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netadmin
2024-08-03 12:27:31 +03:00
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5
comfy_extras/chainner_models/model_loading.py Normal file
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from spandrel import ModelLoader
def load_state_dict(state_dict):
print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
return ModelLoader().load_from_state_dict(state_dict).eval()

112
comfy_extras/nodes_advanced_samplers.py Normal file
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import comfy.samplers
import comfy.utils
import torch
import numpy as np
from tqdm.auto import trange, tqdm
import math
@torch.no_grad()
def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
extra_args = {} if extra_args is None else extra_args
if upscale_steps is None:
upscale_steps = max(len(sigmas) // 2 + 1, 2)
else:
upscale_steps += 1
upscale_steps = min(upscale_steps, len(sigmas) + 1)
upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
orig_shape = x.size()
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
x = denoised
if i < len(upscales):
x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
if sigmas[i + 1] > 0:
x += sigmas[i + 1] * torch.randn_like(x)
return x
class SamplerLCMUpscale:
upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
@classmethod
def INPUT_TYPES(s):
return {"required":
{"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}),
"scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}),
"upscale_method": (s.upscale_methods,),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, scale_ratio, scale_steps, upscale_method):
if scale_steps < 0:
scale_steps = None
sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
return (sampler, )
from comfy.k_diffusion.sampling import to_d
import comfy.model_patcher
@torch.no_grad()
def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
extra_args = {} if extra_args is None else extra_args
temp = [0]
def post_cfg_function(args):
temp[0] = args["uncond_denoised"]
return args["denoised"]
model_options = extra_args.get("model_options", {}).copy()
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
sigma_hat = sigmas[i]
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x - denoised + temp[0], sigmas[i], denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
x = x + d * dt
return x
class SamplerEulerCFGpp:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"version": (["regular", "alternative"],),}
}
RETURN_TYPES = ("SAMPLER",)
# CATEGORY = "sampling/custom_sampling/samplers"
CATEGORY = "_for_testing"
FUNCTION = "get_sampler"
def get_sampler(self, version):
if version == "alternative":
sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
else:
sampler = comfy.samplers.ksampler("euler_cfg_pp")
return (sampler, )
NODE_CLASS_MAPPINGS = {
"SamplerLCMUpscale": SamplerLCMUpscale,
"SamplerEulerCFGpp": SamplerEulerCFGpp,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SamplerEulerCFGpp": "SamplerEulerCFG++",
}

53
comfy_extras/nodes_align_your_steps.py Normal file
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#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
import numpy as np
import torch
def loglinear_interp(t_steps, num_steps):
"""
Performs log-linear interpolation of a given array of decreasing numbers.
"""
xs = np.linspace(0, 1, len(t_steps))
ys = np.log(t_steps[::-1])
new_xs = np.linspace(0, 1, num_steps)
new_ys = np.interp(new_xs, xs, ys)
interped_ys = np.exp(new_ys)[::-1].copy()
return interped_ys
NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582],
"SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582],
"SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}
class AlignYourStepsScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model_type": (["SD1", "SDXL", "SVD"], ),
"steps": ("INT", {"default": 10, "min": 10, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, model_type, steps, denoise):
total_steps = steps
if denoise < 1.0:
if denoise <= 0.0:
return (torch.FloatTensor([]),)
total_steps = round(steps * denoise)
sigmas = NOISE_LEVELS[model_type][:]
if (steps + 1) != len(sigmas):
sigmas = loglinear_interp(sigmas, steps + 1)
sigmas = sigmas[-(total_steps + 1):]
sigmas[-1] = 0
return (torch.FloatTensor(sigmas), )
NODE_CLASS_MAPPINGS = {
"AlignYourStepsScheduler": AlignYourStepsScheduler,
}

120
comfy_extras/nodes_attention_multiply.py Normal file
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def attention_multiply(attn, model, q, k, v, out):
m = model.clone()
sd = model.model_state_dict()
for key in sd:
if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)):
m.add_patches({key: (None,)}, 0.0, q)
if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)):
m.add_patches({key: (None,)}, 0.0, k)
if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)):
m.add_patches({key: (None,)}, 0.0, v)
if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)):
m.add_patches({key: (None,)}, 0.0, out)
return m
class UNetSelfAttentionMultiply:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing/attention_experiments"
def patch(self, model, q, k, v, out):
m = attention_multiply("attn1", model, q, k, v, out)
return (m, )
class UNetCrossAttentionMultiply:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing/attention_experiments"
def patch(self, model, q, k, v, out):
m = attention_multiply("attn2", model, q, k, v, out)
return (m, )
class CLIPAttentionMultiply:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip": ("CLIP",),
"q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "patch"
CATEGORY = "_for_testing/attention_experiments"
def patch(self, clip, q, k, v, out):
m = clip.clone()
sd = m.patcher.model_state_dict()
for key in sd:
if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"):
m.add_patches({key: (None,)}, 0.0, q)
if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"):
m.add_patches({key: (None,)}, 0.0, k)
if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"):
m.add_patches({key: (None,)}, 0.0, v)
if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"):
m.add_patches({key: (None,)}, 0.0, out)
return (m, )
class UNetTemporalAttentionMultiply:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"self_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"self_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"cross_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"cross_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing/attention_experiments"
def patch(self, model, self_structural, self_temporal, cross_structural, cross_temporal):
m = model.clone()
sd = model.model_state_dict()
for k in sd:
if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")):
if '.time_stack.' in k:
m.add_patches({k: (None,)}, 0.0, self_temporal)
else:
m.add_patches({k: (None,)}, 0.0, self_structural)
elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")):
if '.time_stack.' in k:
m.add_patches({k: (None,)}, 0.0, cross_temporal)
else:
m.add_patches({k: (None,)}, 0.0, cross_structural)
return (m, )
NODE_CLASS_MAPPINGS = {
"UNetSelfAttentionMultiply": UNetSelfAttentionMultiply,
"UNetCrossAttentionMultiply": UNetCrossAttentionMultiply,
"CLIPAttentionMultiply": CLIPAttentionMultiply,
"UNetTemporalAttentionMultiply": UNetTemporalAttentionMultiply,
}

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comfy_extras/nodes_audio.py Normal file
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import torchaudio
import torch
import comfy.model_management
import folder_paths
import os
import io
import json
import struct
import random
import hashlib
from comfy.cli_args import args
class EmptyLatentAudio:
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(s):
return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
CATEGORY = "latent/audio"
def generate(self, seconds):
batch_size = 1
length = round((seconds * 44100 / 2048) / 2) * 2
latent = torch.zeros([batch_size, 64, length], device=self.device)
return ({"samples":latent, "type": "audio"}, )
class VAEEncodeAudio:
@classmethod
def INPUT_TYPES(s):
return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "encode"
CATEGORY = "latent/audio"
def encode(self, vae, audio):
sample_rate = audio["sample_rate"]
if 44100 != sample_rate:
waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
else:
waveform = audio["waveform"]
t = vae.encode(waveform.movedim(1, -1))
return ({"samples":t}, )
class VAEDecodeAudio:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
RETURN_TYPES = ("AUDIO",)
FUNCTION = "decode"
CATEGORY = "latent/audio"
def decode(self, vae, samples):
audio = vae.decode(samples["samples"]).movedim(-1, 1)
return ({"waveform": audio, "sample_rate": 44100}, )
def create_vorbis_comment_block(comment_dict, last_block):
vendor_string = b'ComfyUI'
vendor_length = len(vendor_string)
comments = []
for key, value in comment_dict.items():
comment = f"{key}={value}".encode('utf-8')
comments.append(struct.pack('<I', len(comment)) + comment)
user_comment_list_length = len(comments)
user_comments = b''.join(comments)
comment_data = struct.pack('<I', vendor_length) + vendor_string + struct.pack('<I', user_comment_list_length) + user_comments
if last_block:
id = b'\x84'
else:
id = b'\x04'
comment_block = id + struct.pack('>I', len(comment_data))[1:] + comment_data
return comment_block
def insert_or_replace_vorbis_comment(flac_io, comment_dict):
if len(comment_dict) == 0:
return flac_io
flac_io.seek(4)
blocks = []
last_block = False
while not last_block:
header = flac_io.read(4)
last_block = (header[0] & 0x80) != 0
block_type = header[0] & 0x7F
block_length = struct.unpack('>I', b'\x00' + header[1:])[0]
block_data = flac_io.read(block_length)
if block_type == 4 or block_type == 1:
pass
else:
header = bytes([(header[0] & (~0x80))]) + header[1:]
blocks.append(header + block_data)
blocks.append(create_vorbis_comment_block(comment_dict, last_block=True))
new_flac_io = io.BytesIO()
new_flac_io.write(b'fLaC')
for block in blocks:
new_flac_io.write(block)
new_flac_io.write(flac_io.read())
return new_flac_io
class SaveAudio:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""
@classmethod
def INPUT_TYPES(s):
return {"required": { "audio": ("AUDIO", ),
"filename_prefix": ("STRING", {"default": "audio/ComfyUI"})},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
FUNCTION = "save_audio"
OUTPUT_NODE = True
CATEGORY = "audio"
def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
results = list()
metadata = {}
if not args.disable_metadata:
if prompt is not None:
metadata["prompt"] = json.dumps(prompt)
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata[x] = json.dumps(extra_pnginfo[x])
for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
file = f"{filename_with_batch_num}_{counter:05}_.flac"
buff = io.BytesIO()
torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC")
buff = insert_or_replace_vorbis_comment(buff, metadata)
with open(os.path.join(full_output_folder, file), 'wb') as f:
f.write(buff.getbuffer())
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})
counter += 1
return { "ui": { "audio": results } }
class PreviewAudio(SaveAudio):
def __init__(self):
self.output_dir = folder_paths.get_temp_directory()
self.type = "temp"
self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
@classmethod
def INPUT_TYPES(s):
return {"required":
{"audio": ("AUDIO", ), },
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
class LoadAudio:
SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif')
@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))
and f.endswith(LoadAudio.SUPPORTED_FORMATS)
)
]
return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
CATEGORY = "audio"
RETURN_TYPES = ("AUDIO", )
FUNCTION = "load"
def load(self, audio):
audio_path = folder_paths.get_annotated_filepath(audio)
waveform, sample_rate = torchaudio.load(audio_path)
audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
return (audio, )
@classmethod
def IS_CHANGED(s, audio):
image_path = folder_paths.get_annotated_filepath(audio)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, audio):
if not folder_paths.exists_annotated_filepath(audio):
return "Invalid audio file: {}".format(audio)
return True
NODE_CLASS_MAPPINGS = {
"EmptyLatentAudio": EmptyLatentAudio,
"VAEEncodeAudio": VAEEncodeAudio,
"VAEDecodeAudio": VAEDecodeAudio,
"SaveAudio": SaveAudio,
"LoadAudio": LoadAudio,
"PreviewAudio": PreviewAudio,
}

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comfy_extras/nodes_canny.py Normal file
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from kornia.filters import canny
import comfy.model_management
class Canny:
@classmethod
def INPUT_TYPES(s):
return {"required": {"image": ("IMAGE",),
"low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}),
"high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01})
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "detect_edge"
CATEGORY = "image/preprocessors"
def detect_edge(self, image, low_threshold, high_threshold):
output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
return (img_out,)
NODE_CLASS_MAPPINGS = {
"Canny": Canny,
}

56
comfy_extras/nodes_clip_sdxl.py Normal file
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import torch
from nodes import MAX_RESOLUTION
class CLIPTextEncodeSDXLRefiner:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, ascore, width, height, text):
tokens = clip.tokenize(text)
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return ([[cond, {"pooled_output": pooled, "aesthetic_score": ascore, "width": width,"height": height}]], )
class CLIPTextEncodeSDXL:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
"crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
"target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
"text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l):
tokens = clip.tokenize(text_g)
tokens["l"] = clip.tokenize(text_l)["l"]
if len(tokens["l"]) != len(tokens["g"]):
empty = clip.tokenize("")
while len(tokens["l"]) < len(tokens["g"]):
tokens["l"] += empty["l"]
while len(tokens["l"]) > len(tokens["g"]):
tokens["g"] += empty["g"]
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], )
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner,
"CLIPTextEncodeSDXL": CLIPTextEncodeSDXL,
}

