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Commit fdf57325 authored by pythongosssss's avatar pythongosssss
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Merge remote-tracking branch 'origin/master' into tiled-progress

parents 27df7410 93c64afa
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extra_model_paths.yaml.example
View file @ fdf57325
......@@ -13,6 +13,7 @@ a111:
models/ESRGAN
models/SwinIR
embeddings: embeddings
hypernetworks: models/hypernetworks
controlnet: models/ControlNet
#other_ui:
......
folder_paths.py
View file @ fdf57325
......@@ -69,6 +69,46 @@ def get_directory_by_type(type_name):
return None
# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format
# otherwise use default_path as base_dir
def annotated_filepath(name):
if name.endswith("[output]"):
base_dir = get_output_directory()
name = name[:-9]
elif name.endswith("[input]"):
base_dir = get_input_directory()
name = name[:-8]
elif name.endswith("[temp]"):
base_dir = get_temp_directory()
name = name[:-7]
else:
return name, None
return name, base_dir
def get_annotated_filepath(name, default_dir=None):
name, base_dir = annotated_filepath(name)
if base_dir is None:
if default_dir is not None:
base_dir = default_dir
else:
base_dir = get_input_directory() # fallback path
return os.path.join(base_dir, name)
def exists_annotated_filepath(name):
name, base_dir = annotated_filepath(name)
if base_dir is None:
base_dir = get_input_directory() # fallback path
filepath = os.path.join(base_dir, name)
return os.path.exists(filepath)
def add_model_folder_path(folder_name, full_folder_path):
global folder_names_and_paths
if folder_name in folder_names_and_paths:
......
main.py
View file @ fdf57325
......@@ -5,6 +5,7 @@ import shutil
import threading
from comfy.cli_args import args
import comfy.utils
if os.name == "nt":
import logging
......@@ -39,14 +40,9 @@ async def run(server, address='', port=8188, verbose=True, call_on_start=None):
await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop())
def hijack_progress(server):
from tqdm.auto import tqdm
orig_func = getattr(tqdm, "update")
def wrapped_func(*args, **kwargs):
pbar = args[0]
v = orig_func(*args, **kwargs)
server.send_sync("progress", { "value": pbar.n, "max": pbar.total}, server.client_id)
return v
setattr(tqdm, "update", wrapped_func)
def hook(value, total):
server.send_sync("progress", { "value": value, "max": total}, server.client_id)
comfy.utils.set_progress_bar_global_hook(hook)
def cleanup_temp():
temp_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
......
nodes.py
View file @ fdf57325
......@@ -5,6 +5,7 @@ import sys
import json
import hashlib
import traceback
import math
from PIL import Image
from PIL.PngImagePlugin import PngInfo
......@@ -16,6 +17,7 @@ sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "co
import comfy.diffusers_convert
import comfy.samplers
import comfy.sample
import comfy.sd
import comfy.utils
......@@ -58,14 +60,44 @@ class ConditioningCombine:
def combine(self, conditioning_1, conditioning_2):
return (conditioning_1 + conditioning_2, )
class ConditioningAverage :
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ),
"conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "addWeighted"
CATEGORY = "conditioning"
def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
out = []
if len(conditioning_from) > 1:
print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
cond_from = conditioning_from[0][0]
for i in range(len(conditioning_to)):
t1 = conditioning_to[i][0]
t0 = cond_from[:,:t1.shape[1]]
if t0.shape[1] < t1.shape[1]:
t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)
tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))
n = [tw, conditioning_to[i][1].copy()]
out.append(n)
return (out, )
class ConditioningSetArea:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning": ("CONDITIONING", ),
"width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("CONDITIONING",)
......@@ -79,11 +111,41 @@ class ConditioningSetArea:
n = [t[0], t[1].copy()]
n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
n[1]['strength'] = strength
n[1]['set_area_to_bounds'] = False
n[1]['min_sigma'] = min_sigma
n[1]['max_sigma'] = max_sigma
c.append(n)
return (c, )
class ConditioningSetMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning": ("CONDITIONING", ),
