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| Author | SHA1 | Date | |
|---|---|---|---|
 Dr.Lt.Data
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izmp
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@@ -32,6 +32,31 @@ NOTE: The UltralyticsDetectorProvider node is not part of the ComfyUI-Impact-Pac
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* With the addition of wildcard support in FaceDetailer, the structure of DETAILER_PIPE-related nodes and Detailer nodes has changed. There may be malfunctions when using the existing workflow.
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## How To Install
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### **Recommended**
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* Install via [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager).
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### **Manual**
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* Navigate to `ComfyUI/custom_nodes` in your terminal (cmd).
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* Clone the repository under the `custom_nodes` directory using the following command:
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```
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git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack comfyui-impact-pack
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cd comfyui-impact-pack
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```
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* Install dependencies in your Python environment.
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* For Windows Portable, run the following command inside `ComfyUI\custom_nodes\comfyui-impact-pack`:
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```
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..\..\..\python_embeded\python.exe -m pip install -r requirements.txt
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```
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* If using venv or conda, activate your Python environment first, then run:
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```
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pip install -r requirements.txt
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```
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### Companion Pack
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* If you need the `Ultralytics Detector Provider` to use various YOLO detection models, you should also install [ComfyUI-Impact-Subpack](https://github.com/ltdrdata/ComfyUI-Impact-Subpack).
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## Custom Nodes
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### [Detector nodes](https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/detectors.md)
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* `SAMLoader` - Loads the SAM model.
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@@ -18,7 +18,6 @@ modules_path = os.path.join(os.path.dirname(__file__), "modules")
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sys.path.append(modules_path)
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import impact.config
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import impact.sample_error_enhancer
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print(f"### Loading: ComfyUI-Impact-Pack ({impact.config.version})")
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# Core
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Binary file not shown.
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After Width: | Height: | Size: 63 KiB |
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Load Diff
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""
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""
|
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},
|
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{
|
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"link": null,
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|
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|
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"localized_name": "vae"
|
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},
|
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{
|
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|
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|
||||
"localized_name": "positive"
|
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|
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|
||||
"localized_name": "negative"
|
||||
}
|
||||
],
|
||||
"mode": 0,
|
||||
"order": 3,
|
||||
"outputs": [
|
||||
{
|
||||
"links": null,
|
||||
"name": "basic_pipe",
|
||||
"shape": 3,
|
||||
"slot_index": 0,
|
||||
"type": "BASIC_PIPE",
|
||||
"localized_name": "basic_pipe"
|
||||
}
|
||||
],
|
||||
"pos": [
|
||||
1320,
|
||||
360
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "ToBasicPipe"
