1362 lines
50 KiB
Python
1362 lines
50 KiB
Python
import torch
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import os
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import sys
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import json
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import hashlib
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import traceback
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import math
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import time
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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import numpy as np
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
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import comfy.diffusers_convert
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import comfy.samplers
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import comfy.sample
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import comfy.sd
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import comfy.utils
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import comfy.clip_vision
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import comfy.model_management
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import importlib
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import folder_paths
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def before_node_execution():
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comfy.model_management.throw_exception_if_processing_interrupted()
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def interrupt_processing(value=True):
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comfy.model_management.interrupt_current_processing(value)
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MAX_RESOLUTION=8192
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class CLIPTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "encode"
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CATEGORY = "conditioning"
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def encode(self, clip, text):
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return ([[clip.encode(text), {}]], )
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class ConditioningCombine:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "combine"
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CATEGORY = "conditioning"
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def combine(self, conditioning_1, conditioning_2):
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return (conditioning_1 + conditioning_2, )
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class ConditioningAverage :
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ),
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"conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "addWeighted"
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CATEGORY = "conditioning"
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def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
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out = []
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if len(conditioning_from) > 1:
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print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
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cond_from = conditioning_from[0][0]
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for i in range(len(conditioning_to)):
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t1 = conditioning_to[i][0]
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t0 = cond_from[:,:t1.shape[1]]
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if t0.shape[1] < t1.shape[1]:
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t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)
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tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))
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n = [tw, conditioning_to[i][1].copy()]
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out.append(n)
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return (out, )
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class ConditioningSetArea:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conditioning": ("CONDITIONING", ),
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"width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
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"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "append"
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CATEGORY = "conditioning"
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def append(self, conditioning, width, height, x, y, strength):
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c = []
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for t in conditioning:
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n = [t[0], t[1].copy()]
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n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
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n[1]['strength'] = strength
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n[1]['set_area_to_bounds'] = False
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c.append(n)
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return (c, )
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class ConditioningSetMask:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conditioning": ("CONDITIONING", ),
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"mask": ("MASK", ),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"set_cond_area": (["default", "mask bounds"],),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "append"
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CATEGORY = "conditioning"
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def append(self, conditioning, mask, set_cond_area, strength):
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c = []
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set_area_to_bounds = False
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if set_cond_area != "default":
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set_area_to_bounds = True
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if len(mask.shape) < 3:
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mask = mask.unsqueeze(0)
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for t in conditioning:
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n = [t[0], t[1].copy()]
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_, h, w = mask.shape
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n[1]['mask'] = mask
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n[1]['set_area_to_bounds'] = set_area_to_bounds
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n[1]['mask_strength'] = strength
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c.append(n)
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return (c, )
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class VAEDecode:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "decode"
