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on
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Running
on
Zero
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": 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: | |
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 = "conditioning/controlnet" | |
NODE_CLASS_MAPPINGS = { | |
"TripleCLIPLoader": TripleCLIPLoader, | |
"EmptySD3LatentImage": EmptySD3LatentImage, | |
"CLIPTextEncodeSD3": CLIPTextEncodeSD3, | |
"ControlNetApplySD3": ControlNetApplySD3, | |
} | |