import folder_paths import comfy.sd import comfy.model_management import nodes import torch class TripleCLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), ) }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" def load_clip(self, clip_name1, clip_name2, clip_name3): clip_path1 = folder_paths.get_full_path("clip", clip_name1) clip_path2 = folder_paths.get_full_path("clip", clip_name2) clip_path3 = folder_paths.get_full_path("clip", clip_name3) clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings")) return (clip,) class EmptySD3LatentImage: def __init__(self): self.device = comfy.model_management.intermediate_device() @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} RETURN_TYPES = ("LATENT",) FUNCTION = "generate" CATEGORY = "latent/sd3" def generate(self, width, height, batch_size=1): latent = torch.ones([batch_size, 16, height // 8, width // 8], device=self.device) * 0.0609 return ({"samples":latent}, ) class CLIPTextEncodeSD3: @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP", ), "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), "empty_padding": (["none", "empty_prompt"], ) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "advanced/conditioning" def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding): no_padding = empty_padding == "none" tokens = clip.tokenize(clip_g) if len(clip_g) == 0 and no_padding: tokens["g"] = [] if len(clip_l) == 0 and no_padding: tokens["l"] = [] else: tokens["l"] = clip.tokenize(clip_l)["l"] if len(t5xxl) == 0 and no_padding: tokens["t5xxl"] = [] else: tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] if len(tokens["l"]) != len(tokens["g"]): empty = clip.tokenize("") while len(tokens["l"]) < len(tokens["g"]): tokens["l"] += empty["l"] while len(tokens["l"]) > len(tokens["g"]): tokens["g"] += empty["g"] cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled}]], ) class ControlNetApplySD3(nodes.ControlNetApplyAdvanced): @classmethod def INPUT_TYPES(s): return {"required": {"positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "control_net": ("CONTROL_NET", ), "vae": ("VAE", ), "image": ("IMAGE", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) }} CATEGORY = "conditioning/controlnet" NODE_CLASS_MAPPINGS = { "TripleCLIPLoader": TripleCLIPLoader, "EmptySD3LatentImage": EmptySD3LatentImage, "CLIPTextEncodeSD3": CLIPTextEncodeSD3, "ControlNetApplySD3": ControlNetApplySD3, }