I will do lora training and deploy comfyui workflow into serverless api, runpod

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comfyuistudio
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comfyuistudio
David C
Alcune informazioni sono riportate in lingua inglese.

Informazioni su questo servizio

I will train a custom LoRA AI influencer model focused on realism, consistency, and long-term reuse using SDXL, FLUX, and ComfyUI workflows. This service is built for creators, brands, and agencies that need a stable AI identity for social media, marketing, or scalable contentwithout face drift or overfitting.


Your LoRA is trained from your image dataset using a controlled process that prioritizes data quality, correct tagging, and proper training parameters. The result is a model that preserves identity while allowing flexible styles, poses, lighting, and outfits. Youll also receive a ready-to-run ComfyUI workflow, so you can generate results immediately without rebuilding nodes or guessing settings.


Software Used

ComfyUI, Stable Diffusion XL (SDXL), FLUX, Kohya tools, Python training pipeline.


Deliverables

Custom LoRA (.safetensors)

ComfyUI workflow (.json)

Prompt notes (.txt)

Test images (.png / .jpg)


I focus on clean training and usable workflowsnot presets.


Message me before ordering to review your dataset and choose the right package.

Preferenza per lo stile di consegna

Informa il freelance di eventuali preferenze o preoccupazioni relative all'uso di strumenti di IA nel completamento e/o nella consegna dell'ordine.

Scopri di più su David C

David C

ComfyUI workflows, automation, and custom AI models

  • DaStati Uniti
  • Membro dafeb 2026
  • Tempo di risposta medio4 ore
  • Ultima consegna2 mesi
  • Lingue

    Inglese, Spagnolo, Tedesco, Francese
I specialize in building custom ComfyUI workflows, AI image automation, and tailored LoRA model training for Stable Diffusion, Flux, and Wan 2.0. My goal is simple: help you achieve consistent, reliable AI image results without confusion, broken setups, or trial and error. I create clean, production-ready workflows with the latest ComfyUI updates, including optimized node structures, batching, scheduler tuning, and performance enhancements—delivering practical, reusable systems that are easy to load, scale, and use.

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