"class_type": "UpscaleModelLoader", "inputs": {"model_name": "$upscaler"}}, # enlarges, then fit brings it to UPSCALE_BY
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"enlarge": {"class_type": "ImageUpscaleWithModel", "inputs": {"upscale_model": ["upscaler", 0], "image": ["$save.images", "*"]}},
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"fit": {"class_type": "ImageScaleBy", "inputs": {"image": ["enlarge", 0], "upscale_method": "lanczos", "scale_by": "$fit"}},
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"encode": {"class_type": "VAEEncode", "inputs": {"pixels": ["fit", 0], "vae": ["$decode.vae", "*"]}},
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"redraw": {"class_type": "KSampler", "like": "$sampler", "inputs": {
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"model": ["$sampler.model", "*"], "positive": ["$sampler.positive", "*"], "negative": ["$sampler.negative", "*"],
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"latent_image": ["encode", 0], "denoise": UPSCALE_DENOISE}},
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"decode": {"class_type": "VAEDecode", "inputs": {"samples": ["redraw", 0], "vae": ["$decode.vae", "*"]}},
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"save": {"class_type": "SaveImage", "inputs": {"images": ["decode", 0], "filename_prefix": "upscaled"}},
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},
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"after": f"run_workflow saves the picture as before and the larger one beside it (prefix upscaled). The redraw "
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f"sampler has the first one's steps and cfg at denoise {UPSCALE_DENOISE}: lower keeps more of the "
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"picture, higher adds more (and above 0.5 may change it). For 2× or more use upscale_tiled.",
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},
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"upscale_tiled": {
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"job": "upscale", "short": "2×, in tiles",
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"packs": {"UltimateSDUpscale": "ComfyUI_UltimateSDUpscale"},
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"instead": "upscale",
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"upscaler": True,
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"nodes": {
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"upscaler": {"class_type": "UpscaleModelLoader", "inputs": {"model_name": "$upscaler"}},
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"tiles": {"class_type": "UltimateSDUpscale", "like": "$sampler", "inputs": {
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"image": ["$save.images", "*"], "model": ["$sampler.model", "*"], "positive": ["$sampler.positive", "*"],
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"negative": ["$sampler.negative", "*"], "vae": ["$decode.vae", "*"], "upscale_model": ["upscaler", 0],
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"upscale_by": 2.0, "denoise": 0.3, "tile_width": 1024, "tile_height": 1024}},
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"save": {"class_type": "SaveImage", "inputs": {"images": ["tiles", 0], "filename_prefix": "upscaled"}},
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},
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"after": "run_workflow saves the picture as before and the 2× one beside it (prefix upscaled); each tile is "
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"redrawn with the sampler's steps and cfg at denoise 0.3 (higher adds more detail, and may add seams).",
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},
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"control_pose": {
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"job": "follow", "short": "its pose",
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"packs": {"OpenposePreprocessor": "comfyui_controlnet_aux"},
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"takes": REFERENCE_IMAGE,
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"control": "pose",
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"nodes": {
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"reference": {"class_type": "LoadImage", "inputs": {"image": "$image"}},
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"pose": {"class_type": "OpenposePreprocessor", "inputs": {
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"image": ["reference", 0], "detect_hand": "enable", "detect_body": "enable", "detect_face": "enable", "resolution": 1024}},
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"controlnet": {"class_type": "ControlNetLoader", "inputs": {"control_net_name": "$controlnet"}},
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"steer": {"class_type": "ControlNetApplyAdvanced", "inputs": {
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"positive": ["$sampler.positive", "*"], "negative": ["$sampler.negative", "*"], "control_net": ["controlnet", 0],
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"image": ["pose", 0], "vae": ["$decode.vae", "*"], "strength": 0.8, "start_percent": 0.0, "end_percent": 0.8},
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"wire_to": "$sampler"},
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},
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"after": "run_workflow: the picture now takes that pose (strength 0.8, for the first 80% of the steps; lower "
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"either for a looser pose). The prompt still says who and what: change it if it fights the pose.",
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},
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"control_depth": {
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"job": "follow", "short": "its layout and shapes",
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"packs": {"DepthAnythingV2Preprocessor": "comfyui_controlnet_aux"},
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"takes": REFERENCE_IMAGE,
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"control": "depth",
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"nodes": {