"mask": ("MASK", ),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"set_cond_area": (["default", "mask bounds"],),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "append"
CATEGORY = "conditioning"
def append(self, conditioning, mask, set_cond_area, strength):
c = []
set_area_to_bounds = False
if set_cond_area != "default":
set_area_to_bounds = True
if len(mask.shape) < 3:
mask = mask.unsqueeze(0)
for t in conditioning:
n = [t[0], t[1].copy()]
_, h, w = mask.shape
n[1]['mask'] = mask
n[1]['set_area_to_bounds'] = set_area_to_bounds
n[1]['mask_strength'] = strength
c.append(n)
return (c, )
class VAEDecode:
def __init__(self, device="cpu"):
self.device = device
......@@ -126,16 +188,21 @@ class VAEEncode:
CATEGORY = "latent"
def encode(self, vae, pixels):
x = (pixels.shape[1] // 64) * 64
y = (pixels.shape[2] // 64) * 64
@staticmethod
def vae_encode_crop_pixels(pixels):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
if pixels.shape[1] != x or pixels.shape[2] != y:
pixels = pixels[:,:x,:y,:]
t = vae.encode(pixels[:,:,:,:3])
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, :]
return pixels
def encode(self, vae, pixels):
pixels = self.vae_encode_crop_pixels(pixels)
t = vae.encode(pixels[:,:,:,:3])
return ({"samples":t}, )
class VAEEncodeTiled:
def __init__(self, device="cpu"):
self.device = device
......@@ -149,46 +216,51 @@ class VAEEncodeTiled:
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,:]
pixels = VAEEncode.vae_encode_crop_pixels(pixels)
t = vae.encode_tiled(pixels[:,:,:,:3])
return ({"samples":t}, )
class VAEEncodeForInpaint:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", )}}
return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}}
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]
def encode(self, vae, pixels, mask, grow_mask_by=6):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
pixels = pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
pixels = pixels[:,:x,:y,:]
mask = mask[:x,: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,:]
mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
#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())
if grow_mask_by == 0:
mask_erosion = mask
else:
kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
padding = math.ceil((grow_mask_by - 1) / 2)
mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
m = (1.0 - mask.round()).squeeze(1)
for i in range(3):
pixels[:,:,:,i] -= 0.5
pixels[:,:,:,i] *= m
pixels[:,:,:,i] += 0.5
t = vae.encode(pixels)
return ({"samples":t, "noise_mask": (mask_erosion[0][:x,:y].round())}, )
return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
class CheckpointLoader:
@classmethod
......@@ -542,8 +614,8 @@ class EmptyLatentImage:
@classmethod
def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
......@@ -581,8 +653,8 @@ class LatentUpscale:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"crop": (s.crop_methods,)}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale"
......@@ -684,8 +756,8 @@ class LatentCrop:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",),
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
}}
......@@ -710,16 +782,6 @@ class LatentCrop:
new_width = width // 8
to_x = new_width + x
to_y = new_height + y
def enforce_image_dim(d, to_d, max_d):
if to_d > max_d:
leftover = (to_d - max_d) % 8
to_d = max_d
d -= leftover
return (d, to_d)
#make sure size is always multiple of 64
x, to_x = enforce_image_dim(x, to_x, samples.shape[3])
y, to_y = enforce_image_dim(y, to_y, samples.shape[2])
s['samples'] = samples[:,:,y:to_y, x:to_x]
return (s,)
......@@ -739,79 +801,27 @@ class SetLatentNoiseMask:
s["noise_mask"] = mask
return (s,)
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"]
noise_mask = None
device = comfy.model_management.get_torch_device()
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_index = 0
if "batch_index" in latent:
batch_index = latent["batch_index"]
generator = torch.manual_seed(seed)
for i in range(batch_index):
noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
skip = latent["batch_index"] if "batch_index" in latent else 0
noise = comfy.sample.prepare_noise(latent_image, seed, skip)
noise_mask = None
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