215
comfy_extras/nodes_compositing.py Normal file
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import numpy as np
import torch
import comfy.utils
from enum import Enum
def resize_mask(mask, shape):
return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1)
class PorterDuffMode(Enum):
ADD = 0
CLEAR = 1
DARKEN = 2
DST = 3
DST_ATOP = 4
DST_IN = 5
DST_OUT = 6
DST_OVER = 7
LIGHTEN = 8
MULTIPLY = 9
OVERLAY = 10
SCREEN = 11
SRC = 12
SRC_ATOP = 13
SRC_IN = 14
SRC_OUT = 15
SRC_OVER = 16
XOR = 17
def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):
# convert mask to alpha
src_alpha = 1 - src_alpha
dst_alpha = 1 - dst_alpha
# premultiply alpha
src_image = src_image * src_alpha
dst_image = dst_image * dst_alpha
# composite ops below assume alpha-premultiplied images
if mode == PorterDuffMode.ADD:
out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
out_image = torch.clamp(src_image + dst_image, 0, 1)
elif mode == PorterDuffMode.CLEAR:
out_alpha = torch.zeros_like(dst_alpha)
out_image = torch.zeros_like(dst_image)
elif mode == PorterDuffMode.DARKEN:
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
elif mode == PorterDuffMode.DST:
out_alpha = dst_alpha
out_image = dst_image
elif mode == PorterDuffMode.DST_ATOP:
out_alpha = src_alpha
out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image
elif mode == PorterDuffMode.DST_IN:
out_alpha = src_alpha * dst_alpha
out_image = dst_image * src_alpha
elif mode == PorterDuffMode.DST_OUT:
out_alpha = (1 - src_alpha) * dst_alpha
out_image = (1 - src_alpha) * dst_image
elif mode == PorterDuffMode.DST_OVER:
out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha
out_image = dst_image + (1 - dst_alpha) * src_image
elif mode == PorterDuffMode.LIGHTEN:
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image)
elif mode == PorterDuffMode.MULTIPLY:
out_alpha = src_alpha * dst_alpha
out_image = src_image * dst_image
elif mode == PorterDuffMode.OVERLAY:
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image,
src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image))
elif mode == PorterDuffMode.SCREEN:
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
out_image = src_image + dst_image - src_image * dst_image
elif mode == PorterDuffMode.SRC:
out_alpha = src_alpha
out_image = src_image
elif mode == PorterDuffMode.SRC_ATOP:
out_alpha = dst_alpha
out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image
elif mode == PorterDuffMode.SRC_IN:
out_alpha = src_alpha * dst_alpha
out_image = src_image * dst_alpha
elif mode == PorterDuffMode.SRC_OUT:
out_alpha = (1 - dst_alpha) * src_alpha
out_image = (1 - dst_alpha) * src_image
elif mode == PorterDuffMode.SRC_OVER:
out_alpha = src_alpha + (1 - src_alpha) * dst_alpha
out_image = src_image + (1 - src_alpha) * dst_image
elif mode == PorterDuffMode.XOR:
out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
else:
return None, None
# back to non-premultiplied alpha
out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image))
out_image = torch.clamp(out_image, 0, 1)
# convert alpha to mask
out_alpha = 1 - out_alpha
return out_image, out_alpha
class PorterDuffImageComposite:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"source": ("IMAGE",),
"source_alpha": ("MASK",),
"destination": ("IMAGE",),
"destination_alpha": ("MASK",),
"mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}),
},
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "composite"
CATEGORY = "mask/compositing"
def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode):
batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
out_images = []
out_alphas = []
for i in range(batch_size):
src_image = source[i]
dst_image = destination[i]
assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels
src_alpha = source_alpha[i].unsqueeze(2)
dst_alpha = destination_alpha[i].unsqueeze(2)
if dst_alpha.shape[:2] != dst_image.shape[:2]:
upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
if src_image.shape != dst_image.shape:
upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
if src_alpha.shape != dst_alpha.shape:
upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])
out_images.append(out_image)
out_alphas.append(out_alpha.squeeze(2))
result = (torch.stack(out_images), torch.stack(out_alphas))
return result
class SplitImageWithAlpha:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
}
}
CATEGORY = "mask/compositing"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "split_image_with_alpha"
def split_image_with_alpha(self, image: torch.Tensor):
out_images = [i[:,:,:3] for i in image]
out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image]
result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas))
return result
class JoinImageWithAlpha:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"alpha": ("MASK",),
}
}
CATEGORY = "mask/compositing"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "join_image_with_alpha"
def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor):
batch_size = min(len(image), len(alpha))
out_images = []
alpha = 1.0 - resize_mask(alpha, image.shape[1:])
for i in range(batch_size):
out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
result = (torch.stack(out_images),)
return result
NODE_CLASS_MAPPINGS = {
"PorterDuffImageComposite": PorterDuffImageComposite,
"SplitImageWithAlpha": SplitImageWithAlpha,
"JoinImageWithAlpha": JoinImageWithAlpha,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PorterDuffImageComposite": "Porter-Duff Image Composite",
"SplitImageWithAlpha": "Split Image with Alpha",
"JoinImageWithAlpha": "Join Image with Alpha",
}

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class CLIPTextEncodeControlnet:
@classmethod
def INPUT_TYPES(s):
return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "_for_testing/conditioning"
def encode(self, clip, conditioning, text):
tokens = clip.tokenize(text)
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
c = []
for t in conditioning:
n = [t[0], t[1].copy()]
n[1]['cross_attn_controlnet'] = cond
n[1]['pooled_output_controlnet'] = pooled
c.append(n)
return (c, )
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeControlnet": CLIPTextEncodeControlnet
}

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from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
class SetUnionControlNetType:
@classmethod
def INPUT_TYPES(s):
return {"required": {"control_net": ("CONTROL_NET", ),
"type": (["auto"] + list(UNION_CONTROLNET_TYPES.keys()),)
}}
CATEGORY = "conditioning/controlnet"
RETURN_TYPES = ("CONTROL_NET",)
FUNCTION = "set_controlnet_type"
def set_controlnet_type(self, control_net, type):
control_net = control_net.copy()
type_number = UNION_CONTROLNET_TYPES.get(type, -1)
if type_number >= 0:
control_net.set_extra_arg("control_type", [type_number])
else:
control_net.set_extra_arg("control_type", [])
return (control_net,)
NODE_CLASS_MAPPINGS = {
"SetUnionControlNetType": SetUnionControlNetType,
}

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import comfy.samplers
import comfy.sample
from comfy.k_diffusion import sampling as k_diffusion_sampling
import latent_preview
import torch
import comfy.utils
import node_helpers
class BasicScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"scheduler": (comfy.samplers.SCHEDULER_NAMES, ),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, model, scheduler, steps, denoise):
total_steps = steps
if denoise < 1.0:
if denoise <= 0.0:
return (torch.FloatTensor([]),)
total_steps = int(steps/denoise)
sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu()
sigmas = sigmas[-(steps + 1):]
return (sigmas, )
class KarrasScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
"rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, steps, sigma_max, sigma_min, rho):
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
return (sigmas, )
class ExponentialScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, steps, sigma_max, sigma_min):
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max)
return (sigmas, )
class PolyexponentialScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
"rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, steps, sigma_max, sigma_min, rho):
sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
return (sigmas, )
class SDTurboScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"steps": ("INT", {"default": 1, "min": 1, "max": 10}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, model, steps, denoise):
start_step = 10 - int(10 * denoise)
timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
sigmas = model.get_model_object("model_sampling").sigma(timesteps)
sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
return (sigmas, )
class BetaSamplingScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"alpha": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}),
"beta": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, model, steps, alpha, beta):
sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta)
return (sigmas, )
class VPScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), #TODO: fix default values
"beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
"eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, steps, beta_d, beta_min, eps_s):
sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s)
return (sigmas, )
class SplitSigmas:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sigmas": ("SIGMAS", ),
"step": ("INT", {"default": 0, "min": 0, "max": 10000}),
}
}
RETURN_TYPES = ("SIGMAS","SIGMAS")
RETURN_NAMES = ("high_sigmas", "low_sigmas")
CATEGORY = "sampling/custom_sampling/sigmas"
FUNCTION = "get_sigmas"
def get_sigmas(self, sigmas, step):
sigmas1 = sigmas[:step + 1]
sigmas2 = sigmas[step:]
return (sigmas1, sigmas2)
class SplitSigmasDenoise:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sigmas": ("SIGMAS", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS","SIGMAS")
RETURN_NAMES = ("high_sigmas", "low_sigmas")
CATEGORY = "sampling/custom_sampling/sigmas"
FUNCTION = "get_sigmas"
def get_sigmas(self, sigmas, denoise):
steps = max(sigmas.shape[-1] - 1, 0)
total_steps = round(steps * denoise)
sigmas1 = sigmas[:-(total_steps)]
sigmas2 = sigmas[-(total_steps + 1):]
return (sigmas1, sigmas2)
class FlipSigmas:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sigmas": ("SIGMAS", ),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/sigmas"
FUNCTION = "get_sigmas"
def get_sigmas(self, sigmas):
if len(sigmas) == 0:
return (sigmas,)
sigmas = sigmas.flip(0)
if sigmas[0] == 0:
sigmas[0] = 0.0001
return (sigmas,)
class KSamplerSelect:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sampler_name": (comfy.samplers.SAMPLER_NAMES, ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, sampler_name):
sampler = comfy.samplers.sampler_object(sampler_name)
return (sampler, )
class SamplerDPMPP_3M_SDE:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"noise_device": (['gpu', 'cpu'], ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise, noise_device):
if noise_device == 'cpu':
sampler_name = "dpmpp_3m_sde"
else:
sampler_name = "dpmpp_3m_sde_gpu"
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise})
return (sampler, )
class SamplerDPMPP_2M_SDE:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"solver_type": (['midpoint', 'heun'], ),
"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"noise_device": (['gpu', 'cpu'], ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, solver_type, eta, s_noise, noise_device):
if noise_device == 'cpu':
sampler_name = "dpmpp_2m_sde"
else:
sampler_name = "dpmpp_2m_sde_gpu"
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
return (sampler, )
class SamplerDPMPP_SDE:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"noise_device": (['gpu', 'cpu'], ),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise, r, noise_device):
if noise_device == 'cpu':
sampler_name = "dpmpp_sde"
else:
sampler_name = "dpmpp_sde_gpu"
sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
return (sampler, )
class SamplerDPMPP_2S_Ancestral:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise):
sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise})
return (sampler, )
class SamplerEulerAncestral:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise):
sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
return (sampler, )
class SamplerEulerAncestralCFGPP:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}),
}}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, eta, s_noise):
sampler = comfy.samplers.ksampler(
"euler_ancestral_cfg_pp",
{"eta": eta, "s_noise": s_noise})
return (sampler, )
class SamplerLMS:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"order": ("INT", {"default": 4, "min": 1, "max": 100}),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, order):
sampler = comfy.samplers.ksampler("lms", {"order": order})
return (sampler, )
class SamplerDPMAdaptative:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"order": ("INT", {"default": 3, "min": 2, "max": 3}),
"rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
"s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise):
sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff,
"icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta,
"s_noise":s_noise })
return (sampler, )
class Noise_EmptyNoise:
def __init__(self):
self.seed = 0
def generate_noise(self, input_latent):
latent_image = input_latent["samples"]
return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
class Noise_RandomNoise:
def __init__(self, seed):
self.seed = seed
def generate_noise(self, input_latent):
latent_image = input_latent["samples"]
batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None
return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds)
class SamplerCustom:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"add_noise": ("BOOLEAN", {"default": True}),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"sampler": ("SAMPLER", ),
"sigmas": ("SIGMAS", ),
"latent_image": ("LATENT", ),
}
}
RETURN_TYPES = ("LATENT","LATENT")
RETURN_NAMES = ("output", "denoised_output")
FUNCTION = "sample"
CATEGORY = "sampling/custom_sampling"
def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image):
latent = latent_image
latent_image = latent["samples"]
latent = latent.copy()
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
latent["samples"] = latent_image
if not add_noise:
noise = Noise_EmptyNoise().generate_noise(latent)
else:
noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
x0_output = {}
callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
out = latent.copy()
out["samples"] = samples
if "x0" in x0_output:
out_denoised = latent.copy()
out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
else:
out_denoised = out
return (out, out_denoised)
class Guider_Basic(comfy.samplers.CFGGuider):
def set_conds(self, positive):
self.inner_set_conds({"positive": positive})
class BasicGuider:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"conditioning": ("CONDITIONING", ),
}
}
RETURN_TYPES = ("GUIDER",)
FUNCTION = "get_guider"
CATEGORY = "sampling/custom_sampling/guiders"
def get_guider(self, model, conditioning):
guider = Guider_Basic(model)
guider.set_conds(conditioning)
return (guider,)
class CFGGuider:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
}
}
RETURN_TYPES = ("GUIDER",)
FUNCTION = "get_guider"
CATEGORY = "sampling/custom_sampling/guiders"
def get_guider(self, model, positive, negative, cfg):
guider = comfy.samplers.CFGGuider(model)
guider.set_conds(positive, negative)
guider.set_cfg(cfg)
return (guider,)
class Guider_DualCFG(comfy.samplers.CFGGuider):
def set_cfg(self, cfg1, cfg2):
self.cfg1 = cfg1
self.cfg2 = cfg2
def set_conds(self, positive, middle, negative):
middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"})
self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative})
def predict_noise(self, x, timestep, model_options={}, seed=None):
negative_cond = self.conds.get("negative", None)
middle_cond = self.conds.get("middle", None)
out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options)
return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
class DualCFGGuider:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"cond1": ("CONDITIONING", ),
"cond2": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
"cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
}
}
RETURN_TYPES = ("GUIDER",)
FUNCTION = "get_guider"
CATEGORY = "sampling/custom_sampling/guiders"
def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative):
guider = Guider_DualCFG(model)
guider.set_conds(cond1, cond2, negative)
guider.set_cfg(cfg_conds, cfg_cond2_negative)
return (guider,)
class DisableNoise:
@classmethod
def INPUT_TYPES(s):
return {"required":{
}
}
RETURN_TYPES = ("NOISE",)
FUNCTION = "get_noise"
CATEGORY = "sampling/custom_sampling/noise"
def get_noise(self):
return (Noise_EmptyNoise(),)
class RandomNoise(DisableNoise):
@classmethod
def INPUT_TYPES(s):
return {"required":{
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
}
}
def get_noise(self, noise_seed):
return (Noise_RandomNoise(noise_seed),)
class SamplerCustomAdvanced:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise": ("NOISE", ),
"guider": ("GUIDER", ),
"sampler": ("SAMPLER", ),
"sigmas": ("SIGMAS", ),
"latent_image": ("LATENT", ),
}
}
RETURN_TYPES = ("LATENT","LATENT")
RETURN_NAMES = ("output", "denoised_output")
FUNCTION = "sample"
CATEGORY = "sampling/custom_sampling"
def sample(self, noise, guider, sampler, sigmas, latent_image):
latent = latent_image
latent_image = latent["samples"]
latent = latent.copy()
latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
latent["samples"] = latent_image
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
x0_output = {}
callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
samples = samples.to(comfy.model_management.intermediate_device())
out = latent.copy()
out["samples"] = samples
if "x0" in x0_output:
out_denoised = latent.copy()
out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
else:
out_denoised = out
return (out, out_denoised)
class AddNoise:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"noise": ("NOISE", ),
"sigmas": ("SIGMAS", ),
"latent_image": ("LATENT", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "add_noise"
CATEGORY = "_for_testing/custom_sampling/noise"
def add_noise(self, model, noise, sigmas, latent_image):
if len(sigmas) == 0:
return latent_image
latent = latent_image
latent_image = latent["samples"]
noisy = noise.generate_noise(latent)
model_sampling = model.get_model_object("model_sampling")
process_latent_out = model.get_model_object("process_latent_out")
process_latent_in = model.get_model_object("process_latent_in")
if len(sigmas) > 1:
scale = torch.abs(sigmas[0] - sigmas[-1])
else:
scale = sigmas[0]
if torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
latent_image = process_latent_in(latent_image)
noisy = model_sampling.noise_scaling(scale, noisy, latent_image)
noisy = process_latent_out(noisy)
noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0)
out = latent.copy()
out["samples"] = noisy
return (out,)
NODE_CLASS_MAPPINGS = {
"SamplerCustom": SamplerCustom,
"BasicScheduler": BasicScheduler,
"KarrasScheduler": KarrasScheduler,
"ExponentialScheduler": ExponentialScheduler,
"PolyexponentialScheduler": PolyexponentialScheduler,
"VPScheduler": VPScheduler,
"BetaSamplingScheduler": BetaSamplingScheduler,
"SDTurboScheduler": SDTurboScheduler,
"KSamplerSelect": KSamplerSelect,
"SamplerEulerAncestral": SamplerEulerAncestral,
"SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP,
"SamplerLMS": SamplerLMS,
"SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE,
"SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
"SamplerDPMPP_SDE": SamplerDPMPP_SDE,
"SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral,
"SamplerDPMAdaptative": SamplerDPMAdaptative,
"SplitSigmas": SplitSigmas,
"SplitSigmasDenoise": SplitSigmasDenoise,
"FlipSigmas": FlipSigmas,
"CFGGuider": CFGGuider,
"DualCFGGuider": DualCFGGuider,
"BasicGuider": BasicGuider,
"RandomNoise": RandomNoise,
"DisableNoise": DisableNoise,
"AddNoise": AddNoise,
"SamplerCustomAdvanced": SamplerCustomAdvanced,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++",
}

