wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-01-18 18:30:19 +08:00
Code Issues Actions 16 Packages Projects Releases Wiki Activity

Minor changes and extra nodes

Browse Source
This commit is contained in:
Silversith 2023-03-21 12:18:06 +02:00
parent aa2ddfabb9
commit 96dc344b0c
5 changed files with 1935 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

244
comfy_extras/silver_custom.py Normal file
View File

@ -0,0 +1,244 @@
import datetime
import torch
import os
import sys
import json
import hashlib
import copy
import traceback
from PIL import Image
from PIL.PngImagePlugin import PngInfo
import numpy as np
import comfy.samplers
import comfy.sd
import comfy.utils
import comfy_extras.clip_vision
import model_management
import importlib
import folder_paths
class Note:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {"required": {"text": ("STRING", {"multiline": True})}}
RETURN_TYPES = ()
FUNCTION = "Note"
OUTPUT_NODE = False
CATEGORY = "silver_custom"
class SaveImageList:
def __init__(self):
current_dir = os.path.abspath(os.getcwd())
print(current_dir)
self.output_dir = os.path.join(current_dir, "output")
print(self.output_dir)
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_list"
OUTPUT_NODE = True
CATEGORY = "silver_custom"
def save_images_list(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]))
now = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
file = f"{filename}-{now}_{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
return self.get_all_files()
def get_all_files(self):
results = []
for root, dirs, files in os.walk(self.output_dir):
for file in files:
subfolder = os.path.relpath(root, self.output_dir)
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})
sorted_results = sorted(results, key=lambda x: x["filename"])
return {"ui": {"images": sorted_results}}
def custom_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, in_seed=None):
latent_image = latent["samples"]
noise_mask = None
device = model_management.get_torch_device()
if in_seed is not None:
seed = in_seed
print(seed)
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
return (out, seed,)
class CustomKSampler:
@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}),
},
"optional":
{
"in_seed": ()
}
}
RETURN_TYPES = ("LATENT", "seed",)
FUNCTION = "sample"
CATEGORY = "silver_custom"
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0,
in_seed=None):
return custom_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, in_seed=in_seed)
NODE_CLASS_MAPPINGS = {
"Note": Note,
"SaveImageList": SaveImageList,
"CustomKSampler": CustomKSampler,
}

