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Minor changes and extra nodes

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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
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244
comfy_extras/silver_custom.py Normal file
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@ -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
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@ -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,
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1,
11,
4,
"seed"
],
[
16,
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0,
8,
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"LATENT"
],
[
17,
8,
0,
12,
0,
"IMAGE"
],
[
18,
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0,
13,
0,
"IMAGE"
],
[
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"LATENT"
],
[
20,
4,
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11,
0,
"MODEL"
],
[
21,
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"CONDITIONING"
],
[
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2,
"CONDITIONING"
],
[
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0,
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0,
"LATENT"
],
[
24,
16,
0,
8,
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"VAE"
],
[
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15,
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"VAE"
],
[
26,
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14,
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"IMAGE"
],
[
27,
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13,
1,
"VAE"
],
[
28,
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0,
17,
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"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_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
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@ -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 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];