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comfy_extras/nodes_differential_diffusion.py Normal file
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# code adapted from https://github.com/exx8/differential-diffusion
import torch
class DifferentialDiffusion():
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL", ),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "apply"
CATEGORY = "_for_testing"
INIT = False
def apply(self, model):
model = model.clone()
model.set_model_denoise_mask_function(self.forward)
return (model,)
def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict):
model = extra_options["model"]
step_sigmas = extra_options["sigmas"]
sigma_to = model.inner_model.model_sampling.sigma_min
if step_sigmas[-1] > sigma_to:
sigma_to = step_sigmas[-1]
sigma_from = step_sigmas[0]
ts_from = model.inner_model.model_sampling.timestep(sigma_from)
ts_to = model.inner_model.model_sampling.timestep(sigma_to)
current_ts = model.inner_model.model_sampling.timestep(sigma[0])
threshold = (current_ts - ts_to) / (ts_from - ts_to)
return (denoise_mask >= threshold).to(denoise_mask.dtype)
NODE_CLASS_MAPPINGS = {
"DifferentialDiffusion": DifferentialDiffusion,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"DifferentialDiffusion": "Differential Diffusion",
}

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comfy_extras/nodes_flux.py Normal file
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import node_helpers
class CLIPTextEncodeFlux:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning/flux"
def encode(self, clip, clip_l, t5xxl, guidance):
tokens = clip.tokenize(clip_l)
tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
cond = output.pop("cond")
output["guidance"] = guidance
return ([[cond, output]], )
class FluxGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"conditioning": ("CONDITIONING", ),
"guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "append"
CATEGORY = "advanced/conditioning/flux"
def append(self, conditioning, guidance):
c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
return (c, )
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeFlux": CLIPTextEncodeFlux,
"FluxGuidance": FluxGuidance,
}

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comfy_extras/nodes_freelunch.py Normal file
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#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
import torch
import logging
def Fourier_filter(x, threshold, scale):
# FFT
x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))
B, C, H, W = x_freq.shape
mask = torch.ones((B, C, H, W), device=x.device)
crow, ccol = H // 2, W //2
mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
x_freq = x_freq * mask
# IFFT
x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real
return x_filtered.to(x.dtype)
class FreeU:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}),
"b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}),
"s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}),
"s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "model_patches/unet"
def patch(self, model, b1, b2, s1, s2):
model_channels = model.model.model_config.unet_config["model_channels"]
scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
on_cpu_devices = {}
def output_block_patch(h, hsp, transformer_options):
scale = scale_dict.get(int(h.shape[1]), None)
if scale is not None:
h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]
if hsp.device not in on_cpu_devices:
try:
hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
except:
logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
on_cpu_devices[hsp.device] = True
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
else:
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
return h, hsp
m = model.clone()
m.set_model_output_block_patch(output_block_patch)
return (m, )
class FreeU_V2:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}),
"b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}),
"s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}),
"s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "model_patches/unet"
def patch(self, model, b1, b2, s1, s2):
model_channels = model.model.model_config.unet_config["model_channels"]
scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
on_cpu_devices = {}
def output_block_patch(h, hsp, transformer_options):
scale = scale_dict.get(int(h.shape[1]), None)
if scale is not None:
hidden_mean = h.mean(1).unsqueeze(1)
B = hidden_mean.shape[0]
hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1)
if hsp.device not in on_cpu_devices:
try:
hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
except:
logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
on_cpu_devices[hsp.device] = True
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
else:
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
return h, hsp
m = model.clone()
m.set_model_output_block_patch(output_block_patch)
return (m, )
NODE_CLASS_MAPPINGS = {
"FreeU": FreeU,
"FreeU_V2": FreeU_V2,
}

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comfy_extras/nodes_gits.py Normal file
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# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main
import numpy as np
import torch
def loglinear_interp(t_steps, num_steps):
"""
Performs log-linear interpolation of a given array of decreasing numbers.
"""
xs = np.linspace(0, 1, len(t_steps))
ys = np.log(t_steps[::-1])
new_xs = np.linspace(0, 1, num_steps)
new_ys = np.interp(new_xs, xs, ys)
interped_ys = np.exp(new_ys)[::-1].copy()
return interped_ys
NOISE_LEVELS = {
0.80: [
[14.61464119, 7.49001646, 0.02916753],
[14.61464119, 11.54541874, 6.77309084, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
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[14.61464119, 2.45070267, 1.162866, 0.64427125, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.36964464, 0.803307, 0.50118381, 0.32104823, 0.19894916, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.41535246, 0.83188516, 0.54755926, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 0.95350921, 0.64427125, 0.45573691, 0.32104823, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 3.07277966, 1.61558151, 1.05362725, 0.74807048, 0.54755926, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 5.85520077, 2.45070267, 1.51179266, 1.01931262, 0.74807048, 0.57119018, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
1.40: [
[14.61464119, 0.59516323, 0.02916753],
[14.61464119, 0.95350921, 0.34370604, 0.02916753],
[14.61464119, 1.08895338, 0.43325692, 0.13792117, 0.02916753],
[14.61464119, 1.56271636, 0.64427125, 0.27464288, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.803307, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
[14.61464119, 2.05039096, 0.95350921, 0.54755926, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.24153244, 0.72133851, 0.43325692, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.52423614, 0.36617002, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.41535246, 0.86115354, 0.59516323, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.4783645, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.72133851, 0.54755926, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.43325692, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
1.45: [
[14.61464119, 0.59516323, 0.02916753],
[14.61464119, 0.803307, 0.25053367, 0.02916753],
[14.61464119, 0.95350921, 0.34370604, 0.09824532, 0.02916753],
[14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
[14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 1.91321158, 0.95350921, 0.57119018, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 2.19988537, 1.08895338, 0.64427125, 0.41087446, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
1.50: [
[14.61464119, 0.54755926, 0.02916753],
[14.61464119, 0.803307, 0.25053367, 0.02916753],
[14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
[14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
[14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
}
class GITSScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}),
"steps": ("INT", {"default": 10, "min": 2, "max": 1000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, coeff, steps, denoise):
total_steps = steps
if denoise < 1.0:
if denoise <= 0.0:
return (torch.FloatTensor([]),)
total_steps = round(steps * denoise)
if steps <= 20:
sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:]
else:
sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:]
sigmas = loglinear_interp(sigmas, steps + 1)
sigmas = sigmas[-(total_steps + 1):]
sigmas[-1] = 0
return (torch.FloatTensor(sigmas), )
NODE_CLASS_MAPPINGS = {
"GITSScheduler": GITSScheduler,
}

24
comfy_extras/nodes_hunyuan.py Normal file
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class CLIPTextEncodeHunyuanDiT:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"bert": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, bert, mt5xl):
tokens = clip.tokenize(bert)
tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]
output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
cond = output.pop("cond")
return ([[cond, output]], )
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
}

