diff --git a/README.md b/README.md new file mode 100644 index 0000000..7a56188 --- /dev/null +++ b/README.md @@ -0,0 +1,70 @@ +# ComfyUI-Toggle-Pass + +DynamicRef 系列节点——多参考图动态编码，支持 1~10 张参考图，每张图独立编码输出。 + +--- + +## 节点说明 + +### DynamicRefIndependent — 多参考图独立编码（核心节点） + +每张参考图独立 VAE 编码，输出各自独立的 CONDITIONING 和 LATENT，适合多图各自控制风格/内容的场景。 + +**输入**（动态，由 `num_images` 控制）： +- `clip` — CLIP 模型 +- `vae` — VAE 模型 +- `num_images` — 参考图数量（1~10） +- `instruction` — 系统提示词（可留空使用默认） +- `image1` ~ `imageN` — 参考图 +- `prompt1` ~ `promptN` — 每张图对应的提示词（控件在节点内，可折叠） + +**输出**（动态，N×2 个）： +- `cond_1`, `latent1`, `cond_2`, `latent2`, ... `cond_N`, `latentN` + +**用法**： +1. 拖入节点，默认 1 张参考图 +2. 调整 `num_images` 到需要的数量（1~10），输入槽和输出槽自动增减 +3. 每张图连接自己的参考图，填写对应 prompt +4. `cond_N` 连到 sampler 的 conditioning，`latentN` 连到 VAE Encode 的 latent（供参考图编码使用） + +--- + +### DynamicRefImageEncode — 动态参考图编码 + +单张 prompt 控制，动态增删参考图输入槽。 + +**输入**：`clip`, `num_images`, `prompt`, `image1` ~ `imageN` +**输出**：`CONDITIONING`, `LATENT`（所有图共享同一份 conditioning，分别输出 latent） + +--- + +### DynamicRefLatentInput — 动态 Latent 输入 + +动态增删 LATENT 输入槽，配合 DynamicRef 系列使用。 + +--- + +### ImageBatcher — 图像批处理 + +动态增删 IMAGE 输入槽，将多张图打包成批次。 + +--- + +### OneImageMultiPrompt — 单图多提示词 + +一张参考图对应多个提示词，分别输出 conditioning。 + +--- + +## 注意事项 + +- `num_images` 调整时，输入/输出槽会自动增减，**已有连线不会被断开** +- 切换页面再回来，节点会自动恢复到正确的槽位数量 +- 需要 Qwen2.5-VL 或兼容的 CLIP 模型支持 +- 默认 instruction 适用于 Qwen-Image-Edit 类模型，可按需修改 + +--- + +## 安装 + +将本文件夹放到 `ComfyUI/custom_nodes/` 下，重启 ComfyUI 即可。 diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..28745b0 --- /dev/null +++ b/__init__.py @@ -0,0 +1,197 @@ +""" +ComfyUI Toggle Pass - Toggle nodes + Image IO + Multi-Ref Encode +Author: Clever +""" +from .multi_ref_encode import NODE_CLASS_MAPPINGS as MULTI_REF_NODES, NODE_DISPLAY_NAME_MAPPINGS as MULTI_REF_NAMES + +import os +import json +import torch +import numpy as np +from PIL import Image, ImageOps, ImageSequence, ImageFile +from PIL.PngImagePlugin import PngInfo +import comfy.model_management +import comfy.utils +import folder_paths +import node_helpers + + +WEB_DIRECTORY = "./js" + +_last_saved_path = {} + + +class SaveLoadImage: + """Save image AND output it. Toggle OFF to auto-load last saved image instead.""" + + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "use_saved": (["Save", "Load"],), + }, + "optional": { + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO" + }, + } + + RETURN_TYPES = ("IMAGE", "MASK") + RETURN_NAMES = ("IMAGE", "MASK") + OUTPUT_NODE = True + FUNCTION = "execute" + CATEGORY = "Toggle-Pass" + DESCRIPTION = "Save image and output IMAGE. Toggle use_saved=ON to reuse last saved image." + + def execute(self, images, use_saved="Save", filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + if use_saved == "Load": + return self._load_last() + else: + return self._save(images, filename_prefix, prompt, extra_pnginfo) + + def _save(self, images, filename_prefix, prompt, extra_pnginfo): + if images is None: + images = torch.zeros((1, 64, 64, 3), dtype=torch.float32) + + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path( + filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0] + ) + results = list() + for (batch_number, image) in enumerate(images): + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + if not comfy.utils.args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.png" + filepath = os.path.join(full_output_folder, file) + img.save(filepath, pnginfo=metadata, compress_level=self.compress_level) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + if id(self) not in _last_saved_path: + _last_saved_path[id(self)] = filepath + + counter += 1 + + last_file = os.path.join(full_output_folder, results[0]["filename"]) + _last_saved_path[id(self)] = last_file + + mask = torch.zeros((images.shape[0], images.shape[1], images.shape[2]), dtype=torch.float32) + return {"result": (images, mask), "ui": {"images": results}} + + def _load_last(self): + node_id = id(self) + if node_id in _last_saved_path and os.path.isfile(_last_saved_path[node_id]): + filepath = _last_saved_path[node_id] + img = node_helpers.pillow(Image.open, filepath) + output_images = [] + output_masks = [] + w, h = None, None + dtype = comfy.model_management.intermediate_dtype() + + for i in ImageSequence.Iterator(img): + i = node_helpers.pillow(ImageOps.exif_transpose, i) + if i.mode == 'I': + i = i.point(lambda x: x * (1 / 255)) + image = i.convert("RGB") + if len(output_images) == 0: + w = image.size[0] + h = image.size[1] + if image.size[0] != w or image.size[1] != h: + continue + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + elif i.mode == 'P' and 'transparency' in i.info: + mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((h, w), dtype=torch.float32, device="cpu") + output_images.append(image.to(dtype=dtype)) + output_masks.append(mask.unsqueeze(0).to(dtype=dtype)) + if img.format == "MPO": + break + + if len(output_images) > 1: + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) + else: + output_image = output_images[0] + output_mask = output_masks[0] + + return {"result": (output_image, output_mask), "ui": {"images": []}} + + dummy = torch.zeros((1, 64, 64, 3), dtype=torch.float32) + dummy_mask = torch.zeros((1, 64, 64), dtype=torch.float32) + return {"result": (dummy, dummy_mask), "ui": {"images": []}} + + +class ToggleEmptyLatent: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "width": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}), + "height": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "enabled": (["ON", "OFF"],), + }, + "optional": { + "fallback_width": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}), + "fallback_height": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}), + }, + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("LATENT",) + FUNCTION = "generate" + CATEGORY = "Toggle-Pass" + + def generate(self, width, height, batch_size=1, enabled="ON", fallback_width=None, fallback_height=None): + if fallback_width is None: + fallback_width = 1024 + if fallback_height is None: + fallback_height = 1024 + if enabled != "ON": + width = fallback_width + height = fallback_height + latent = torch.zeros([batch_size, 4, height // 8, width // 8], + device=comfy.model_management.intermediate_device(), + dtype=comfy.model_management.intermediate_dtype()) + return ({"samples": latent, "downscale_ratio_spacial": 8},) + + +NODE_CLASS_MAPPINGS = { + **MULTI_REF_NODES, + "ToggleEmptyLatent": ToggleEmptyLatent, + "SaveLoadImage": SaveLoadImage, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + **MULTI_REF_NAMES, + "ToggleEmptyLatent": "Toggle Empty Latent", + "SaveLoadImage": "Save/Load Image", +} 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"../../scripts/app.js"; + +const EXT_NAME = "ComfyUI-Toggle-Pass"; + +/* ============ 通用节点（DynamicRefLatentInput / ImageBatcher / DynamicRefImageEncode）============ */ + +const DYNAMIC_NODES = { + "DynamicRefLatentInput": { widget: "num_latents", inputType: "LATENT", max: 10, prefix: "latent" }, + "ImageBatcher": { widget: "num_images", inputType: "IMAGE", max: 10, prefix: "image" }, + "DynamicRefImageEncode": { widget: "num_images", inputType: "IMAGE", max: 10, prefix: "image" }, +}; + +app.registerExtension({ + name: EXT_NAME + "-generic", + beforeRegisterNodeDef(nodeType, nodeData, app) { + const cfg = DYNAMIC_NODES[nodeData.name]; + if (!cfg) return; + + // onConfigure：从 JSON 恢复后触发，widget 值已正确 + const origConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + