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