693
comfy_extras/silver_custom/Custom.json Normal file
View File

@ -0,0 +1,693 @@
{
"last_node_id": 17,
"last_link_id": 28,
"nodes": [
{
"id": 4,
"type": "CheckpointLoaderSimple",
"pos": [
-343,
487
],
"size": {
"0": 315,
"1": 98
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
10,
20
],
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": [
3,
5
],
"slot_index": 1
},
{
"name": "VAE",
"type": "VAE",
"links": [],
"slot_index": 2
}
],
"properties": {},
"widgets_values": [
"dreamshaper_331BakedVae.safetensors"
]
},
{
"id": 6,
"type": "CLIPTextEncode",
"pos": [
-8,
171
],
"size": {
"0": 422.84503173828125,
"1": 164.31304931640625
},
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 3
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
11,
21
],
"slot_index": 0
}
],
"properties": {},
"widgets_values": [
"masterpiece best quality girl"
]
},
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
-3,
383
],
"size": {
"0": 425.27801513671875,
"1": 180.6060791015625
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 5
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
12,
22
],
"slot_index": 0
}
],
"properties": {},
"widgets_values": [
"bad hands"
]
},
{
"id": 5,
"type": "EmptyLatentImage",
"pos": [
-7,
32
],
"size": {
"0": 315,
"1": 106
},
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
13
],
"slot_index": 0
}
],
"properties": {},
"widgets_values": [
512,
512,
1
]
},
{
"id": 15,
"type": "VAEDecode",
"pos": [
2153,
102
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 23
},
{
"name": "vae",
"type": "VAE",
"link": 25
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
26
],
"slot_index": 0
}
],
"properties": {}
},
{
"id": 12,
"type": "ImageScale",
"pos": [
993,
101
],
"size": {
"0": 315,
"1": 130
},
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 17
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
18
],
"slot_index": 0
}
],
"properties": {},
"widgets_values": [
"nearest-exact",
768,
768,
"disabled"
]
},
{
"id": 13,
"type": "VAEEncode",
"pos": [
1368,
113
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "pixels",
"type": "IMAGE",
"link": 18
},
{
"name": "vae",
"type": "VAE",
"link": 27
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
19
],
"slot_index": 0
}
],
"properties": {}
},
{
"id": 10,
"type": "CustomKSampler",
"pos": [
486,
494
],
"size": {
"0": 315,
"1": 282
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 10
},
{
"name": "positive",
"type": "CONDITIONING",
"link": 11
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 12
},
{
"name": "latent_image",
"type": "LATENT",
"link": 13
},
{
"name": "in_seed",
"type": 0,
"link": null
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
16
],
"slot_index": 0
},
{
"name": "seed",
"type": "seed",
"links": [
15
],
"slot_index": 1
}
],
"properties": {},
"widgets_values": [
57521337950617,
true,
20,
8,
"dpmpp_2m",
"normal",
1
]
},
{
"id": 16,
"type": "VAELoader",
"pos": [
366,
-3
],
"size": {
"0": 315,
"1": 58
},
"flags": {},
"order": 2,
"mode": 0,
"outputs": [
{
"name": "VAE",
"type": "VAE",
"links": [
24,
25,
27
],
"slot_index": 0
}
],
"properties": {},
"widgets_values": [
"vae-ft-mse-840000-ema-pruned.safetensors"
]
},
{
"id": 8,
"type": "VAEDecode",
"pos": [
733,
172
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 16
},
{
"name": "vae",
"type": "VAE",
"link": 24
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
17,
28
],
"slot_index": 0
}
],
"properties": {}
},
{
"id": 17,
"type": "PreviewImage",
"pos": [
2156,
327
],
"size": [
337.8637939453124,
365.6173385620117
],
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 28
}
],
"properties": {}
},
{
"id": 14,
"type": "SaveImageList",
"pos": [
2499,
297
],
"size": [
336.9637939453123,
394.6173385620117
],
"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 26
}
],
"properties": {},
"widgets_values": [
"ComfyUI"
]
},
{
"id": 11,
"type": "CustomKSampler",
"pos": [
1620,
365
],
"size": {
"0": 315,
"1": 282
},
"flags": {},
"order": 10,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 20
},
{
"name": "positive",
"type": "CONDITIONING",
"link": 21
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 22
},
{
"name": "latent_image",
"type": "LATENT",
"link": 19
},
{
"name": "in_seed",
"type": 0,
"link": 15
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
23
],
"slot_index": 0
},
{
"name": "seed",
"type": "seed",
"links": null
}
],
"properties": {},
"widgets_values": [
0,
false,
20,
8,
"dpmpp_2m",
"karras",
0.5
]
}
],
"links": [
[
3,
4,
1,
6,
0,
"CLIP"
],
[
5,
4,
1,
7,
0,
"CLIP"
],
[
10,
4,
0,
10,
0,
"MODEL"
],
[
11,
6,
0,
10,
1,
"CONDITIONING"
],
[
12,
7,
0,
10,
2,
"CONDITIONING"
],
[
13,
5,
0,
10,
3,
"LATENT"
],
[
15,
10,
1,
11,
4,
"seed"
],
[
16,
10,
0,
8,
0,
"LATENT"
],
[
17,
8,
0,
12,
0,
"IMAGE"
],
[
18,
12,
0,
13,
0,
"IMAGE"
],
[
19,
13,
0,
11,
3,
"LATENT"
],
[
20,
4,
0,
11,
0,
"MODEL"
],
[
21,
6,
0,
11,
1,
"CONDITIONING"
],
[
22,
7,
0,
11,
2,
"CONDITIONING"
],
[
23,
11,
0,
15,
0,
"LATENT"
],
[
24,
16,
0,
8,
1,
"VAE"
],
[
25,
16,
0,
15,
1,
"VAE"
],
[
26,
15,
0,
14,
0,
"IMAGE"
],
[
27,
16,
0,
13,
1,
"VAE"
],
[
28,
8,
0,
17,
0,
"IMAGE"
]
],
"groups": [],
"config": {},
"extra": {},
"version": 0.4
}