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CATEGORY = "latent"
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def decode(self, vae, samples):
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return (vae.decode(samples["samples"]), )
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class VAEDecodeTiled:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "decode"
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CATEGORY = "_for_testing"
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def decode(self, vae, samples):
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return (vae.decode_tiled(samples["samples"]), )
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class VAEEncode:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "encode"
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CATEGORY = "latent"
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@staticmethod
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def vae_encode_crop_pixels(pixels):
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x = (pixels.shape[1] // 8) * 8
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y = (pixels.shape[2] // 8) * 8
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if pixels.shape[1] != x or pixels.shape[2] != y:
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x_offset = (pixels.shape[1] % 8) // 2
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y_offset = (pixels.shape[2] % 8) // 2
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pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
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return pixels
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def encode(self, vae, pixels):
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pixels = self.vae_encode_crop_pixels(pixels)
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t = vae.encode(pixels[:,:,:,:3])
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return ({"samples":t}, )
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class VAEEncodeTiled:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "encode"
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CATEGORY = "_for_testing"
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def encode(self, vae, pixels):
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pixels = VAEEncode.vae_encode_crop_pixels(pixels)
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t = vae.encode_tiled(pixels[:,:,:,:3])
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return ({"samples":t}, )
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class VAEEncodeForInpaint:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "encode"
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CATEGORY = "latent/inpaint"
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def encode(self, vae, pixels, mask, grow_mask_by=6):
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x = (pixels.shape[1] // 8) * 8
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y = (pixels.shape[2] // 8) * 8
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mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
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pixels = pixels.clone()
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if pixels.shape[1] != x or pixels.shape[2] != y:
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x_offset = (pixels.shape[1] % 8) // 2
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y_offset = (pixels.shape[2] % 8) // 2
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pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
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mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
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#grow mask by a few pixels to keep things seamless in latent space
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if grow_mask_by == 0:
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mask_erosion = mask
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else:
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kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
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padding = math.ceil((grow_mask_by - 1) / 2)
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mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
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m = (1.0 - mask.round()).squeeze(1)
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for i in range(3):
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pixels[:,:,:,i] -= 0.5
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pixels[:,:,:,i] *= m
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pixels[:,:,:,i] += 0.5
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t = vae.encode(pixels)
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return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
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class CheckpointLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ),
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "advanced/loaders"
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def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
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config_path = folder_paths.get_full_path("configs", config_name)
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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class CheckpointLoaderSimple:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "loaders"
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def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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return out
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class DiffusersLoader:
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@classmethod
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def INPUT_TYPES(cls):
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paths = []
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for search_path in folder_paths.get_folder_paths("diffusers"):
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if os.path.exists(search_path):
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paths += next(os.walk(search_path))[1]
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return {"required": {"model_path": (paths,), }}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "advanced/loaders"
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def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
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for search_path in folder_paths.get_folder_paths("diffusers"):
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if os.path.exists(search_path):
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paths = next(os.walk(search_path))[1]
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if model_path in paths:
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model_path = os.path.join(search_path, model_path)
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break
