text-to-panorama / src /app /engine /upscaleImage.ts
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exhumation of this old project 🧟
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"use server"
import { Client } from "@gradio/client"
export async function upscalePanorama({
image,
prompt
}: {
image: string
prompt: string
}): Promise<string> {
const app = await Client.connect("jbilcke-hf/clarity-upscaler-api")
// const dataUri = await fetch(`data:image/jpeg;base64,${base64Data}`)
const dataUri = await fetch(image)
const imageBlob = await dataUri.blob()
const result = await app.predict("/predict", {
"Secret Token": process.env.MICRO_SERVICE_SECRET_TOKEN || "",
// convert the base64 image to blob
"Image": imageBlob,
Prompt: `360° HDRI panorama photo, ${prompt}`,
"Negative Prompt": "blurry, cropped",
"Scalue Factor": 2,
"Dynamic": 6,
"Creativity": 0.35,
"Resemblance": 0.6,
"tiling_width": 112,
"tiling_height": 144,
// epicrealism_naturalSinRC1VAE.safetensors [84d76a0328]', 'juggernaut_reborn.safetensors [338b85bc4f]', 'flat2DAnimerge_v45Sharp.safetensors'],
"sd_model": "juggernaut_reborn.safetensors [338b85bc4f]",
"scheduler": "DPM++ 3M SDE Karras",
})
/*
inputs.append(gr.Slider(
label="Num Inference Steps", info='''Number of denoising steps''', value=18,
minimum=1, maximum=100, step=1,
))
inputs.append(gr.Number(
label="Seed", info='''Random seed. Leave blank to randomize the seed''', value=1337
))
inputs.append(gr.Checkbox(
label="Downscaling", info='''Downscale the image before upscaling. Can improve quality and speed for images with high resolution but lower quality''', value=False
))
inputs.append(gr.Number(
label="Downscaling Resolution", info='''Downscaling resolution''', value=768
))
inputs.append(gr.Textbox(
label="Lora Links", info='''Link to a lora file you want to use in your upscaling. Multiple links possible, seperated by comma'''
))
inputs.append(gr.Textbox(
label="Custom Sd Model", info='''Link to a custom safetensors checkpoint file you want to use in your upscaling. Will overwrite sd_model checkpoint.'''
))
])
*/
console.log(`result:`, result)
return ""
}