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/smallsd1.5-txt2img"
# Request payload
data = {
"prompt": "a toy panda standing on a pile of broccoli",
"negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, BadDream, UnrealisticDream",
"scheduler": "dpmpp_2m",
"num_inference_steps": 100,
"guidance_scale": 7.5,
"samples": 1,
"seed": 2313248373,
"img_width": 512,
"img_height": 512,
"base64": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
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.
PixelFlow allows you to use all these features
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Segmented Creation Workflow
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customized Output
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Layering Different Models
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Workflow APIs
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Segmind Small-SD: The Next Generation in Efficient Text-to-Image Synthesis
Introducing Segmind Small-SD, an innovative generative AI model from Segmind, designed with the specific goal of accelerating and democratizing AI applications. This new compact and high-speed Stable Diffusion model is open-sourced and readily accessible on Huggingface. The inspiration behind Segmind Small-SD comes from the pioneering research delineated in the paper "On Architectural Compression of Text-to-Image Diffusion Models." Based on this groundwork, Segmind presents two compact versions: SD-Small and SD-Tiny, with SD-Small offering a 35% reduction in parameters compared to the base model while maintaining a similar level of image fidelity.
The technological core of Segmind Small-SD is built around Knowledge Distillation (KD), a concept that mimics a teacher-student learning process within the realm of AI. Here, a larger, pre-trained model (the teacher) assists a smaller model (the student) in training on a condensed dataset. This unique distillation method includes matching outputs at every block of the U-nets from the teacher model, ensuring the maintenance of model quality during the size reduction. The KD process includes a multi-faceted loss function that not only considers the traditional loss but also the variance between the latents generated by the teacher and the student model, and notably, the feature-level loss — the difference between the block outputs from the teacher and student.
The key advantage of the Segmind Small-SD model is its blend of speed, efficiency, and quality. Offering up to 85% faster inferences, these models significantly reduce the time required to produce results, providing a perfect balance between high performance and economic viability. Despite their reduced size, these models can generate high-quality images, making them an excellent solution for tasks that demand rapid image generation without compromising quality.
Segmind Small-SD use cases
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Digital Content Creation: Rapid production of superior-quality images for various digital content platforms, such as blogs, social media, and more.
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Game Asset Creation: For game developers, the model can efficiently generate unique game assets, enhancing creativity and speed.
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Customized Marketing: Quicker generation of personalized visuals for digital marketing efforts, boosting customer interaction.
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AI-Assisted Art and Design: Artists and designers can leverage it for fast creation of distinctive, AI-aided visual content.
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AI Research: For researchers in AI, faster inference means more rapid prototyping, testing, and discoveries, thereby speeding up the overall research process.
Segmind Small-SD License
Segmind Small-SD is licensed under CreativeML Open RAIL-M. This license encourages both the open and responsible use of the model. It is inspired by permissive open-source licenses in terms of granting IP rights while also adding use-based restrictions to prevent misuse of the technology, be it due to technical limitations or ethical considerations. While derivative versions of the model can be released under different licensing terms, they must always include the same use-based restrictions as the original license. Thus, the license strikes a balance between open and responsible AI development, promoting open-science in the field of AI while simultaneously addressing potential misuse.
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