LEOSAMs-HelloWorld-SDXL-Base-Model
This checkpoint model is uploaded on imagepipeline.io
Model details - HelloWorld 2.0 no longer requires trigger words, and the results are comparable in quality to version 1.0 with trigger words.. The trigger word 'leogirl' in 1.0 was highly associated with East Asians. After the cancellation of the trigger words, while words like '1girl' will still likely generate East Asian portraits when race is not specified, you can now specify the race by using keywords like nationality, skin color, etc. For example, the trigger effects for words like 'Chinese', 'Russian', 'Iranian', 'Jamaican', 'Kenyan', 'dark-skinned', 'pale-skinned', etc., are listed below.
You can also get different styles of characters by writing the names of people from different countries and genders in the prompt, such as Han Meimei (China), Sophie Martin (France), Priya Patel (India), Fatima Al-Hassan (Arab), Wanjiru Mwangi (Kenya). The above prompts are just examples, there are many available prompts and ways to play, and you're welcome to explore and share them by yourself.
HelloWorld 2.0 has balanced the quality/color and offers more style options. The 1.0 version, when used with 'leogirl', would likely produce images with a strong film texture. HelloWorld 2.0 is no longer tied to a film texture and can be customized with some quality-related prompts. Some prompts that have been tested and work well include:
high-end fashion photoshoot, product introduction photo, popular Korean makeup, aegyo sal, Sharp High-Quality Photo, studio light, medium format photo, Mamiya photography, analog film, Medium Portrait with Soft Light, real-life image, refined editorial photograph, raw photo, real photo, Scanned Photo, film still
The color effects of these prompts are as follows:
The training set for HelloWorld 2.0 significantly increased the proportion of full-body photos to improve the effects of SDXL in generating full-body and distant view portraits. Although it has improved compared to version 1.0, it is still strongly recommended to use 'adetailer' in the process of generating full-body photos. Also, for users with enough video memory (24g), it is recommended to perform 1.5x high-resolution repair on the image, which can significantly improve facial 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/sdxl/text2image/v1/run"
payload = json.dumps({
"model_id": "94026682-87e0-4531-84f4-91778cb210a1",
"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/sdxl/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
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