origConfigure?.apply(this, arguments); + requestAnimationFrame(() => { + const w = this.widgets?.find(w => w.name === cfg.widget); + if (w) syncInputs(this, cfg, Math.max(1, 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inputs.length; i++) if (inputs[i].type === cfg.inputType) idx.push(i); + for (let i = 0; i < count - target; i++) node.removeInput(idx[idx.length - 1 - i]); + } + node.setSize(node.computeSize()); + app.graph.change(); +} + +/* ============ Ref Independent（四项同步增删）============ */ + +app.registerExtension({ + name: EXT_NAME + "-refind", + beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name !== "DynamicRefIndependent") return; + + // onConfigure：从 JSON 恢复后触发，此时 widget 值已正确，输出槽为后端全量（20个） + // 用 w.value 裁剪到 N*2 个 + const origConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + origConfigure?.apply(this, arguments); + requestAnimationFrame(() => { + const w = this.widgets?.find(w => w.name === "num_images"); + if (!w) return; + const n = Math.max(1, Math.min(10, w.value || 1)); + applyRefInd(this, n); + }); + }; + + // onNodeCreated：新拖入的节点（首次创建，无 JSON 恢复） + const origCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + origCreated?.apply(this, arguments); + if (this._tpRefInd) return; + this._tpRefInd = true; + const w = this.widgets?.find(w => w.name === "num_images"); + if (!w) return; + const n = Math.max(1, Math.min(10, w.value || 1)); + applyRefInd(this, n); + const prev = w.callback; + w.callback = function (v) { + if (prev) prev.call(this, v); + applyRefInd(this._node || this, Math.max(1, Math.min(10, v || 1))); + }; + }; + }, +}); + +/** + * Ref Independent：同步增删 + * 1. IMAGE 输入槽：image1 ~ imageN（增删） + * 2. prompt 控件：prompt1 ~ promptN（hidden 显示/隐藏） + * 3. 输出槽：cond_1, latent1, cond_2, latent2, ..., cond_N, latentN（增删，交替排列） + * + * 关键：输出顺序必须和后端 RETURN_TYPES 交替排列一致： + * RETURN_TYPES = (CONDITIONING, LATENT, CONDITIONING, LATENT, ...) + * 后端固定 20 个输出，前端通过 removeOutput 裁剪到 N*2 个 + * 裁剪时从末尾移除，保留前 N*2 个（index 0~2N-1），这样已有连线不受影响 + */ +function applyRefInd(node, n) { + // ---- 1. 增删 IMAGE 输入槽 ---- + const inputs = node.inputs || []; + let imgCount = 0; + for (const inp of inputs) if (inp.type === "IMAGE") imgCount++; + if (imgCount < n) { + for (let i = imgCount + 1; i <= n; i++) node.addInput(`image${i}`, "IMAGE"); + } else if (imgCount > n) { + const idx = []; + for (let i = 0; i < inputs.length; i++) if (inputs[i].type === "IMAGE") idx.push(i); + for (let i = 0; i < imgCount - n; i++) node.removeInput(idx[idx.length - 1 - i]); + } + + // ---- 2. 隐藏/显示 prompt 控件 ---- + for (const w of (node.widgets || [])) { + if (w.name && w.name.startsWith("prompt")) { + const num = parseInt(w.name.replace("prompt", ""), 10); + w.hidden = num > n; + } + } + + // ---- 3. 调整输出槽 ---- + // 后端固定 20 个输出（10 对 cond_X/latentX），需要裁剪到 N*2 个 + // 策略：只从末尾 removeOutput，不重建前面的，保护已有连线 + const expectedCount = n * 2; + while (node.outputs && node.outputs.length > expectedCount) { + node.removeOutput(node.outputs.length - 1); + } + // 如果不够（理论上不应该发生，除非后端定义改了），补齐 + while (node.outputs && node.outputs.length < expectedCount) { + const i = Math.floor(node.outputs.length / 2) + 1; + if (node.outputs.length % 2 === 0) { + node.addOutput(`cond_${i}`, "CONDITIONING"); + } else { + node.addOutput(`latent${i}`, "LATENT"); + } + } + + node.setSize(node.computeSize()); + app.graph.change(); +} diff --git a/multi_ref_encode.py b/multi_ref_encode.py new file mode 100644 index 0000000..aa8fcf5 --- /dev/null +++ b/multi_ref_encode.py @@ -0,0 +1,576 @@ +""" +Multi-Reference Image Encode Node +Supports 3/5/10 reference images for Qwen2.5-VL / FLUX2 workflows. +Each image is encoded into a reference latent and injected into conditioning. + +Version: 3.0 - Multiple node classes (3/5/10 images) + Custom output size for img2img +""" + +import math +import torch +import comfy.utils +import node_helpers + + +class DynamicRefImageEncode: + """ + 动态 Ref Image Encode 节点，合并原 III/V/X 三个节点。 + 前端 JS 根据 num_images 增删 IMAGE 输入槽（1~10）。 + 所有参考图共享同一个 prompt，输出 1 个 conditioning + 1 个 latent。 + """ + + @classmethod + def INPUT_TYPES(cls): + opt = {} + for i in range(1, 11): + opt[f"image{i}"] = ("IMAGE",) + return { + "required": { + "clip": ("CLIP",), + "vae": ("VAE",), + "num_images": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "instruction": ("STRING", { + "multiline": True, + "default": ( + "Describe the key features of the input image (color, shape, size, " + "texture, objects, background), then explain how the user's text " + "instruction should alter or modify the image. Generate a new image " + "that meets the user's requirements while maintaining consistency " + "with the original input where appropriate." + ), + }), + }, + "optional": { + **opt, + "模型类型": (["Flux2", "SD / SDXL"], {"default": "Flux2"}), + "匹配原图尺寸": (["OFF", "ON"], {"default": "OFF"}), + "输出宽度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}), + "输出高度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}), + }, + } + + RETURN_TYPES = ("CONDITIONING", "LATENT") + RETURN_NAMES = ("conditioning", "latent") + FUNCTION = "encode" + CATEGORY = "Toggle-Pass" + + def _get_system_prompt(self, instruction): + prefix = "<|im_start|>system\n" + suffix = "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + content = instruction if instruction.strip() else ( + "Describe the key features of the input image (color, shape, size, " + "texture, objects, background), then explain how the user's text " + "instruction should alter or modify the image. Generate a new image " + "that meets the user's requirements while maintaining consistency " + "with the original input where appropriate." + ) + if prefix in content: + content = content.split(prefix)[1] + if suffix in content: + content = content.split(suffix)[0] + content = content.replace("{}", "") + return prefix + content + suffix + + def encode(self, clip, num_images, prompt, instruction="", + vae=None, 模型类型="Flux2", 匹配原图尺寸="OFF", + 输出宽度=1024, 输出高度=1024, **kwargs): + + n = max(1, min(10, int(num_images))) + llama_template = self._get_system_prompt(instruction) + + ref_latents = [] + vl_images = [] + image_prompt = "" + + for i in range(1, n + 1): + image = kwargs.get(f"image{i}", None) + if image is None: + continue + + samples = image.movedim(-1, 1) # [B, C, H, W] + current_total = samples.shape[3] * samples.shape[2] + + if 匹配原图尺寸 == "ON": + pixel_width = samples.shape[3] + pixel_height = samples.shape[2] + else: + pixel_width = 输出宽度 + pixel_height = 输出高度 + + if vae is not None: + s = comfy.utils.common_upscale(samples, pixel_width, pixel_height, "lanczos", "center") + img_for_vae = s.movedim(1, -1) + ref_latents.append(vae.encode(img_for_vae[:, :, :, :3])) + + # VL image: resize down for vision-language token + vl_total = 384 * 384 + vl_scale = math.sqrt(vl_total / current_total) + vl_w = round(samples.shape[3] * vl_scale) + vl_h = round(samples.shape[2] * vl_scale) + s_vl = comfy.utils.common_upscale(samples, vl_w, vl_h, "lanczos", "center") + img_vl = s_vl.movedim(1, -1) + + vl_images.append(img_vl) + # 注意：不要手动加 <|vision_start|><|image_pad|><|vision_end|> + # ComfyUI 的 Qwen2.5-VL CLIP tokenizer 会在 images 参数传入时自动插入这些 token + image_prompt += "Picture {}: ".format(len(vl_images)) + + full_prompt = image_prompt + prompt + tokens = clip.tokenize(full_prompt, images=vl_images, llama_template=llama_template) + conditioning = clip.encode_from_tokens_scheduled(tokens) + + if len(ref_latents) > 0: + conditioning = node_helpers.conditioning_set_values( + conditioning, + {"reference_latents": ref_latents}, + append=True, + ) + latent_out = {"samples": ref_latents[0]} + else: + scale = 16 if 模型类型 == "Flux2" else 8 + lw = 输出宽度 // scale + lh = 输出高度 // scale + latent_out = {"samples": torch.zeros(1, 4, lh, lw)} + + return (conditioning, latent_out) + + +NODE_CLASS_MAPPINGS = { + "DynamicRefImageEncode": DynamicRefImageEncode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "DynamicRefImageEncode": "Ref Image Encode", +} + + + + +# ==================== Multi Ref Latent Input Nodes ==================== +# 接收多个 LATENT 输入，打包进 conditioning 的 reference_latents +# 和图片版本不同：这些节点直接接收 latent，不需要 VAE 编码 +# ==================================================================== + + +class DynamicRefLatentInput: + """ + 动态 LATENT 输入节点，前端 JS 提供 +/- 按钮增减输入槽。 + 后端固定定义 latent1~latent10，由 num_latents 控制生效数量。 + """ + @classmethod + def INPUT_TYPES(cls): + optional = {} + for i in range(1, 11): + optional[f"latent{i}"] = ("LATENT",) + return { + "required": { + "conditioning": ("CONDITIONING",), + "num_latents": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": optional, + } + + RETURN_TYPES = ("CONDITIONING",) + RETURN_NAMES = ("conditioning",) + FUNCTION = "pack" + CATEGORY = "Toggle-Pass" + + def pack(self, conditioning, num_latents, **kwargs): + ref_latents = [] + n = max(1, min(10, int(num_latents))) + for i in range(1, n + 1): + lat = kwargs.get(f"latent{i}", None) + if lat is not None and "samples" in lat: + ref_latents.append(lat["samples"]) + + if len(ref_latents) == 0: + return (conditioning,) + + return (node_helpers.conditioning_set_values( + conditioning, + {"reference_latents": ref_latents}, + append=True, + ),) + + +NODE_CLASS_MAPPINGS["DynamicRefLatentInput"] = DynamicRefLatentInput +NODE_DISPLAY_NAME_MAPPINGS["DynamicRefLatentInput"] = "Ref Latent Input" + + + + + +# ==================== Image Batcher ==================== +# 把多个独立 IMAGE 输入打包成 1 个 batch IMAGE 输出 +# ====================================================== + + +class ImageBatcher: + """ + 动态 IMAGE 输入节点，前端 JS 提供 +/- 按钮增减输入槽。 + 把多个独立 IMAGE 打包成一个 batch IMAGE 输出。 + 后端固定定义 image1~image10，由 num_images 控制生效数量。 + """ + + @classmethod + def INPUT_TYPES(cls): + optional = {} + for i in range(1, 11): + optional[f"image{i}"] = ("IMAGE",) + return { + "required": { + "num_images": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + }, + "optional": optional, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "batch" + CATEGORY = "Toggle-Pass" + + def batch(self, num_images, **kwargs): + images = [] + n = max(1, min(10, int(num_images))) + for i in range(1, n + 1): + img = kwargs.get(f"image{i}", None) + if img is not None: + images.append(img) + + if len(images) == 0: + return (torch.zeros(1, 64, 64, 3),) + + # 把所有图片缩放成相同尺寸（以第一张图为基准） + target_h = images[0].shape[1] + target_w = images[0].shape[2] + + resized = [] + for img in images: + h, w = img.shape[1], img.shape[2] + if h != target_h or w != target_w: + img = img.movedim(-1, 1) # [B, H, W, C] -> [B, C, H, W] + img = comfy.utils.common_upscale(img, target_w, target_h, "lanczos", "center") + img = img.movedim(1, -1) # [B, C, H, W] -> [B, H, W, C] + resized.append(img) + + result = torch.cat(resized, dim=0) + return (result,) + + +NODE_CLASS_MAPPINGS["ImageBatcher"] = ImageBatcher +NODE_DISPLAY_NAME_MAPPINGS["ImageBatcher"] = "Image Batcher" + + +# ==================== One Image Multi Prompt ==================== +# 1 张参考图 + 多个 prompt → 多组 conditioning + 1 个共享 latent +# 顺序执行，避免显存爆炸 +# ================================================================ + + +class OneImageMultiPrompt: + """ + 1 张参考图 + 多个 prompt → 多组独立 conditioning + 1 个共享 latent。 + 图片只预处理一次，然后逐个 prompt 顺序编码（节省显存）。 + 每组 conditioning 可外接独立的 Sampler 采样不同效果。 + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "clip": ("CLIP",), + "image": ("IMAGE",), + "prompt数量": ("INT", {"default": 4, "min": 2, "max": 8, "step": 1}), + }, + "optional": { + "vae": ("VAE",), + "instruction": ("STRING", { + "multiline": True, + "default": ( + "Describe the key features of the input image (color, shape, size, " + "texture, objects, background), then explain how the user's text " + "instruction should alter or modify the image. Generate a new image " + "that meets the user's requirements while maintaining consistency " + "with the original input where appropriate." + ), + }), + "模型类型": (["Flux2", "SD / SDXL"], {"default": "Flux2"}), + "匹配原图尺寸": (["OFF", "ON"], {"default": "OFF"}), + "输出宽度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}), + "输出高度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}), + "prompt1": ("STRING", {"multiline": True, "default": ""}), + "prompt2": ("STRING", {"multiline": True, "default": ""}), + "prompt3": ("STRING", {"multiline": True, "default": ""}), + "prompt4": ("STRING", {"multiline": True, "default": ""}), + "prompt5": ("STRING", {"multiline": True, "default": ""}), + "prompt6": ("STRING", {"multiline": True, "default": ""}), + "prompt7": ("STRING", {"multiline": True, "default": ""}), + "prompt8": ("STRING", {"multiline": True, "default": ""}), + }, + } + + RETURN_TYPES = ("CONDITIONING",) * 8 + ("LATENT",) + RETURN_NAMES = ("cond_1", "cond_2", "cond_3", "cond_4", + "cond_5", "cond_6", "cond_7", "cond_8", "latent") + FUNCTION = "encode" + CATEGORY = "Toggle-Pass" + + def _get_system_prompt(self, instruction): + prefix = "<|im_start|>system\n" + suffix = "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + content = instruction if instruction.strip() else ( + "Describe the key features of the input image (color, shape, size, " + "texture, objects, background), then explain how the user's text " + "instruction should alter or modify the image. Generate a new image " + "that meets the user's requirements while maintaining consistency " + "with the original input where appropriate." + ) + if prefix in content: + content = content.split(prefix)[1] + if suffix in content: + content = content.split(suffix)[0] + content = content.replace("{}", "") + return prefix + content + suffix + + def encode(self, clip, image, prompt数量=4, + vae=None, instruction="", 模型类型="Flux2", + 匹配原图尺寸="OFF", 输出宽度=1024, 输出高度=1024, + **kwargs): + + n = max(2, min(8, int(prompt数量))) + llama_template = self._get_system_prompt(instruction) + + # ---- 图片预处理（只做一次）---- + samples = image.movedim(-1, 1) # [B, C, H, W] + current_total = samples.shape[3] * samples.shape[2] + + if 匹配原图尺寸 == "ON": + pixel_width = samples.shape[3] + pixel_height = samples.shape[2] + else: + pixel_width = 输出宽度 + pixel_height = 输出高度 + + # VAE 编码参考图（图生图用） + ref_latent = None + if vae is not None: + s = comfy.utils.common_upscale(samples, pixel_width, pixel_height, "lanczos", "center") + img_for_vae = s.movedim(1, -1) + ref_latent = vae.encode(img_for_vae[:, :, :, :3]) + + # VL 缩放（给 CLIP vision encoder 用的图） + vl_total = 384 * 384 + vl_scale = math.sqrt(vl_total / current_total) + vl_w = round(samples.shape[3] * vl_scale) + vl_h = round(samples.shape[2] * vl_scale) + s_vl = comfy.utils.common_upscale(samples, vl_w, vl_h, "lanczos", "center") + vl_image = s_vl.movedim(1, -1) # [B, H, W, C] + + # 图片 prompt 片段（固定不变） + # 注意：不要手动加 <|vision_start|><|image_pad|><|vision_end|> + # ComfyUI 的 Qwen2.5-VL CLIP tokenizer 会在 images 参数传入时自动插入这些 token + image_prompt = "Picture 1: " + + # ---- 顺序编码每个 prompt ---- + results = [] + for i in range(n): + p = kwargs.get(f"prompt{i + 1}", "") + full_prompt = image_prompt + p + tokens = clip.tokenize(full_prompt, images=[vl_image], llama_template=llama_template) + cond = clip.encode_from_tokens_scheduled(tokens) + + if