|
||||
},
|
||||
"size": {
|
||||
"0": 241.79998779296875,
|
||||
"1": 106
|
||||
},
|
||||
"type": "ToBasicPipe"
|
||||
}
|
||||
],
|
||||
"packname": "Impact",
|
||||
"version": "1.0"
|
||||
}
|
||||
},
|
||||
"controller_panel": {
|
||||
"controllers": {},
|
||||
"hidden": true,
|
||||
"highlight": true,
|
||||
"version": 2,
|
||||
"default_order": []
|
||||
},
|
||||
"node_versions": {
|
||||
"comfy-core": "0.3.14",
|
||||
"comfyui-impact-pack": "1ae7cae2df8cca06027edfa3a24512671239d6c4"
|
||||
},
|
||||
"ue_links": [],
|
||||
"VHS_latentpreview": false,
|
||||
"VHS_latentpreviewrate": 0,
|
||||
"VHS_MetadataImage": true,
|
||||
"VHS_KeepIntermediate": true
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
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|
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|
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|
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|
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|
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Load Diff
+49
-43
@@ -223,29 +223,29 @@ api.addEventListener("executed", progressExecuteHandler);
|
||||
app.registerExtension({
|
||||
name: "Comfy.Impack",
|
||||
|
||||
commands: [
|
||||
{
|
||||
id: 'refresh-impact-wildcard',
|
||||
label: 'Impact: Refresh Wildcard',
|
||||
function: async () => {
|
||||
await api.fetchApi('/impact/wildcards/refresh');
|
||||
await load_wildcards();
|
||||
app.extensionManager.toast.add({
|
||||
severity: 'info',
|
||||
summary: 'Refreshed!',
|
||||
detail: 'Impact Wildcard List is refreshed!!',
|
||||
life: 3000
|
||||
});
|
||||
}
|
||||
}
|
||||
],
|
||||
commands: [
|
||||
{
|
||||
id: 'refresh-impact-wildcard',
|
||||
label: 'Impact: Refresh Wildcard',
|
||||
function: async () => {
|
||||
await api.fetchApi('/impact/wildcards/refresh');
|
||||
await load_wildcards();
|
||||
app.extensionManager.toast.add({
|
||||
severity: 'info',
|
||||
summary: 'Refreshed!',
|
||||
detail: 'Impact Wildcard List is refreshed!!',
|
||||
life: 3000
|
||||
});
|
||||
}
|
||||
}
|
||||
],
|
||||
|
||||
menuCommands: [
|
||||
{
|
||||
path: ['Edit'],
|
||||
commands: ['refresh-impact-wildcard']
|
||||
}
|
||||
],
|
||||
menuCommands: [
|
||||
{
|
||||
path: ['Edit'],
|
||||
commands: ['refresh-impact-wildcard']
|
||||
}
|
||||
],
|
||||
|
||||
loadedGraphNode(node, app) {
|
||||
if (node.comfyClass == "MaskPainter") {
|
||||
@@ -617,12 +617,14 @@ app.registerExtension({
|
||||
|
||||
if(node.comfyClass == "ImpactSEGSLabelFilter" || node.comfyClass == "SEGSLabelFilterDetailerHookProvider") {
|
||||
node.widgets[0].callback = (value, canvas, node, pos, e) => {
|
||||
if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
|
||||
node.widgets[1].value += ", "
|
||||
if(node) {
|
||||
if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
|
||||
node.widgets[1].value += ", "
|
||||
|
||||
node.widgets[1].value += value;
|
||||
if(node.widgets_values)
|
||||
node.widgets_values[1] = node.widgets[1].value;
|
||||
node.widgets[1].value += value;
|
||||
if(node.widgets_values)
|
||||
node.widgets_values[1] = node.widgets[1].value;
|
||||
}
|
||||
}
|
||||
|
||||
Object.defineProperty(node.widgets[0], "value", {
|
||||
@@ -696,18 +698,20 @@ app.registerExtension({
|
||||
break;
|
||||
}
|
||||
|
||||
node.widgets[combo_id+1].callback = (value, canvas, node, pos, e) => {
|
||||
if(node.widgets[tbox_id].value != '')
|
||||
node.widgets[tbox_id].value += ', '
|
||||
node.widgets[combo_id+1].callback = (value, canvas, node, pos, e) => {
|
||||
if(node) {
|
||||
if(node.widgets[tbox_id].value != '')
|
||||
node.widgets[tbox_id].value += ', '
|
||||
|
||||
node.widgets[tbox_id].value += node._wildcard_value;
|
||||
}
|
||||
node.widgets[tbox_id].value += node._wildcard_value;
|
||||
}
|
||||
}
|
||||
|
||||
Object.defineProperty(node.widgets[combo_id+1], "value", {
|
||||
set: (value) => {
|
||||
if (value !== "Select the Wildcard to add to the text")
|
||||
node._wildcard_value = value;
|
||||
},
|
||||
if (value !== "Select the Wildcard to add to the text")
|
||||
node._wildcard_value = value;
|
||||
},
|
||||
get: () => { return "Select the Wildcard to add to the text"; }
|
||||
});
|
||||
|
||||
@@ -720,14 +724,16 @@ app.registerExtension({
|
||||
|
||||
if(has_lora) {
|
||||
node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
|
||||
let lora_name = node._value;
|
||||
if(lora_name.endsWith('.safetensors')) {
|
||||
lora_name = lora_name.slice(0, -12);
|
||||
}
|
||||
if(node) {
|
||||
let lora_name = node._value;
|
||||
if(lora_name.endsWith('.safetensors')) {
|
||||
lora_name = lora_name.slice(0, -12);
|
||||
}
|
||||
|
||||
node.widgets[tbox_id].value += `<lora:${lora_name}>`;
|
||||
if(node.widgets_values) {
|
||||
node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
|
||||
node.widgets[tbox_id].value += `<lora:${lora_name}>`;
|
||||
if(node.widgets_values) {
|
||||