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return comfy.diffusers_convert.load_diffusers(model_path, fp16=comfy.model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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class unCLIPCheckpointLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION")
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FUNCTION = "load_checkpoint"
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CATEGORY = "loaders"
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def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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return out
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class CLIPSetLastLayer:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip": ("CLIP", ),
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"stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
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}}
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RETURN_TYPES = ("CLIP",)
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FUNCTION = "set_last_layer"
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CATEGORY = "conditioning"
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def set_last_layer(self, clip, stop_at_clip_layer):
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clip = clip.clone()
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clip.clip_layer(stop_at_clip_layer)
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return (clip,)
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class LoraLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"clip": ("CLIP", ),
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"lora_name": (folder_paths.get_filename_list("loras"), ),
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"strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL", "CLIP")
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FUNCTION = "load_lora"
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CATEGORY = "loaders"
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def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
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lora_path = folder_paths.get_full_path("loras", lora_name)
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model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip)
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return (model_lora, clip_lora)
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class TomePatchModel:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "_for_testing"
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def patch(self, model, ratio):
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m = model.clone()
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m.set_model_tomesd(ratio)
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return (m, )
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class VAELoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "vae_name": (folder_paths.get_filename_list("vae"), )}}
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RETURN_TYPES = ("VAE",)
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FUNCTION = "load_vae"
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CATEGORY = "loaders"
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#TODO: scale factor?
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def load_vae(self, vae_name):
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vae_path = folder_paths.get_full_path("vae", vae_name)
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vae = comfy.sd.VAE(ckpt_path=vae_path)
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return (vae,)
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class ControlNetLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}
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RETURN_TYPES = ("CONTROL_NET",)
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FUNCTION = "load_controlnet"
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CATEGORY = "loaders"
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def load_controlnet(self, control_net_name):
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = comfy.sd.load_controlnet(controlnet_path)
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return (controlnet,)
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class DiffControlNetLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"control_net_name": (folder_paths.get_filename_list("controlnet"), )}}
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|
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RETURN_TYPES = ("CONTROL_NET",)
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FUNCTION = "load_controlnet"
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CATEGORY = "loaders"
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def load_controlnet(self, model, control_net_name):
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = comfy.sd.load_controlnet(controlnet_path, model)
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return (controlnet,)
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class ControlNetApply:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conditioning": ("CONDITIONING", ),
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"control_net": ("CONTROL_NET", ),
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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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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "apply_controlnet"
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CATEGORY = "conditioning"
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def apply_controlnet(self, conditioning, control_net, image, strength):
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c = []
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control_hint = image.movedim(-1,1)
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for t in conditioning:
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n = [t[0], t[1].copy()]
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c_net = control_net.copy().set_cond_hint(control_hint, strength)
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if 'control' in t[1]:
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c_net.set_previous_controlnet(t[1]['control'])
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n[1]['control'] = c_net
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c.append(n)
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return (c, )
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class CLIPLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip_name": (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 = "loaders"
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def load_clip(self, clip_name):
|
|
clip_path = folder_paths.get_full_path("clip", clip_name)
|
|
clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=folder_paths.get_folder_paths("embeddings"))
|
|
return (clip,)
|
|
|
|
class CLIPVisionLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ),
|
|
}}
|
|