ref_latent is not None: + cond = node_helpers.conditioning_set_values( + cond, + {"reference_latents": [ref_latent]}, + append=True, + ) + + results.append(cond) + + # ---- 共享 latent 输出 ---- + if ref_latent is not None: + latent_out = {"samples": ref_latent} + else: + # 纯文生图 + scale = 16 if 模型类型 == "Flux2" else 8 + lw = 输出宽度 // scale + lh = 输出高度 // scale + latent_out = {"samples": torch.zeros(1, 4, lh, lw)} + + # 补齐到 8 个 conditioning（未使用的填空 conditioning） + while len(results) < 8: + results.append((torch.zeros(1, 768), {})) + + # 释放临时张量 + del samples, vl_image + if ref_latent is not None: + del ref_latent + + return (*results, latent_out) + + +NODE_CLASS_MAPPINGS["OneImageMultiPrompt"] = OneImageMultiPrompt +NODE_DISPLAY_NAME_MAPPINGS["OneImageMultiPrompt"] = "一图多视角" +# ==================== Ref Independent Output ==================== +# 每张图片独立编码，conditioning 和 latent 完全隔离，无交叉 +# 每张图走独立通道，适合需要完全独立控制每张参考图的场景 +# ================================================================ + + + +class DynamicRefIndependent: + """ + 动态独立参考图编码节点。 + 前端 JS 根据 num_images 同步增删： + - IMAGE 输入槽 (image1~imageN) + - prompt 控件 (prompt1~promptN) + - CONDITIONING 输出槽 (cond_1~cond_N) + - LATENT 输出槽 (latent1~latentN) + 每张图独立编码，输出自己的 CONDITIONING 和 LATENT。 + 共 2*N 个输出：cond_1, latent1, cond_2, latent2, ..., cond_N, latentN + """ + + @classmethod + def INPUT_TYPES(cls): + opt = {} + for i in range(1, 11): + opt[f"image{i}"] = ("IMAGE",) + opt[f"prompt{i}"] = ("STRING", {"multiline": True, "default": ""}) + return { + "required": { + "clip": ("CLIP",), + "vae": ("VAE",), + "num_images": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), + "instruction": ("STRING", { + "multiline": True, + "default": ( + "Describe the key features of the input image (color, shape, size, " + "texture, objects, background), then explain how the user's text " + "instruction should alter or modify the image. Generate a new image " + "that meets the user's requirements while maintaining consistency " + "with the original input where appropriate." + ), + }), + }, + "optional": { + **opt, + }, + } + + # 输出顺序：cond_1, latent1, cond_2, latent2, ..., cond_10, latent10（交替） + # 每组：1个 CONDITIONING + 1个 LATENT（每张图各自独立的 latent） + # 前端通过 removeOutput 只保留 N 组（N*2 个输出槽） + RETURN_TYPES = tuple( + item + for i in range(10) + for item in ("CONDITIONING", "LATENT") + ) + RETURN_NAMES = tuple( + item + for i in range(1, 11) + for item in (f"cond_{i}", f"latent{i}") + ) + FUNCTION = "encode" + CATEGORY = "Toggle-Pass" + + def _get_system_prompt(self, instruction): + prefix = "<|im_start|>system\n" + suffix = "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + content = instruction if instruction.strip() else ( + "Describe the key features of the input image (color, shape, size, " + "texture, objects, background), then explain how the user's text " + "instruction should alter or modify the image. Generate a new image " + "that meets the user's requirements while maintaining consistency " + "with the original input where appropriate." + ) + if prefix in content: + content = content.split(prefix)[1] + if suffix in content: + content = content.split(suffix)[0] + content = content.replace("{}", "") + return prefix + content + suffix + + def encode(self, clip, num_images, instruction="", vae=None, **kwargs): + n = max(1, min(10, int(num_images))) + llama_template = self._get_system_prompt(instruction) + conds = [] + latents = [] # 每张图自己的 latent + first_latent = None # 第一张非空图的 latent，用于共享 latent_out + + for i in range(1, n + 1): + img = kwargs.get(f"image{i}", None) + prompt = kwargs.get(f"prompt{i}", "") + + if img is None: + # 空槽：用纯文本 prompt 编码，不带图片，格式正确 + text = prompt or "" + tokens = clip.tokenize(text, llama_template=llama_template) + cond = clip.encode_from_tokens_scheduled(tokens) + conds.append(cond) + latents.append(None) # 空槽没有 latent + continue + + samples = img.movedim(-1, 1) + current_total = samples.shape[3] * samples.shape[2] + + # ---- VAE 编码（每张图独立编码自己的 latent）---- + img_latent = None + if vae is not None: + s = comfy.utils.common_upscale(samples, samples.shape[3], samples.shape[2], "lanczos", "center") + img_for_vae = s.movedim(1, -1) + img_latent = vae.encode(img_for_vae[:, :, :, :3]) + del s, img_for_vae + if first_latent is None: + first_latent = img_latent # 记录第一张图的 latent 作为 latent_out + + # ---- VL 编码 ---- + vl_total = 384 * 384 + vl_scale = math.sqrt(vl_total / current_total) + vl_w = round(samples.shape[3] * vl_scale) + vl_h = round(samples.shape[2] * vl_scale) + s_vl = comfy.utils.common_upscale(samples, vl_w, vl_h, "lanczos", "center") + vl_image = s_vl.movedim(1, -1) + + # ---- tokenize + encode ---- + image_tag = "Picture 1: " + full_prompt = image_tag + (prompt or "") + tokens = clip.tokenize(full_prompt, images=[vl_image], llama_template=llama_template) + cond = clip.encode_from_tokens_scheduled(tokens) + + # ---- 每张图的 conditioning 绑定自己的 latent ---- + if img_latent is not None: + cond = node_helpers.conditioning_set_values( + cond, + {"reference_latents": [img_latent]}, + append=True, + ) + + conds.append(cond) + latents.append(img_latent) + del samples, s_vl, vl_image + + # 补齐到 10 个 conditioning + while len(conds) < 10: + conds.append((torch.zeros(1, 768), {})) + while len(latents) < 10: + latents.append(None) + + # 共享 latent_out（取第一张非空图的 latent，供下游 Empty Latent 参考尺寸用） + if first_latent is None: + first_latent = torch.zeros(1, 4, 1, 1) + latent_out = {"samples": first_latent} + + # 返回：交替排列 cond_1, latent1, cond_2, latent2, ... + # 每个 latentN 槽输出对应图的 latent（空槽用 latent_out 兜底） + results = [] + for i in range(10): + results.append(conds[i]) + lat_i = latents[i] + results.append({"samples": lat_i} if lat_i is not None else latent_out) + return (*results,) + + +NODE_CLASS_MAPPINGS["DynamicRefIndependent"] = DynamicRefIndependent +NODE_DISPLAY_NAME_MAPPINGS["DynamicRefIndependent"] = "Ref Independent" diff --git a/workflow_to_png.py b/workflow_to_png.py new file mode 100644 index 0000000..d137547 --- /dev/null +++ b/workflow_to_png.py @@ -0,0 +1,70 @@ +""" +把 example_workflows/ 目录下的 .json 工作流 +转成可直接拖入 ComfyUI 的 PNG 图片。 + +用法： + python workflow_to_png.py + +输出：每个 JSON 文件旁边生成同名 .png +（PIL 未安装时会自动提示安装命令） +""" + +import json +import os +import sys + +WORKFLOWS_DIR = os.path.join(os.path.dirname(__file__), "example_workflows") + +# ---------- 尝试导入 PIL ---------- +try: + from PIL import Image, PngImagePlugin +except ImportError: + print("缺少 Pillow，请先运行：") + print(" pip install Pillow") + sys.exit(1) + + +def json_to_png(json_path: str) -> str: + """把单个 JSON 工作流嵌入 PNG，返回输出路径。""" + with open(json_path, "r", encoding="utf-8") as f: + workflow_text = f.read() + + # 解析一次确保合法 JSON + json.loads(workflow_text) + + # 生成一张 1×1 的透明占位图（ComfyUI 只需要元数据，图像内容无关紧要） + img = Image.new("RGB", (1, 1), color=(30, 30, 30)) + + meta = PngImagePlugin.PngInfo() + meta.add_text("workflow", workflow_text) + + out_path = os.path.splitext(json_path)[0] + ".png" + img.save(out_path, pnginfo=meta, compress_level=9) + return out_path + + +def main(): + json_files = [ + os.path.join(WORKFLOWS_DIR, f) + for f in os.listdir(WORKFLOWS_DIR) + if f.endswith(".json") + ] + + if not json_files: + print("没有找到 .json 文件") + return + + print(f"共找到 {len(json_files)} 个工作流文件：\n") + for jf in json_files: + try: + out = json_to_png(jf) + print(f" ✓ {os.path.basename(jf)}") + print(f" → {os.path.basename(out)}") + except Exception as e: + print(f" ✗ {os.path.basename(jf)} 错误: {e}") + + print("\n完成！把生成的 .png 直接拖入 ComfyUI 即可恢复工作流。") + + +if __name__ == "__main__": + main()