1018 lines
37 KiB
Python
1018 lines
37 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 copy
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import traceback
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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.samplers
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import comfy.sd
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import comfy.utils
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import comfy_extras.clip_vision
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import model_management
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import importlib
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supported_ckpt_extensions = ['.ckpt', '.pth']
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supported_pt_extensions = ['.ckpt', '.pt', '.bin', '.pth']
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try:
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import safetensors.torch
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supported_ckpt_extensions += ['.safetensors']
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supported_pt_extensions += ['.safetensors']
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except:
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print("Could not import safetensors, safetensors support disabled.")
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def recursive_search(directory):
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result = []
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for root, subdir, file in os.walk(directory, followlinks=True):
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for filepath in file:
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#we os.path,join directory with a blank string to generate a path separator at the end.
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result.append(os.path.join(root, filepath).replace(os.path.join(directory,''),''))
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return result
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def filter_files_extensions(files, extensions):
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return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))
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def before_node_execution():
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model_management.throw_exception_if_processing_interrupted()
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def interrupt_processing(value=True):
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model_management.interrupt_current_processing(value)
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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 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": 4096, "step": 64}),
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"height": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),
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"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),
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"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),
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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, min_sigma=0.0, max_sigma=99.0):
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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]['min_sigma'] = min_sigma
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n[1]['max_sigma'] = max_sigma
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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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def encode(self, vae, pixels):
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x = (pixels.shape[1] // 64) * 64
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y = (pixels.shape[2] // 64) * 64
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if pixels.shape[1] != x or pixels.shape[2] != y:
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pixels = pixels[:,:x,:y,:]
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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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x = (pixels.shape[1] // 64) * 64
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y = (pixels.shape[2] // 64) * 64
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if pixels.shape[1] != x or pixels.shape[2] != y:
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pixels = pixels[:,:x,:y,:]
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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", )}}
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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):
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x = (pixels.shape[1] // 64) * 64
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y = (pixels.shape[2] // 64) * 64
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mask = torch.nn.functional.interpolate(mask[None,None,], size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")[0][0]
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if pixels.shape[1] != x or pixels.shape[2] != y:
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pixels = pixels[:,:x,:y,:]
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mask = mask[:x,:y]
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#grow mask by a few pixels to keep things seamless in latent space
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kernel_tensor = torch.ones((1, 1, 6, 6))
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mask_erosion = torch.clamp(torch.nn.functional.conv2d((mask.round())[None], kernel_tensor, padding=3), 0, 1)
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m = (1.0 - mask.round())
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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[0][:x,:y].round())}, )
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class CheckpointLoader:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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config_dir = os.path.join(models_dir, "configs")
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ckpt_dir = os.path.join(models_dir, "checkpoints")
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embedding_directory = os.path.join(models_dir, "embeddings")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "config_name": (filter_files_extensions(recursive_search(s.config_dir), '.yaml'), ),
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"ckpt_name": (filter_files_extensions(recursive_search(s.ckpt_dir), supported_ckpt_extensions), )}}
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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, config_name, ckpt_name, output_vae=True, output_clip=True):
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config_path = os.path.join(self.config_dir, config_name)
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ckpt_path = os.path.join(self.ckpt_dir, ckpt_name)
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return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=self.embedding_directory)
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class CheckpointLoaderSimple:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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ckpt_dir = os.path.join(models_dir, "checkpoints")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "ckpt_name": (filter_files_extensions(recursive_search(s.ckpt_dir), supported_ckpt_extensions), ),
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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 = os.path.join(self.ckpt_dir, ckpt_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=CheckpointLoader.embedding_directory)