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"reference": {"class_type": "LoadImage", "inputs": {"image": "$image"}},
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"depth": {"class_type": "DepthAnythingV2Preprocessor", "inputs": {
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"image": ["reference", 0], "ckpt_name": "depth_anything_v2_vitl.pth", "resolution": 1024}},
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"controlnet": {"class_type": "ControlNetLoader", "inputs": {"control_net_name": "$controlnet"}},
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"steer": {"class_type": "ControlNetApplyAdvanced", "inputs": {
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"positive": ["$sampler.positive", "*"], "negative": ["$sampler.negative", "*"], "control_net": ["controlnet", 0],
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"image": ["depth", 0], "vae": ["$decode.vae", "*"], "strength": 0.7, "start_percent": 0.0, "end_percent": 0.7},
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"wire_to": "$sampler"},
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},
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"after": "run_workflow: the picture now keeps that layout (strength 0.7, for the first 70% of the steps; lower "
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"either to let the model change more). The prompt still says what is drawn.",
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},
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"detail_face_hands": {
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"job": "detail", "short": "after a picture is made",
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"packs": {"FaceDetailer": "ComfyUI-Impact-Pack", "UltralyticsDetectorProvider": "ComfyUI-Impact-Subpack"},
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"detectors": {"face": "face_yolov8", "hand": "hand_yolov8"},
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"nodes": {
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"face_finder": {"class_type": "UltralyticsDetectorProvider", "inputs": {"model_name": "$face"}},
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"hand_finder": {"class_type": "UltralyticsDetectorProvider", "inputs": {"model_name": "$hand"}},
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"face": {"class_type": "FaceDetailer", "inputs": {
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"image": ["$decode", 0], "model": ["$sampler.model", "*"], "clip": ["$sampler.positive.clip", "*"],
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"vae": ["$decode.vae", "*"], "positive": ["$sampler.positive", "*"], "negative": ["$sampler.negative", "*"],
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"bbox_detector": ["face_finder", 0], "denoise": 0.4, "guide_size": 512}},
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"hands": {"class_type": "FaceDetailer", "inputs": {
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"image": ["face", "image"], "model": ["$sampler.model", "*"], "clip": ["$sampler.positive.clip", "*"],
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"vae": ["$decode.vae", "*"], "positive": ["$sampler.positive", "*"], "negative": ["$sampler.negative", "*"],
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"bbox_detector": ["hand_finder", 0], "denoise": 0.45, "guide_size": 512}},
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"save": {"class_type": "SaveImage", "inputs": {"images": ["hands", "image"], "filename_prefix": "detailed"}},
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},
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"after": "run_workflow saves the picture as before and the detailed one beside it (prefix detailed).",
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},
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}
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class BlockError(ValueError):
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pass
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def directory(info=None):
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"""{job: "block (a few words), …"} for the AI, short enough to stay in the chat; with ComfyUI's node
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list `info`, a block whose nodes are missing says what to install and what to use instead."""
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out = {heading: [] for heading in JOBS.values()}
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for name, block in BLOCKS.items():
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missing = sorted({pack for kind, pack in block["packs"].items() if info is not None and kind not in info})
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needs = f"; needs {', '.join(missing)}" + (f", use {block['instead']}" if block.get("instead") else "") if missing else ""
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out[JOBS[block["job"]]].append(f"{name} ({block['short']}{needs})")
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return {heading: ", ".join(names) for heading, names in out.items()}
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def _choices(info, kind, name):
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"""The files node `kind`'s drop-down `name` offers, in either form ComfyUI's node list gives it
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([[choices], {...}] or ["COMBO", {"options": [choices]}])."""
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try:
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spec = info[kind]["input"]["required"][name]
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found = spec[0] if isinstance(spec[0], list) else spec[1].get("options", []) if spec[0] == "COMBO" else []
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except (KeyError, IndexError, TypeError, AttributeError):
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found = []
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return [item for item in found if isinstance(item, str)]
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def _detector(info, prefix):
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"""The detector model file for `prefix` this ComfyUI has, preferring the larger kinds (m over s over n)."""
