  Recent data-driven image colorization methods have enabled automatic or
reference-based colorization, while still suffering from unsatisfactory and
inaccurate object-level color control. To address these issues, we propose a
new method called DiffColor that leverages the power of pre-trained diffusion
models to recover vivid colors conditioned on a prompt text, without any
additional inputs. DiffColor mainly contains two stages: colorization with
generative color prior and in-context controllable colorization. Specifically,
we first fine-tune a pre-trained text-to-image model to generate colorized
images using a CLIP-based contrastive loss. Then we try to obtain an optimized
text embedding aligning the colorized image and the text prompt, and a
fine-tuned diffusion model enabling high-quality image reconstruction. Our
method can produce vivid and diverse colors with a few iterations, and keep the
structure and background intact while having colors well-aligned with the
target language guidance. Moreover, our method allows for in-context
colorization, i.e., producing different colorization results by modifying
prompt texts without any fine-tuning, and can achieve object-level controllable
colorization results. Extensive experiments and user studies demonstrate that
DiffColor outperforms previous works in terms of visual quality, color
fidelity, and diversity of colorization options.
