@classmethod

def INPUT_TYPES(s):

return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}}

RETURN_TYPES = ("CONDITIONING",)

FUNCTION = "encode"

CATEGORY = "conditioning"

def encode(self, clip, text):

@classmethod

def INPUT_TYPES(s):

return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}

RETURN_TYPES = ("CONDITIONING",)

FUNCTION = "combine"

CATEGORY = "conditioning"

def combine(self, conditioning_1, conditioning_2):

@classmethod

def INPUT_TYPES(s):

return {"required": {"conditioning": ("CONDITIONING", ),

"width": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),

"height": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),

"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),

"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),

"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),

}}

RETURN_TYPES = ("CONDITIONING",)

FUNCTION = "append"

CATEGORY = "conditioning"

def append(self, conditioning, width, height, x, y, strength, min_sigma=0.0, max_sigma=99.0):

c = []

for t in conditioning:

n = [t[0], t[1].copy()]

n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)

n[1]['strength'] = strength

n[1]['min_sigma'] = min_sigma

n[1]['max_sigma'] = max_sigma

c.append(n)

def __init__(self, device="cpu"):

self.device = device

@classmethod

def INPUT_TYPES(s):

return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "decode"

CATEGORY = "latent"

def decode(self, vae, samples):

def __init__(self, device="cpu"):

self.device = device

@classmethod

def INPUT_TYPES(s):

return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "decode"

CATEGORY = "_for_testing"

def decode(self, vae, samples):

def __init__(self, device="cpu"):

self.device = device

@classmethod

def INPUT_TYPES(s):

return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "encode"

CATEGORY = "latent"

def encode(self, vae, pixels):

x = (pixels.shape[1] // 64) * 64

y = (pixels.shape[2] // 64) * 64

if pixels.shape[1] != x or pixels.shape[2] != y:

pixels = pixels[:,:x,:y,:]

t = vae.encode(pixels[:,:,:,:3])

def __init__(self, device="cpu"):

self.device = device

@classmethod

def INPUT_TYPES(s):

return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "encode"

CATEGORY = "_for_testing"

def encode(self, vae, pixels):

x = (pixels.shape[1] // 64) * 64

y = (pixels.shape[2] // 64) * 64

if pixels.shape[1] != x or pixels.shape[2] != y:

pixels = pixels[:,:x,:y,:]

t = vae.encode_tiled(pixels[:,:,:,:3])

def __init__(self, device="cpu"):

self.device = device

@classmethod

def INPUT_TYPES(s):

return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", )}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "encode"

CATEGORY = "latent/inpaint"

def encode(self, vae, pixels, mask):

x = (pixels.shape[1] // 64) * 64

y = (pixels.shape[2] // 64) * 64

mask = torch.nn.functional.interpolate(mask[None,None,], size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")[0][0]

pixels = pixels.clone()

if pixels.shape[1] != x or pixels.shape[2] != y:

pixels = pixels[:,:x,:y,:]

mask = mask[:x,:y]

#grow mask by a few pixels to keep things seamless in latent space

kernel_tensor = torch.ones((1, 1, 6, 6))

mask_erosion = torch.clamp(torch.nn.functional.conv2d((mask.round())[None], kernel_tensor, padding=3), 0, 1)

m = (1.0 - mask.round())

for i in range(3):

pixels[:,:,:,i] -= 0.5

pixels[:,:,:,i] *= m

pixels[:,:,:,i] += 0.5

t = vae.encode(pixels)

@classmethod

def INPUT_TYPES(s):

return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ),

"ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}}

RETURN_TYPES = ("MODEL", "CLIP", "VAE")

FUNCTION = "load_checkpoint"

CATEGORY = "loaders"

def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):

config_path = folder_paths.get_full_path("configs", config_name)

ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)

@classmethod

def INPUT_TYPES(s):

return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),

}}

RETURN_TYPES = ("MODEL", "CLIP", "VAE")

FUNCTION = "load_checkpoint"

CATEGORY = "loaders"

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=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))

@classmethod

def INPUT_TYPES(s):

return {"required": { "clip": ("CLIP", ),

"stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),

}}

RETURN_TYPES = ("CLIP",)

FUNCTION = "set_last_layer"

CATEGORY = "conditioning"

def set_last_layer(self, clip, stop_at_clip_layer):

clip = clip.clone()

clip.clip_layer(stop_at_clip_layer)