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found = sorted((name for name in _choices(info, "UltralyticsDetectorProvider", "model_name") if prefix in name.lower()),
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key=lambda name: ("m." not in name, "s." not in name, name))
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if not found:
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raise BlockError(f"No {prefix.split('_')[0]} detector model in models/ultralytics/bbox ({prefix}*.pt): "
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"ask the user to get one (ComfyUI-Manager's model list has them).")
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return found[0]
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def _upscaler(info):
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"""The upscale model file this ComfyUI has, preferring a 4× one, and how many times it enlarges."""
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files = _choices(info, "UpscaleModelLoader", "model_name")
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if not files:
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raise BlockError("No upscale model in models/upscale_models: ask the user to get one (4x-UltraSharp, "
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"ComfyUI-Manager's model list has it).")
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times = {name: int(found.group(1) or found.group(2)) for name in files
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if (found := re.search(r"(?<![0-9])([1-8])x|x([1-8])(?![0-9])", name.lower()))}
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name = sorted(files, key=lambda item: (times.get(item) != 4, item.lower()))[0]
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return name, times.get(name, 4)
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def _controlnet(info, kind, main, base_of):
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"""The ControlNet file for `kind` (CONTROLS) whose base model (`base_of(name)`) is of the family of
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`main`, the canvas's model's: one for that kind before a union one, one of that very base first.
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LLLite files need their own loader."""
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from .model_advice import family
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wanted = family(main)
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files = [name for name in _choices(info, "ControlNetLoader", "control_net_name") if "lllite" not in name.lower()]
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own = [name for name in files if any(word in name.lower() for word in CONTROLS[kind])]
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union = [name for name in files if any(word in name.lower() for word in ANY_CONTROL) and name not in own]
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fitting = [name for name in own + union if not wanted or family(base_of(name)) == wanted]
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if not fitting:
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raise BlockError(f"No {kind} ControlNet{f' for {wanted} models' if wanted else ''} in models/controlnet: ask the "
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"user to get one (Anomalous can download one they pick on Civitai).")
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return min(fitting, key=lambda name: (name in union, base_of(name).lower() != str(main).lower()))
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def nodes_of(name, info, image=None, main_base=lambda: "", base_of=lambda name: ""):
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"""Block `name` as add_nodes' group, its values filled in, or BlockError saying why not. A control
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block asks `main_base()` for the canvas's model's base model and `base_of` for each ControlNet's."""
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block = BLOCKS.get(name)
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if not block:
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raise BlockError(f"No block {name!r}; there are: {', '.join(BLOCKS)}.")
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missing = sorted({pack for kind, pack in block["packs"].items() if kind not in info})
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if missing:
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instead = f" Use block {block['instead']} instead, or ask the user to install it with ComfyUI-Manager." if block.get("instead") else \
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" Ask the user to install it with ComfyUI-Manager."
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raise BlockError(f"Block {name} needs {', '.join(missing)}, not installed here.{instead}")
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if block.get("takes") and not (isinstance(image, str) and image.strip()):
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raise BlockError(f"Block {name} needs image: {block['takes']}.")
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fills = {"$image": (image or "").strip()}
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for role, prefix in (block.get("detectors") or {}).items():
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fills[f"${role}"] = _detector(info, prefix)
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if block.get("upscaler"):
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fills["$upscaler"], times = _upscaler(info)
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fills["$fit"] = round(UPSCALE_BY / times, 4) # the model's 4× brought back to UPSCALE_BY
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if block.get("control"):
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fills["$controlnet"] = _controlnet(info, block["control"], main_base(), base_of)
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return {key: {**spec, "inputs": {input: fills.get(value, value) if isinstance(value, str) else value
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for input, value in spec["inputs"].items()}} for key, spec in block["nodes"].items()}
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def roles_of(name):
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"""The settings block `name` changes on canvas nodes it finds ({role: {setting: value}}), as the page applies them."""
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return BLOCKS[name].get("roles") or {}
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def after(name):
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return BLOCKS[name]["after"]
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