584 lines
17 KiB
Python
584 lines
17 KiB
Python
import logging
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import pytest
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from comfy_execution.graph_utils import GraphBuilder
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from prompt_control.nodes_lazy import (
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PCLazyLoraLoader,
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PCLazyLoraLoaderAdvanced,
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PCLazyTextEncode,
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PCLazyTextEncodeAdvanced,
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)
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log = logging.getLogger("comfyui-prompt-control")
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def reset_graphbuilder_state():
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GraphBuilder.set_default_prefix("UID", 0, 0)
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def find_file(name):
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names = {"test": "test.safetensors", "other": "some/other.safetensors"}
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return names.get(name)
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def as_dict(out):
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return {"result": out.result, "expand": out.expand}
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def loraloader(text, adv=False, **kwargs):
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reset_graphbuilder_state()
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cls = PCLazyLoraLoaderAdvanced if adv else PCLazyLoraLoader
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model = [0, 1]
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clip = [0, 0]
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return as_dict(cls.execute(model=model, clip=clip, text=text, **kwargs))
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def te(text, adv=False, **kwargs):
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cls = PCLazyTextEncode if adv else PCLazyTextEncodeAdvanced
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reset_graphbuilder_state()
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clip = [0, 0]
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return as_dict(cls.execute(clip=clip, text=text, **kwargs))
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@pytest.fixture(autouse=True)
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def patch_lora_name_to_file(monkeypatch):
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import prompt_control.utils
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monkeypatch.setattr(prompt_control.utils, "lora_name_to_file", find_file)
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@pytest.fixture(autouse=True)
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def patch_torch_cuda_current_device(monkeypatch):
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import torch.cuda
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monkeypatch.setattr(torch.cuda, "current_device", lambda: "cpu")
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def test_textencode_expansion():
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for p in ["test", "[test:0.2] test", "[test[test::0.5]]<lora:test:1>"]:
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r1 = te(p)
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r2 = te(p, adv=True)
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assert r1 == r2
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def test_textencode_alternating():
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r = te("[a|b]")
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expected_result = {
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"expand": {
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"UID.0.0.1": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "a",
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},
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},
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"UID.0.0.10": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.9",
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0,
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],
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"end": 0.5,
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"start": 0.4,
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},
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},
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"UID.0.0.11": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "b",
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},
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},
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"UID.0.0.12": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.11",
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0,
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],
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"end": 0.6,
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"start": 0.5,
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},
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},
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"UID.0.0.13": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "a",
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},
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},
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"UID.0.0.14": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.13",
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0,
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],
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"end": 0.7,
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"start": 0.6,
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},
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},
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"UID.0.0.15": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "b",
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},
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},
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"UID.0.0.16": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.15",
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0,
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],
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"end": 0.8,
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"start": 0.7,
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},
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},
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"UID.0.0.17": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "a",
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},
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},
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"UID.0.0.18": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.17",
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0,
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],
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"end": 0.9,
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"start": 0.8,
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},
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},
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"UID.0.0.19": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "b",
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},
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},
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"UID.0.0.2": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.1",
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0,
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],
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"end": 0.1,
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"start": 0.0,
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},
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},
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"UID.0.0.20": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.19",
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0,
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],
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"end": 1.0,
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"start": 0.9,
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},
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},
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"UID.0.0.21": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.2",
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0,
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],
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"conditioning_2": [
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"UID.0.0.4",
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0,
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],
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},
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},
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"UID.0.0.22": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.21",
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0,
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],
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"conditioning_2": [
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"UID.0.0.6",
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0,
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],
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},
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},
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"UID.0.0.23": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.22",
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0,
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],
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"conditioning_2": [
