Initial release
This commit is contained in:
electricy00
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"""
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Multi-Reference Image Encode Node
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Supports 3/5/10 reference images for Qwen2.5-VL / FLUX2 workflows.
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Each image is encoded into a reference latent and injected into conditioning.
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Version: 3.0 - Multiple node classes (3/5/10 images) + Custom output size for img2img
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"""
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import math
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import torch
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import comfy.utils
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import node_helpers
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class DynamicRefImageEncode:
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"""
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动态 Ref Image Encode 节点，合并原 III/V/X 三个节点。
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前端 JS 根据 num_images 增删 IMAGE 输入槽（1~10）。
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所有参考图共享同一个 prompt，输出 1 个 conditioning + 1 个 latent。
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"""
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@classmethod
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def INPUT_TYPES(cls):
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opt = {}
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for i in range(1, 11):
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opt[f"image{i}"] = ("IMAGE",)
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return {
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"required": {
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"clip": ("CLIP",),
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"vae": ("VAE",),
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"num_images": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
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"prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
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"instruction": ("STRING", {
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"multiline": True,
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"default": (
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"Describe the key features of the input image (color, shape, size, "
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"texture, objects, background), then explain how the user's text "
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"instruction should alter or modify the image. Generate a new image "
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"that meets the user's requirements while maintaining consistency "
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"with the original input where appropriate."
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),
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}),
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},
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"optional": {
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**opt,
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"模型类型": (["Flux2", "SD / SDXL"], {"default": "Flux2"}),
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"匹配原图尺寸": (["OFF", "ON"], {"default": "OFF"}),
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"输出宽度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
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"输出高度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
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},
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}
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RETURN_TYPES = ("CONDITIONING", "LATENT")
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RETURN_NAMES = ("conditioning", "latent")
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FUNCTION = "encode"
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CATEGORY = "Toggle-Pass"
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def _get_system_prompt(self, instruction):
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prefix = "<|im_start|>system\n"
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suffix = "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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content = instruction if instruction.strip() else (
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"Describe the key features of the input image (color, shape, size, "
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"texture, objects, background), then explain how the user's text "
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"instruction should alter or modify the image. Generate a new image "
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"that meets the user's requirements while maintaining consistency "
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"with the original input where appropriate."
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)
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if prefix in content:
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content = content.split(prefix)[1]
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if suffix in content:
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content = content.split(suffix)[0]
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content = content.replace("{}", "")
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return prefix + content + suffix
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def encode(self, clip, num_images, prompt, instruction="",
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vae=None, 模型类型="Flux2", 匹配原图尺寸="OFF",
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输出宽度=1024, 输出高度=1024, **kwargs):
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n = max(1, min(10, int(num_images)))
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llama_template = self._get_system_prompt(instruction)
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ref_latents = []
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vl_images = []
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image_prompt = ""
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for i in range(1, n + 1):
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image = kwargs.get(f"image{i}", None)
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if image is None:
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continue
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samples = image.movedim(-1, 1) # [B, C, H, W]
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current_total = samples.shape[3] * samples.shape[2]
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if 匹配原图尺寸 == "ON":
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pixel_width = samples.shape[3]
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pixel_height = samples.shape[2]
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else:
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pixel_width = 输出宽度
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pixel_height = 输出高度
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if vae is not None:
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s = comfy.utils.common_upscale(samples, pixel_width, pixel_height, "lanczos", "center")
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img_for_vae = s.movedim(1, -1)
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ref_latents.append(vae.encode(img_for_vae[:, :, :, :3]))
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# VL image: resize down for vision-language token
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vl_total = 384 * 384
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vl_scale = math.sqrt(vl_total / current_total)
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vl_w = round(samples.shape[3] * vl_scale)
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vl_h = round(samples.shape[2] * vl_scale)
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s_vl = comfy.utils.common_upscale(samples, vl_w, vl_h, "lanczos", "center")
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img_vl = s_vl.movedim(1, -1)
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vl_images.append(img_vl)
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# 注意：不要手动加 <|vision_start|><|image_pad|><|vision_end|>
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# ComfyUI 的 Qwen2.5-VL CLIP tokenizer 会在 images 参数传入时自动插入这些 token
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image_prompt += "Picture {}: ".format(len(vl_images))
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full_prompt = image_prompt + prompt
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tokens = clip.tokenize(full_prompt, images=vl_images, llama_template=llama_template)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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if len(ref_latents) > 0:
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conditioning = node_helpers.conditioning_set_values(
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conditioning,
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{"reference_latents": ref_latents},
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append=True,
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)
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latent_out = {"samples": ref_latents[0]}
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else:
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scale = 16 if 模型类型 == "Flux2" else 8