RETURN_TYPES = ("CLIP_VISION",)
|
|
FUNCTION = "load_clip"
|
|
|
|
CATEGORY = "loaders"
|
|
|
|
def load_clip(self, clip_name):
|
|
clip_path = folder_paths.get_full_path("clip_vision", clip_name)
|
|
clip_vision = comfy.clip_vision.load(clip_path)
|
|
return (clip_vision,)
|
|
|
|
class CLIPVisionEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "clip_vision": ("CLIP_VISION",),
|
|
"image": ("IMAGE",)
|
|
}}
|
|
RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
|
|
FUNCTION = "encode"
|
|
|
|
CATEGORY = "conditioning"
|
|
|
|
def encode(self, clip_vision, image):
|
|
output = clip_vision.encode_image(image)
|
|
return (output,)
|
|
|
|
class StyleModelLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}}
|
|
|
|
RETURN_TYPES = ("STYLE_MODEL",)
|
|
FUNCTION = "load_style_model"
|
|
|
|
CATEGORY = "loaders"
|
|
|
|
def load_style_model(self, style_model_name):
|
|
style_model_path = folder_paths.get_full_path("style_models", style_model_name)
|
|
style_model = comfy.sd.load_style_model(style_model_path)
|
|
return (style_model,)
|
|
|
|
|
|
class StyleModelApply:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {"conditioning": ("CONDITIONING", ),
|
|
"style_model": ("STYLE_MODEL", ),
|
|
"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
|
|
}}
|
|
RETURN_TYPES = ("CONDITIONING",)
|
|
FUNCTION = "apply_stylemodel"
|
|
|
|
CATEGORY = "conditioning/style_model"
|
|
|
|
def apply_stylemodel(self, clip_vision_output, style_model, conditioning):
|
|
cond = style_model.get_cond(clip_vision_output)
|
|
c = []
|
|
for t in conditioning:
|
|
n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
|
|
c.append(n)
|
|
return (c, )
|
|
|
|
class unCLIPConditioning:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {"conditioning": ("CONDITIONING", ),
|
|
"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
|
|
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
"noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
}}
|
|
RETURN_TYPES = ("CONDITIONING",)
|
|
FUNCTION = "apply_adm"
|
|
|
|
CATEGORY = "conditioning"
|
|
|
|
def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation):
|
|
c = []
|
|
for t in conditioning:
|
|
o = t[1].copy()
|
|
x = (clip_vision_output, strength, noise_augmentation)
|
|
if "adm" in o:
|
|
o["adm"] = o["adm"][:] + [x]
|
|
else:
|
|
o["adm"] = [x]
|
|
n = [t[0], o]
|
|
c.append(n)
|
|
return (c, )
|
|
|
|
class GLIGENLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}}
|
|
|
|
RETURN_TYPES = ("GLIGEN",)
|
|
FUNCTION = "load_gligen"
|
|
|
|
CATEGORY = "loaders"
|
|
|
|
def load_gligen(self, gligen_name):
|
|
gligen_path = folder_paths.get_full_path("gligen", gligen_name)
|
|
gligen = comfy.sd.load_gligen(gligen_path)
|
|
return (gligen,)
|
|
|
|
class GLIGENTextBoxApply:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {"conditioning_to": ("CONDITIONING", ),
|
|
"clip": ("CLIP", ),
|
|
"gligen_textbox_model": ("GLIGEN", ),
|
|
"text": ("STRING", {"multiline": True}),
|
|
"width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
|
|
"height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
|
|
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
}}
|
|
RETURN_TYPES = ("CONDITIONING",)
|
|
FUNCTION = "append"
|
|
|
|
CATEGORY = "conditioning/gligen"
|
|
|
|
def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y):
|
|
c = []
|
|
cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
|
|
for t in conditioning_to:
|
|
n = [t[0], t[1].copy()]
|
|
position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]
|
|
prev = []
|
|
if "gligen" in n[1]:
|
|
prev = n[1]['gligen'][2]
|
|
|
|
n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params)
|
|
c.append(n)
|
|
return (c, )
|
|
|
|
class EmptyLatentImage:
|
|
def __init__(self, device="cpu"):
|
|
self.device = device
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
|
|
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
|
|
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "generate"
|
|
|
|
CATEGORY = "latent"
|
|
|
|
def generate(self, width, height, batch_size=1):
|
|
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
|
|
return ({"samples":latent}, )
|
|
|
|
|
|
class LatentFromBatch:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "samples": ("LATENT",),
|
|
"batch_index": ("INT", {"default": 0, "min": 0, "max": 63}),
|
|
"length": ("INT", {"default": 1, "min": 1, "max": 64}),
|
|
}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "frombatch"
|
|
|
|
CATEGORY = "latent/batch"
|
|
|
|
def frombatch(self, samples, batch_index, length):
|
|
s = samples.copy()
|
|
s_in = samples["samples"]
|
|
batch_index = min(s_in.shape[0] - 1, batch_index)
|
|
length = min(s_in.shape[0] - batch_index, length)
|
|
s["samples"] = s_in[batch_index:batch_index + length].clone()
|
|
if "noise_mask" in samples:
|
|
masks = samples["noise_mask"]
|
|
if masks.shape[0] == 1:
|
|
s["noise_mask"] = masks.clone()
|
|
else:
|
|
if masks.shape[0] < s_in.shape[0]:
|
|
masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]
|
|
s["noise_mask"] = masks[batch_index:batch_index + length].clone()
|
|
if "batch_index" not in s:
|
|
s["batch_index"] = [x for x in range(batch_index, batch_index+length)]
|
|
else:
|
|
s["batch_index"] = samples["batch_index"][batch_index:batch_index + length]
|
|
return (s,)
|
|
|
|
class RepeatLatentBatch:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "samples": ("LATENT",),
|
|
"amount": ("INT", {"default": 1, "min": 1, "max": 64}),
|
|
}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "repeat"
|
|
|
|
CATEGORY = "latent/batch"
|
|
|
|
def repeat(self, samples, amount):
|
|
s = samples.copy()
|
|
s_in = samples["samples"]
|
|
|
|
s["samples"] = s_in.repeat((amount, 1,1,1))
|
|
if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1:
|
|
masks = samples["noise_mask"]
|
|
if masks.shape[0] < s_in.shape[0]:
|
|
masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]
|
|
s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1))
|
|
if "batch_index" in s:
|
|
offset = max(s["batch_index"]) - min(s["batch_index"]) + 1
|
|
s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]]
|
|
return (s,)
|
|
|
|
class LatentUpscale:
|
|
upscale_methods = ["nearest-exact", "bilinear", "area"]
|
|
crop_methods = ["disabled", "center"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
|
|
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
|
|
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
|
|
"crop": (s.crop_methods,)}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "upscale"
|
|
|
|
CATEGORY = "latent"
|
|
|
|
def upscale(self, samples, upscale_method, width, height, crop):
|
|
s = samples.copy()
|
|
s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)
|
|
return (s,)
|
|
|
|
class LatentRotate:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "samples": ("LATENT",),
|
|
"rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],),
|
|
}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "rotate"
|
|
|
|
CATEGORY = "latent/transform"
|
|
|
|
def rotate(self, samples, rotation):
|
|
s = samples.copy()
|
|
rotate_by = 0
|
|
if rotation.startswith("90"):
|
|
rotate_by = 1
|
|
elif rotation.startswith("180"):
|
|
rotate_by = 2
