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Checking backward compatibility'

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Sirshendu Ganguly
2026-01-27 21:16:07 -05:00
parent 7dc69a9305
commit b002d6e66d
4 changed files with 254 additions and 1 deletions
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__init__.py
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@@ -7,6 +7,8 @@ from .modules.modelSearch import modelSearch
from .modules.bridges import mainRoute
from .modules.controlNet import controlNet
from .modules.multiInference import multiInference
from .modules.runwareBFL import runwareKontext
from .modules.runwareImagen import runwareImagen
from .modules.loraSearch import loraSearch
from .modules.loraCombine import loraCombine
from .modules.refiner import refiner
@@ -89,6 +91,8 @@ NODE_CLASS_MAPPINGS = {
"Runware PhotoMaker V2": photoMaker,
"Runware Image Upscaler": upscaler,
"Runware Model Search": modelSearch,
"Runware Kontext Inference": runwareKontext,
"Runware Imagen Inference": runwareImagen,
"Runware Multi Inference": multiInference,
"Runware Lora Search": loraSearch,
"Runware Embedding Search": embeddingSearch,
modules/imageInference.py
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@@ -1,6 +1,5 @@
from .utils import runwareUtils as rwUtils
import comfy.model_management
import os
class txt2img:
modules/runwareBFL.py
+139
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@@ -0,0 +1,139 @@
from .utils import runwareUtils as rwUtils
class runwareKontext:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"Model": ([
"bfl:3@1 (FLUX.1 Kontext Pro)",
"bfl:4@1 (FLUX.1 Kontext Max)",
], {
"tooltip": "Select The Model You Want For Image Generation or Image Editing.",
"default": "bfl:3@1 (FLUX.1 Kontext Pro)",
}),
"positivePrompt": ("STRING", {
"multiline": True,
"placeholder": "Text instruction to guide the model on generating the image. It is usually a sentence or a paragraph that provides positive guidance for the task. This parameter is essential to shape the desired results.\n\nYou Can Press (Ctrl + Alt + E) To Enhance The Prompt!",
"tooltip": "Text instruction to guide the model on generating the image. You Can Also Press (Ctrl + Alt + E) To Enhance The Prompt!"
}),
"Prompt Upsampling": ("BOOLEAN", {
"tooltip": "If Enabled it Will automatically modify the prompt at generation time for more creative results.",
"default": False,
"label_on": "Enabled",
"label_off": "Disabled",
}),
"Aspect Ratio": ([
"21:9 (Ultra-Wide / Landscape)",
"16:9 (Wide / Landscape)",
"4:3 (Standard / Landscape)",
"3:2 (Classic Photo / Landscape)",
"1:1 (Square)",
"2:3 (Classic Photo / Portrait)",
"3:4 (Standard / Portrait)",
"9:16 (Vertical Video / Portrait)",
"9:21 (Ultra-Tall / Portrait)"
], {
"default": "16:9 (Wide / Landscape)",
"tooltip": "Adjust the dimensions of the generated image. This setting allows you to choose from various aspect ratios, such as 16:9 for wide images or 1:1 for square images. The width and height will be automatically adjusted based on the selected aspect ratio.",
}),
"seed": ("INT", {
"tooltip": "A value used to randomize the image generation. If you want to make images reproducible (generate the same image multiple times), you can use the same seed value. Set to 0 to auto-generate a random seed.",
"default": 0,
"min": 0,
"max": 4294967295,
}),
"Multi Inference Mode": ("BOOLEAN", {
"tooltip": "If Enabled the node will skip the image generation process and will only return the Runware Task Object to be used in the Multi Inference Node.",
"default": False,
"label_on": "Enabled",
"label_off": "Disabled",
}),
"batchSize": ("INT", {
"tooltip": "The number of images to generate in a single request.",
"default": 1,
"min": 1,
"max": 10,
}),
},
"optional": {
"Reference Images": ("RUNWAREREFERENCEIMAGES", {
"tooltip": "Connect a Runware Reference Images Node to use reference images for image editing or generation.",
}),
}
}
@classmethod
def VALIDATE_INPUTS(cls, positivePrompt):
if (positivePrompt is not None and (positivePrompt == "" or len(positivePrompt) < 3 or len(positivePrompt) > 2000)):
raise Exception(
"Positive Prompt Must Be Between 3 And 2000 characters!")
return True
DESCRIPTION = "Generate And Edit Images Using Runware BFL Kontext Models."