1
nodes.py
View File

@ -980,3 +980,4 @@ def load_custom_nodes():
load_custom_nodes() load_custom_nodes()
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py")) load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "silver_custom.py"))

993
patch.diff Normal file
View File

@ -0,0 +1,993 @@
From 15c9574acd2864a0a986e23b967030f565455003 Mon Sep 17 00:00:00 2001
From: Jaco van der Gryp <jacov@bbd.co.za>
Date: Tue, 21 Mar 2023 12:15:11 +0200
Subject: [PATCH] Added some custom nodes for minor upgrades that I wanted in
the UI
---
comfy_extras/silver_custom.py | 244 +++++++++
comfy_extras/silver_custom/Custom.json | 693 +++++++++++++++++++++++++
nodes.py | 1 +
web/scripts/app.js | 5 +-
4 files changed, 942 insertions(+), 1 deletion(-)
create mode 100644 comfy_extras/silver_custom.py
create mode 100644 comfy_extras/silver_custom/Custom.json
diff --git a/comfy_extras/silver_custom.py b/comfy_extras/silver_custom.py
new file mode 100644
index 0000000..9bab2fa
--- /dev/null
+++ b/comfy_extras/silver_custom.py
@@ -0,0 +1,244 @@
+import datetime
+
+import torch
+
+import os
+import sys
+import json
+import hashlib
+import copy
+import traceback
+
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+import numpy as np
+import comfy.samplers
+import comfy.sd
+import comfy.utils
+import comfy_extras.clip_vision
+import model_management
+import importlib
+import folder_paths
+
+
+class Note:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"text": ("STRING", {"multiline": True})}}
+
+ RETURN_TYPES = ()
+ FUNCTION = "Note"
+
+ OUTPUT_NODE = False
+
+ CATEGORY = "silver_custom"
+
+
+class SaveImageList:
+ def __init__(self):
+ current_dir = os.path.abspath(os.getcwd())
+ print(current_dir)
+ self.output_dir = os.path.join(current_dir, "output")
+ print(self.output_dir)
+ 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_list"
+
+ OUTPUT_NODE = True
+
+ CATEGORY = "silver_custom"
+
+ def save_images_list(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]))
+
+ now = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+ file = f"{filename}-{now}_{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
+
+ return self.get_all_files()
+
+ def get_all_files(self):
+ results = []
+ for root, dirs, files in os.walk(self.output_dir):
+ for file in files:
+ subfolder = os.path.relpath(root, self.output_dir)
+ results.append({
+ "filename": file,
+ "subfolder": subfolder,
+ "type": self.type
+ })
+ sorted_results = sorted(results, key=lambda x: x["filename"])
+ return {"ui": {"images": sorted_results}}
+
+
+def custom_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, in_seed=None):
+ latent_image = latent["samples"]
+ noise_mask = None
+ device = model_management.get_torch_device()
+ if in_seed is not None:
+ seed = in_seed
+ print(seed)
+ 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
+ return (out, seed,)
+
+
+class CustomKSampler:
+ @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}),
+ },
+ "optional":
+ {
+ "in_seed": ()
+ }
+ }
+
+ RETURN_TYPES = ("LATENT", "seed",)
+ FUNCTION = "sample"
+
+ CATEGORY = "silver_custom"
+