comfy.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 = []
def get_models(cond):
models = []
for c in cond:
if 'control' in c[1]:
models += [c[1]['control']]
if 'gligen' in c[1]:
models += [c[1]['gligen'][1]]
return models
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)
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)
negative_copy += [[t] + n[1:]]
models = get_models(positive) + get_models(negative)
comfy.model_management.load_controlnet_gpu(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, model_options=model.model_options)
else:
#other samplers
pass
noise_mask = latent["noise_mask"]
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 m in models:
m.cleanup()
pbar = comfy.utils.ProgressBar(steps)
def callback(step, x0, x):
pbar.update_absolute(step + 1)
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback)
out = latent.copy()
out["samples"] = samples
return (out, )
......@@ -974,8 +984,7 @@ class LoadImage:
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image"
def load_image(self, image):
input_dir = folder_paths.get_input_directory()
image_path = os.path.join(input_dir, image)
image_path = folder_paths.get_annotated_filepath(image)
i = Image.open(image_path)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
......@@ -989,20 +998,27 @@ class LoadImage:
@classmethod
def IS_CHANGED(s, image):
input_dir = folder_paths.get_input_directory()
image_path = os.path.join(input_dir, image)
image_path = folder_paths.get_annotated_filepath(image)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image):
if not folder_paths.exists_annotated_filepath(image):
return "Invalid image file: {}".format(image)
return True
class LoadImageMask:
_color_channels = ["alpha", "red", "green", "blue"]
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
return {"required":
{"image": (sorted(os.listdir(input_dir)), ),
"channel": (["alpha", "red", "green", "blue"], ),}
"channel": (s._color_channels, ),}
}
CATEGORY = "mask"
......@@ -1010,8 +1026,7 @@ class LoadImageMask:
RETURN_TYPES = ("MASK",)
FUNCTION = "load_image"
def load_image(self, image, channel):
input_dir = folder_paths.get_input_directory()
image_path = os.path.join(input_dir, image)
image_path = folder_paths.get_annotated_filepath(image)
i = Image.open(image_path)
if i.getbands() != ("R", "G", "B", "A"):
i = i.convert("RGBA")
......@@ -1028,13 +1043,22 @@ class LoadImageMask:
@classmethod
def IS_CHANGED(s, image, channel):
input_dir = folder_paths.get_input_directory()
image_path = os.path.join(input_dir, image)
image_path = folder_paths.get_annotated_filepath(image)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image, channel):
if not folder_paths.exists_annotated_filepath(image):
return "Invalid image file: {}".format(image)
if channel not in s._color_channels:
return "Invalid color channel: {}".format(channel)
return True
class ImageScale:
upscale_methods = ["nearest-exact", "bilinear", "area"]
crop_methods = ["disabled", "center"]
......@@ -1079,10 +1103,10 @@ class ImagePadForOutpaint:
return {
"required": {
"image": ("IMAGE",),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
}
}
......@@ -1154,8 +1178,10 @@ NODE_CLASS_MAPPINGS = {
"ImageScale": ImageScale,
"ImageInvert": ImageInvert,
"ImagePadForOutpaint": ImagePadForOutpaint,
"ConditioningAverage ": ConditioningAverage ,
"ConditioningCombine": ConditioningCombine,
"ConditioningSetArea": ConditioningSetArea,
"ConditioningSetMask": ConditioningSetMask,
"KSamplerAdvanced": KSamplerAdvanced,
"SetLatentNoiseMask": SetLatentNoiseMask,
"LatentComposite": LatentComposite,
......@@ -1204,7 +1230,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"CLIPTextEncode": "CLIP Text Encode (Prompt)",
"CLIPSetLastLayer": "CLIP Set Last Layer",
"ConditioningCombine": "Conditioning (Combine)",
"ConditioningAverage ": "Conditioning (Average)",
"ConditioningSetArea": "Conditioning (Set Area)",
"ConditioningSetMask": "Conditioning (Set Mask)",
"ControlNetApply": "Apply ControlNet",
# Latent
"VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
......
notebooks/comfyui_colab.ipynb
View file @ fdf57325
......@@ -47,7 +47,7 @@
" !git pull\n",
"\n",
"!echo -= Install dependencies =-\n",
"!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118"
"!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117"
]
},
{
......