120
comfy_extras/nodes_hypernetwork.py Normal file
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import comfy.utils
import folder_paths
import torch
import logging
def load_hypernetwork_patch(path, strength):
sd = comfy.utils.load_torch_file(path, safe_load=True)
activation_func = sd.get('activation_func', 'linear')
is_layer_norm = sd.get('is_layer_norm', False)
use_dropout = sd.get('use_dropout', False)
activate_output = sd.get('activate_output', False)
last_layer_dropout = sd.get('last_layer_dropout', False)
valid_activation = {
"linear": torch.nn.Identity,
"relu": torch.nn.ReLU,
"leakyrelu": torch.nn.LeakyReLU,
"elu": torch.nn.ELU,
"swish": torch.nn.Hardswish,
"tanh": torch.nn.Tanh,
"sigmoid": torch.nn.Sigmoid,
"softsign": torch.nn.Softsign,
"mish": torch.nn.Mish,
}
if activation_func not in valid_activation:
logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {} {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout))
return None
out = {}
for d in sd:
try:
dim = int(d)
except:
continue
output = []
for index in [0, 1]:
attn_weights = sd[dim][index]
keys = attn_weights.keys()
linears = filter(lambda a: a.endswith(".weight"), keys)
linears = list(map(lambda a: a[:-len(".weight")], linears))
layers = []
i = 0
while i < len(linears):
lin_name = linears[i]
last_layer = (i == (len(linears) - 1))
penultimate_layer = (i == (len(linears) - 2))
lin_weight = attn_weights['{}.weight'.format(lin_name)]
lin_bias = attn_weights['{}.bias'.format(lin_name)]
layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0])
layer.load_state_dict({"weight": lin_weight, "bias": lin_bias})
layers.append(layer)
if activation_func != "linear":
if (not last_layer) or (activate_output):
layers.append(valid_activation[activation_func]())
if is_layer_norm:
i += 1
ln_name = linears[i]
ln_weight = attn_weights['{}.weight'.format(ln_name)]
ln_bias = attn_weights['{}.bias'.format(ln_name)]
ln = torch.nn.LayerNorm(ln_weight.shape[0])
ln.load_state_dict({"weight": ln_weight, "bias": ln_bias})
layers.append(ln)
if use_dropout:
if (not last_layer) and (not penultimate_layer or last_layer_dropout):
layers.append(torch.nn.Dropout(p=0.3))
i += 1
output.append(torch.nn.Sequential(*layers))
out[dim] = torch.nn.ModuleList(output)
class hypernetwork_patch:
def __init__(self, hypernet, strength):
self.hypernet = hypernet
self.strength = strength
def __call__(self, q, k, v, extra_options):
dim = k.shape[-1]
if dim in self.hypernet:
hn = self.hypernet[dim]
k = k + hn[0](k) * self.strength
v = v + hn[1](v) * self.strength
return q, k, v
def to(self, device):
for d in self.hypernet.keys():
self.hypernet[d] = self.hypernet[d].to(device)
return self
return hypernetwork_patch(out, strength)
class HypernetworkLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ),
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "load_hypernetwork"
CATEGORY = "loaders"
def load_hypernetwork(self, model, hypernetwork_name, strength):
hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name)
model_hypernetwork = model.clone()
patch = load_hypernetwork_patch(hypernetwork_path, strength)
if patch is not None:
model_hypernetwork.set_model_attn1_patch(patch)
model_hypernetwork.set_model_attn2_patch(patch)
return (model_hypernetwork,)
NODE_CLASS_MAPPINGS = {
"HypernetworkLoader": HypernetworkLoader
}

83
comfy_extras/nodes_hypertile.py Normal file
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@@ -0,0 +1,83 @@
#Taken from: https://github.com/tfernd/HyperTile/
import math
from einops import rearrange
# Use torch rng for consistency across generations
from torch import randint
def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
min_value = min(min_value, value)
# All big divisors of value (inclusive)
divisors = [i for i in range(min_value, value + 1) if value % i == 0]
ns = [value // i for i in divisors[:max_options]] # has at least 1 element
if len(ns) - 1 > 0:
idx = randint(low=0, high=len(ns) - 1, size=(1,)).item()
else:
idx = 0
return ns[idx]
class HyperTile:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}),
"swap_size": ("INT", {"default": 2, "min": 1, "max": 128}),
"max_depth": ("INT", {"default": 0, "min": 0, "max": 10}),
"scale_depth": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "model_patches/unet"
def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
model_channels = model.model.model_config.unet_config["model_channels"]
latent_tile_size = max(32, tile_size) // 8
self.temp = None
def hypertile_in(q, k, v, extra_options):
model_chans = q.shape[-2]
orig_shape = extra_options['original_shape']
apply_to = []
for i in range(max_depth + 1):
apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i)))
if model_chans in apply_to:
shape = extra_options["original_shape"]
aspect_ratio = shape[-1] / shape[-2]
hw = q.size(1)
h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1
nh = random_divisor(h, latent_tile_size * factor, swap_size)
nw = random_divisor(w, latent_tile_size * factor, swap_size)
if nh * nw > 1:
q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
self.temp = (nh, nw, h, w)
return q, k, v
return q, k, v
def hypertile_out(out, extra_options):
if self.temp is not None:
nh, nw, h, w = self.temp
self.temp = None
out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
return out
m = model.clone()
m.set_model_attn1_patch(hypertile_in)
m.set_model_attn1_output_patch(hypertile_out)
return (m, )
NODE_CLASS_MAPPINGS = {
"HyperTile": HyperTile,
}

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import nodes
import folder_paths
from comfy.cli_args import args
from PIL import Image
from PIL.PngImagePlugin import PngInfo
import numpy as np
import json
import os
MAX_RESOLUTION = nodes.MAX_RESOLUTION
class ImageCrop:
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",),
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "crop"
CATEGORY = "image/transform"
def crop(self, image, width, height, x, y):
x = min(x, image.shape[2] - 1)
y = min(y, image.shape[1] - 1)
to_x = width + x
to_y = height + y
img = image[:,y:to_y, x:to_x, :]
return (img,)
class RepeatImageBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",),
"amount": ("INT", {"default": 1, "min": 1, "max": 4096}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "repeat"
CATEGORY = "image/batch"
def repeat(self, image, amount):
s = image.repeat((amount, 1,1,1))
return (s,)
class ImageFromBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",),
"batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}),
"length": ("INT", {"default": 1, "min": 1, "max": 4096}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "frombatch"
CATEGORY = "image/batch"
def frombatch(self, image, batch_index, length):
s_in = image
batch_index = min(s_in.shape[0] - 1, batch_index)
length = min(s_in.shape[0] - batch_index, length)
s = s_in[batch_index:batch_index + length].clone()
return (s,)
class SaveAnimatedWEBP:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""
methods = {"default": 4, "fastest": 0, "slowest": 6}
@classmethod
def INPUT_TYPES(s):
return {"required":
{"images": ("IMAGE", ),
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
"fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
"lossless": ("BOOLEAN", {"default": True}),
"quality": ("INT", {"default": 80, "min": 0, "max": 100}),
"method": (list(s.methods.keys()),),
# "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
FUNCTION = "save_images"
OUTPUT_NODE = True
CATEGORY = "image/animation"
def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None):
method = self.methods.get(method)
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
results = list()
pil_images = []
for image in images:
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
pil_images.append(img)
metadata = pil_images[0].getexif()
if not args.disable_metadata:
if prompt is not None:
metadata[0x0110] = "prompt:{}".format(json.dumps(prompt))
if extra_pnginfo is not None:
inital_exif = 0x010f
for x in extra_pnginfo:
metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x]))
inital_exif -= 1
if num_frames == 0:
num_frames = len(pil_images)
c = len(pil_images)
for i in range(0, c, num_frames):
file = f"{filename}_{counter:05}_.webp"
pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method)
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})
counter += 1
animated = num_frames != 1
return { "ui": { "images": results, "animated": (animated,) } }
class SaveAnimatedPNG:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""
@classmethod
def INPUT_TYPES(s):
return {"required":
{"images": ("IMAGE", ),
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
"fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
"compress_level": ("INT", {"default": 4, "min": 0, "max": 9})
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
FUNCTION = "save_images"
OUTPUT_NODE = True
CATEGORY = "image/animation"
def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
results = list()
pil_images = []
for image in images:
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
pil_images.append(img)
metadata = None
if not args.disable_metadata:
metadata = PngInfo()
if prompt is not None:
metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True)
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True)
file = f"{filename}_{counter:05}_.png"
pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:])
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})
return { "ui": { "images": results, "animated": (True,)} }
NODE_CLASS_MAPPINGS = {
"ImageCrop": ImageCrop,
"RepeatImageBatch": RepeatImageBatch,
"ImageFromBatch": ImageFromBatch,
"SaveAnimatedWEBP": SaveAnimatedWEBP,
"SaveAnimatedPNG": SaveAnimatedPNG,
}

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import torch
class InstructPixToPixConditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": {"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"vae": ("VAE", ),
"pixels": ("IMAGE", ),
}}
RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/instructpix2pix"
def encode(self, positive, negative, pixels, vae):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
concat_latent = vae.encode(pixels)
out_latent = {}
out_latent["samples"] = torch.zeros_like(concat_latent)
out = []
for conditioning in [positive, negative]:
c = []
for t in conditioning:
d = t[1].copy()
d["concat_latent_image"] = concat_latent
n = [t[0], d]
c.append(n)
out.append(c)
return (out[0], out[1], out_latent)
NODE_CLASS_MAPPINGS = {
"InstructPixToPixConditioning": InstructPixToPixConditioning,
}

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import comfy.utils
import torch
def reshape_latent_to(target_shape, latent):
if latent.shape[1:] != target_shape[1:]:
latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
class LatentAdd:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples1, samples2):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
samples_out["samples"] = s1 + s2
return (samples_out,)
class LatentSubtract:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples1, samples2):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
samples_out["samples"] = s1 - s2
return (samples_out,)
class LatentMultiply:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",),
"multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples, multiplier):
samples_out = samples.copy()
s1 = samples["samples"]
samples_out["samples"] = s1 * multiplier
return (samples_out,)
class LatentInterpolate:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",),
"samples2": ("LATENT",),
"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples1, samples2, ratio):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
m1 = torch.linalg.vector_norm(s1, dim=(1))
m2 = torch.linalg.vector_norm(s2, dim=(1))
s1 = torch.nan_to_num(s1 / m1)
s2 = torch.nan_to_num(s2 / m2)
t = (s1 * ratio + s2 * (1.0 - ratio))
mt = torch.linalg.vector_norm(t, dim=(1))
st = torch.nan_to_num(t / mt)
samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
return (samples_out,)
class LatentBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "batch"
CATEGORY = "latent/batch"
def batch(self, samples1, samples2):
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
if s1.shape[1:] != s2.shape[1:]:
s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
s = torch.cat((s1, s2), dim=0)
samples_out["samples"] = s
samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
return (samples_out,)
class LatentBatchSeedBehavior:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",),
"seed_behavior": (["random", "fixed"],{"default": "fixed"}),}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "op"
CATEGORY = "latent/advanced"
def op(self, samples, seed_behavior):
samples_out = samples.copy()
latent = samples["samples"]
if seed_behavior == "random":
if 'batch_index' in samples_out:
samples_out.pop('batch_index')
elif seed_behavior == "fixed":
batch_number = samples_out.get("batch_index", [0])[0]
samples_out["batch_index"] = [batch_number] * latent.shape[0]
return (samples_out,)
NODE_CLASS_MAPPINGS = {
"LatentAdd": LatentAdd,
"LatentSubtract": LatentSubtract,
"LatentMultiply": LatentMultiply,
"LatentInterpolate": LatentInterpolate,
"LatentBatch": LatentBatch,
"LatentBatchSeedBehavior": LatentBatchSeedBehavior,
}