@classmethod

def INPUT_TYPES(s):

return {"required": { "model": ("MODEL",),

"clip": ("CLIP", ),

"lora_name": (folder_paths.get_filename_list("loras"), ),

"strength_model": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),

"strength_clip": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),

}}

RETURN_TYPES = ("MODEL", "CLIP")

FUNCTION = "load_lora"

CATEGORY = "loaders"

def load_lora(self, model, clip, lora_name, strength_model, strength_clip):

lora_path = folder_paths.get_full_path("loras", lora_name)

model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip)

@classmethod

def INPUT_TYPES(s):

return {"required": { "vae_name": (folder_paths.get_filename_list("vae"), )}}

RETURN_TYPES = ("VAE",)

FUNCTION = "load_vae"

CATEGORY = "loaders"

#TODO: scale factor?

def load_vae(self, vae_name):

vae_path = folder_paths.get_full_path("vae", vae_name)

vae = comfy.sd.VAE(ckpt_path=vae_path)

@classmethod

def INPUT_TYPES(s):

return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}

RETURN_TYPES = ("CONTROL_NET",)

FUNCTION = "load_controlnet"

CATEGORY = "loaders"

def load_controlnet(self, control_net_name):

controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)

controlnet = comfy.sd.load_controlnet(controlnet_path)

@classmethod

def INPUT_TYPES(s):

return {"required": { "model": ("MODEL",),

"control_net_name": (folder_paths.get_filename_list("controlnet"), )}}

RETURN_TYPES = ("CONTROL_NET",)

FUNCTION = "load_controlnet"

CATEGORY = "loaders"

def load_controlnet(self, model, control_net_name):

controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)

controlnet = comfy.sd.load_controlnet(controlnet_path, model)

@classmethod

def INPUT_TYPES(s):

return {"required": {"conditioning": ("CONDITIONING", ),

"control_net": ("CONTROL_NET", ),

"image": ("IMAGE", ),

"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})

}}

RETURN_TYPES = ("CONDITIONING",)

FUNCTION = "apply_controlnet"

CATEGORY = "conditioning"

def apply_controlnet(self, conditioning, control_net, image, strength):

c = []

control_hint = image.movedim(-1,1)

print(control_hint.shape)

for t in conditioning:

n = [t[0], t[1].copy()]

c_net = control_net.copy().set_cond_hint(control_hint, strength)

if 'control' in t[1]:

c_net.set_previous_controlnet(t[1]['control'])

n[1]['control'] = c_net

c.append(n)

@classmethod

def INPUT_TYPES(s):

return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),

}}

RETURN_TYPES = ("CLIP",)

FUNCTION = "load_clip"

CATEGORY = "loaders"

def load_clip(self, clip_name):

clip_path = folder_paths.get_full_path("clip", clip_name)

clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=folder_paths.get_folder_paths("embeddings"))

@classmethod

def INPUT_TYPES(s):

return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ),

}}

RETURN_TYPES = ("CLIP_VISION",)

FUNCTION = "load_clip"

CATEGORY = "loaders"

def load_clip(self, clip_name):

clip_path = folder_paths.get_full_path("clip_vision", clip_name)

clip_vision = comfy_extras.clip_vision.load(clip_path)

@classmethod

def INPUT_TYPES(s):

return {"required": { "clip_vision": ("CLIP_VISION",),

"image": ("IMAGE",)

}}

RETURN_TYPES = ("CLIP_VISION_OUTPUT",)

FUNCTION = "encode"

CATEGORY = "conditioning/style_model"

def encode(self, clip_vision, image):

output = clip_vision.encode_image(image)

@classmethod

def INPUT_TYPES(s):

return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}}

RETURN_TYPES = ("STYLE_MODEL",)

FUNCTION = "load_style_model"

CATEGORY = "loaders"

def load_style_model(self, style_model_name):

style_model_path = folder_paths.get_full_path("style_models", style_model_name)

style_model = comfy.sd.load_style_model(style_model_path)

@classmethod

def INPUT_TYPES(s):

return {"required": {"conditioning": ("CONDITIONING", ),

"style_model": ("STYLE_MODEL", ),

"clip_vision_output": ("CLIP_VISION_OUTPUT", ),

}}

RETURN_TYPES = ("CONDITIONING",)

