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The Training: Mastering Diffusers for AI Image Creation: From Theory to Practice training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
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Presentation of Diffusers architectures (DDPM, DDIM, Stable Diffusion…). Overview of uses and applications. Introduction to text-to-image and image-to-image pipelines. Installation of Python environments and dependencies (PyTorch, Transformers, Diffusers).
Presentation and handling of the Diffusers library. Loading pre-trained models (Stable Diffusion, DreamBooth…). Image generation from textual prompts and parameterization (guidance, steps, seed). Evaluation of results, GPU memory management, batch processing.
Preparing a dataset for training or fine-tuning. Adapting a Diffusers model with your own images (classification and text+image pipelines). Advanced usage: style controls, integration of content control tools (inpainting, outpainting). Deploying an API or microservice for image generation, optimization tips for production.
Target audience
Developers, data scientists, AI engineers, graphic designers, and IT professionals wishing to explore image generation using Diffusers models.
Prerequisites
Good knowledge of Python and basics in machine learning or deep learning.
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