+ def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0,
+ in_seed=None):
+ return custom_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+ denoise=denoise, in_seed=in_seed)
+
+
+NODE_CLASS_MAPPINGS = {
+ "Note": Note,
+ "SaveImageList": SaveImageList,
+ "CustomKSampler": CustomKSampler,
+}
diff --git a/comfy_extras/silver_custom/Custom.json b/comfy_extras/silver_custom/Custom.json
new file mode 100644
index 0000000..1ad2baf
--- /dev/null
+++ b/comfy_extras/silver_custom/Custom.json
@@ -0,0 +1,693 @@
+{
+ "last_node_id": 17,
+ "last_link_id": 28,
+ "nodes": [
+ {
+ "id": 4,
+ "type": "CheckpointLoaderSimple",
+ "pos": [
+ -343,
+ 487
+ ],
+ "size": {
+ "0": 315,
+ "1": 98
+ },
+ "flags": {},
+ "order": 0,
+ "mode": 0,
+ "outputs": [
+ {
+ "name": "MODEL",
+ "type": "MODEL",
+ "links": [
+ 10,
+ 20
+ ],
+ "slot_index": 0
+ },
+ {
+ "name": "CLIP",
+ "type": "CLIP",
+ "links": [
+ 3,
+ 5
+ ],
+ "slot_index": 1
+ },
+ {
+ "name": "VAE",
+ "type": "VAE",
+ "links": [],
+ "slot_index": 2
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ "dreamshaper_331BakedVae.safetensors"
+ ]
+ },
+ {
+ "id": 6,
+ "type": "CLIPTextEncode",
+ "pos": [
+ -8,
+ 171
+ ],
+ "size": {
+ "0": 422.84503173828125,
+ "1": 164.31304931640625
+ },
+ "flags": {},
+ "order": 3,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "clip",
+ "type": "CLIP",
+ "link": 3
+ }
+ ],
+ "outputs": [
+ {
+ "name": "CONDITIONING",
+ "type": "CONDITIONING",
+ "links": [
+ 11,
+ 21
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ "masterpiece best quality girl"
+ ]
+ },
+ {
+ "id": 7,
+ "type": "CLIPTextEncode",
+ "pos": [
+ -3,
+ 383
+ ],
+ "size": {
+ "0": 425.27801513671875,
+ "1": 180.6060791015625
+ },
+ "flags": {},
+ "order": 4,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "clip",
+ "type": "CLIP",
+ "link": 5
+ }
+ ],
+ "outputs": [
+ {
+ "name": "CONDITIONING",
+ "type": "CONDITIONING",
+ "links": [
+ 12,
+ 22
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ "bad hands"
+ ]
+ },
+ {
+ "id": 5,
+ "type": "EmptyLatentImage",
+ "pos": [
+ -7,
+ 32
+ ],
+ "size": {
+ "0": 315,
+ "1": 106
+ },
+ "flags": {},
+ "order": 1,
+ "mode": 0,
+ "outputs": [
+ {
+ "name": "LATENT",
+ "type": "LATENT",
+ "links": [
+ 13
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ 512,
+ 512,
+ 1
+ ]
+ },
+ {
+ "id": 15,
+ "type": "VAEDecode",
+ "pos": [
+ 2153,
+ 102
+ ],
+ "size": {
+ "0": 210,
+ "1": 46
+ },
+ "flags": {},
+ "order": 11,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "samples",
+ "type": "LATENT",
+ "link": 23
+ },
+ {
+ "name": "vae",
+ "type": "VAE",
+ "link": 25
+ }
+ ],
+ "outputs": [
+ {
+ "name": "IMAGE",
+ "type": "IMAGE",
+ "links": [
+ 26
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {}
+ },
+ {
+ "id": 12,
+ "type": "ImageScale",
+ "pos": [
+ 993,
+ 101
+ ],
+ "size": {
+ "0": 315,
+ "1": 130
+ },
+ "flags": {},
+ "order": 7,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "image",
+ "type": "IMAGE",
+ "link": 17
+ }
+ ],
+ "outputs": [
+ {
+ "name": "IMAGE",
+ "type": "IMAGE",
+ "links": [
+ 18