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lw = 输出宽度 // scale
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lh = 输出高度 // scale
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latent_out = {"samples": torch.zeros(1, 4, lh, lw)}
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return (conditioning, latent_out)
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NODE_CLASS_MAPPINGS = {
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"DynamicRefImageEncode": DynamicRefImageEncode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DynamicRefImageEncode": "Ref Image Encode",
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}
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# ==================== Multi Ref Latent Input Nodes ====================
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# 接收多个 LATENT 输入，打包进 conditioning 的 reference_latents
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# 和图片版本不同：这些节点直接接收 latent，不需要 VAE 编码
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# ====================================================================
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class DynamicRefLatentInput:
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"""
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动态 LATENT 输入节点，前端 JS 提供 +/- 按钮增减输入槽。
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后端固定定义 latent1~latent10，由 num_latents 控制生效数量。
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"""
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@classmethod
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def INPUT_TYPES(cls):
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optional = {}
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for i in range(1, 11):
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optional[f"latent{i}"] = ("LATENT",)
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return {
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"required": {
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"conditioning": ("CONDITIONING",),
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"num_latents": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
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},
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"optional": optional,
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}
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RETURN_TYPES = ("CONDITIONING",)
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RETURN_NAMES = ("conditioning",)
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FUNCTION = "pack"
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CATEGORY = "Toggle-Pass"
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def pack(self, conditioning, num_latents, **kwargs):
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ref_latents = []
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n = max(1, min(10, int(num_latents)))
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for i in range(1, n + 1):
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lat = kwargs.get(f"latent{i}", None)
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if lat is not None and "samples" in lat:
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ref_latents.append(lat["samples"])
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if len(ref_latents) == 0:
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return (conditioning,)
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return (node_helpers.conditioning_set_values(
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conditioning,
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{"reference_latents": ref_latents},
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append=True,
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),)
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NODE_CLASS_MAPPINGS["DynamicRefLatentInput"] = DynamicRefLatentInput
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NODE_DISPLAY_NAME_MAPPINGS["DynamicRefLatentInput"] = "Ref Latent Input"
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# ==================== Image Batcher ====================
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# 把多个独立 IMAGE 输入打包成 1 个 batch IMAGE 输出
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# ======================================================
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class ImageBatcher:
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"""
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动态 IMAGE 输入节点，前端 JS 提供 +/- 按钮增减输入槽。
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把多个独立 IMAGE 打包成一个 batch IMAGE 输出。
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后端固定定义 image1~image10，由 num_images 控制生效数量。
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"""
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@classmethod
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def INPUT_TYPES(cls):
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optional = {}
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for i in range(1, 11):
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optional[f"image{i}"] = ("IMAGE",)
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return {
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"required": {
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"num_images": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
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},
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"optional": optional,
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "batch"
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CATEGORY = "Toggle-Pass"
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def batch(self, num_images, **kwargs):
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images = []
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n = max(1, min(10, int(num_images)))
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for i in range(1, n + 1):
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img = kwargs.get(f"image{i}", None)
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if img is not None:
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images.append(img)
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if len(images) == 0:
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return (torch.zeros(1, 64, 64, 3),)
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# 把所有图片缩放成相同尺寸（以第一张图为基准）
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target_h = images[0].shape[1]
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target_w = images[0].shape[2]
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resized = []
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for img in images:
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h, w = img.shape[1], img.shape[2]
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if h != target_h or w != target_w:
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img = img.movedim(-1, 1) # [B, H, W, C] -> [B, C, H, W]
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img = comfy.utils.common_upscale(img, target_w, target_h, "lanczos", "center")
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img = img.movedim(1, -1) # [B, C, H, W] -> [B, H, W, C]
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resized.append(img)
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result = torch.cat(resized, dim=0)
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return (result,)
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NODE_CLASS_MAPPINGS["ImageBatcher"] = ImageBatcher
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NODE_DISPLAY_NAME_MAPPINGS["ImageBatcher"] = "Image Batcher"
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# ==================== One Image Multi Prompt ====================
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# 1 张参考图 + 多个 prompt → 多组 conditioning + 1 个共享 latent
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# 顺序执行，避免显存爆炸
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# ================================================================
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class OneImageMultiPrompt:
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"""
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1 张参考图 + 多个 prompt → 多组独立 conditioning + 1 个共享 latent。
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图片只预处理一次，然后逐个 prompt 顺序编码（节省显存）。
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每组 conditioning 可外接独立的 Sampler 采样不同效果。
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"clip": ("CLIP",),
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"image": ("IMAGE",),
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"prompt数量": ("INT", {"default": 4, "min": 2, "max": 8, "step": 1}),
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},
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"optional": {
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"vae": ("VAE",),
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"instruction": ("STRING", {
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"multiline": True,
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"default": (
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"Describe the key features of the input image (color, shape, size, "
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"texture, objects, background), then explain how the user's text "
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"instruction should alter or modify the image. Generate a new image "
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"that meets the user's requirements while maintaining consistency "
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"with the original input where appropriate."
