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| import folder_paths | |
| import comfy.sd | |
| import comfy.model_management | |
| import nodes | |
| import torch | |
| class TripleCLIPLoader: | |
| 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() | |
| 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: | |
| 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): | |
| 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 = "_for_testing/sd3" | |
| NODE_CLASS_MAPPINGS = { | |
| "TripleCLIPLoader": TripleCLIPLoader, | |
| "EmptySD3LatentImage": EmptySD3LatentImage, | |
| "CLIPTextEncodeSD3": CLIPTextEncodeSD3, | |
| "ControlNetApplySD3": ControlNetApplySD3, | |
| } | |