API
If you're looking for an API, you can choose from your desired programming language.
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import requests
import base64
# Use this function to convert an image file from the filesystem to base64
def image_file_to_base64(image_path):
with open(image_path, 'rb') as f:
image_data = f.read()
return base64.b64encode(image_data).decode('utf-8')
# Use this function to fetch an image from a URL and convert it to base64
def image_url_to_base64(image_url):
response = requests.get(image_url)
image_data = response.content
return base64.b64encode(image_data).decode('utf-8')
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/sd1.5-flat2d"
# Request payload
data = {
"prompt": "(best-quality:0.8), (best-quality:0.8), perfect anime illustration of a Celestial Morbid Gaelic [Doghouse:Toyota:5] with a face, detailed with Cloth patterns, inside a Worn-Out Bar, Summer, equirectangular 360, Masterpiece, Sad, Surrealism Art, backlight, telephoto lens, Kodak portra, Lavender background, full of color, trending on artstation, trending on CGSociety",
"negative_prompt": "(worst quality:0.8), verybadimagenegative_v1.3 easynegative, (surreal:0.8), (modernism:0.8), (art deco:0.8), (art nouveau:0.8)",
"scheduler": "dpmpp_2m",
"num_inference_steps": 20,
"guidance_scale": 7,
"samples": 1,
"seed": 80952715457,
"img_width": 512,
"img_height": 768,
"base64": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated image
Attributes
Prompt to render
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 20,
max : 100
Scale for classifier-free guidance
min : 0.1,
max : 25
Number of samples to generate.
min : 1,
max : 4
Seed for image generation.
Width of the image.
Allowed values:
Height of the Image
Allowed values:
Base64 encoding of the output image.
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
Flat 2D
The Flat-2D model is the result of merging select anime-based and cartoon-based models. This fusion ensures a balanced output that aligns closely with classic anime styles. For those seeking enhanced clarity, upscaling the generated images using ESRGAN proves to be highly effective, adding an extra layer of finesse to the visuals.
Advantages
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Unique Anime-Cartoon Fusion: Flat-2D stands out with its distinctive cartoony anime style, offering a fresh perspective in the world of digital art.
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Authentic Anime Aesthetics: The model prioritizes traditional anime visuals over hyper-detailed representations, ensuring genuine anime feel.
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User-Centric Design: Tailored for anime and cartoon enthusiasts, the model provides an intuitive platform for generating desired visuals.
Use Cases
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Anime Art Creation: Artists can craft visuals that align closely with classic anime styles, perfect for various platforms.
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Animation Projects: Animators can utilize Flat-2D for creating scenes and characters that resonate with traditional anime aesthetics.
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Merchandise Design: Businesses can design merchandise, from T-shirts to posters, infused with the unique cartoony anime style.
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Educational Platforms: Students of animation and design can explore the nuances of anime and cartoon fusion, enhancing their learning experience.
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Content Creation: Content creators can produce engaging visuals for their audience, tapping into the popular anime-cartoon blend.
Other Popular Models
sdxl-img2img
SDXL Img2Img is used for text-guided image-to-image translation. This model uses the weights from Stable Diffusion to generate new images from an input image using StableDiffusionImg2ImgPipeline from diffusers

sdxl1.0-txt2img
The SDXL model is the official upgrade to the v1.5 model. The model is released as open-source software

codeformer
CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.

sd2.1-faceswapper
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training