node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -766,7 +772,7 @@ app.registerExtension({
|
||||
else
|
||||
node._mode_value = value; // combo value
|
||||
|
||||
populated_text_widget.inputEl.disabled = node._mode_value != 'populate';
|
||||
populated_text_widget.inputEl.disabled = node._mode_value == 'populate';
|
||||
},
|
||||
get: () => {
|
||||
if(node._mode_value != undefined)
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import configparser
|
||||
import os
|
||||
|
||||
version_code = [8, 7]
|
||||
version_code = [8, 8, 5]
|
||||
version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
|
||||
|
||||
dependency_version = 24
|
||||
|
||||
@@ -96,9 +96,13 @@ class SAMLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
models = [x for x in folder_paths.get_filename_list("sams") if 'hq' not in x]
|
||||
|
||||
if 'ESAM_ModelLoader_Zho' in nodes.NODE_CLASS_MAPPINGS:
|
||||
models.append('ESAM')
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"model_name": (models + ['ESAM'], {"tooltip": "The detection accuracy varies depending on the SAM model. ESAM can only be used if ComfyUI-YoloWorld-EfficientSAM is installed."}),
|
||||
"model_name": (models, {"tooltip": "The detection accuracy varies depending on the SAM model. ESAM can only be used if ComfyUI-YoloWorld-EfficientSAM is installed."}),
|
||||
"device_mode": (["AUTO", "Prefer GPU", "CPU"], {"tooltip": "AUTO: Only applicable when a GPU is available. It temporarily loads the SAM_MODEL into VRAM only when the detection function is used.\n"
|
||||
"Prefer GPU: Tries to keep the SAM_MODEL on the GPU whenever possible. This can be used when there is sufficient VRAM available.\n"
|
||||
"CPU: Always loads only on the CPU."}),
|
||||
|
||||
@@ -46,65 +46,27 @@ def get_noise_sampler(x, cpu, total_sigmas, **kwargs):
|
||||
|
||||
|
||||
def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
|
||||
if sampler_name == "dpmpp_sde":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
if sampler_name in ["dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu"]:
|
||||
if sampler_name == "dpmpp_sde":
|
||||
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde
|
||||
elif sampler_name == "dpmpp_sde_gpu":
|
||||
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_sde_gpu
|
||||
elif sampler_name == "dpmpp_2m_sde":
|
||||
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde
|
||||
elif sampler_name == "dpmpp_2m_sde_gpu":
|
||||
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_2m_sde_gpu
|
||||
elif sampler_name == "dpmpp_3m_sde":
|
||||
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde
|
||||
elif sampler_name == "dpmpp_3m_sde_gpu":
|
||||
orig_sampler_function = k_diffusion_sampling.sample_dpmpp_3m_sde_gpu
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_sde(model, x, sigmas, **kwargs)
|
||||
def sampler_function_wrapper(model, x, sigmas, **kwargs):
|
||||
if 'noise_sampler' not in kwargs:
|
||||
kwargs['noise_sampler'] = get_noise_sampler(x, 'gpu' not in sampler_name, total_sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
return orig_sampler_function(model, x, sigmas, **kwargs)
|
||||
|
||||
elif sampler_name == "dpmpp_sde_gpu":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_sde_gpu(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_2m_sde":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_2m_sde(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_2m_sde_gpu":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_2m_sde_gpu(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_3m_sde":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_3m_sde(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
|
||||
elif sampler_name == "dpmpp_3m_sde_gpu":
|
||||
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
|
||||
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
|
||||
if noise_sampler is not None:
|
||||
kwargs['noise_sampler'] = noise_sampler
|
||||
|
||||
return k_diffusion_sampling.sample_dpmpp_3m_sde_gpu(model, x, sigmas, **kwargs)
|
||||
|
||||
sampler_function = sample_dpmpp_sde
|
||||
sampler_function = sampler_function_wrapper
|
||||
|
||||
else:
|
||||
return comfy.samplers.sampler_object(sampler_name)
|
||||
|
||||
@@ -701,6 +701,9 @@ class ImpactControlBridge:
|
||||
return (value, )
|
||||
else:
|
||||
return (ExecutionBlocker(None), )
|
||||
elif extra_pnginfo is None:
|
||||
logging.warn(f"[Impact Pack] limitation: '{behavior}' behavior cannot be used in API execution.")
|
||||
return (value,)
|
||||
else:
|
||||
workflow_nodes, links = workflow_to_map(extra_pnginfo['workflow'])
|
||||
|
||||
|
||||
@@ -1,25 +0,0 @@
|
||||
import comfy.sample
|
||||
import traceback
|
||||
|
||||
original_sample = comfy.sample.sample
|
||||
|
||||
|
||||
def informative_sample(*args, **kwargs):
|
||||
try:
|
||||
return original_sample(*args, **kwargs) # This code helps interpret error messages that occur within exceptions but does not have any impact on other operations.