server.py
View file @ fdf57325
......@@ -112,13 +112,20 @@ class PromptServer():
@routes.post("/upload/image")
async def upload_image(request):
upload_dir = folder_paths.get_input_directory()
post = await request.post()
image = post.get("image")
if post.get("type") is None:
upload_dir = folder_paths.get_input_directory()
elif post.get("type") == "input":
upload_dir = folder_paths.get_input_directory()
elif post.get("type") == "temp":
upload_dir = folder_paths.get_temp_directory()
elif post.get("type") == "output":
upload_dir = folder_paths.get_output_directory()
if not os.path.exists(upload_dir):
os.makedirs(upload_dir)
post = await request.post()
image = post.get("image")
if image and image.file:
filename = image.filename
......
web/extensions/core/colorPalette.js
View file @ fdf57325
......@@ -232,10 +232,27 @@ app.registerExtension({
"name": "My Color Palette",
"colors": {
"node_slot": {
},
"litegraph_base": {
},
"comfy_base": {
}
}
};
// Copy over missing keys from default color palette
const defaultColorPalette = colorPalettes[defaultColorPaletteId];
for (const key in defaultColorPalette.colors.litegraph_base) {
if (!colorPalette.colors.litegraph_base[key]) {
colorPalette.colors.litegraph_base[key] = "";
}
}
for (const key in defaultColorPalette.colors.comfy_base) {
if (!colorPalette.colors.comfy_base[key]) {
colorPalette.colors.comfy_base[key] = "";
}
}
return completeColorPalette(colorPalette);
};
......
web/extensions/core/slotDefaults.js
View file @ fdf57325
......@@ -6,6 +6,7 @@ app.registerExtension({
name: "Comfy.SlotDefaults",
suggestionsNumber: null,
init() {
LiteGraph.search_filter_enabled = true;
LiteGraph.middle_click_slot_add_default_node = true;
this.suggestionsNumber = app.ui.settings.addSetting({
id: "Comfy.NodeSuggestions.number",
......@@ -43,6 +44,14 @@ app.registerExtension({
}
if (this.slot_types_default_out[type].includes(nodeId)) continue;
this.slot_types_default_out[type].push(nodeId);
// Input types have to be stored as lower case
// Store each node that can handle this input type
const lowerType = type.toLocaleLowerCase();
if (!(lowerType in LiteGraph.registered_slot_in_types)) {
LiteGraph.registered_slot_in_types[lowerType] = { nodes: [] };
}
LiteGraph.registered_slot_in_types[lowerType].nodes.push(nodeType.comfyClass);
}
var outputs = nodeData["output"];
......@@ -53,6 +62,16 @@ app.registerExtension({
}
this.slot_types_default_in[type].push(nodeId);
// Store each node that can handle this output type
if (!(type in LiteGraph.registered_slot_out_types)) {
LiteGraph.registered_slot_out_types[type] = { nodes: [] };
}
LiteGraph.registered_slot_out_types[type].nodes.push(nodeType.comfyClass);
if(!LiteGraph.slot_types_out.includes(type)) {
LiteGraph.slot_types_out.push(type);
}
}
var maxNum = this.suggestionsNumber.value;
this.setDefaults(maxNum);
......
web/lib/litegraph.core.js
View file @ fdf57325
......@@ -3628,6 +3628,18 @@
return size;
};
LGraphNode.prototype.inResizeCorner = function(canvasX, canvasY) {
var rows = this.outputs ? this.outputs.length : 1;
var outputs_offset = (this.constructor.slot_start_y || 0) + rows * LiteGraph.NODE_SLOT_HEIGHT;
return isInsideRectangle(canvasX,
canvasY,
this.pos[0] + this.size[0] - 15,
this.pos[1] + Math.max(this.size[1] - 15, outputs_offset),
20,
20
);
}
/**
* returns all the info available about a property of this node.