FUNCTION = "apply_stylemodel"

CATEGORY = "conditioning/style_model"

def apply_stylemodel(self, clip_vision_output, style_model, conditioning):

cond = style_model.get_cond(clip_vision_output)

c = []

for t in conditioning:

n = [torch.cat((t[0], cond), dim=1), t[1].copy()]

c.append(n)

def __init__(self, device="cpu"):

self.device = device

@classmethod

def INPUT_TYPES(s):

return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),

"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),

"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "generate"

CATEGORY = "latent"

def generate(self, width, height, batch_size=1):

latent = torch.zeros([batch_size, 4, height // 8, width // 8])

upscale_methods = ["nearest-exact", "bilinear", "area"]

crop_methods = ["disabled", "center"]

@classmethod

def INPUT_TYPES(s):

return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),

"width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),

"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),

"crop": (s.crop_methods,)}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "upscale"

CATEGORY = "latent"

def upscale(self, samples, upscale_method, width, height, crop):

s = samples.copy()

s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)

@classmethod

def INPUT_TYPES(s):

return {"required": { "samples": ("LATENT",),

"rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],),

}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "rotate"

CATEGORY = "latent/transform"

def rotate(self, samples, rotation):

s = samples.copy()

rotate_by = 0

if rotation.startswith("90"):

rotate_by = 1

elif rotation.startswith("180"):

rotate_by = 2

elif rotation.startswith("270"):

rotate_by = 3

s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2])

@classmethod

def INPUT_TYPES(s):

return {"required": { "samples": ("LATENT",),

"flip_method": (["x-axis: vertically", "y-axis: horizontally"],),

}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "flip"

CATEGORY = "latent/transform"

def flip(self, samples, flip_method):

s = samples.copy()

if flip_method.startswith("x"):

s["samples"] = torch.flip(samples["samples"], dims=[2])

elif flip_method.startswith("y"):

s["samples"] = torch.flip(samples["samples"], dims=[3])

@classmethod

def INPUT_TYPES(s):

return {"required": { "samples_to": ("LATENT",),

"samples_from": ("LATENT",),

"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),

"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),

"feather": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),

}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "composite"

CATEGORY = "latent"

def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0):

x = x // 8

y = y // 8

feather = feather // 8

samples_out = samples_to.copy()

s = samples_to["samples"].clone()

samples_to = samples_to["samples"]

samples_from = samples_from["samples"]

if feather == 0:

s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]

else:

samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]

mask = torch.ones_like(samples_from)

for t in range(feather):

if y != 0:

mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))

if y + samples_from.shape[2] < samples_to.shape[2]:

mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))

if x != 0:

mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))

if x + samples_from.shape[3] < samples_to.shape[3]:

mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))

rev_mask = torch.ones_like(mask) - mask

s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask

samples_out["samples"] = s

@classmethod

def INPUT_TYPES(s):

return {"required": { "samples": ("LATENT",),

"width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),

"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),

"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),

"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}),

}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "crop"

CATEGORY = "latent/transform"

def crop(self, samples, width, height, x, y):

s = samples.copy()

samples = samples['samples']

x = x // 8

y = y // 8

#enfonce minimum size of 64

if x > (samples.shape[3] - 8):

x = samples.shape[3] - 8

if y > (samples.shape[2] - 8):

y = samples.shape[2] - 8

new_height = height // 8

new_width = width // 8

to_x = new_width + x

to_y = new_height + y

def enforce_image_dim(d, to_d, max_d):

if to_d > max_d:

leftover = (to_d - max_d) % 8

to_d = max_d

d -= leftover

return (d, to_d)

#make sure size is always multiple of 64

x, to_x = enforce_image_dim(x, to_x, samples.shape[3])

y, to_y = enforce_image_dim(y, to_y, samples.shape[2])

s['samples'] = samples[:,:,y:to_y, x:to_x]

@classmethod

def INPUT_TYPES(s):

return {"required": { "samples": ("LATENT",),

"mask": ("MASK",),

}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "set_mask"