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import numpy as np
import scipy.ndimage
import torch
import comfy.utils
from nodes import MAX_RESOLUTION
def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
source = source.to(destination.device)
if resize_source:
source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
left, top = (x // multiplier, y // multiplier)
right, bottom = (left + source.shape[3], top + source.shape[2],)
if mask is None:
mask = torch.ones_like(source)
else:
mask = mask.to(destination.device, copy=True)
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
# calculate the bounds of the source that will be overlapping the destination
# this prevents the source trying to overwrite latent pixels that are out of bounds
# of the destination
visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
mask = mask[:, :, :visible_height, :visible_width]
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[:, :, :visible_height, :visible_width]
destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
destination[:, :, top:bottom, left:right] = source_portion + destination_portion
return destination
class LatentCompositeMasked:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"destination": ("LATENT",),
"source": ("LATENT",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"resize_source": ("BOOLEAN", {"default": False}),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "composite"
CATEGORY = "latent"
def composite(self, destination, source, x, y, resize_source, mask = None):
output = destination.copy()
destination = destination["samples"].clone()
source = source["samples"]
output["samples"] = composite(destination, source, x, y, mask, 8, resize_source)
return (output,)
class ImageCompositeMasked:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"destination": ("IMAGE",),
"source": ("IMAGE",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"resize_source": ("BOOLEAN", {"default": False}),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "composite"
CATEGORY = "image"
def composite(self, destination, source, x, y, resize_source, mask = None):
destination = destination.clone().movedim(-1, 1)
output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
return (output,)
class MaskToImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "mask_to_image"
def mask_to_image(self, mask):
result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
return (result,)
class ImageToMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"channel": (["red", "green", "blue", "alpha"],),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "image_to_mask"
def image_to_mask(self, image, channel):
channels = ["red", "green", "blue", "alpha"]
mask = image[:, :, :, channels.index(channel)]
return (mask,)
class ImageColorToMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "image_to_mask"
def image_to_mask(self, image, color):
temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]
mask = torch.where(temp == color, 255, 0).float()
return (mask,)
class SolidMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "solid"
def solid(self, value, width, height):
out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu")
return (out,)
class InvertMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "invert"
def invert(self, mask):
out = 1.0 - mask
return (out,)
class CropMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "crop"
def crop(self, mask, x, y, width, height):
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
out = mask[:, y:y + height, x:x + width]
return (out,)
class MaskComposite:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"destination": ("MASK",),
"source": ("MASK",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"operation": (["multiply", "add", "subtract", "and", "or", "xor"],),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "combine"
def combine(self, destination, source, x, y, operation):
output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
source = source.reshape((-1, source.shape[-2], source.shape[-1]))
left, top = (x, y,)
right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
visible_width, visible_height = (right - left, bottom - top,)
source_portion = source[:, :visible_height, :visible_width]
destination_portion = destination[:, top:bottom, left:right]
if operation == "multiply":
output[:, top:bottom, left:right] = destination_portion * source_portion
elif operation == "add":
output[:, top:bottom, left:right] = destination_portion + source_portion
elif operation == "subtract":
output[:, top:bottom, left:right] = destination_portion - source_portion
elif operation == "and":
output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
elif operation == "or":
output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()
elif operation == "xor":
output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()
output = torch.clamp(output, 0.0, 1.0)
return (output,)
class FeatherMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "feather"
def feather(self, mask, left, top, right, bottom):
output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()
left = min(left, output.shape[-1])
right = min(right, output.shape[-1])
top = min(top, output.shape[-2])
bottom = min(bottom, output.shape[-2])
for x in range(left):
feather_rate = (x + 1.0) / left
output[:, :, x] *= feather_rate
for x in range(right):
feather_rate = (x + 1) / right
output[:, :, -x] *= feather_rate
for y in range(top):
feather_rate = (y + 1) / top
output[:, y, :] *= feather_rate
for y in range(bottom):
feather_rate = (y + 1) / bottom
output[:, -y, :] *= feather_rate
return (output,)
class GrowMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
"tapered_corners": ("BOOLEAN", {"default": True}),
},
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "expand_mask"
def expand_mask(self, mask, expand, tapered_corners):
c = 0 if tapered_corners else 1
kernel = np.array([[c, 1, c],
[1, 1, 1],
[c, 1, c]])
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
out = []
for m in mask:
output = m.numpy()
for _ in range(abs(expand)):
if expand < 0:
output = scipy.ndimage.grey_erosion(output, footprint=kernel)
else:
output = scipy.ndimage.grey_dilation(output, footprint=kernel)
output = torch.from_numpy(output)
out.append(output)
return (torch.stack(out, dim=0),)
class ThresholdMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "image_to_mask"
def image_to_mask(self, mask, value):
mask = (mask > value).float()
return (mask,)
NODE_CLASS_MAPPINGS = {
"LatentCompositeMasked": LatentCompositeMasked,
"ImageCompositeMasked": ImageCompositeMasked,
"MaskToImage": MaskToImage,
"ImageToMask": ImageToMask,
"ImageColorToMask": ImageColorToMask,
"SolidMask": SolidMask,
"InvertMask": InvertMask,
"CropMask": CropMask,
"MaskComposite": MaskComposite,
"FeatherMask": FeatherMask,
"GrowMask": GrowMask,
"ThresholdMask": ThresholdMask,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ImageToMask": "Convert Image to Mask",
"MaskToImage": "Convert Mask to Image",
}

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import folder_paths
import comfy.sd
import comfy.model_sampling
import comfy.latent_formats
import torch
class LCM(comfy.model_sampling.EPS):
def calculate_denoised(self, sigma, model_output, model_input):
timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
x0 = model_input - model_output * sigma
sigma_data = 0.5
scaled_timestep = timestep * 10.0 #timestep_scaling
c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
return c_out * x0 + c_skip * model_input
class X0(comfy.model_sampling.EPS):
def calculate_denoised(self, sigma, model_output, model_input):
return model_output
class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
original_timesteps = 50
def __init__(self, model_config=None):
super().__init__(model_config)
self.skip_steps = self.num_timesteps // self.original_timesteps
sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
for x in range(self.original_timesteps):
sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps]
self.set_sigmas(sigmas_valid)
def timestep(self, sigma):
log_sigma = sigma.log()
dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device)
def sigma(self, timestep):
t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1))
low_idx = t.floor().long()
high_idx = t.ceil().long()
w = t.frac()
log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
return log_sigma.exp().to(timestep.device)
def rescale_zero_terminal_snr_sigmas(sigmas):
alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
alphas_bar_sqrt = alphas_cumprod.sqrt()
# Store old values.
alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
# Shift so the last timestep is zero.
alphas_bar_sqrt -= (alphas_bar_sqrt_T)
# Scale so the first timestep is back to the old value.
alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
# Convert alphas_bar_sqrt to betas
alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
alphas_bar[-1] = 4.8973451890853435e-08
return ((1 - alphas_bar) / alphas_bar) ** 0.5
class ModelSamplingDiscrete:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["eps", "v_prediction", "lcm", "x0"],),
"zsnr": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, sampling, zsnr):
m = model.clone()
sampling_base = comfy.model_sampling.ModelSamplingDiscrete
if sampling == "eps":
sampling_type = comfy.model_sampling.EPS
elif sampling == "v_prediction":
sampling_type = comfy.model_sampling.V_PREDICTION
elif sampling == "lcm":
sampling_type = LCM
sampling_base = ModelSamplingDiscreteDistilled
elif sampling == "x0":
sampling_type = X0
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
if zsnr:
model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class ModelSamplingStableCascade:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, shift):
m = model.clone()
sampling_base = comfy.model_sampling.StableCascadeSampling
sampling_type = comfy.model_sampling.EPS
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class ModelSamplingSD3:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, shift, multiplier=1000):
m = model.clone()
sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
sampling_type = comfy.model_sampling.CONST
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift=shift, multiplier=multiplier)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class ModelSamplingAuraFlow(ModelSamplingSD3):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"shift": ("FLOAT", {"default": 1.73, "min": 0.0, "max": 100.0, "step":0.01}),
}}
FUNCTION = "patch_aura"
def patch_aura(self, model, shift):
return self.patch(model, shift, multiplier=1.0)
class ModelSamplingContinuousEDM:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
"sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, sampling, sigma_max, sigma_min):
m = model.clone()
latent_format = None
sigma_data = 1.0
if sampling == "eps":
sampling_type = comfy.model_sampling.EPS
elif sampling == "v_prediction":
sampling_type = comfy.model_sampling.V_PREDICTION
elif sampling == "edm_playground_v2.5":
sampling_type = comfy.model_sampling.EDM
sigma_data = 0.5
latent_format = comfy.latent_formats.SDXL_Playground_2_5()
class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
m.add_object_patch("model_sampling", model_sampling)
if latent_format is not None:
m.add_object_patch("latent_format", latent_format)
return (m, )
class ModelSamplingContinuousV:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["v_prediction"],),
"sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, sampling, sigma_max, sigma_min):
m = model.clone()
latent_format = None
sigma_data = 1.0
if sampling == "v_prediction":
sampling_type = comfy.model_sampling.V_PREDICTION
class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class RescaleCFG:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, multiplier):
def rescale_cfg(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
sigma = args["sigma"]
sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
x_orig = args["input"]
#rescale cfg has to be done on v-pred model output
x = x_orig / (sigma * sigma + 1.0)
cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
#rescalecfg
x_cfg = uncond + cond_scale * (cond - uncond)
ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
x_rescaled = x_cfg * (ro_pos / ro_cfg)
x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)
m = model.clone()
m.set_model_sampler_cfg_function(rescale_cfg)
return (m, )
NODE_CLASS_MAPPINGS = {
"ModelSamplingDiscrete": ModelSamplingDiscrete,
"ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
"ModelSamplingContinuousV": ModelSamplingContinuousV,
"ModelSamplingStableCascade": ModelSamplingStableCascade,
"ModelSamplingSD3": ModelSamplingSD3,
"ModelSamplingAuraFlow": ModelSamplingAuraFlow,
"RescaleCFG": RescaleCFG,
}

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import torch
import comfy.utils
class PatchModelAddDownscale:
upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"]
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}),
"downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}),
"downscale_after_skip": ("BOOLEAN", {"default": True}),
"downscale_method": (s.upscale_methods,),
"upscale_method": (s.upscale_methods,),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
model_sampling = model.get_model_object("model_sampling")
sigma_start = model_sampling.percent_to_sigma(start_percent)
sigma_end = model_sampling.percent_to_sigma(end_percent)
def input_block_patch(h, transformer_options):
if transformer_options["block"][1] == block_number:
sigma = transformer_options["sigmas"][0].item()
if sigma <= sigma_start and sigma >= sigma_end:
h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled")
return h
def output_block_patch(h, hsp, transformer_options):
if h.shape[2] != hsp.shape[2]:
h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled")
return h, hsp
m = model.clone()
if downscale_after_skip:
m.set_model_input_block_patch_after_skip(input_block_patch)
else:
m.set_model_input_block_patch(input_block_patch)
m.set_model_output_block_patch(output_block_patch)
return (m, )
NODE_CLASS_MAPPINGS = {
"PatchModelAddDownscale": PatchModelAddDownscale,
}
NODE_DISPLAY_NAME_MAPPINGS = {
# Sampling
"PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)",
}

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import comfy.sd
import comfy.utils
import comfy.model_base
import comfy.model_management
import comfy.model_sampling
import torch
import folder_paths
import json
import os
from comfy.cli_args import args
class ModelMergeSimple:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model1": ("MODEL",),
"model2": ("MODEL",),
"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, model1, model2, ratio):
m = model1.clone()
kp = model2.get_key_patches("diffusion_model.")
for k in kp:
m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
return (m, )
class ModelSubtract:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model1": ("MODEL",),
"model2": ("MODEL",),
"multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, model1, model2, multiplier):
m = model1.clone()
kp = model2.get_key_patches("diffusion_model.")
for k in kp:
m.add_patches({k: kp[k]}, - multiplier, multiplier)
return (m, )
class ModelAdd:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model1": ("MODEL",),
"model2": ("MODEL",),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, model1, model2):
m = model1.clone()
kp = model2.get_key_patches("diffusion_model.")
for k in kp:
m.add_patches({k: kp[k]}, 1.0, 1.0)
return (m, )
class CLIPMergeSimple:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip1": ("CLIP",),
"clip2": ("CLIP",),
"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, clip1, clip2, ratio):
m = clip1.clone()
kp = clip2.get_key_patches()
for k in kp:
if k.endswith(".position_ids") or k.endswith(".logit_scale"):
continue
m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
return (m, )
class CLIPSubtract:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip1": ("CLIP",),
"clip2": ("CLIP",),
"multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, clip1, clip2, multiplier):
m = clip1.clone()
kp = clip2.get_key_patches()
for k in kp:
if k.endswith(".position_ids") or k.endswith(".logit_scale"):
continue
m.add_patches({k: kp[k]}, - multiplier, multiplier)
return (m, )
class CLIPAdd:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip1": ("CLIP",),
"clip2": ("CLIP",),
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, clip1, clip2):
m = clip1.clone()
kp = clip2.get_key_patches()
for k in kp:
if k.endswith(".position_ids") or k.endswith(".logit_scale"):
continue
m.add_patches({k: kp[k]}, 1.0, 1.0)
return (m, )
class ModelMergeBlocks:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model1": ("MODEL",),
"model2": ("MODEL",),
"input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "merge"
CATEGORY = "advanced/model_merging"
def merge(self, model1, model2, **kwargs):
m = model1.clone()
kp = model2.get_key_patches("diffusion_model.")
default_ratio = next(iter(kwargs.values()))
for k in kp:
ratio = default_ratio
k_unet = k[len("diffusion_model."):]
last_arg_size = 0
for arg in kwargs:
if k_unet.startswith(arg) and last_arg_size < len(arg):
ratio = kwargs[arg]
last_arg_size = len(arg)
m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
return (m, )
def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None):
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir)
prompt_info = ""
if prompt is not None:
prompt_info = json.dumps(prompt)
metadata = {}
enable_modelspec = True
if isinstance(model.model, comfy.model_base.SDXL):
if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix):
metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit"
else:
metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
elif isinstance(model.model, comfy.model_base.SDXLRefiner):
metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
elif isinstance(model.model, comfy.model_base.SVD_img2vid):
metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1"
elif isinstance(model.model, comfy.model_base.SD3):
metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants
else:
enable_modelspec = False
if enable_modelspec:
metadata["modelspec.sai_model_spec"] = "1.0.0"
metadata["modelspec.implementation"] = "sgm"
metadata["modelspec.title"] = "{} {}".format(filename, counter)
#TODO:
# "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512",
# "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
# "v2-inpainting"
extra_keys = {}
model_sampling = model.get_model_object("model_sampling")
if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM):
if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION):
extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float()
extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float()
if model.model.model_type == comfy.model_base.ModelType.EPS:
metadata["modelspec.predict_key"] = "epsilon"
elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION:
metadata["modelspec.predict_key"] = "v"
if not args.disable_metadata:
metadata["prompt"] = prompt_info
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata[x] = json.dumps(extra_pnginfo[x])
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys)
class CheckpointSave:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip": ("CLIP",),
"vae": ("VAE",),
"filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
RETURN_TYPES = ()
FUNCTION = "save"
OUTPUT_NODE = True
CATEGORY = "advanced/model_merging"
def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
return {}
class CLIPSave:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip": ("CLIP",),
"filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
RETURN_TYPES = ()
FUNCTION = "save"
OUTPUT_NODE = True
CATEGORY = "advanced/model_merging"
def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None):
prompt_info = ""
if prompt is not None:
prompt_info = json.dumps(prompt)
metadata = {}
if not args.disable_metadata:
metadata["prompt"] = prompt_info
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata[x] = json.dumps(extra_pnginfo[x])
comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True)
clip_sd = clip.get_sd()
for prefix in ["clip_l.", "clip_g.", ""]:
k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys()))
current_clip_sd = {}
for x in k:
current_clip_sd[x] = clip_sd.pop(x)
if len(current_clip_sd) == 0:
continue
p = prefix[:-1]
replace_prefix = {}
filename_prefix_ = filename_prefix
if len(p) > 0:
filename_prefix_ = "{}_{}".format(filename_prefix_, p)
replace_prefix[prefix] = ""
replace_prefix["transformer."] = ""
full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir)
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)
comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata)
return {}
class VAESave:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
@classmethod
def INPUT_TYPES(s):
return {"required": { "vae": ("VAE",),
"filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
RETURN_TYPES = ()
FUNCTION = "save"
OUTPUT_NODE = True
CATEGORY = "advanced/model_merging"
def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
prompt_info = ""
if prompt is not None:
prompt_info = json.dumps(prompt)
metadata = {}
if not args.disable_metadata:
metadata["prompt"] = prompt_info
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata[x] = json.dumps(extra_pnginfo[x])
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata)
return {}
NODE_CLASS_MAPPINGS = {
"ModelMergeSimple": ModelMergeSimple,
"ModelMergeBlocks": ModelMergeBlocks,
"ModelMergeSubtract": ModelSubtract,
"ModelMergeAdd": ModelAdd,
"CheckpointSave": CheckpointSave,
"CLIPMergeSimple": CLIPMergeSimple,
"CLIPMergeSubtract": CLIPSubtract,
"CLIPMergeAdd": CLIPAdd,
"CLIPSave": CLIPSave,
"VAESave": VAESave,
}