|
|
elif rotation.startswith("270"):
|
|
rotate_by = 3
|
|
|
|
s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2])
|
|
return (s,)
|
|
|
|
class LatentFlip:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "samples": ("LATENT",),
|
|
"flip_method": (["x-axis: vertically", "y-axis: horizontally"],),
|
|
}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "flip"
|
|
|
|
CATEGORY = "latent/transform"
|
|
|
|
def flip(self, samples, flip_method):
|
|
s = samples.copy()
|
|
if flip_method.startswith("x"):
|
|
s["samples"] = torch.flip(samples["samples"], dims=[2])
|
|
elif flip_method.startswith("y"):
|
|
s["samples"] = torch.flip(samples["samples"], dims=[3])
|
|
|
|
return (s,)
|
|
|
|
class LatentComposite:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "samples_to": ("LATENT",),
|
|
"samples_from": ("LATENT",),
|
|
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
"feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "composite"
|
|
|
|
CATEGORY = "latent"
|
|
|
|
def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0):
|
|
x = x // 8
|
|
y = y // 8
|
|
feather = feather // 8
|
|
samples_out = samples_to.copy()
|
|
s = samples_to["samples"].clone()
|
|
samples_to = samples_to["samples"]
|
|
samples_from = samples_from["samples"]
|
|
if feather == 0:
|
|
s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
|
|
else:
|
|
samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
|
|
mask = torch.ones_like(samples_from)
|
|
for t in range(feather):
|
|
if y != 0:
|
|
mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
|
|
|
|
if y + samples_from.shape[2] < samples_to.shape[2]:
|
|
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
|
|
if x != 0:
|
|
mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
|
|
if x + samples_from.shape[3] < samples_to.shape[3]:
|
|
mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
|
|
rev_mask = torch.ones_like(mask) - mask
|
|
s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask
|
|
samples_out["samples"] = s
|
|
return (samples_out,)
|
|
|
|
class LatentCrop:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "samples": ("LATENT",),
|
|
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
|
|
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
|
|
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "crop"
|
|
|
|
CATEGORY = "latent/transform"
|
|
|
|
def crop(self, samples, width, height, x, y):
|
|
s = samples.copy()
|
|
samples = samples['samples']
|
|
x = x // 8
|
|
y = y // 8
|
|
|
|
#enfonce minimum size of 64
|
|
if x > (samples.shape[3] - 8):
|
|
x = samples.shape[3] - 8
|
|
if y > (samples.shape[2] - 8):
|
|
y = samples.shape[2] - 8
|
|
|
|
new_height = height // 8
|
|
new_width = width // 8
|
|
to_x = new_width + x
|
|
to_y = new_height + y
|
|
s['samples'] = samples[:,:,y:to_y, x:to_x]
|
|
return (s,)
|
|
|
|
class SetLatentNoiseMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "samples": ("LATENT",),
|
|
"mask": ("MASK",),
|
|
}}
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "set_mask"
|
|
|
|
CATEGORY = "latent/inpaint"
|
|
|
|
def set_mask(self, samples, mask):
|
|
s = samples.copy()
|
|
s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
|
|
return (s,)
|
|
|
|
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
|
|
device = comfy.model_management.get_torch_device()
|
|
latent_image = latent["samples"]
|
|
|
|
if disable_noise:
|
|
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
|
|
else:
|
|
batch_inds = latent["batch_index"] if "batch_index" in latent else None
|
|
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
|
|
|
|
noise_mask = None
|
|
if "noise_mask" in latent:
|
|
noise_mask = latent["noise_mask"]
|
|
|
|
pbar = comfy.utils.ProgressBar(steps)
|
|
def callback(step, x0, x, total_steps):
|
|
pbar.update_absolute(step + 1, total_steps)
|
|
|
|
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
|
|
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
|
|
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback)
|
|
out = latent.copy()
|
|
out["samples"] = samples
|
|
return (out, )
|
|
|
|
class KSampler:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"model": ("MODEL",),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
|
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
|
|
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
|
|
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
|
|
"positive": ("CONDITIONING", ),
|
|
"negative": ("CONDITIONING", ),
|
|
"latent_image": ("LATENT", ),
|
|
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
}}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "sample"
|
|
|
|
CATEGORY = "sampling"
|
|
|
|
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
|
|
return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
|
|
|
|
class KSamplerAdvanced:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"model": ("MODEL",),
|
|
"add_noise": (["enable", "disable"], ),
|
|
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
|
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
|
|
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
|
|
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
|
|
"positive": ("CONDITIONING", ),
|
|
"negative": ("CONDITIONING", ),
|
|
"latent_image": ("LATENT", ),
|
|
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
|
|
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
|
|
"return_with_leftover_noise": (["disable", "enable"], ),
|
|
}}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
FUNCTION = "sample"
|
|
|
|
CATEGORY = "sampling"
|
|
|
|
def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
|
|
force_full_denoise = True
|
|
if return_with_leftover_noise == "enable":
|
|
force_full_denoise = False
|
|
disable_noise = False
|
|
if add_noise == "disable":
|
|
disable_noise = True
|
|
return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
|
|
|
|
class SaveImage:
|
|
def __init__(self):
|
|
self.output_dir = folder_paths.get_output_directory()
|
|
self.type = "output"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"images": ("IMAGE", ),
|
|
"filename_prefix": ("STRING", {"default": "ComfyUI"})},
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "save_images"
|
|
|
|
OUTPUT_NODE = True
|
|
|
|
CATEGORY = "image"
|
|
|
|
def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
|
|
def map_filename(filename):
|
|
prefix_len = len(os.path.basename(filename_prefix))
|
|
prefix = filename[:prefix_len + 1]
|
|
try:
|
|
digits = int(filename[prefix_len + 1:].split('_')[0])
|
|
except:
|
|
digits = 0
|
|
return (digits, prefix)
|
|
|
|
def compute_vars(input):
|
|
input = input.replace("%width%", str(images[0].shape[1]))
|
|
input = input.replace("%height%", str(images[0].shape[0]))
|
|
return input
|
|
|
|
filename_prefix = compute_vars(filename_prefix)
|
|
|
|
subfolder = os.path.dirname(os.path.normpath(filename_prefix))
|
|
filename = os.path.basename(os.path.normpath(filename_prefix))
|
|
|
|
full_output_folder = os.path.join(self.output_dir, subfolder)
|
|
|
|
if os.path.commonpath((self.output_dir, os.path.abspath(full_output_folder))) != self.output_dir:
|
|
print("Saving image outside the output folder is not allowed.")
