Oodles builds production-grade Stable Diffusion applications using a robust AI and ML technology stack. Our solutions are developed with Python, PyTorch, Hugging Face Diffusers, and SDXL / SD 1.5 / SDXL Turbo models. We implement custom fine-tuning with LoRA and DreamBooth, advanced conditioning with ControlNet and IP-Adapter, and deploy optimized inference pipelines on private cloud or on-premise GPU infrastructure.
Stable Diffusion is an open-source latent diffusion model used for text-to-image, image-to-image, in-painting, and out-painting workflows. It is typically implemented using Python, PyTorch, and Hugging Face Diffusers, allowing enterprises to maintain full control over models, data, and infrastructure without relying on third-party hosted APIs.
Oodles engineers Stable Diffusion pipelines using PyTorch-based training and inference stacks, ControlNet for structural guidance, IP-Adapter for style transfer, custom schedulers, and FastAPI-based serving layers. These pipelines are containerized with Docker and deployed on GPU-enabled environments to deliver scalable, secure, and high-performance image generation systems.
Trained specifically on your data
Structure & composition guidance
Your data stays on your infrastructure
Built for production workloads
A systematic approach from data preparation to deployed image generation pipelines.
1
Discovery & Data Strategy: Define use cases (e.g., product shots, assets), identify style requirements, and curate datasets for model fine-tuning.
2
Fine-tuning & Training: Train Stable Diffusion LoRAs, DreamBooth models, or SDXL adapters to align image outputs with brand identity, products, or artistic styles.
3
Pipeline Controller Setup: Integrate ControlNet for pose/edge detection, IP-Adapter for style transfer, and other conditioning tools to ensure precise outputs.
4
Testing, Eval & Optimizing: Optimize Stable Diffusion inference using TensorRT, xFormers, batching strategies, and GPU memory optimization for low-latency generation.
5
Deployment & Integration: Deploy Stable Diffusion as scalable REST APIs using FastAPI, Ray Serve, or NVIDIA Triton and integrate with internal tools or frontends.
Structure-aware image generation using Canny, Depth, OpenPose, Scribble, and Segmentation ControlNet models.
Train lightweight adapters (LoRAs) on your products, characters, or art styles without retraining the entire model.
Intelligently modify parts of an image or extend image borders seamlessly using mask-based generation.
Transform existing images into new styles while preserving the original composition using IP-Adapter.
Sub-second image generation using SDXL Turbo, Latent Consistency Models (LCM), and accelerated schedulers.
Full ownership of Stable Diffusion models, weights, and datasets through isolated cloud or on-prem GPU deployments.
Production-ready image generation powered by Stable Diffusion across creative and industrial domains.
Generate photorealistic product variations on models, changing clothing, backgrounds, or lighting instantly.
Rapidly produce textures, sprites, character designs, and environmental concepts to accelerate game development.
Create unlimited variations of ad creatives, social media visuals, and customized brand imagery on demand.
Turn rough sketches and CAD drawings into photorealistic interior and exterior renders using ControlNet.
Generate personalized avatars, hero images, and unique visual content for user engagement.