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ "nearest-exact",
+ 768,
+ 768,
+ "disabled"
+ ]
+ },
+ {
+ "id": 13,
+ "type": "VAEEncode",
+ "pos": [
+ 1368,
+ 113
+ ],
+ "size": {
+ "0": 210,
+ "1": 46
+ },
+ "flags": {},
+ "order": 9,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "pixels",
+ "type": "IMAGE",
+ "link": 18
+ },
+ {
+ "name": "vae",
+ "type": "VAE",
+ "link": 27
+ }
+ ],
+ "outputs": [
+ {
+ "name": "LATENT",
+ "type": "LATENT",
+ "links": [
+ 19
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {}
+ },
+ {
+ "id": 10,
+ "type": "CustomKSampler",
+ "pos": [
+ 486,
+ 494
+ ],
+ "size": {
+ "0": 315,
+ "1": 282
+ },
+ "flags": {},
+ "order": 5,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "model",
+ "type": "MODEL",
+ "link": 10
+ },
+ {
+ "name": "positive",
+ "type": "CONDITIONING",
+ "link": 11
+ },
+ {
+ "name": "negative",
+ "type": "CONDITIONING",
+ "link": 12
+ },
+ {
+ "name": "latent_image",
+ "type": "LATENT",
+ "link": 13
+ },
+ {
+ "name": "in_seed",
+ "type": 0,
+ "link": null
+ }
+ ],
+ "outputs": [
+ {
+ "name": "LATENT",
+ "type": "LATENT",
+ "links": [
+ 16
+ ],
+ "slot_index": 0
+ },
+ {
+ "name": "seed",
+ "type": "seed",
+ "links": [
+ 15
+ ],
+ "slot_index": 1
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ 57521337950617,
+ true,
+ 20,
+ 8,
+ "dpmpp_2m",
+ "normal",
+ 1
+ ]
+ },
+ {
+ "id": 16,
+ "type": "VAELoader",
+ "pos": [
+ 366,
+ -3
+ ],
+ "size": {
+ "0": 315,
+ "1": 58
+ },
+ "flags": {},
+ "order": 2,
+ "mode": 0,
+ "outputs": [
+ {
+ "name": "VAE",
+ "type": "VAE",
+ "links": [
+ 24,
+ 25,
+ 27
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ "vae-ft-mse-840000-ema-pruned.safetensors"
+ ]
+ },
+ {
+ "id": 8,
+ "type": "VAEDecode",
+ "pos": [
+ 733,
+ 172
+ ],
+ "size": {
+ "0": 210,
+ "1": 46
+ },
+ "flags": {},
+ "order": 6,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "samples",
+ "type": "LATENT",
+ "link": 16
+ },
+ {
+ "name": "vae",
+ "type": "VAE",
+ "link": 24
+ }
+ ],
+ "outputs": [
+ {
+ "name": "IMAGE",
+ "type": "IMAGE",
+ "links": [
+ 17,
+ 28
+ ],
+ "slot_index": 0
+ }
+ ],
+ "properties": {}
+ },
+ {
+ "id": 17,
+ "type": "PreviewImage",
+ "pos": [
+ 2156,
+ 327
+ ],
+ "size": [
+ 337.8637939453124,
+ 365.6173385620117
+ ],
+ "flags": {},
+ "order": 8,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "images",
+ "type": "IMAGE",
+ "link": 28
+ }
+ ],
+ "properties": {}
+ },
+ {
+ "id": 14,
+ "type": "SaveImageList",
+ "pos": [
+ 2499,
+ 297
+ ],
+ "size": [
+ 336.9637939453123,
+ 394.6173385620117
+ ],
+ "flags": {},
+ "order": 12,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "images",
+ "type": "IMAGE",
+ "link": 26
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ "ComfyUI"
+ ]
+ },
+ {
+ "id": 11,
+ "type": "CustomKSampler",
+ "pos": [
+ 1620,
+ 365
+ ],
+ "size": {
+ "0": 315,
+ "1": 282
+ },
+ "flags": {},
+ "order": 10,
+ "mode": 0,
+ "inputs": [
+ {
+ "name": "model",
+ "type": "MODEL",
+ "link": 20
+ },
+ {
+ "name": "positive",
+ "type": "CONDITIONING",
+ "link": 21
+ },
+ {
+ "name": "negative",
+ "type": "CONDITIONING",
+ "link": 22