CATEGORY = "latent/inpaint"

def set_mask(self, samples, mask):

s = samples.copy()

s["noise_mask"] = mask

latent_image = latent["samples"]

noise_mask = None

device = model_management.get_torch_device()

if disable_noise:

noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")

else:

noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu")

if "noise_mask" in latent:

noise_mask = latent['noise_mask']

noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")

noise_mask = noise_mask.round()

noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)

noise_mask = torch.cat([noise_mask] * noise.shape[0])

noise_mask = noise_mask.to(device)

real_model = None

model_management.load_model_gpu(model)

real_model = model.model

noise = noise.to(device)

latent_image = latent_image.to(device)

positive_copy = []

negative_copy = []

control_nets = []

for p in positive:

t = p[0]

if t.shape[0] < noise.shape[0]:

t = torch.cat([t] * noise.shape[0])

t = t.to(device)

if 'control' in p[1]:

control_nets += [p[1]['control']]

positive_copy += [[t] + p[1:]]

for n in negative:

t = n[0]

if t.shape[0] < noise.shape[0]:

t = torch.cat([t] * noise.shape[0])

t = t.to(device)

if 'control' in n[1]:

control_nets += [n[1]['control']]

negative_copy += [[t] + n[1:]]

control_net_models = []

for x in control_nets:

control_net_models += x.get_control_models()

model_management.load_controlnet_gpu(control_net_models)

if sampler_name in comfy.samplers.KSampler.SAMPLERS:

sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)

else:

#other samplers

pass

samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask)

samples = samples.cpu()

for c in control_nets:

c.cleanup()

out = latent.copy()

out["samples"] = samples

@classmethod

def INPUT_TYPES(s):

return {"required":

{"model": ("MODEL",),

"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),

"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),

"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),

"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),

"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),

"positive": ("CONDITIONING", ),

"negative": ("CONDITIONING", ),

"latent_image": ("LATENT", ),

"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),

}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "sample"

CATEGORY = "sampling"

def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):

@classmethod

def INPUT_TYPES(s):

return {"required":

{"model": ("MODEL",),

"add_noise": (["enable", "disable"], ),

"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),

"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),

"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),

"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),

"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),

"positive": ("CONDITIONING", ),

"negative": ("CONDITIONING", ),

"latent_image": ("LATENT", ),

"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),

"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),

"return_with_leftover_noise": (["disable", "enable"], ),

}}

RETURN_TYPES = ("LATENT",)

FUNCTION = "sample"

CATEGORY = "sampling"

def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):

force_full_denoise = True

if return_with_leftover_noise == "enable":

force_full_denoise = False

disable_noise = False

if add_noise == "disable":

disable_noise = True

def __init__(self):

self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")

self.type = "output"

@classmethod

def INPUT_TYPES(s):

return {"required":

{"images": ("IMAGE", ),

"filename_prefix": ("STRING", {"default": "ComfyUI"})},

"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},

}

RETURN_TYPES = ()

FUNCTION = "save_images"

OUTPUT_NODE = True

CATEGORY = "image"

def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):

def map_filename(filename):

prefix_len = len(os.path.basename(filename_prefix))

prefix = filename[:prefix_len + 1]

try:

digits = int(filename[prefix_len + 1:].split('_')[0])

except:

digits = 0

return (digits, prefix)

subfolder = os.path.dirname(os.path.normpath(filename_prefix))

filename = os.path.basename(os.path.normpath(filename_prefix))

full_output_folder = os.path.join(self.output_dir, subfolder)

if os.path.commonpath((self.output_dir, os.path.realpath(full_output_folder))) != self.output_dir:

print("Saving image outside the output folder is not allowed.")

return {}

try:

counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1

except ValueError:

counter = 1

except FileNotFoundError:

os.makedirs(full_output_folder, exist_ok=True)

counter = 1

if not os.path.exists(self.output_dir):

os.makedirs(self.output_dir)

results = list()

for image in images:

i = 255. * image.cpu().numpy()

img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))

metadata = PngInfo()

if prompt is not None:

metadata.add_text("prompt", json.dumps(prompt))

if extra_pnginfo is not None:

for x in extra_pnginfo:

metadata.add_text(x, json.dumps(extra_pnginfo[x]))

file = f"{filename}_{counter:05}_.png"

img.save(os.path.join(full_output_folder, file), pnginfo=metadata, optimize=True)

results.append({

"filename": file,

"subfolder": subfolder,

"type": self.type

});

counter += 1

def __init__(self):

self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")

self.type = "temp"

@classmethod

def INPUT_TYPES(s):

return {"required":

