imagepipeline/epiCRealism-Inpaint

epiCRealism-Inpaint

This checkpoint model is uploaded on imagepipeline.io

Model details -

How to try this model ?

You can try using it locally or send an API call to test the output quality.

Get your API_KEY from imagepipeline.io. No payment required.

Coding in php javascript node etc ? Checkout our documentation

import requests  
import json  
  
url =  "https://imagepipeline.io/sd/text2image/v1/run"  
  
payload = json.dumps({  
"model_id":  "58e8200e-8cec-4280-8b60-2151727d6afa",  
"prompt":  "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",  
"negative_prompt":  "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",  
"width":  "512",  
"height":  "512",  
"samples":  "1",  
"num_inference_steps":  "30",  
"safety_checker":  false,   
"guidance_scale":  7.5,  
"multi_lingual":  "no",  
"embeddings":  "", 
"lora_models": "", 
"lora_weights":  "" 
})  
  
headers =  {  
'Content-Type':  'application/json',
'API-Key': 'your_api_key'
}  
  
response = requests.request("POST", url, headers=headers, data=payload)  
  
print(response.text)

}

Get more ready to use MODELS like this for SD 1.5 and SDXL :

API Reference

Generate Image

  https://api.imagepipeline.io/sd/text2image/v1

Headers	Type	Description
API-Key	str	Get your API_KEY from imagepipeline.io
Content-Type	str	application/json - content type of the request body

Parameter	Type	Description
model_id	str	Your base model, find available lists in models page or upload your own
prompt	str	Text Prompt. Check our Prompt Guide for tips
num_inference_steps	int [1-50]	Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM)
guidance_scale	float [1-20]	Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5
lora_models	str, array	Pass the model_id(s) of LoRA models that can be found in models page
lora_weights	str, array	Strength of the LoRA effect

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license: creativeml-openrail-m tags:

• imagepipeline
• imagepipeline.io
• text-to-image
• ultra-realistic pinned: false pipeline_tag: text-to-image

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