+ },
+ {
+ "name": "latent_image",
+ "type": "LATENT",
+ "link": 19
+ },
+ {
+ "name": "in_seed",
+ "type": 0,
+ "link": 15
+ }
+ ],
+ "outputs": [
+ {
+ "name": "LATENT",
+ "type": "LATENT",
+ "links": [
+ 23
+ ],
+ "slot_index": 0
+ },
+ {
+ "name": "seed",
+ "type": "seed",
+ "links": null
+ }
+ ],
+ "properties": {},
+ "widgets_values": [
+ 0,
+ false,
+ 20,
+ 8,
+ "dpmpp_2m",
+ "karras",
+ 0.5
+ ]
+ }
+ ],
+ "links": [
+ [
+ 3,
+ 4,
+ 1,
+ 6,
+ 0,
+ "CLIP"
+ ],
+ [
+ 5,
+ 4,
+ 1,
+ 7,
+ 0,
+ "CLIP"
+ ],
+ [
+ 10,
+ 4,
+ 0,
+ 10,
+ 0,
+ "MODEL"
+ ],
+ [
+ 11,
+ 6,
+ 0,
+ 10,
+ 1,
+ "CONDITIONING"
+ ],
+ [
+ 12,
+ 7,
+ 0,
+ 10,
+ 2,
+ "CONDITIONING"
+ ],
+ [
+ 13,
+ 5,
+ 0,
+ 10,
+ 3,
+ "LATENT"
+ ],
+ [
+ 15,
+ 10,
+ 1,
+ 11,
+ 4,
+ "seed"
+ ],
+ [
+ 16,
+ 10,
+ 0,
+ 8,
+ 0,
+ "LATENT"
+ ],
+ [
+ 17,
+ 8,
+ 0,
+ 12,
+ 0,
+ "IMAGE"
+ ],
+ [
+ 18,
+ 12,
+ 0,
+ 13,
+ 0,
+ "IMAGE"
+ ],
+ [
+ 19,
+ 13,
+ 0,
+ 11,
+ 3,
+ "LATENT"
+ ],
+ [
+ 20,
+ 4,
+ 0,
+ 11,
+ 0,
+ "MODEL"
+ ],
+ [
+ 21,
+ 6,
+ 0,
+ 11,
+ 1,
+ "CONDITIONING"
+ ],
+ [
+ 22,
+ 7,
+ 0,
+ 11,
+ 2,
+ "CONDITIONING"
+ ],
+ [
+ 23,
+ 11,
+ 0,
+ 15,
+ 0,
+ "LATENT"
+ ],
+ [
+ 24,
+ 16,
+ 0,
+ 8,
+ 1,
+ "VAE"
+ ],
+ [
+ 25,
+ 16,
+ 0,
+ 15,
+ 1,
+ "VAE"
+ ],
+ [
+ 26,
+ 15,
+ 0,
+ 14,
+ 0,
+ "IMAGE"
+ ],
+ [
+ 27,
+ 16,
+ 0,
+ 13,
+ 1,
+ "VAE"
+ ],
+ [
+ 28,
+ 8,
+ 0,
+ 17,
+ 0,
+ "IMAGE"
+ ]
+ ],
+ "groups": [],
+ "config": {},
+ "extra": {},
+ "version": 0.4
+}
\ No newline at end of file
diff --git a/nodes.py b/nodes.py
index cb4d772..03bb185 100644
--- a/nodes.py
+++ b/nodes.py
@@ -980,3 +980,4 @@ def load_custom_nodes():
load_custom_nodes()
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
+load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "silver_custom.py"))
diff --git a/web/scripts/app.js b/web/scripts/app.js
index 3f06629..bbd607c 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -551,7 +551,10 @@ class ComfyApp {
const nodeData = defs[nodeId];
const node = Object.assign(
function ComfyNode() {
- const inputs = nodeData["input"]["required"];
+ var inputs = nodeData["input"]["required"];
+ if (nodeData["input"]["optional"] != undefined){
+ inputs = Object.assign({}, nodeData["input"]["required"], nodeData["input"]["optional"])
+ }
const config = { minWidth: 1, minHeight: 1 };
for (const inputName in inputs) {
const inputData = inputs[inputName];
--
2.24.1.windows.2

5
web/scripts/app.js
View File

@ -551,7 +551,10 @@ class ComfyApp {
const nodeData = defs[nodeId]; const nodeData = defs[nodeId];
const node = Object.assign( const node = Object.assign(
function ComfyNode() { function ComfyNode() {
const inputs = nodeData["input"]["required"]; var inputs = nodeData["input"]["required"];
if (nodeData["input"]["optional"] != undefined){
inputs = Object.assign({}, nodeData["input"]["required"], nodeData["input"]["optional"])
}
const config = { minWidth: 1, minHeight: 1 }; const config = { minWidth: 1, minHeight: 1 };
for (const inputName in inputs) { for (const inputName in inputs) {
const inputData = inputs[inputName]; const inputData = inputs[inputName];