FUNCTION = "kontextInference"
RETURN_TYPES = ("IMAGE", "RUNWARETASK")
RETURN_NAMES = ("IMAGE", "RW-Task")
CATEGORY = "Runware"
def kontextInference(self, **kwargs):
runwareModel = kwargs.get("Model")
runwareModel = runwareModel.split(" ")[0]
positivePrompt = kwargs.get("positivePrompt")
promptUpsampling = kwargs.get("Prompt Upsampling")
aspectRatio = kwargs.get("Aspect Ratio")
multiInferenceMode = kwargs.get("Multi Inference Mode", False)
referenceImages = kwargs.get("Reference Images", None)
seed = kwargs.get("seed")
batchSize = kwargs.get("batchSize", 1)
aspectRatioMap = {
"21:9": (1568, 672),
"16:9": (1392, 752),
"4:3": (1184, 880),
"3:2": (1248, 832),
"1:1": (1024, 1024),
"2:3": (832, 1248),
"3:4": (880, 1184),
"9:16": (752, 1392),
"9:21": (672, 1568),
}
secAspectRatio = aspectRatio.split(" ")[0]
width, height = aspectRatioMap.get(secAspectRatio, (1024, 1024))
genConfig = [
{
"taskType": "imageInference",
"taskUUID": rwUtils.genRandUUID(),
"positivePrompt": positivePrompt,
"height": height,
"width": width,
"model": runwareModel,
"seed": seed,
"outputType": "base64Data",
"outputFormat": rwUtils.OUTPUT_FORMAT,
"outputQuality": rwUtils.OUTPUT_QUALITY,
"numberResults": batchSize,
}
]
if (referenceImages is not None):
genConfig[0]["referenceImages"] = referenceImages
if (promptUpsampling):
genConfig[0]["providerSettings"] = {
"bfl": {
"promptUpsampling": True
}
}
# For Debugging Purposes Only
print(f"[Debugging] Task UUID: {genConfig[0]['taskUUID']}")
if (multiInferenceMode):
return (None, genConfig)
else:
genResult = rwUtils.inferenecRequest(genConfig)
images = rwUtils.convertImageB64List(genResult)
return (images, None)
modules/runwareImagen.py
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@@ -0,0 +1,111 @@
from .utils import runwareUtils as rwUtils
class runwareImagen:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"Model": ([
"google:1@1 (Imagen 3)",
"google:1@2 (Imagen 3 Fast)",
"google:2@1 (Imagen 4 Preview)",
"google:2@2 (Imagen 4 Ultra)",
"google:2@3 (Imagen 4 Fast)",
], {
"tooltip": "Select The Google Imagen Model For Image Generation.",
"default": "google:1@1 (Imagen 3)",
}),
"positivePrompt": ("STRING", {
"multiline": True,
"placeholder": "Text instruction to guide the model on generating the image. It is usually a sentence or a paragraph that provides positive guidance for the task. This parameter is essential to shape the desired results.",
"tooltip": "Text instruction to guide the model on generating the image."
}),
"Aspect Ratio": ([
"1:1 (Square)",
"9:16 (Vertical)",
"16:9 (Wide)",
"3:4 (Portrait)",
"4:3 (Landscape)"
], {
"default": "1:1 (Square)",
"tooltip": "Adjust the dimensions of the generated image. The width and height will be automatically adjusted based on the selected aspect ratio.",
}),
"seed": ("INT", {
"tooltip": "A value used to randomize the image generation. If you want to make images reproducible (generate the same image multiple times), you can use the same seed value.",
"default": 0,
"min": 1,
"max": 9223372036854776000,
}),
"Multi Inference Mode": ("BOOLEAN", {
"tooltip": "If Enabled the node will skip the image generation process and will only return the Runware Task Object to be used in the Multi Inference Node.",
"default": False,
"label_on": "Enabled",
"label_off": "Disabled",
}),
"batchSize": ("INT", {
"tooltip": "The number of images to generate in a single request.",
"default": 1,
"min": 1,
"max": 10,
}),
}
}
@classmethod
def VALIDATE_INPUTS(cls, positivePrompt):
if (positivePrompt is not None and (positivePrompt == "" or len(positivePrompt) < 3 or len(positivePrompt) > 2000)):
raise Exception(
"Positive Prompt Must Be Between 3 And 2000 characters!")
return True
DESCRIPTION = "Generate Images Using Google Imagen Models."
FUNCTION = "imagenInference"
RETURN_TYPES = ("IMAGE", "RUNWARETASK")
RETURN_NAMES = ("IMAGE", "RW-Task")
CATEGORY = "Runware"
def imagenInference(self, **kwargs):
runwareModel = kwargs.get("Model")
runwareModel = runwareModel.split(" ")[0]
positivePrompt = kwargs.get("positivePrompt")
aspectRatio = kwargs.get("Aspect Ratio")
multiInferenceMode = kwargs.get("Multi Inference Mode", False)
seed = kwargs.get("seed")
batchSize = kwargs.get("batchSize", 1)
aspectRatioMap = {
"1:1": (1024, 1024),
"9:16": (768, 1408),
"16:9": (1408, 768),
"3:4": (896, 1280),
"4:3": (1280, 896),
}
secAspectRatio = aspectRatio.split(" ")[0]
width, height = aspectRatioMap.get(secAspectRatio, (1024, 1024))
genConfig = [
{
"taskType": "imageInference",
"taskUUID": rwUtils.genRandUUID(),
"positivePrompt": positivePrompt,
"height": height,
"width": width,
"model": runwareModel,
"seed": seed,
"outputType": "base64Data",
"outputFormat": rwUtils.OUTPUT_FORMAT,
"outputQuality": rwUtils.OUTPUT_QUALITY,
"numberResults": batchSize
}
]
# For Debugging Purposes Only
print(f"[Debugging] Task UUID: {genConfig[0]['taskUUID']}")
if (multiInferenceMode):
return (None, genConfig)
else:
genResult = rwUtils.inferenecRequest(genConfig)
images = rwUtils.convertImageB64List(genResult)
return (images, None)