bytedance/DreamO

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1649 stars · 136 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DreamO is the official PyTorch implementation of a unified framework for image customization, built on FLUX diffusion models. It supports tasks like subject-driven and identity-preserving image generation, with a Gradio demo, quantization support, and consumer-GPU compatibility.

Use cases

• generate customized images from reference subjects
• identity-preserving portrait generation
• run image customization models on consumer GPUs
• quantize a diffusion model for faster inference
• try a unified image customization demo locally
• integrate image customization into ComfyUI workflows

When to choose

• you need subject-driven or identity-preserving image generation with a FLUX-based model
• you want a research-backed unified image customization framework with a ready demo
• you need to run image customization on 16-24GB consumer GPUs via quantization and offloading

When to avoid

• you need a lightweight CPU-only image editing tool
• you want a production image-generation service rather than a research codebase
• you need training/fine-tuning pipelines rather than inference

Facets

library · maturity active

image-processing machine-learning deep-learning image-processing artificial-intelligence machine-learning python cross-platform image-customization diffusion flux text-to-image gradio siggraph-asia-2025 subject-driven-generation model-quantization gpu

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markdown · JSON · MCP: product_card(name="bytedance/DreamO")