|
|
return {}
|
|
|
|
try:
|
|
counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
|
|
except ValueError:
|
|
counter = 1
|
|
except FileNotFoundError:
|
|
os.makedirs(full_output_folder, exist_ok=True)
|
|
counter = 1
|
|
|
|
results = list()
|
|
for image in images:
|
|
i = 255. * image.cpu().numpy()
|
|
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
|
metadata = PngInfo()
|
|
if prompt is not None:
|
|
metadata.add_text("prompt", json.dumps(prompt))
|
|
if extra_pnginfo is not None:
|
|
for x in extra_pnginfo:
|
|
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
|
|
|
file = f"{filename}_{counter:05}_.png"
|
|
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
|
|
results.append({
|
|
"filename": file,
|
|
"subfolder": subfolder,
|
|
"type": self.type
|
|
})
|
|
counter += 1
|
|
|
|
return { "ui": { "images": results } }
|
|
|
|
class PreviewImage(SaveImage):
|
|
def __init__(self):
|
|
self.output_dir = folder_paths.get_temp_directory()
|
|
self.type = "temp"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"images": ("IMAGE", ), },
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
|
}
|
|
|
|
class LoadImage:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
input_dir = folder_paths.get_input_directory()
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
|
|
return {"required":
|
|
{"image": (sorted(files), )},
|
|
}
|
|
|
|
CATEGORY = "image"
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK")
|
|
FUNCTION = "load_image"
|
|
def load_image(self, image):
|
|
image_path = folder_paths.get_annotated_filepath(image)
|
|
i = Image.open(image_path)
|
|
image = i.convert("RGB")
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image)[None,]
|
|
if 'A' in i.getbands():
|
|
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
|
|
mask = 1. - torch.from_numpy(mask)
|
|
else:
|
|
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
|
|
return (image, mask)
|
|
|
|
@classmethod
|
|
def IS_CHANGED(s, image):
|
|
image_path = folder_paths.get_annotated_filepath(image)
|
|
m = hashlib.sha256()
|
|
with open(image_path, 'rb') as f:
|
|
m.update(f.read())
|
|
return m.digest().hex()
|
|
|
|
@classmethod
|
|
def VALIDATE_INPUTS(s, image):
|
|
if not folder_paths.exists_annotated_filepath(image):
|
|
return "Invalid image file: {}".format(image)
|
|
|
|
return True
|
|
|
|
class LoadImageMask:
|
|
_color_channels = ["alpha", "red", "green", "blue"]
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
input_dir = folder_paths.get_input_directory()
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
|
|
return {"required":
|
|
{"image": (sorted(files), ),
|
|
"channel": (s._color_channels, ), }
|
|
}
|
|
|
|
CATEGORY = "mask"
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "load_image"
|
|
def load_image(self, image, channel):
|
|
image_path = folder_paths.get_annotated_filepath(image)
|
|
i = Image.open(image_path)
|
|
if i.getbands() != ("R", "G", "B", "A"):
|
|
i = i.convert("RGBA")
|
|
mask = None
|
|
c = channel[0].upper()
|
|
if c in i.getbands():
|
|
mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
|
|
mask = torch.from_numpy(mask)
|
|
if c == 'A':
|
|
mask = 1. - mask
|
|
else:
|
|
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
|
|
return (mask,)
|
|
|
|
@classmethod
|
|
def IS_CHANGED(s, image, channel):
|
|
image_path = folder_paths.get_annotated_filepath(image)
|
|
m = hashlib.sha256()
|
|
with open(image_path, 'rb') as f:
|
|
m.update(f.read())
|
|
return m.digest().hex()
|
|
|
|
@classmethod
|
|
def VALIDATE_INPUTS(s, image, channel):
|
|
if not folder_paths.exists_annotated_filepath(image):
|
|
return "Invalid image file: {}".format(image)
|
|
|
|
if channel not in s._color_channels:
|
|
return "Invalid color channel: {}".format(channel)
|
|
|
|
return True
|
|
|
|
class ImageScale:
|
|
upscale_methods = ["nearest-exact", "bilinear", "area"]
|
|
crop_methods = ["disabled", "center"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
|
|
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
|
|
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
