mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-11 14:50:49 +08:00
468 lines
12 KiB
Python
468 lines
12 KiB
Python
import pytest
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from comfy.api.components.schema.prompt import Prompt
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from comfy.client.embedded_comfy_client import EmbeddedComfyClient
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from comfy.model_downloader import add_known_models, KNOWN_LORAS
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from comfy.model_downloader_types import CivitFile
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_workflows = {
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"audio_1": {
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"14": {
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"inputs": {
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"ckpt_name": "stable_audio_open_1.0.safetensors"
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},
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"class_type": "CheckpointLoaderSimple",
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"_meta": {
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"title": "Load Checkpoint"
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}
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},
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"15": {
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"inputs": {
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"clip_name": "t5_base.safetensors",
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"type": "stable_audio"
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},
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"class_type": "CLIPLoader",
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"_meta": {
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"title": "Load CLIP"
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}
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},
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"16": {
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"inputs": {
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"text": "hard bop, upright bass, slappy bass, low frequencies, drum kit brushed hi-hat, snare with ghost notes, syncopated, groove",
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"clip": [
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"15",
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0
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]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Prompt)"
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}
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},
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"17": {
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"inputs": {
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"text": "",
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"clip": [
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"15",
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0
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]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Prompt)"
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}
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},
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"19": {
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"inputs": {
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"cfg": 7,
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"model": [
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"14",
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0
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],
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"positive": [
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"16",
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0
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],
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"negative": [
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"17",
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0
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]
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},
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"class_type": "CFGGuider",
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"_meta": {
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"title": "CFGGuider"
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}
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},
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"21": {
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"inputs": {
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"noise_seed": 600769511872395
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},
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"class_type": "RandomNoise",
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"_meta": {
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"title": "RandomNoise"
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}
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},
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"22": {
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"inputs": {
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"noise": [
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"21",
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0
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],
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"guider": [
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"19",
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0
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],
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"sampler": [
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"29",
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0
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],
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"sigmas": [
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"24",
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0
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],
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"latent_image": [
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"30",
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0
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]
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},
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"class_type": "SamplerCustomAdvanced",
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"_meta": {
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"title": "SamplerCustomAdvanced"
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}
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},
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"24": {
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"inputs": {
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"scheduler": "exponential",
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"steps": 20,
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"denoise": 1,
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"model": [
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"14",
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0
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]
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},
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"class_type": "BasicScheduler",
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"_meta": {
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"title": "BasicScheduler"
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}
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},
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"26": {
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"inputs": {
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"samples": [
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"22",
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1
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],
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"vae": [
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"14",
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2
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]
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},
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"class_type": "VAEDecodeAudio",
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"_meta": {
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"title": "VAEDecodeAudio"
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}
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},
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"27": {
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"inputs": {
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"filename_prefix": "audio/objectobject",
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"audio": [
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"26",
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0
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]
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},
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"class_type": "SaveAudio",
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"_meta": {
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"title": "SaveAudio"
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}
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},
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"29": {
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"inputs": {
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"version": "regular"
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},
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"class_type": "SamplerEulerCFGpp",
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"_meta": {
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"title": "SamplerEulerCFG++"
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}
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},
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"30": {
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"inputs": {
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"seconds": 47.6
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},
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"class_type": "EmptyLatentAudio",
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"_meta": {
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"title": "EmptyLatentAudio"
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}
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},
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"31": {
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"inputs": {
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"filename_prefix": "latents/ComfyUI",
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"samples": [
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"22",
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1
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]
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},
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"class_type": "SaveLatent",
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"_meta": {
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"title": "SaveLatent"
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}
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}
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},
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"auraflow_1": {
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"1": {
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"inputs": {
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"ckpt_name": "aura_flow_0.1.safetensors"
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},
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"class_type": "CheckpointLoaderSimple",
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"_meta": {
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"title": "Load Checkpoint"
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}
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},
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"2": {
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"inputs": {
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"shift": 1.73,
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"model": [
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"1",
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0
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]
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},
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"class_type": "ModelSamplingAuraFlow",
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"_meta": {
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"title": "ModelSamplingAuraFlow"
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}
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},
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"3": {
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"inputs": {
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"seed": 232240565010917,
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"steps": 25,
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"cfg": 3.5,
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"sampler_name": "uni_pc",
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"scheduler": "normal",
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"denoise": 1,
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"model": [
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"2",
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0
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],
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"positive": [
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"4",
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0
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],
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"negative": [
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"5",
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0
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],
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"latent_image": [