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comfy_extras/nodes_model_merging_model_specific.py Normal file
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import comfy_extras.nodes_model_merging
class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
CATEGORY = "advanced/model_merging/model_specific"
@classmethod
def INPUT_TYPES(s):
arg_dict = { "model1": ("MODEL",),
"model2": ("MODEL",)}
argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
arg_dict["time_embed."] = argument
arg_dict["label_emb."] = argument
for i in range(12):
arg_dict["input_blocks.{}.".format(i)] = argument
for i in range(3):
arg_dict["middle_block.{}.".format(i)] = argument
for i in range(12):
arg_dict["output_blocks.{}.".format(i)] = argument
arg_dict["out."] = argument
return {"required": arg_dict}
class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
CATEGORY = "advanced/model_merging/model_specific"
@classmethod
def INPUT_TYPES(s):
arg_dict = { "model1": ("MODEL",),
"model2": ("MODEL",)}
argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
arg_dict["time_embed."] = argument
arg_dict["label_emb."] = argument
for i in range(9):
arg_dict["input_blocks.{}".format(i)] = argument
for i in range(3):
arg_dict["middle_block.{}".format(i)] = argument
for i in range(9):
arg_dict["output_blocks.{}".format(i)] = argument
arg_dict["out."] = argument
return {"required": arg_dict}
class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
CATEGORY = "advanced/model_merging/model_specific"
@classmethod
def INPUT_TYPES(s):
arg_dict = { "model1": ("MODEL",),
"model2": ("MODEL",)}
argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
arg_dict["pos_embed."] = argument
arg_dict["x_embedder."] = argument
arg_dict["context_embedder."] = argument
arg_dict["y_embedder."] = argument
arg_dict["t_embedder."] = argument
for i in range(24):
arg_dict["joint_blocks.{}.".format(i)] = argument
arg_dict["final_layer."] = argument
return {"required": arg_dict}
NODE_CLASS_MAPPINGS = {
"ModelMergeSD1": ModelMergeSD1,
"ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
"ModelMergeSDXL": ModelMergeSDXL,
"ModelMergeSD3_2B": ModelMergeSD3_2B,
}

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import torch
import comfy.model_management
from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat
class Morphology:
@classmethod
def INPUT_TYPES(s):
return {"required": {"image": ("IMAGE",),
"operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],),
"kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "process"
CATEGORY = "image/postprocessing"
def process(self, image, operation, kernel_size):
device = comfy.model_management.get_torch_device()
kernel = torch.ones(kernel_size, kernel_size, device=device)
image_k = image.to(device).movedim(-1, 1)
if operation == "erode":
output = erosion(image_k, kernel)
elif operation == "dilate":
output = dilation(image_k, kernel)
elif operation == "open":
output = opening(image_k, kernel)
elif operation == "close":
output = closing(image_k, kernel)
elif operation == "gradient":
output = gradient(image_k, kernel)
elif operation == "top_hat":
output = top_hat(image_k, kernel)
elif operation == "bottom_hat":
output = bottom_hat(image_k, kernel)
else:
raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'")
img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1)
return (img_out,)
NODE_CLASS_MAPPINGS = {
"Morphology": Morphology,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Morphology": "ImageMorphology",
}

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comfy_extras/nodes_pag.py Normal file
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#Modified/simplified version of the node from: https://github.com/pamparamm/sd-perturbed-attention
#If you want the one with more options see the above repo.
#My modified one here is more basic but has less chances of breaking with ComfyUI updates.
import comfy.model_patcher
import comfy.samplers
class PerturbedAttentionGuidance:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.01}),
}
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "model_patches/unet"
def patch(self, model, scale):
unet_block = "middle"
unet_block_id = 0
m = model.clone()
def perturbed_attention(q, k, v, extra_options, mask=None):
return v
def post_cfg_function(args):
model = args["model"]
cond_pred = args["cond_denoised"]
cond = args["cond"]
cfg_result = args["denoised"]
sigma = args["sigma"]
model_options = args["model_options"].copy()
x = args["input"]
if scale == 0:
return cfg_result
# Replace Self-attention with PAG
model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, perturbed_attention, "attn1", unet_block, unet_block_id)
(pag,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
return cfg_result + (cond_pred - pag) * scale
m.set_model_sampler_post_cfg_function(post_cfg_function)
return (m,)
NODE_CLASS_MAPPINGS = {
"PerturbedAttentionGuidance": PerturbedAttentionGuidance,
}

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import torch
import comfy.model_management
import comfy.sampler_helpers
import comfy.samplers
import comfy.utils
import node_helpers
def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale):
pos = noise_pred_pos - noise_pred_nocond
neg = noise_pred_neg - noise_pred_nocond
perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos
perp_neg = perp * neg_scale
cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)
return cfg_result
#TODO: This node should be removed, it has been replaced with PerpNegGuider
class PerpNeg:
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL", ),
"empty_conditioning": ("CONDITIONING", ),
"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
def patch(self, model, empty_conditioning, neg_scale):
m = model.clone()
nocond = comfy.sampler_helpers.convert_cond(empty_conditioning)
def cfg_function(args):
model = args["model"]
noise_pred_pos = args["cond_denoised"]
noise_pred_neg = args["uncond_denoised"]
cond_scale = args["cond_scale"]
x = args["input"]
sigma = args["sigma"]
model_options = args["model_options"]
nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative")
(noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options)
cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale)
return cfg_result
m.set_model_sampler_cfg_function(cfg_function)
return (m, )
class Guider_PerpNeg(comfy.samplers.CFGGuider):
def set_conds(self, positive, negative, empty_negative_prompt):
empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"})
self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative})
def set_cfg(self, cfg, neg_scale):
self.cfg = cfg
self.neg_scale = neg_scale
def predict_noise(self, x, timestep, model_options={}, seed=None):
# in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg
# but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly
positive_cond = self.conds.get("positive", None)
negative_cond = self.conds.get("negative", None)
empty_cond = self.conds.get("empty_negative_prompt", None)
(noise_pred_pos, noise_pred_neg, noise_pred_empty) = \
comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options)
cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg)
# normally this would be done in cfg_function, but we skipped
# that for efficiency: we can compute the noise predictions in
# a single call to calc_cond_batch() (rather than two)
# so we replicate the hook here
for fn in model_options.get("sampler_post_cfg_function", []):
args = {
"denoised": cfg_result,
"cond": positive_cond,
"uncond": negative_cond,
"model": self.inner_model,
"uncond_denoised": noise_pred_neg,
"cond_denoised": noise_pred_pos,
"sigma": timestep,
"model_options": model_options,
"input": x,
# not in the original call in samplers.py:cfg_function, but made available for future hooks
"empty_cond": empty_cond,
"empty_cond_denoised": noise_pred_empty,}
cfg_result = fn(args)
return cfg_result
class PerpNegGuider:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"empty_conditioning": ("CONDITIONING", ),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
"neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
}
}
RETURN_TYPES = ("GUIDER",)
FUNCTION = "get_guider"
CATEGORY = "_for_testing"
def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale):
guider = Guider_PerpNeg(model)
guider.set_conds(positive, negative, empty_conditioning)
guider.set_cfg(cfg, neg_scale)
return (guider,)
NODE_CLASS_MAPPINGS = {
"PerpNeg": PerpNeg,
"PerpNegGuider": PerpNegGuider,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)",
}

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import torch
import torch.nn as nn
import folder_paths
import comfy.clip_model
import comfy.clip_vision
import comfy.ops
# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0
VISION_CONFIG_DICT = {
"hidden_size": 1024,
"image_size": 224,
"intermediate_size": 4096,
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 24,
"patch_size": 14,
"projection_dim": 768,
"hidden_act": "quick_gelu",
}
class MLP(nn.Module):
def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops):
super().__init__()
if use_residual:
assert in_dim == out_dim
self.layernorm = operations.LayerNorm(in_dim)
self.fc1 = operations.Linear(in_dim, hidden_dim)
self.fc2 = operations.Linear(hidden_dim, out_dim)
self.use_residual = use_residual
self.act_fn = nn.GELU()
def forward(self, x):
residual = x
x = self.layernorm(x)
x = self.fc1(x)
x = self.act_fn(x)
x = self.fc2(x)
if self.use_residual:
x = x + residual
return x
class FuseModule(nn.Module):
def __init__(self, embed_dim, operations):
super().__init__()
self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations)
self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations)
self.layer_norm = operations.LayerNorm(embed_dim)
def fuse_fn(self, prompt_embeds, id_embeds):
stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)
stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds
stacked_id_embeds = self.mlp2(stacked_id_embeds)
stacked_id_embeds = self.layer_norm(stacked_id_embeds)
return stacked_id_embeds
def forward(
self,
prompt_embeds,
id_embeds,
class_tokens_mask,
) -> torch.Tensor:
# id_embeds shape: [b, max_num_inputs, 1, 2048]
id_embeds = id_embeds.to(prompt_embeds.dtype)
num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
batch_size, max_num_inputs = id_embeds.shape[:2]
# seq_length: 77
seq_length = prompt_embeds.shape[1]
# flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
flat_id_embeds = id_embeds.view(
-1, id_embeds.shape[-2], id_embeds.shape[-1]
)
# valid_id_mask [b*max_num_inputs]
valid_id_mask = (
torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
< num_inputs[:, None]
)
valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]
prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
class_tokens_mask = class_tokens_mask.view(-1)
valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
# slice out the image token embeddings
image_token_embeds = prompt_embeds[class_tokens_mask]
stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
return updated_prompt_embeds
class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection):
def __init__(self):
self.load_device = comfy.model_management.text_encoder_device()
offload_device = comfy.model_management.text_encoder_offload_device()
dtype = comfy.model_management.text_encoder_dtype(self.load_device)
super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast)
self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False)
self.fuse_module = FuseModule(2048, comfy.ops.manual_cast)
def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask):
b, num_inputs, c, h, w = id_pixel_values.shape
id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)
shared_id_embeds = self.vision_model(id_pixel_values)[2]
id_embeds = self.visual_projection(shared_id_embeds)
id_embeds_2 = self.visual_projection_2(shared_id_embeds)
id_embeds = id_embeds.view(b, num_inputs, 1, -1)
id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)
id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)
return updated_prompt_embeds
class PhotoMakerLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}}
RETURN_TYPES = ("PHOTOMAKER",)
FUNCTION = "load_photomaker_model"
CATEGORY = "_for_testing/photomaker"
def load_photomaker_model(self, photomaker_model_name):
photomaker_model_path = folder_paths.get_full_path("photomaker", photomaker_model_name)
photomaker_model = PhotoMakerIDEncoder()
data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True)
if "id_encoder" in data:
data = data["id_encoder"]
photomaker_model.load_state_dict(data)
return (photomaker_model,)
class PhotoMakerEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": { "photomaker": ("PHOTOMAKER",),
"image": ("IMAGE",),
"clip": ("CLIP", ),
"text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "apply_photomaker"
CATEGORY = "_for_testing/photomaker"
def apply_photomaker(self, photomaker, image, clip, text):
special_token = "photomaker"
pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()
try:
index = text.split(" ").index(special_token) + 1
except ValueError:
index = -1
tokens = clip.tokenize(text, return_word_ids=True)
out_tokens = {}
for k in tokens:
out_tokens[k] = []
for t in tokens[k]:
f = list(filter(lambda x: x[2] != index, t))
while len(f) < len(t):
f.append(t[-1])
out_tokens[k].append(f)
cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True)
if index > 0:
token_index = index - 1
num_id_images = 1
class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)]
out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device),
class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0))
else:
out = cond
return ([[out, {"pooled_output": pooled}]], )
NODE_CLASS_MAPPINGS = {
"PhotoMakerLoader": PhotoMakerLoader,
"PhotoMakerEncode": PhotoMakerEncode,
}