*
......@@ -5877,14 +5889,7 @@ LGraphNode.prototype.executeAction = function(action)
if ( !this.connecting_node && !node.flags.collapsed && !this.live_mode ) {
//Search for corner for resize
if ( !skip_action &&
node.resizable !== false &&
isInsideRectangle( e.canvasX,
e.canvasY,
node.pos[0] + node.size[0] - 5,
node.pos[1] + node.size[1] - 5,
10,
10
)
node.resizable !== false && node.inResizeCorner(e.canvasX, e.canvasY)
) {
this.graph.beforeChange();
this.resizing_node = node;
......@@ -6424,16 +6429,7 @@ LGraphNode.prototype.executeAction = function(action)
//Search for corner
if (this.canvas) {
if (
isInsideRectangle(
e.canvasX,
e.canvasY,
node.pos[0] + node.size[0] - 5,
node.pos[1] + node.size[1] - 5,
5,
5
)
) {
if (node.inResizeCorner(e.canvasX, e.canvasY)) {
this.canvas.style.cursor = "se-resize";
} else {
this.canvas.style.cursor = "crosshair";
......@@ -9953,11 +9949,11 @@ LGraphNode.prototype.executeAction = function(action)
}
break;
case "slider":
var range = w.options.max - w.options.min;
var old_value = w.value;
var nvalue = Math.clamp((x - 15) / (widget_width - 30), 0, 1);
if(w.options.read_only) break;
w.value = w.options.min + (w.options.max - w.options.min) * nvalue;
if (w.callback) {
if (old_value != w.value) {
setTimeout(function() {
inner_value_change(w, w.value);
}, 20);
......@@ -10044,7 +10040,7 @@ LGraphNode.prototype.executeAction = function(action)
if (event.click_time < 200 && delta == 0) {
this.prompt("Value",w.value,function(v) {
// check if v is a valid equation or a number
if (/^[0-9+\-*/()\s]+$/.test(v)) {
if (/^[0-9+\-*/()\s]+|\d+\.\d+$/.test(v)) {
try {//solve the equation if possible
v = eval(v);
} catch (e) { }
......
web/scripts/app.js
View file @ fdf57325
......@@ -20,6 +20,12 @@ export class ComfyApp {
*/
#processingQueue = false;
/**
* Content Clipboard
* @type {serialized node object}
*/
static clipspace = null;
constructor() {
this.ui = new ComfyUI(this);
......@@ -130,6 +136,83 @@ export class ComfyApp {
);
}
}
options.push(
{
content: "Copy (Clipspace)",
callback: (obj) => {
var widgets = null;
if(this.widgets) {
widgets = this.widgets.map(({ type, name, value }) => ({ type, name, value }));
}
let img = new Image();
var imgs = undefined;
if(this.imgs != undefined) {
img.src = this.imgs[0].src;
imgs = [img];
}
ComfyApp.clipspace = {
'widgets': widgets,
'imgs': imgs,
'original_imgs': imgs,
'images': this.images
};
}
});
if(ComfyApp.clipspace != null) {
options.push(
{
content: "Paste (Clipspace)",
callback: () => {
if(ComfyApp.clipspace != null) {
if(ComfyApp.clipspace.widgets != null && this.widgets != null) {
ComfyApp.clipspace.widgets.forEach(({ type, name, value }) => {
const prop = Object.values(this.widgets).find(obj => obj.type === type && obj.name === name);
if (prop) {
prop.callback(value);
}
});
}
// image paste
if(ComfyApp.clipspace.imgs != undefined && this.imgs != undefined && this.widgets != null) {
var filename = "";
if(this.images && ComfyApp.clipspace.images) {
this.images = ComfyApp.clipspace.images;
}
if(ComfyApp.clipspace.images != undefined) {
const clip_image = ComfyApp.clipspace.images[0];
if(clip_image.subfolder != '')
filename = `${clip_image.subfolder}/`;
filename += `${clip_image.filename} [${clip_image.type}]`;
}
else if(ComfyApp.clipspace.widgets != undefined) {
const index_in_clip = ComfyApp.clipspace.widgets.findIndex(obj => obj.name === 'image');
if(index_in_clip >= 0) {
filename = `${ComfyApp.clipspace.widgets[index_in_clip].value}`;
}
}
const index = this.widgets.findIndex(obj => obj.name === 'image');
if(index >= 0 && filename != "" && ComfyApp.clipspace.imgs != undefined) {
this.imgs = ComfyApp.clipspace.imgs;
this.widgets[index].value = filename;
if(this.widgets_values != undefined) {
this.widgets_values[index] = filename;
}
}
}
this.trigger('changed');
}
}
}
);
}
};
}
......@@ -888,8 +971,10 @@ export class ComfyApp {
loadGraphData(graphData) {
this.clean();
let reset_invalid_values = false;
if (!graphData) {
graphData = structuredClone(defaultGraph);
reset_invalid_values = true;
}
const missingNodeTypes = [];
......@@ -975,6 +1060,13 @@ export class ComfyApp {
}
}
}
if (reset_invalid_values) {
if (widget.type == "combo") {
if (!widget.options.values.includes(widget.value) && widget.options.values.length > 0) {
widget.value = widget.options.values[0];
}
}
}
}
}
......