|
|
"crop": (s.crop_methods,)}}
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "upscale"
|
|
|
|
CATEGORY = "image/upscaling"
|
|
|
|
def upscale(self, image, upscale_method, width, height, crop):
|
|
samples = image.movedim(-1,1)
|
|
s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop)
|
|
s = s.movedim(1,-1)
|
|
return (s,)
|
|
|
|
class ImageInvert:
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "image": ("IMAGE",)}}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "invert"
|
|
|
|
CATEGORY = "image"
|
|
|
|
def invert(self, image):
|
|
s = 1.0 - image
|
|
return (s,)
|
|
|
|
|
|
class ImagePadForOutpaint:
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
|
|
"feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK")
|
|
FUNCTION = "expand_image"
|
|
|
|
CATEGORY = "image"
|
|
|
|
def expand_image(self, image, left, top, right, bottom, feathering):
|
|
d1, d2, d3, d4 = image.size()
|
|
|
|
new_image = torch.zeros(
|
|
(d1, d2 + top + bottom, d3 + left + right, d4),
|
|
dtype=torch.float32,
|
|
)
|
|
new_image[:, top:top + d2, left:left + d3, :] = image
|
|
|
|
mask = torch.ones(
|
|
(d2 + top + bottom, d3 + left + right),
|
|
dtype=torch.float32,
|
|
)
|
|
|
|
t = torch.zeros(
|
|
(d2, d3),
|
|
dtype=torch.float32
|
|
)
|
|
|
|
if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3:
|
|
|
|
for i in range(d2):
|
|
for j in range(d3):
|
|
dt = i if top != 0 else d2
|
|
db = d2 - i if bottom != 0 else d2
|
|
|
|
dl = j if left != 0 else d3
|
|
dr = d3 - j if right != 0 else d3
|
|
|
|
d = min(dt, db, dl, dr)
|
|
|
|
if d >= feathering:
|
|
continue
|
|
|
|
v = (feathering - d) / feathering
|
|
|
|
t[i, j] = v * v
|
|
|
|
mask[top:top + d2, left:left + d3] = t
|
|
|
|
return (new_image, mask)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"KSampler": KSampler,
|
|
"CheckpointLoaderSimple": CheckpointLoaderSimple,
|
|
"CLIPTextEncode": CLIPTextEncode,
|
|
"CLIPSetLastLayer": CLIPSetLastLayer,
|
|
"VAEDecode": VAEDecode,
|
|
"VAEEncode": VAEEncode,
|
|
"VAEEncodeForInpaint": VAEEncodeForInpaint,
|
|
"VAELoader": VAELoader,
|
|
"EmptyLatentImage": EmptyLatentImage,
|
|
"LatentUpscale": LatentUpscale,
|
|
"LatentFromBatch": LatentFromBatch,
|
|
"RepeatLatentBatch": RepeatLatentBatch,
|
|
"SaveImage": SaveImage,
|
|
"PreviewImage": PreviewImage,
|
|
"LoadImage": LoadImage,
|
|
"LoadImageMask": LoadImageMask,
|
|
"ImageScale": ImageScale,
|
|
"ImageInvert": ImageInvert,
|
|
"ImagePadForOutpaint": ImagePadForOutpaint,
|
|
"ConditioningAverage ": ConditioningAverage ,
|
|
"ConditioningCombine": ConditioningCombine,
|
|
"ConditioningSetArea": ConditioningSetArea,
|
|
"ConditioningSetMask": ConditioningSetMask,
|
|
"KSamplerAdvanced": KSamplerAdvanced,
|
|
"SetLatentNoiseMask": SetLatentNoiseMask,
|
|
"LatentComposite": LatentComposite,
|
|
"LatentRotate": LatentRotate,
|
|
"LatentFlip": LatentFlip,
|
|
"LatentCrop": LatentCrop,
|
|
"LoraLoader": LoraLoader,
|
|
"CLIPLoader": CLIPLoader,
|
|
"CLIPVisionEncode": CLIPVisionEncode,
|
|
"StyleModelApply": StyleModelApply,
|
|
"unCLIPConditioning": unCLIPConditioning,
|
|
"ControlNetApply": ControlNetApply,
|
|
"ControlNetLoader": ControlNetLoader,
|
|
"DiffControlNetLoader": DiffControlNetLoader,
|
|
"StyleModelLoader": StyleModelLoader,
|
|
"CLIPVisionLoader": CLIPVisionLoader,
|
|
"VAEDecodeTiled": VAEDecodeTiled,
|
|
"VAEEncodeTiled": VAEEncodeTiled,
|
|
"TomePatchModel": TomePatchModel,
|
|
"unCLIPCheckpointLoader": unCLIPCheckpointLoader,
|
|
"GLIGENLoader": GLIGENLoader,
|
|
"GLIGENTextBoxApply": GLIGENTextBoxApply,
|
|
|
|
"CheckpointLoader": CheckpointLoader,
|
|
"DiffusersLoader": DiffusersLoader,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
# Sampling
|
|
"KSampler": "KSampler",
|
|
"KSamplerAdvanced": "KSampler (Advanced)",
|
|
# Loaders
|
|
"CheckpointLoader": "Load Checkpoint (With Config)",
|
|
"CheckpointLoaderSimple": "Load Checkpoint",
|
|
"VAELoader": "Load VAE",
|
|
"LoraLoader": "Load LoRA",
|
|
"CLIPLoader": "Load CLIP",
|
|
"ControlNetLoader": "Load ControlNet Model",
|
|