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comfy_extras/nodes_post_processing.py Normal file
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import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
import math
import comfy.utils
import comfy.model_management
class Blend:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image1": ("IMAGE",),
"image2": ("IMAGE",),
"blend_factor": ("FLOAT", {
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}),
"blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "blend_images"
CATEGORY = "image/postprocessing"
def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
image2 = image2.to(image1.device)
if image1.shape != image2.shape:
image2 = image2.permute(0, 3, 1, 2)
image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
image2 = image2.permute(0, 2, 3, 1)
blended_image = self.blend_mode(image1, image2, blend_mode)
blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
blended_image = torch.clamp(blended_image, 0, 1)
return (blended_image,)
def blend_mode(self, img1, img2, mode):
if mode == "normal":
return img2
elif mode == "multiply":
return img1 * img2
elif mode == "screen":
return 1 - (1 - img1) * (1 - img2)
elif mode == "overlay":
return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
elif mode == "soft_light":
return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
elif mode == "difference":
return img1 - img2
else:
raise ValueError(f"Unsupported blend mode: {mode}")
def g(self, x):
return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
def gaussian_kernel(kernel_size: int, sigma: float, device=None):
x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij")
d = torch.sqrt(x * x + y * y)
g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
return g / g.sum()
class Blur:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"blur_radius": ("INT", {
"default": 1,
"min": 1,
"max": 31,
"step": 1
}),
"sigma": ("FLOAT", {
"default": 1.0,
"min": 0.1,
"max": 10.0,
"step": 0.1
}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "blur"
CATEGORY = "image/postprocessing"
def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
if blur_radius == 0:
return (image,)
image = image.to(comfy.model_management.get_torch_device())
batch_size, height, width, channels = image.shape
kernel_size = blur_radius * 2 + 1
kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1)
image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect')
blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]
blurred = blurred.permute(0, 2, 3, 1)
return (blurred.to(comfy.model_management.intermediate_device()),)
class Quantize:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"colors": ("INT", {
"default": 256,
"min": 1,
"max": 256,
"step": 1
}),
"dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "quantize"
CATEGORY = "image/postprocessing"
def bayer(im, pal_im, order):
def normalized_bayer_matrix(n):
if n == 0:
return np.zeros((1,1), "float32")
else:
q = 4 ** n
m = q * normalized_bayer_matrix(n - 1)
return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q
num_colors = len(pal_im.getpalette()) // 3
spread = 2 * 256 / num_colors
bayer_n = int(math.log2(order))
bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5)
result = torch.from_numpy(np.array(im).astype(np.float32))
tw = math.ceil(result.shape[0] / bayer_matrix.shape[0])
th = math.ceil(result.shape[1] / bayer_matrix.shape[1])
tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1)
result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255)
result = result.to(dtype=torch.uint8)
im = Image.fromarray(result.cpu().numpy())
im = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
return im
def quantize(self, image: torch.Tensor, colors: int, dither: str):
batch_size, height, width, _ = image.shape
result = torch.zeros_like(image)
for b in range(batch_size):
im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB')
pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836
if dither == "none":
quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
elif dither == "floyd-steinberg":
quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG)
elif dither.startswith("bayer"):
order = int(dither.split('-')[-1])
quantized_image = Quantize.bayer(im, pal_im, order)
quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
result[b] = quantized_array
return (result,)
class Sharpen:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"sharpen_radius": ("INT", {
"default": 1,
"min": 1,
"max": 31,
"step": 1
}),
"sigma": ("FLOAT", {
"default": 1.0,
"min": 0.1,
"max": 10.0,
"step": 0.01
}),
"alpha": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 5.0,
"step": 0.01
}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "sharpen"
CATEGORY = "image/postprocessing"
def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float):
if sharpen_radius == 0:
return (image,)
batch_size, height, width, channels = image.shape
image = image.to(comfy.model_management.get_torch_device())
kernel_size = sharpen_radius * 2 + 1
kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10)
center = kernel_size // 2
kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
sharpened = sharpened.permute(0, 2, 3, 1)
result = torch.clamp(sharpened, 0, 1)
return (result.to(comfy.model_management.intermediate_device()),)
class ImageScaleToTotalPixels:
upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
crop_methods = ["disabled", "center"]
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
"megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image/upscaling"
def upscale(self, image, upscale_method, megapixels):
samples = image.movedim(-1,1)
total = int(megapixels * 1024 * 1024)
scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
width = round(samples.shape[3] * scale_by)
height = round(samples.shape[2] * scale_by)
s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
s = s.movedim(1,-1)
return (s,)
NODE_CLASS_MAPPINGS = {
"ImageBlend": Blend,
"ImageBlur": Blur,
"ImageQuantize": Quantize,
"ImageSharpen": Sharpen,
"ImageScaleToTotalPixels": ImageScaleToTotalPixels,
}

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import torch
class LatentRebatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "latents": ("LATENT",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
}}
RETURN_TYPES = ("LATENT",)
INPUT_IS_LIST = True
OUTPUT_IS_LIST = (True, )
FUNCTION = "rebatch"
CATEGORY = "latent/batch"
@staticmethod
def get_batch(latents, list_ind, offset):
'''prepare a batch out of the list of latents'''
samples = latents[list_ind]['samples']
shape = samples.shape
mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu')
if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]:
torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear")
if mask.shape[0] < samples.shape[0]:
mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]]
if 'batch_index' in latents[list_ind]:
batch_inds = latents[list_ind]['batch_index']
else:
batch_inds = [x+offset for x in range(shape[0])]
return samples, mask, batch_inds
@staticmethod
def get_slices(indexable, num, batch_size):
'''divides an indexable object into num slices of length batch_size, and a remainder'''
slices = []
for i in range(num):
slices.append(indexable[i*batch_size:(i+1)*batch_size])
if num * batch_size < len(indexable):
return slices, indexable[num * batch_size:]
else:
return slices, None
@staticmethod
def slice_batch(batch, num, batch_size):
result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch]
return list(zip(*result))
@staticmethod
def cat_batch(batch1, batch2):
if batch1[0] is None:
return batch2
result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)]
return result
def rebatch(self, latents, batch_size):
batch_size = batch_size[0]
output_list = []
current_batch = (None, None, None)
processed = 0
for i in range(len(latents)):
# fetch new entry of list
#samples, masks, indices = self.get_batch(latents, i)
next_batch = self.get_batch(latents, i, processed)
processed += len(next_batch[2])
# set to current if current is None
if current_batch[0] is None:
current_batch = next_batch
# add previous to list if dimensions do not match
elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]:
sliced, _ = self.slice_batch(current_batch, 1, batch_size)
output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
current_batch = next_batch
# cat if everything checks out
else:
current_batch = self.cat_batch(current_batch, next_batch)
# add to list if dimensions gone above target batch size
if current_batch[0].shape[0] > batch_size:
num = current_batch[0].shape[0] // batch_size
sliced, remainder = self.slice_batch(current_batch, num, batch_size)
for i in range(num):
output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})
current_batch = remainder
#add remainder
if current_batch[0] is not None:
sliced, _ = self.slice_batch(current_batch, 1, batch_size)
output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
#get rid of empty masks
for s in output_list:
if s['noise_mask'].mean() == 1.0:
del s['noise_mask']
return (output_list,)
class ImageRebatch:
@classmethod
def INPUT_TYPES(s):
return {"required": { "images": ("IMAGE",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
}}
RETURN_TYPES = ("IMAGE",)
INPUT_IS_LIST = True
OUTPUT_IS_LIST = (True, )
FUNCTION = "rebatch"
CATEGORY = "image/batch"
def rebatch(self, images, batch_size):
batch_size = batch_size[0]
output_list = []
all_images = []
for img in images:
for i in range(img.shape[0]):
all_images.append(img[i:i+1])
for i in range(0, len(all_images), batch_size):
output_list.append(torch.cat(all_images[i:i+batch_size], dim=0))
return (output_list,)
NODE_CLASS_MAPPINGS = {
"RebatchLatents": LatentRebatch,
"RebatchImages": ImageRebatch,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"RebatchLatents": "Rebatch Latents",
"RebatchImages": "Rebatch Images",
}

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import torch
from torch import einsum
import torch.nn.functional as F
import math
from einops import rearrange, repeat
from comfy.ldm.modules.attention import optimized_attention
import comfy.samplers
# from comfy/ldm/modules/attention.py
# but modified to return attention scores as well as output
def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None):
b, _, dim_head = q.shape
dim_head //= heads
scale = dim_head ** -0.5
h = heads
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, -1, heads, dim_head)
.permute(0, 2, 1, 3)
.reshape(b * heads, -1, dim_head)
.contiguous(),
(q, k, v),
)
# force cast to fp32 to avoid overflowing
if attn_precision == torch.float32:
sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * scale
del q, k
if mask is not None:
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
sim = sim.softmax(dim=-1)
out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
out = (
out.unsqueeze(0)
.reshape(b, heads, -1, dim_head)
.permute(0, 2, 1, 3)
.reshape(b, -1, heads * dim_head)
)
return (out, sim)
def create_blur_map(x0, attn, sigma=3.0, threshold=1.0):
# reshape and GAP the attention map
_, hw1, hw2 = attn.shape
b, _, lh, lw = x0.shape
attn = attn.reshape(b, -1, hw1, hw2)
# Global Average Pool
mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length()
mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
# Reshape
mask = (
mask.reshape(b, *mid_shape)
.unsqueeze(1)
.type(attn.dtype)
)
# Upsample
mask = F.interpolate(mask, (lh, lw))
blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma)
blurred = blurred * mask + x0 * (1 - mask)
return blurred
def gaussian_blur_2d(img, kernel_size, sigma):
ksize_half = (kernel_size - 1) * 0.5
x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
pdf = torch.exp(-0.5 * (x / sigma).pow(2))
x_kernel = pdf / pdf.sum()
x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
img = F.pad(img, padding, mode="reflect")
img = F.conv2d(img, kernel2d, groups=img.shape[-3])
return img
class SelfAttentionGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.01}),
"blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
def patch(self, model, scale, blur_sigma):
m = model.clone()
attn_scores = None
# TODO: make this work properly with chunked batches
# currently, we can only save the attn from one UNet call
def attn_and_record(q, k, v, extra_options):
nonlocal attn_scores
# if uncond, save the attention scores
heads = extra_options["n_heads"]
cond_or_uncond = extra_options["cond_or_uncond"]
b = q.shape[0] // len(cond_or_uncond)
if 1 in cond_or_uncond:
uncond_index = cond_or_uncond.index(1)
# do the entire attention operation, but save the attention scores to attn_scores
(out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"])
# when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn]
n_slices = heads * b
attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)]
return out
else:
return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"])
def post_cfg_function(args):
nonlocal attn_scores
uncond_attn = attn_scores
sag_scale = scale
sag_sigma = blur_sigma
sag_threshold = 1.0
model = args["model"]
uncond_pred = args["uncond_denoised"]
uncond = args["uncond"]
cfg_result = args["denoised"]
sigma = args["sigma"]
model_options = args["model_options"]
x = args["input"]
if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding
return cfg_result
# create the adversarially blurred image
degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold)
degraded_noised = degraded + x - uncond_pred
# call into the UNet
(sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options)
return cfg_result + (degraded - sag) * sag_scale
m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True)
# from diffusers:
# unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch
m.set_model_attn1_replace(attn_and_record, "middle", 0, 0)
return (m, )
NODE_CLASS_MAPPINGS = {
"SelfAttentionGuidance": SelfAttentionGuidance,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SelfAttentionGuidance": "Self-Attention Guidance",
}

