license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
Photon
This checkpoint model is uploaded on imagepipeline.io
Model details - Recommendation for generating the first image with Photon: Prompt: A simple sentence in natural language describing the image. Negative: cartoon, painting, illustration, (worst quality, low quality, normal quality:2) Sampler: DPM++ 2M Karras | Steps: 20 | CFG Scale: 6 Size: 512x768 or 768x512 Hires.fix: R-ESRGAN 4x+ | Steps: 10 | Denoising: 0.45 | Upscale x 2 (avoid using negative embeddings unless absolutely necessary)From this initial point, experiment by adding positive and negative tags and adjusting the settings. Most of the sample images follow this format
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": "982b1053-d809-4cc2-9701-8d22f641ed3c",
"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 |
license: creativeml-openrail-m tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic pinned: false pipeline_tag: text-to-image
Feedback
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