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"UID.0.0.8",
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0,
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],
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},
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},
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"UID.0.0.24": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.23",
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0,
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],
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"conditioning_2": [
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"UID.0.0.10",
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0,
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],
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},
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},
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"UID.0.0.25": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.24",
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0,
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],
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"conditioning_2": [
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"UID.0.0.12",
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0,
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],
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},
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},
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"UID.0.0.26": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.25",
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0,
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],
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"conditioning_2": [
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"UID.0.0.14",
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0,
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],
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},
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},
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"UID.0.0.27": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.26",
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0,
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],
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"conditioning_2": [
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"UID.0.0.16",
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0,
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],
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},
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},
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"UID.0.0.28": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.27",
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0,
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],
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"conditioning_2": [
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"UID.0.0.18",
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0,
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],
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},
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},
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"UID.0.0.29": {
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"class_type": "ConditioningCombine",
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"inputs": {
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"conditioning_1": [
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"UID.0.0.28",
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0,
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],
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"conditioning_2": [
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"UID.0.0.20",
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0,
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],
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},
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},
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"UID.0.0.3": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "b",
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},
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},
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"UID.0.0.4": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.3",
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0,
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],
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"end": 0.2,
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"start": 0.1,
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},
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},
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"UID.0.0.5": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "a",
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},
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},
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"UID.0.0.6": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.5",
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0,
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],
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"end": 0.3,
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"start": 0.2,
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},
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},
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"UID.0.0.7": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "b",
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},
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},
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"UID.0.0.8": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {
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"conditioning": [
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"UID.0.0.7",
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0,
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],
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"end": 0.4,
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"start": 0.3,
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},
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},
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"UID.0.0.9": {
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"class_type": "PCTextEncode",
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"inputs": {
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"clip": [
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0,
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0,
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],
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"text": "a",
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},
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},
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},
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"result": (
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[
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"UID.0.0.29",
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0,
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],
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),
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}
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assert r == expected_result
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def test_textencode_lora():
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reset_graphbuilder_state()
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r = te("test<lora:test:1>")
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assert r == {
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"result": (["UID.0.0.2", 0],),
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"expand": {
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"UID.0.0.1": {"class_type": "PCTextEncode", "inputs": {"clip": [0, 0], "text": "test"}},
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"UID.0.0.2": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {"conditioning": ["UID.0.0.1", 0], "start": 0.0, "end": 1.0},
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},
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},
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}
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def test_textencode_lora_with_schedule():
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r = te("simple [test:0.1,0.5] prompt<lora:test:1>")
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assert r == {
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"result": (["UID.0.0.8", 0],),
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"expand": {
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"UID.0.0.1": {
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"class_type": "PCTextEncode",
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"inputs": {"clip": [0, 0], "text": "simple prompt"},
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},
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"UID.0.0.2": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {"conditioning": ["UID.0.0.1", 0], "start": 0.0, "end": 0.1},
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},
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"UID.0.0.3": {
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"class_type": "PCTextEncode",
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"inputs": {"clip": [0, 0], "text": "simple test prompt"},
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},
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"UID.0.0.4": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {"conditioning": ["UID.0.0.3", 0], "start": 0.1, "end": 0.5},
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},
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"UID.0.0.5": {
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"class_type": "PCTextEncode",