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

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import torch
import comfy.utils
class SD_4XUpscale_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "images": ("IMAGE",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/upscale_diffusion"
def encode(self, images, positive, negative, scale_ratio, noise_augmentation):
width = max(1, round(images.shape[-2] * scale_ratio))
height = max(1, round(images.shape[-3] * scale_ratio))
pixels = comfy.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center")
out_cp = []
out_cn = []
for t in positive:
n = [t[0], t[1].copy()]
n[1]['concat_image'] = pixels
n[1]['noise_augmentation'] = noise_augmentation
out_cp.append(n)
for t in negative:
n = [t[0], t[1].copy()]
n[1]['concat_image'] = pixels
n[1]['noise_augmentation'] = noise_augmentation
out_cn.append(n)
latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
return (out_cp, out_cn, {"samples":latent})
NODE_CLASS_MAPPINGS = {
"SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning,
}

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import torch
import nodes
import comfy.utils
def camera_embeddings(elevation, azimuth):
elevation = torch.as_tensor([elevation])
azimuth = torch.as_tensor([azimuth])
embeddings = torch.stack(
[
torch.deg2rad(
(90 - elevation) - (90)
), # Zero123 polar is 90-elevation
torch.sin(torch.deg2rad(azimuth)),
torch.cos(torch.deg2rad(azimuth)),
torch.deg2rad(
90 - torch.full_like(elevation, 0)
),
], dim=-1).unsqueeze(1)
return embeddings
class StableZero123_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
t = vae.encode(encode_pixels)
cam_embeds = camera_embeddings(elevation, azimuth)
cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1)
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
class StableZero123_Conditioning_Batched:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
t = vae.encode(encode_pixels)
cam_embeds = []
for i in range(batch_size):
cam_embeds.append(camera_embeddings(elevation, azimuth))
elevation += elevation_batch_increment
azimuth += azimuth_batch_increment
cam_embeds = torch.cat(cam_embeds, dim=0)
cond = torch.cat([comfy.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1)
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
class SV3D_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
t = vae.encode(encode_pixels)
azimuth = 0
azimuth_increment = 360 / (max(video_frames, 2) - 1)
elevations = []
azimuths = []
for i in range(video_frames):
elevations.append(elevation)
azimuths.append(azimuth)
azimuth += azimuth_increment
positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
latent = torch.zeros([video_frames, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
NODE_CLASS_MAPPINGS = {
"StableZero123_Conditioning": StableZero123_Conditioning,
"StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
"SV3D_Conditioning": SV3D_Conditioning,
}

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"""
This file is part of ComfyUI.
Copyright (C) 2024 Stability AI
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
import torch
import nodes
import comfy.utils
class StableCascade_EmptyLatentImage:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
"compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})
}}
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("stage_c", "stage_b")
FUNCTION = "generate"
CATEGORY = "latent/stable_cascade"
def generate(self, width, height, compression, batch_size=1):
c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
return ({
"samples": c_latent,
}, {
"samples": b_latent,
})
class StableCascade_StageC_VAEEncode:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"vae": ("VAE", ),
"compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
}}
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("stage_c", "stage_b")
FUNCTION = "generate"
CATEGORY = "latent/stable_cascade"
def generate(self, image, vae, compression):
width = image.shape[-2]
height = image.shape[-3]
out_width = (width // compression) * vae.downscale_ratio
out_height = (height // compression) * vae.downscale_ratio
s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1)
c_latent = vae.encode(s[:,:,:,:3])
b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2])
return ({
"samples": c_latent,
}, {
"samples": b_latent,
})
class StableCascade_StageB_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "conditioning": ("CONDITIONING",),
"stage_c": ("LATENT",),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "set_prior"
CATEGORY = "conditioning/stable_cascade"
def set_prior(self, conditioning, stage_c):
c = []
for t in conditioning:
d = t[1].copy()
d['stable_cascade_prior'] = stage_c['samples']
n = [t[0], d]
c.append(n)
return (c, )
class StableCascade_SuperResolutionControlnet:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"vae": ("VAE", ),
}}
RETURN_TYPES = ("IMAGE", "LATENT", "LATENT")
RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
FUNCTION = "generate"
CATEGORY = "_for_testing/stable_cascade"
def generate(self, image, vae):
width = image.shape[-2]
height = image.shape[-3]
batch_size = image.shape[0]
controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)
c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
return (controlnet_input, {
"samples": c_latent,
}, {
"samples": b_latent,
})
NODE_CLASS_MAPPINGS = {
"StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage,
"StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning,
"StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode,
"StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet,
}

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#Taken from: https://github.com/dbolya/tomesd
import torch
from typing import Tuple, Callable
import math
def do_nothing(x: torch.Tensor, mode:str=None):
return x
def mps_gather_workaround(input, dim, index):
if input.shape[-1] == 1:
return torch.gather(
input.unsqueeze(-1),
dim - 1 if dim < 0 else dim,
index.unsqueeze(-1)
).squeeze(-1)
else:
return torch.gather(input, dim, index)
def bipartite_soft_matching_random2d(metric: torch.Tensor,
w: int, h: int, sx: int, sy: int, r: int,
no_rand: bool = False) -> Tuple[Callable, Callable]:
"""
Partitions the tokens into src and dst and merges r tokens from src to dst.
Dst tokens are partitioned by choosing one randomy in each (sx, sy) region.
Args:
- metric [B, N, C]: metric to use for similarity
- w: image width in tokens
- h: image height in tokens
- sx: stride in the x dimension for dst, must divide w
- sy: stride in the y dimension for dst, must divide h
- r: number of tokens to remove (by merging)
- no_rand: if true, disable randomness (use top left corner only)
"""
B, N, _ = metric.shape
if r <= 0 or w == 1 or h == 1:
return do_nothing, do_nothing
gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather
with torch.no_grad():
hsy, wsx = h // sy, w // sx
# For each sy by sx kernel, randomly assign one token to be dst and the rest src
if no_rand:
rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64)
else:
rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device)
# The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead
idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64)
idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype))
idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx)
# Image is not divisible by sx or sy so we need to move it into a new buffer
if (hsy * sy) < h or (wsx * sx) < w:
idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64)
idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view
else:
idx_buffer = idx_buffer_view
# We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices
rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1)
# We're finished with these
del idx_buffer, idx_buffer_view
# rand_idx is currently dst|src, so split them
num_dst = hsy * wsx
a_idx = rand_idx[:, num_dst:, :] # src
b_idx = rand_idx[:, :num_dst, :] # dst
def split(x):
C = x.shape[-1]
src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C))
dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C))
return src, dst
# Cosine similarity between A and B
metric = metric / metric.norm(dim=-1, keepdim=True)
a, b = split(metric)
scores = a @ b.transpose(-1, -2)
# Can't reduce more than the # tokens in src
r = min(a.shape[1], r)
# Find the most similar greedily
node_max, node_idx = scores.max(dim=-1)
edge_idx = node_max.argsort(dim=-1, descending=True)[..., None]
unm_idx = edge_idx[..., r:, :] # Unmerged Tokens
src_idx = edge_idx[..., :r, :] # Merged Tokens
dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx)
def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
src, dst = split(x)
n, t1, c = src.shape
unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c))
src = gather(src, dim=-2, index=src_idx.expand(n, r, c))
dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)
return torch.cat([unm, dst], dim=1)
def unmerge(x: torch.Tensor) -> torch.Tensor:
unm_len = unm_idx.shape[1]
unm, dst = x[..., :unm_len, :], x[..., unm_len:, :]
_, _, c = unm.shape
src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c))
# Combine back to the original shape
out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype)
out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst)
out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm)
out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src)
return out
return merge, unmerge
def get_functions(x, ratio, original_shape):
b, c, original_h, original_w = original_shape
original_tokens = original_h * original_w
downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1])))
stride_x = 2
stride_y = 2
max_downsample = 1
if downsample <= max_downsample:
w = int(math.ceil(original_w / downsample))
h = int(math.ceil(original_h / downsample))
r = int(x.shape[1] * ratio)
no_rand = False
m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand)
return m, u
nothing = lambda y: y
return nothing, nothing
class TomePatchModel:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "_for_testing"
def patch(self, model, ratio):
self.u = None
def tomesd_m(q, k, v, extra_options):
#NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q
#however from my basic testing it seems that using q instead gives better results
m, self.u = get_functions(q, ratio, extra_options["original_shape"])
return m(q), k, v
def tomesd_u(n, extra_options):
return self.u(n)
m = model.clone()
m.set_model_attn1_patch(tomesd_m)
m.set_model_attn1_output_patch(tomesd_u)
return (m, )
NODE_CLASS_MAPPINGS = {
"TomePatchModel": TomePatchModel,
}

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import os
import logging
from spandrel import ModelLoader, ImageModelDescriptor
from comfy import model_management
import torch
import comfy.utils
import folder_paths
try:
from spandrel_extra_arches import EXTRA_REGISTRY
from spandrel import MAIN_REGISTRY
MAIN_REGISTRY.add(*EXTRA_REGISTRY)
logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.")
except:
pass
class UpscaleModelLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ),
}}
RETURN_TYPES = ("UPSCALE_MODEL",)
FUNCTION = "load_model"
CATEGORY = "loaders"
def load_model(self, model_name):
model_path = folder_paths.get_full_path("upscale_models", model_name)
sd = comfy.utils.load_torch_file(model_path, safe_load=True)
if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""})
out = ModelLoader().load_from_state_dict(sd).eval()
if not isinstance(out, ImageModelDescriptor):
raise Exception("Upscale model must be a single-image model.")
return (out, )
class ImageUpscaleWithModel:
@classmethod
def INPUT_TYPES(s):
return {"required": { "upscale_model": ("UPSCALE_MODEL",),
"image": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image/upscaling"
def upscale(self, upscale_model, image):
device = model_management.get_torch_device()
memory_required = model_management.module_size(upscale_model.model)
memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate
memory_required += image.nelement() * image.element_size()
model_management.free_memory(memory_required, device)
upscale_model.to(device)
in_img = image.movedim(-1,-3).to(device)
tile = 512
overlap = 32
oom = True
while oom:
try:
steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
pbar = comfy.utils.ProgressBar(steps)
s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
oom = False
except model_management.OOM_EXCEPTION as e:
tile //= 2
if tile < 128:
raise e
upscale_model.to("cpu")
s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
return (s,)
NODE_CLASS_MAPPINGS = {
"UpscaleModelLoader": UpscaleModelLoader,
"ImageUpscaleWithModel": ImageUpscaleWithModel
}

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import nodes
import torch
import comfy.utils
import comfy.sd
import folder_paths
import comfy_extras.nodes_model_merging
class ImageOnlyCheckpointLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
}}
RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE")
FUNCTION = "load_checkpoint"
CATEGORY = "loaders/video_models"
def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return (out[0], out[3], out[2])
class SVD_img2vid_Conditioning:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
"motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
"fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
"augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
if augmentation_level > 0:
encode_pixels += torch.randn_like(pixels) * augmentation_level
t = vae.encode(encode_pixels)
positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([video_frames, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
class VideoLinearCFGGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "sampling/video_models"
def patch(self, model, min_cfg):
def linear_cfg(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1))
return uncond + scale * (cond - uncond)
m = model.clone()
m.set_model_sampler_cfg_function(linear_cfg)
return (m, )
class VideoTriangleCFGGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "sampling/video_models"
def patch(self, model, min_cfg):
def linear_cfg(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
period = 1.0
values = torch.linspace(0, 1, cond.shape[0], device=cond.device)
values = 2 * (values / period - torch.floor(values / period + 0.5)).abs()
scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1))
return uncond + scale * (cond - uncond)
m = model.clone()
m.set_model_sampler_cfg_function(linear_cfg)
return (m, )
class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
CATEGORY = "_for_testing"
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip_vision": ("CLIP_VISION",),
"vae": ("VAE",),
"filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None):
comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
return {}
NODE_CLASS_MAPPINGS = {
"ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader,
"SVD_img2vid_Conditioning": SVD_img2vid_Conditioning,
"VideoLinearCFGGuidance": VideoLinearCFGGuidance,
"VideoTriangleCFGGuidance": VideoTriangleCFGGuidance,
"ImageOnlyCheckpointSave": ImageOnlyCheckpointSave,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)",
}

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comfy_extras/nodes_webcam.py Normal file
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import nodes
import folder_paths
MAX_RESOLUTION = nodes.MAX_RESOLUTION
class WebcamCapture(nodes.LoadImage):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("WEBCAM", {}),
"width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"capture_on_queue": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "load_capture"
CATEGORY = "image"
def load_capture(s, image, **kwargs):
return super().load_image(folder_paths.get_annotated_filepath(image))
NODE_CLASS_MAPPINGS = {
"WebcamCapture": WebcamCapture,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"WebcamCapture": "Webcam Capture",
}