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"6",
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0
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]
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},
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"class_type": "KSampler",
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"_meta": {
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"title": "KSampler"
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}
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},
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"4": {
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"inputs": {
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"text": "close-up portrait of cat",
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"clip": [
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"1",
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1
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]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Prompt)"
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}
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},
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"5": {
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"inputs": {
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"text": "",
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"clip": [
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"1",
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1
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]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Prompt)"
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}
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},
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"6": {
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"inputs": {
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"width": 1024,
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"height": 1024,
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"batch_size": 1
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},
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"class_type": "EmptyLatentImage",
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"_meta": {
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"title": "Empty Latent Image"
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}
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},
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"7": {
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"inputs": {
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"samples": [
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"3",
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0
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],
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"vae": [
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"1",
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2
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]
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},
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"class_type": "VAEDecode",
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"_meta": {
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"title": "VAE Decode"
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}
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},
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"8": {
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"inputs": {
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"filename_prefix": "ComfyUI",
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"images": [
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"7",
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0
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]
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},
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"class_type": "SaveImage",
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"_meta": {
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"title": "Save Image"
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}
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}
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},
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"lora_1": {
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"3": {
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"inputs": {
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"seed": 851616030078638,
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"steps": 20,
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"cfg": 8,
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"sampler_name": "euler",
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"scheduler": "normal",
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"denoise": 1,
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"model": [
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"10",
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0
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],
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"positive": [
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"6",
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0
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],
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"negative": [
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"7",
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0
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],
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"latent_image": [
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"5",
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0
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]
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},
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"class_type": "KSampler",
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"_meta": {
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"title": "KSampler"
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}
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},
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"4": {
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"inputs": {
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"ckpt_name": "v1-5-pruned-emaonly.safetensors"
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},
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"class_type": "CheckpointLoaderSimple",
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"_meta": {
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"title": "Load Checkpoint"
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}
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},
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"5": {
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"inputs": {
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"width": 512,
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"height": 512,
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"batch_size": 1
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},
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"class_type": "EmptyLatentImage",
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"_meta": {
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"title": "Empty Latent Image"
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}
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},
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"6": {
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"inputs": {
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"text": "masterpiece best quality girl",
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"clip": [
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"10",
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1
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]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Prompt)"
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}
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},
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"7": {
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"inputs": {
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"text": "bad hands",
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"clip": [
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"10",
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1
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]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Prompt)"
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}
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},
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"8": {
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"inputs": {
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"samples": [
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"3",
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0
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],
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"vae": [
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"4",
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2
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]
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},
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"class_type": "VAEDecode",
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"_meta": {
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"title": "VAE Decode"
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}
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},
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"9": {
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"inputs": {
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"filename_prefix": "ComfyUI",
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"images": [
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"8",
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0
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]
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},
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"class_type": "SaveImage",
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"_meta": {
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"title": "Save Image"
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}
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},
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"10": {
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"inputs": {
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"lora_name": "epi_noiseoffset2.safetensors",
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"strength_model": 1,
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"strength_clip": 1,
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"model": [
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"4",
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0
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],
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"clip": [
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"4",
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1
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]
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},
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"class_type": "LoraLoader",
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"_meta": {
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"title": "Load LoRA"
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}
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}
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}
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}
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@pytest.fixture(scope="module", autouse=False)
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@pytest.mark.asyncio
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async def client(tmp_path_factory) -> EmbeddedComfyClient:
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async with EmbeddedComfyClient() as client:
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yield client
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@pytest.mark.asyncio
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@pytest.mark.parametrize("workflow_name, workflow", _workflows.items())
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async def test_workflow(workflow_name: str, workflow: dict, has_gpu: bool, client: EmbeddedComfyClient):
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if not has_gpu:
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pytest.skip("requires gpu")
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if "audio" in workflow_name:
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try:
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import torchaudio
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except (ImportError, ModuleNotFoundError):
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pytest.skip("requires torchaudio")
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prompt = Prompt.validate(workflow)
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add_known_models("loras", KNOWN_LORAS, CivitFile(13941, 16576, "epi_noiseoffset2.safetensors"))
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# todo: add all the models we want to test a bit more elegantly
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outputs = await client.queue_prompt(prompt)
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if any(v.class_type == "SaveImage" for v in prompt.values()):
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save_image_node_id = next(key for key in prompt if prompt[key].class_type == "SaveImage")
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assert outputs[save_image_node_id]["images"][0]["abs_path"] is not None
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elif any(v.class_type == "SaveAudio" for v in prompt.values()):
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save_image_node_id = next(key for key in prompt if prompt[key].class_type == "SaveAudio")
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assert outputs[save_image_node_id]["audio"][0]["filename"] is not None
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