| 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": 16}),
|
| "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
|
| "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,
|
| }
|
|
|