"DiffControlNetLoader": "Load ControlNet Model (diff)",
|
|
"StyleModelLoader": "Load Style Model",
|
|
"CLIPVisionLoader": "Load CLIP Vision",
|
|
"UpscaleModelLoader": "Load Upscale Model",
|
|
# Conditioning
|
|
"CLIPVisionEncode": "CLIP Vision Encode",
|
|
"StyleModelApply": "Apply Style Model",
|
|
"CLIPTextEncode": "CLIP Text Encode (Prompt)",
|
|
"CLIPSetLastLayer": "CLIP Set Last Layer",
|
|
"ConditioningCombine": "Conditioning (Combine)",
|
|
"ConditioningAverage ": "Conditioning (Average)",
|
|
"ConditioningSetArea": "Conditioning (Set Area)",
|
|
"ConditioningSetMask": "Conditioning (Set Mask)",
|
|
"ControlNetApply": "Apply ControlNet",
|
|
# Latent
|
|
"VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
|
|
"SetLatentNoiseMask": "Set Latent Noise Mask",
|
|
"VAEDecode": "VAE Decode",
|
|
"VAEEncode": "VAE Encode",
|
|
"LatentRotate": "Rotate Latent",
|
|
"LatentFlip": "Flip Latent",
|
|
"LatentCrop": "Crop Latent",
|
|
"EmptyLatentImage": "Empty Latent Image",
|
|
"LatentUpscale": "Upscale Latent",
|
|
"LatentComposite": "Latent Composite",
|
|
"LatentFromBatch" : "Latent From Batch",
|
|
"RepeatLatentBatch": "Repeat Latent Batch",
|
|
# Image
|
|
"SaveImage": "Save Image",
|
|
"PreviewImage": "Preview Image",
|
|
"LoadImage": "Load Image",
|
|
"LoadImageMask": "Load Image (as Mask)",
|
|
"ImageScale": "Upscale Image",
|
|
"ImageUpscaleWithModel": "Upscale Image (using Model)",
|
|
"ImageInvert": "Invert Image",
|
|
"ImagePadForOutpaint": "Pad Image for Outpainting",
|
|
# _for_testing
|
|
"VAEDecodeTiled": "VAE Decode (Tiled)",
|
|
"VAEEncodeTiled": "VAE Encode (Tiled)",
|
|
}
|
|
|
|
def load_custom_node(module_path):
|
|
module_name = os.path.basename(module_path)
|
|
if os.path.isfile(module_path):
|
|
sp = os.path.splitext(module_path)
|
|
module_name = sp[0]
|
|
try:
|
|
if os.path.isfile(module_path):
|
|
module_spec = importlib.util.spec_from_file_location(module_name, module_path)
|
|
else:
|
|
module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py"))
|
|
module = importlib.util.module_from_spec(module_spec)
|
|
sys.modules[module_name] = module
|
|
module_spec.loader.exec_module(module)
|
|
if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
|
|
NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
|
|
if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
|
|
NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
|
|
return True
|
|
else:
|
|
print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
|
|
return False
|
|
except Exception as e:
|
|
print(traceback.format_exc())
|
|
print(f"Cannot import {module_path} module for custom nodes:", e)
|
|
return False
|
|
|
|
def load_custom_nodes():
|
|
node_paths = folder_paths.get_folder_paths("custom_nodes")
|
|
node_import_times = []
|
|
for custom_node_path in node_paths:
|
|
possible_modules = os.listdir(custom_node_path)
|
|
if "__pycache__" in possible_modules:
|
|
possible_modules.remove("__pycache__")
|
|
|
|
for possible_module in possible_modules:
|
|
module_path = os.path.join(custom_node_path, possible_module)
|
|
if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
|
|
time_before = time.perf_counter()
|
|
success = load_custom_node(module_path)
|
|
node_import_times.append((time.perf_counter() - time_before, module_path, success))
|
|
|
|
if len(node_import_times) > 0:
|
|
print("\nImport times for custom nodes:")
|
|
for n in sorted(node_import_times):
|
|
if n[2]:
|
|
import_message = ""
|
|
else:
|
|
import_message = " (IMPORT FAILED)"
|
|
print("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
|
|
print()
|
|
|
|
def init_custom_nodes():
|
|
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_hypernetwork.py"))
|
|
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
|
|
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
|
|
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))
|
|
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_rebatch.py"))
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load_custom_nodes()
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