|
||||
except RuntimeError as e:
|
||||
is_model_mix_issue = False
|
||||
try:
|
||||
if 'mat1 and mat2 shapes cannot be multiplied' in e.args[0]:
|
||||
if 'torch.nn.functional.linear' in traceback.format_exc().strip().split('\n')[-3]:
|
||||
is_model_mix_issue = True
|
||||
except:
|
||||
pass
|
||||
|
||||
if is_model_mix_issue:
|
||||
raise RuntimeError("\n\n#### It seems that models and clips are mixed and interconnected between SDXL Base, SDXL Refiner, SD1.x, and SD2.x. Please verify. ####\n\n")
|
||||
else:
|
||||
raise e
|
||||
|
||||
|
||||
comfy.sample.sample = informative_sample
|
||||
+38
-13
@@ -44,7 +44,9 @@ def read_wildcard(k, v):
|
||||
elif isinstance(v, str):
|
||||
k = wildcard_normalize(k)
|
||||
wildcard_dict[k] = [v]
|
||||
|
||||
elif isinstance(v, (int, float)):
|
||||
k = wildcard_normalize(k)
|
||||
wildcard_dict[k] = [str(v)]
|
||||
|
||||
def read_wildcard_dict(wildcard_path):
|
||||
global wildcard_dict
|
||||
@@ -135,6 +137,8 @@ def process(text, seed=None):
|
||||
b = r.group(3)
|
||||
if b is not None:
|
||||
b = b.strip()
|
||||
else:
|
||||
b = "-1"
|
||||
|
||||
if r is not None:
|
||||
if b is not None and is_numeric_string(a) and is_numeric_string(b):
|
||||
@@ -145,26 +149,32 @@ def process(text, seed=None):
|
||||
x = int(a)
|
||||
select_range = (x, x)
|
||||
|
||||
# Expand wildcard path or return the string after $$
|
||||
def expand_wildcard_or_return_string(options, pattern, wildcard_pattern):
|
||||
matches = re.findall(wildcard_pattern, pattern)
|
||||
if len(options) == 1 and matches:
|
||||
# $$<single wildcard>
|
||||
return get_wildcard_options(pattern)
|
||||
else:
|
||||
# $$opt1|opt2|...
|
||||
options[0] = pattern
|
||||
return options
|
||||
|
||||
if select_range is not None and len(multi_select_pattern) == 2:
|
||||
# PATTERN: count$$
|
||||
matches = re.findall(wildcard_pattern, multi_select_pattern[1])
|
||||
if len(options) == 1 and matches:
|
||||
# count$$<single wildcard>
|
||||
options = get_wildcard_options(multi_select_pattern[1])
|
||||
else:
|
||||
# count$$opt1|opt2|...
|
||||
options[0] = multi_select_pattern[1]
|
||||
options = expand_wildcard_or_return_string(options, multi_select_pattern[1], wildcard_pattern )
|
||||
elif select_range is not None and len(multi_select_pattern) == 3:
|
||||
# PATTERN: count$$ sep $$
|
||||
select_sep = multi_select_pattern[1]
|
||||
options[0] = multi_select_pattern[2]
|
||||
options = expand_wildcard_or_return_string(options, multi_select_pattern[2], wildcard_pattern )
|
||||
|
||||
adjusted_probabilities = []
|
||||
|
||||
total_prob = 0
|
||||
|
||||
for option in options:
|
||||
parts = option.split('::', 1)
|
||||
parts = option.split('::', 1) if isinstance(option, str) else f"{option}".split('::', 1)
|
||||
|
||||
if len(parts) == 2 and is_numeric_string(parts[0].strip()):
|
||||
config_value = float(parts[0].strip())
|
||||
else:
|
||||
@@ -178,15 +188,30 @@ def process(text, seed=None):
|
||||
if select_range is None:
|
||||
select_count = 1
|
||||
else:
|
||||
select_count = random_gen.integers(low=select_range[0], high=select_range[1]+1, size=1)
|
||||
def calculate_max(_options_length, _max_select_range):
|
||||
return min(_max_select_range + 1, _options_length + 1) if _max_select_range > 0 else _options_length + 1
|
||||
|
||||
if select_count > len(options):
|
||||
def calculate_select_count(_max_value, _min_select_range, random_gen):
|
||||
if max(_max_value, _min_select_range) <= 0:
|
||||
return 0
|
||||
# fix: low >= high
|
||||
elif _max_value == _min_select_range:
|
||||
return _max_value
|
||||
else:
|
||||
# fix: low >= high
|
||||
_low_value = min(_min_select_range, _max_value)
|
||||
_high_value = max(_min_select_range, _max_value)
|
||||
return random_gen.integers(low=_low_value, high=_high_value, size=1)
|
||||
select_count = calculate_select_count(calculate_max(len(options), select_range[1]), select_range[0], random_gen)
|
||||
|
||||
if select_count > len(options) or total_prob <= 1:
|
||||
random_gen.shuffle(options)
|
||||
selected_items = options
|
||||
else:
|
||||
selected_items = random_gen.choice(options, p=normalized_probabilities, size=select_count, replace=False)
|
||||
|
||||
selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', x, 1) for x in selected_items]
|
||||
# x may be numpy.int32, convert to string
|
||||
selected_items2 = [re.sub(r'^\s*[0-9.]+::', '', str(x), 1) for x in selected_items]
|
||||
replacement = select_sep.join(selected_items2)
|
||||
if '::' in replacement:
|
||||
pass
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "comfyui-impact-pack"
|
||||
description = "This node pack offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
|
||||
version = "8.7"
|
||||
version = "8.8.5"
|
||||
license = { file = "LICENSE.txt" }
|
||||
dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
|
||||
|
||||
|
||||
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File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user