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),
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}),
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"模型类型": (["Flux2", "SD / SDXL"], {"default": "Flux2"}),
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"匹配原图尺寸": (["OFF", "ON"], {"default": "OFF"}),
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"输出宽度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
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"输出高度": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 8}),
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"prompt1": ("STRING", {"multiline": True, "default": ""}),
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"prompt2": ("STRING", {"multiline": True, "default": ""}),
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"prompt3": ("STRING", {"multiline": True, "default": ""}),
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"prompt4": ("STRING", {"multiline": True, "default": ""}),
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"prompt5": ("STRING", {"multiline": True, "default": ""}),
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"prompt6": ("STRING", {"multiline": True, "default": ""}),
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"prompt7": ("STRING", {"multiline": True, "default": ""}),
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"prompt8": ("STRING", {"multiline": True, "default": ""}),
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},
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}
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RETURN_TYPES = ("CONDITIONING",) * 8 + ("LATENT",)
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RETURN_NAMES = ("cond_1", "cond_2", "cond_3", "cond_4",
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"cond_5", "cond_6", "cond_7", "cond_8", "latent")
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FUNCTION = "encode"
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CATEGORY = "Toggle-Pass"
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def _get_system_prompt(self, instruction):
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prefix = "<|im_start|>system\n"
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suffix = "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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content = instruction if instruction.strip() else (
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"Describe the key features of the input image (color, shape, size, "
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"texture, objects, background), then explain how the user's text "
|
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"instruction should alter or modify the image. Generate a new image "
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"that meets the user's requirements while maintaining consistency "
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"with the original input where appropriate."
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)
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if prefix in content:
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content = content.split(prefix)[1]
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if suffix in content:
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content = content.split(suffix)[0]
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content = content.replace("{}", "")
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return prefix + content + suffix
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def encode(self, clip, image, prompt数量=4,
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vae=None, instruction="", 模型类型="Flux2",
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匹配原图尺寸="OFF", 输出宽度=1024, 输出高度=1024,
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**kwargs):
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n = max(2, min(8, int(prompt数量)))
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llama_template = self._get_system_prompt(instruction)
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# ---- 图片预处理（只做一次）----
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samples = image.movedim(-1, 1) # [B, C, H, W]
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current_total = samples.shape[3] * samples.shape[2]
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if 匹配原图尺寸 == "ON":
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pixel_width = samples.shape[3]
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pixel_height = samples.shape[2]
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else:
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pixel_width = 输出宽度
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pixel_height = 输出高度
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# VAE 编码参考图（图生图用）
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ref_latent = None
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if vae is not None:
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s = comfy.utils.common_upscale(samples, pixel_width, pixel_height, "lanczos", "center")
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img_for_vae = s.movedim(1, -1)
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ref_latent = vae.encode(img_for_vae[:, :, :, :3])
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# VL 缩放（给 CLIP vision encoder 用的图）
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vl_total = 384 * 384
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vl_scale = math.sqrt(vl_total / current_total)
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vl_w = round(samples.shape[3] * vl_scale)
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vl_h = round(samples.shape[2] * vl_scale)
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s_vl = comfy.utils.common_upscale(samples, vl_w, vl_h, "lanczos", "center")
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vl_image = s_vl.movedim(1, -1) # [B, H, W, C]
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||||
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||||
# 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: "
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||||
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||||
# ---- 顺序编码每个 prompt ----
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results = []
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for i in range(n):
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p = kwargs.get(f"prompt{i + 1}", "")
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full_prompt = image_prompt + p
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tokens = clip.tokenize(full_prompt, images=[vl_image], llama_template=llama_template)
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cond = clip.encode_from_tokens_scheduled(tokens)
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||||
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||||
if ref_latent is not None:
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cond = node_helpers.conditioning_set_values(
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||||
cond,
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{"reference_latents": [ref_latent]},
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||||
append=True,
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||||
)
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||||
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||||
results.append(cond)
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||||
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||||
# ---- 共享 latent 输出 ----
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||||
if ref_latent is not None:
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||||
latent_out = {"samples": ref_latent}
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||||
else:
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||||
# 纯文生图
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||||
scale = 16 if 模型类型 == "Flux2" else 8
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||||
lw = 输出宽度 // scale
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||||
lh = 输出高度 // scale
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latent_out = {"samples": torch.zeros(1, 4, lh, lw)}
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||||
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||||
# 补齐到 8 个 conditioning（未使用的填空 conditioning）
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||||
while len(results) < 8:
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results.append((torch.zeros(1, 768), {}))
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||||
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||||
# 释放临时张量
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||||
del samples, vl_image
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||||
if ref_latent is not None:
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||||
del ref_latent
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||||
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||||
return (*results, latent_out)
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||||
|
||||
|
||||
NODE_CLASS_MAPPINGS["OneImageMultiPrompt"] = OneImageMultiPrompt
|
||||
NODE_DISPLAY_NAME_MAPPINGS["OneImageMultiPrompt"] = "一图多视角"
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||||
# ==================== 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"
|
||||
Reference in New Issue
Block a user