web/scripts/widgets.js
View file @ fdf57325
......@@ -136,9 +136,11 @@ function addMultilineWidget(node, name, opts, app) {
left: `${t.a * margin + t.e}px`,
top: `${t.d * (y + widgetHeight - margin - 3) + t.f}px`,
width: `${(widgetWidth - margin * 2 - 3) * t.a}px`,
background: (!node.color)?'':node.color,
height: `${(this.parent.inputHeight - margin * 2 - 4) * t.d}px`,
position: "absolute",
zIndex: 1,
color: (!node.color)?'':'white',
zIndex: app.graph._nodes.indexOf(node),
fontSize: `${t.d * 10.0}px`,
});
this.inputEl.hidden = !visible;
......@@ -270,6 +272,9 @@ export const ComfyWidgets = {
app.graph.setDirtyCanvas(true);
};
img.src = `/view?filename=${name}&type=input`;
if ((node.size[1] - node.imageOffset) < 100) {
node.size[1] = 250 + node.imageOffset;
}
}
// Add our own callback to the combo widget to render an image when it changes
......
web/style.css
View file @ fdf57325
......@@ -120,7 +120,7 @@ body {
.comfy-menu > button,
.comfy-menu-btns button,
.comfy-menu .comfy-list button,
.comfy-modal button{
.comfy-modal button {
color: var(--input-text);
background-color: var(--comfy-input-bg);
border-radius: 8px;
......@@ -129,6 +129,15 @@ body {
margin-top: 2px;
}
.comfy-menu > button:hover,
.comfy-menu-btns button:hover,
.comfy-menu .comfy-list button:hover,
.comfy-modal button:hover,
.comfy-settings-btn:hover {
filter: brightness(1.2);
cursor: pointer;
}
.comfy-menu span.drag-handle {
width: 10px;
height: 20px;
......@@ -248,8 +257,11 @@ button.comfy-queue-btn {
}
}
/* Input popup */
.graphdialog {
min-height: 1em;
background-color: var(--comfy-menu-bg);
}
.graphdialog .name {
......@@ -273,15 +285,66 @@ button.comfy-queue-btn {
border-radius: 12px 0 0 12px;
}
/* Context menu */
.litegraph .litemenu-entry.has_submenu {
position: relative;
padding-right: 20px;
}
}
.litemenu-entry.has_submenu::after {
.litemenu-entry.has_submenu::after {
content: ">";
position: absolute;
top: 0;
right: 2px;
}
\ No newline at end of file
}
.litegraph.litecontextmenu,
.litegraph.litecontextmenu.dark {
z-index: 9999 !important;
background-color: var(--comfy-menu-bg) !important;
filter: brightness(95%);
}
.litegraph.litecontextmenu .litemenu-entry:hover:not(.disabled):not(.separator) {
background-color: var(--comfy-menu-bg) !important;
filter: brightness(155%);
color: var(--input-text);
}
.litegraph.litecontextmenu .litemenu-entry.submenu,
.litegraph.litecontextmenu.dark .litemenu-entry.submenu {
background-color: var(--comfy-menu-bg) !important;
color: var(--input-text);
}
.litegraph.litecontextmenu input {
background-color: var(--comfy-input-bg) !important;
color: var(--input-text) !important;
}
/* Search box */
.litegraph.litesearchbox {
z-index: 9999 !important;
background-color: var(--comfy-menu-bg) !important;
overflow: hidden;
}
.litegraph.litesearchbox input,
.litegraph.litesearchbox select {
background-color: var(--comfy-input-bg) !important;
color: var(--input-text);
}
.litegraph.lite-search-item {
color: var(--input-text);
background-color: var(--comfy-input-bg);
filter: brightness(80%);
padding-left: 0.2em;
}
.litegraph.lite-search-item.generic_type {
color: var(--input-text);
filter: brightness(50%);
}
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