{"images": ("IMAGE", ), },

"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},

input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")

@classmethod

def INPUT_TYPES(s):

if not os.path.exists(s.input_dir):

os.makedirs(s.input_dir)

return {"required":

{"image": (sorted(os.listdir(s.input_dir)), )},

}

CATEGORY = "image"

RETURN_TYPES = ("IMAGE", "MASK")

FUNCTION = "load_image"

def load_image(self, image):

image_path = os.path.join(self.input_dir, image)

i = Image.open(image_path)

image = i.convert("RGB")

image = np.array(image).astype(np.float32) / 255.0

image = torch.from_numpy(image)[None,]

if 'A' in i.getbands():

mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0

mask = 1. - torch.from_numpy(mask)

else:

mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")

return (image, mask)

@classmethod

def IS_CHANGED(s, image):

image_path = os.path.join(s.input_dir, image)

m = hashlib.sha256()

with open(image_path, 'rb') as f:

m.update(f.read())

input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")

@classmethod

def INPUT_TYPES(s):

return {"required":

{"image": (sorted(os.listdir(s.input_dir)), ),

"channel": (["alpha", "red", "green", "blue"], ),}

}

CATEGORY = "image"

RETURN_TYPES = ("MASK",)

FUNCTION = "load_image"

def load_image(self, image, channel):

image_path = os.path.join(self.input_dir, image)

i = Image.open(image_path)

mask = None

c = channel[0].upper()

if c in i.getbands():

mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0

mask = torch.from_numpy(mask)

if c == 'A':

mask = 1. - mask

else:

mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")

return (mask,)

@classmethod

def IS_CHANGED(s, image, channel):

image_path = os.path.join(s.input_dir, image)

m = hashlib.sha256()

with open(image_path, 'rb') as f:

m.update(f.read())

upscale_methods = ["nearest-exact", "bilinear", "area"]

crop_methods = ["disabled", "center"]

@classmethod

def INPUT_TYPES(s):

return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),

"width": ("INT", {"default": 512, "min": 1, "max": 4096, "step": 1}),

"height": ("INT", {"default": 512, "min": 1, "max": 4096, "step": 1}),

"crop": (s.crop_methods,)}}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "upscale"

CATEGORY = "image/upscaling"

def upscale(self, image, upscale_method, width, height, crop):

samples = image.movedim(-1,1)

s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop)

s = s.movedim(1,-1)

@classmethod

def INPUT_TYPES(s):

return {"required": { "image": ("IMAGE",)}}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "invert"

CATEGORY = "image"

def invert(self, image):

s = 1.0 - image

"KSampler": KSampler,

"CheckpointLoader": CheckpointLoader,

"CheckpointLoaderSimple": CheckpointLoaderSimple,

"CLIPTextEncode": CLIPTextEncode,

"CLIPSetLastLayer": CLIPSetLastLayer,

"VAEDecode": VAEDecode,

"VAEEncode": VAEEncode,

"VAEEncodeForInpaint": VAEEncodeForInpaint,

"VAELoader": VAELoader,

"EmptyLatentImage": EmptyLatentImage,

"LatentUpscale": LatentUpscale,

"SaveImage": SaveImage,

"PreviewImage": PreviewImage,

"LoadImage": LoadImage,

"LoadImageMask": LoadImageMask,

"ImageScale": ImageScale,

"ImageInvert": ImageInvert,

"ConditioningCombine": ConditioningCombine,

"ConditioningSetArea": ConditioningSetArea,

"KSamplerAdvanced": KSamplerAdvanced,

"SetLatentNoiseMask": SetLatentNoiseMask,

"LatentComposite": LatentComposite,

"LatentRotate": LatentRotate,

"LatentFlip": LatentFlip,

"LatentCrop": LatentCrop,

"LoraLoader": LoraLoader,

"CLIPLoader": CLIPLoader,

"CLIPVisionEncode": CLIPVisionEncode,

"StyleModelApply": StyleModelApply,

"ControlNetApply": ControlNetApply,

"ControlNetLoader": ControlNetLoader,

"DiffControlNetLoader": DiffControlNetLoader,

"StyleModelLoader": StyleModelLoader,

"CLIPVisionLoader": CLIPVisionLoader,

"VAEDecodeTiled": VAEDecodeTiled,

"VAEEncodeTiled": VAEEncodeTiled,