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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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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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lora_dir = os.path.join(models_dir, "loras")
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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": (filter_files_extensions(recursive_search(s.lora_dir), supported_pt_extensions), ),
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"strength_model": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"strength_clip": ("FLOAT", {"default": 1.0, "min": 0.0, "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 = os.path.join(self.lora_dir, 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 VAELoader:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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vae_dir = os.path.join(models_dir, "vae")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "vae_name": (filter_files_extensions(recursive_search(s.vae_dir), supported_pt_extensions), )}}
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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 = os.path.join(self.vae_dir, 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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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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controlnet_dir = os.path.join(models_dir, "controlnet")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "control_net_name": (filter_files_extensions(recursive_search(s.controlnet_dir), supported_pt_extensions), )}}
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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 = os.path.join(self.controlnet_dir, 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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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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controlnet_dir = os.path.join(models_dir, "controlnet")
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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": (filter_files_extensions(recursive_search(s.controlnet_dir), supported_pt_extensions), )}}
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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 = os.path.join(self.controlnet_dir, 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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print(control_hint.shape)
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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 T2IAdapterLoader:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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t2i_adapter_dir = os.path.join(models_dir, "t2i_adapter")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "t2i_adapter_name": (filter_files_extensions(recursive_search(s.t2i_adapter_dir), supported_pt_extensions), )}}
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RETURN_TYPES = ("CONTROL_NET",)
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FUNCTION = "load_t2i_adapter"
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CATEGORY = "loaders"
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def load_t2i_adapter(self, t2i_adapter_name):
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t2i_path = os.path.join(self.t2i_adapter_dir, t2i_adapter_name)
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t2i_adapter = comfy.sd.load_t2i_adapter(t2i_path)
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return (t2i_adapter,)
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class CLIPLoader:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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clip_dir = os.path.join(models_dir, "clip")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip_name": (filter_files_extensions(recursive_search(s.clip_dir), supported_pt_extensions), ),
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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):
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clip_path = os.path.join(self.clip_dir, clip_name)
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clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=CheckpointLoader.embedding_directory)
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return (clip,)
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class CLIPVisionLoader:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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clip_dir = os.path.join(models_dir, "clip_vision")
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@classmethod
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def INPUT_TYPES(s):
|
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return {"required": { "clip_name": (filter_files_extensions(recursive_search(s.clip_dir), supported_pt_extensions), ),
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}}
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RETURN_TYPES = ("CLIP_VISION",)
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FUNCTION = "load_clip"
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CATEGORY = "loaders"
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def load_clip(self, clip_name):
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clip_path = os.path.join(self.clip_dir, clip_name)
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clip_vision = comfy_extras.clip_vision.load(clip_path)
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return (clip_vision,)
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class CLIPVisionEncode:
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@classmethod
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def INPUT_TYPES(s):
|
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return {"required": { "clip_vision": ("CLIP_VISION",),
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"image": ("IMAGE",)
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}}
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RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
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FUNCTION = "encode"
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CATEGORY = "conditioning/style_model"
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def encode(self, clip_vision, image):
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output = clip_vision.encode_image(image)
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return (output,)
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class StyleModelLoader:
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models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
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style_model_dir = os.path.join(models_dir, "style_models")
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "style_model_name": (filter_files_extensions(recursive_search(s.style_model_dir), supported_pt_extensions), )}}
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RETURN_TYPES = ("STYLE_MODEL",)
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FUNCTION = "load_style_model"
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CATEGORY = "loaders"
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def load_style_model(self, style_model_name):
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style_model_path = os.path.join(self.style_model_dir, style_model_name)
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style_model = comfy.sd.load_style_model(style_model_path)
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return (style_model,)
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|
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class StyleModelApply:
|
|
@classmethod
|
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def INPUT_TYPES(s):
|
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return {"required": {"conditioning": ("CONDITIONING", ),
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"style_model": ("STYLE_MODEL", ),
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"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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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 EmptyLatentImage:
|
|
def __init__(self, device="cpu"):
|
|
self.device = device
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
|
|
"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
|
|
"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 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": 4096, "step": 64}),
|
|
"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
|
|
"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": 4096, "step": 8}),
|
|
"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),
|
|
"feather": ("INT", {"default": 0, "min": 0, "max": 4096, "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": 4096, "step": 64}),
|
|
"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
|
|
"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),
|
|
"y": ("INT", {"default": 0, "min": 0, "max": 4096, "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
|
|
def enforce_image_dim(d, to_d, max_d):
|
|
if to_d > max_d:
|
|
leftover = (to_d - max_d) % 8
|
|
to_d = max_d
|
|
d -= leftover
|
|
return (d, to_d)
|
|
|
|
#make sure size is always multiple of 64
|
|
x, to_x = enforce_image_dim(x, to_x, samples.shape[3])
|
|
y, to_y = enforce_image_dim(y, to_y, samples.shape[2])
|
|
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
|
|
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):
|
|
latent_image = latent["samples"]
|
|
noise_mask = None
|
|
device = model_management.get_torch_device()
|
|
|
|
if disable_noise:
|
|
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
|
|
else:
|
|
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu")
|
|
|
|
if "noise_mask" in latent:
|
|
noise_mask = latent['noise_mask']
|
|
noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")
|
|
noise_mask = noise_mask.round()
|
|
noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
|
|
noise_mask = torch.cat([noise_mask] * noise.shape[0])
|
|
noise_mask = noise_mask.to(device)
|
|
|
|
real_model = None
|
|
model_management.load_model_gpu(model)
|
|
real_model = model.model
|
|
|
|
noise = noise.to(device)
|
|
latent_image = latent_image.to(device)
|
|
|
|
positive_copy = []
|
|
negative_copy = []
|
|
|
|
control_nets = []
|
|
for p in positive:
|
|
t = p[0]
|
|
if t.shape[0] < noise.shape[0]:
|
|
t = torch.cat([t] * noise.shape[0])
|
|
t = t.to(device)
|
|
if 'control' in p[1]:
|
|
control_nets += [p[1]['control']]
|
|
positive_copy += [[t] + p[1:]]
|
|
for n in negative:
|
|
t = n[0]
|
|
if t.shape[0] < noise.shape[0]:
|
|
t = torch.cat([t] * noise.shape[0])
|
|
t = t.to(device)
|
|
if 'control' in p[1]:
|
|
control_nets += [p[1]['control']]
|
|
negative_copy += [[t] + n[1:]]
|
|
|
|
control_net_models = []
|
|
for x in control_nets:
|
|
control_net_models += x.get_control_models()
|
|
model_management.load_controlnet_gpu(control_net_models)
|
|
|
|
if sampler_name in comfy.samplers.KSampler.SAMPLERS:
|
|
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)
|
|
else:
|
|
#other samplers
|
|
pass
|
|
|
|
samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask)
|
|
samples = samples.cpu()
|
|
for c in control_nets:
|
|
c.cleanup()
|
|
|
|
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 = os.path.join(os.path.dirname(os.path.realpath(__file__)), "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)
|
|
|
|
subfolder = os.path.dirname(filename_prefix)
|
|
filename = os.path.basename(filename_prefix)
|
|
comfy_output_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
|
|
full_output_folder = os.path.join(comfy_output_folder, subfolder)
|
|
|
|
if os.path.commonpath((comfy_output_folder, os.path.realpath(full_output_folder))) != comfy_output_folder:
|
|
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
|
|
|
|
paths = 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, optimize=True)
|
|
paths.append(os.path.join(subfolder, file))
|
|
counter += 1
|
|
return { "ui": { "images": paths } }
|
|
|
|
class LoadImage:
|
|
input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
if not os.path.exists(s.input_dir):
|
|
os.makedirs(s.input_dir)
|
|
return {"required":
|
|
{"image": (sorted(os.listdir(s.input_dir)), )},
|
|
}
|
|
|
|
CATEGORY = "image"
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK")
|
|
FUNCTION = "load_image"
|
|
def load_image(self, image):
|
|
image_path = os.path.join(self.input_dir, 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 = os.path.join(s.input_dir, image)
|
|
m = hashlib.sha256()
|
|
with open(image_path, 'rb') as f:
|
|
m.update(f.read())
|
|
return m.digest().hex()
|
|
|
|
class LoadImageMask:
|
|
input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"image": (sorted(os.listdir(s.input_dir)), ),
|
|
"channel": (["alpha", "red", "green", "blue"], ),}
|
|
}
|
|
|
|
CATEGORY = "image"
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "load_image"
|
|
def load_image(self, image, channel):
|
|
image_path = os.path.join(self.input_dir, image)
|
|
i = Image.open(image_path)
|
|
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 = os.path.join(s.input_dir, image)
|
|
m = hashlib.sha256()
|
|
with open(image_path, 'rb') as f:
|
|
m.update(f.read())
|
|
return m.digest().hex()
|
|
|
|
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": 4096, "step": 1}),
|
|
"height": ("INT", {"default": 512, "min": 1, "max": 4096, "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,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"KSampler": KSampler,
|
|
"CheckpointLoader": CheckpointLoader,
|
|
"CheckpointLoaderSimple": CheckpointLoaderSimple,
|
|
"CLIPTextEncode": CLIPTextEncode,
|
|
"CLIPSetLastLayer": CLIPSetLastLayer,
|
|
"VAEDecode": VAEDecode,
|
|
"VAEEncode": VAEEncode,
|
|
"VAEEncodeForInpaint": VAEEncodeForInpaint,
|
|
"VAELoader": VAELoader,
|
|
"EmptyLatentImage": EmptyLatentImage,
|
|
"LatentUpscale": LatentUpscale,
|
|
"SaveImage": SaveImage,
|
|
"LoadImage": LoadImage,
|
|
"LoadImageMask": LoadImageMask,
|
|
"ImageScale": ImageScale,
|
|
"ImageInvert": ImageInvert,
|
|
"ConditioningCombine": ConditioningCombine,
|
|
"ConditioningSetArea": ConditioningSetArea,
|
|
"KSamplerAdvanced": KSamplerAdvanced,
|
|
"SetLatentNoiseMask": SetLatentNoiseMask,
|
|
"LatentComposite": LatentComposite,
|
|
"LatentRotate": LatentRotate,
|
|
"LatentFlip": LatentFlip,
|
|
"LatentCrop": LatentCrop,
|
|
"LoraLoader": LoraLoader,
|
|
"CLIPLoader": CLIPLoader,
|
|
"CLIPVisionEncode": CLIPVisionEncode,
|
|
"StyleModelApply": StyleModelApply,
|
|
"ControlNetApply": ControlNetApply,
|
|
"ControlNetLoader": ControlNetLoader,
|
|
"DiffControlNetLoader": DiffControlNetLoader,
|
|
"T2IAdapterLoader": T2IAdapterLoader,
|
|
"StyleModelLoader": StyleModelLoader,
|
|
"CLIPVisionLoader": CLIPVisionLoader,
|
|
"VAEDecodeTiled": VAEDecodeTiled,
|
|
"VAEEncodeTiled": VAEEncodeTiled,
|
|
}
|
|
|
|
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)
|
|
else:
|
|
print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
|
|
except Exception as e:
|
|
print(traceback.format_exc())
|
|
print(f"Cannot import {module_path} module for custom nodes:", e)
|
|
|
|
def load_custom_nodes():
|
|
CUSTOM_NODE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "custom_nodes")
|
|
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
|
|
load_custom_node(module_path)
|
|
|
|
load_custom_nodes()
|
|
|
|
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
|