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"inputs": {"clip": [0, 0], "text": "simple prompt"},
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},
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"UID.0.0.6": {
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"class_type": "ConditioningSetTimestepRange",
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"inputs": {"conditioning": ["UID.0.0.5", 0], "start": 0.5, "end": 1.0},
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},
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"UID.0.0.7": {
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"class_type": "ConditioningCombine",
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"inputs": {"conditioning_1": ["UID.0.0.2", 0], "conditioning_2": ["UID.0.0.4", 0]},
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},
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"UID.0.0.8": {
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"class_type": "ConditioningCombine",
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"inputs": {"conditioning_1": ["UID.0.0.7", 0], "conditioning_2": ["UID.0.0.6", 0]},
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},
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},
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}
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def test_loraloader_empty(monkeypatch, caplog):
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result = loraloader("prompt here <lora:nonexistent:1.0:0.5>")["expand"]
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result_adv = loraloader("prompt here <lora:nonexistent:1.0:0.5>", adv=True)["expand"]
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assert result == {}
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assert result_adv == {}
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def test_loraloader_duplicate_results():
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result = loraloader("<lora:test:1>")["expand"]
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result2 = loraloader("prompt here <lora:test:1.0:0.5><lora:test:0:0.5>")["expand"]
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result3 = loraloader("prompt here <lora:test:1.0:0.5><lora:test:0:0.5>", adv=True)["expand"]
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assert result == result2
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assert result2 == result3
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assert result == {
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"UID.0.0.1": {
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"class_type": "LoraLoader",
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"inputs": {
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"model": [0, 1],
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"clip": [0, 0],
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"strength_model": 1.0,
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"strength_clip": 1.0,
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"lora_name": "test.safetensors",
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},
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}
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}
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def test_loraloader_multiple_loras():
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result = loraloader("<lora:test:1><lora:other:0.5>")["expand"]
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assert result == {
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"UID.0.0.1": {
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"class_type": "LoraLoader",
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"inputs": {
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"model": [0, 1],
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"clip": [0, 0],
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"strength_model": 1.0,
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"strength_clip": 1.0,
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"lora_name": "test.safetensors",
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},
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},
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"UID.0.0.2": {
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"class_type": "LoraLoader",
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"inputs": {
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"model": ["UID.0.0.1", 0],
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"clip": ["UID.0.0.1", 1],
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"strength_model": 0.5,
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"strength_clip": 0.5,
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"lora_name": "some/other.safetensors",
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},
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},
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}
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|
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|
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def test_loraloader_strength_clip():
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result = loraloader("prompt here <lora:test:1.0:0.5>")["expand"]
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assert result == {
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"UID.0.0.1": {
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"class_type": "LoraLoader",
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"inputs": {
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"model": [0, 1],
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"clip": [0, 0],
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"strength_model": 1.0,
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"strength_clip": 0.5,
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"lora_name": "test.safetensors",
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},
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}
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}
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def test_loraloader_scheduled_compare():
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result = loraloader("prompt [<lora:test:0.5>:0.5]")["expand"]
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result2 = loraloader("prompt [<lora:test:0.5>:0.5]", adv=True)["expand"]
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assert result == result2
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expected = {
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"UID.0.0.1": {
|
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"class_type": "CreateHookLora",
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"inputs": {"lora_name": "test.safetensors", "strength_model": 0.5, "strength_clip": 0.5},
|
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},
|
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"UID.0.0.2": {
|
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"class_type": "CreateHookKeyframe",
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"inputs": {"strength_mult": 0.0, "start_percent": 0.0},
|
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},
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"UID.0.0.3": {
|
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"class_type": "CreateHookKeyframe",
|
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"inputs": {"start_percent": 0.5, "prev_hook_kf": ["UID.0.0.2", 0], "strength_mult": 1.0},
|
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},
|
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"UID.0.0.4": {
|
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"class_type": "SetHookKeyframes",
|
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"inputs": {"hooks": ["UID.0.0.1", 0], "hook_kf": ["UID.0.0.3", 0]},
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},
|
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"UID.0.0.5": {
|
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"class_type": "SetClipHooks",
|
|
"inputs": {
|
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"clip": [0, 0],
|
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"hooks": ["UID.0.0.4", 0],
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"apply_to_conds": True,
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"schedule_clip": True,
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},
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},
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}
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assert result == expected
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def test_loraloader_adv_start():
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result2 = loraloader("prompt [<lora:test:0.5>:0.5]", adv=True, start=0.6)["expand"]
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assert result2 == {
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"UID.0.0.1": {
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"class_type": "LoraLoader",
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"inputs": {
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"model": [0, 1],
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"clip": [0, 0],
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"strength_model": 0.5,
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"strength_clip": 0.5,
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"lora_name": "test.safetensors",
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},
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}
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}
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def test_loraloader_end_zero():
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result2 = loraloader("prompt [<lora:test:0.5>:0.5]", adv=True, end=0.5)["expand"]
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assert result2 == {}
|