SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Color palette matching. To perform low-level matching, we utilize the Pyllette tool [59] to extract a set of 10 palette colors. Subsequently, we conduct a bipartite matching between the color palette of each generated image and the reference palette colors in the RGB color space. Before matching, each color is scaled to  $[0,1]$ . The matching result is obtained by calculating the sum of L1 distances.
User study. To evaluate the effectiveness of our approach against other methods, we conducted a user study using an online form. The user study interface, depicted in Figure 22, was designed to elicit
* 01 Specification
Thank you for taking part in this survey. For each question in this questionnaire, we will provide a reference image, a corresponding text description, and image results generated using four methods based on these two conditions. For each case, please rank the four methods from high to low based on two aspects:
1. image authenticity (how difficult it is to distinguish from images in the real world (photo/painting)) and
2. the degree of matching with the given conditions (both the reference image and the text prompt).
$\bigcirc$  I understood.
(b) Shuffled results
Figure 22: User study user interface. In this case, four methods are: (A). SD ImageVar [53], (B). ELITE [20], (C). DALL-E 2[16], (D). RIVAL (ours).
(a) Specification
(c) Ranking questions
method	SD [11]	ImgVar [53]	ELITE [20]	UnCLIP [16]	RIVAL
base model	V1-5	V1-3	V1-4	V2-1	V1-5
KID ↓	17.1	18.5	25.7	13.5	13.2
Table 6: Quantitative comparisons for KID  $(\times 10^{3})$ . All methods are Stable Diffusion based.
user rankings of image variation results. We collected 41 questionnaire responses, encompassing 16 cases of ranking comparisons.
KID evaluation. To provide a comprehensive assessment of the quality, we utilize Kernel Inception Distance (KID)[60] to evaluate the perceptual generation quality of our test set. As depicted in Table6, with Stable Diffusion V1-5, our method achieves the best KID score, which is slightly superior to the UnCLIP [16], employing the advanced Stable Diffusion V2-1.
F Additional Considerations
Data acquisition. To comprehensively evaluate our method, we collected diverse source exemplars from multiple public datasets, such as DreamBooth [12] and Interactive Video Stylization [61]. Some exemplars were obtained from Google and Behance solely for research purposes. We will not release our self-collected example data due to license restrictions.
Societal impacts. This paper introduces a novel framework for image generation that leverages a hybrid image-text condition, facilitating the generation of diverse image variations. Although this application has the potential to be misused by malicious actors for disinformation purposes, significant advancements have been achieved in detecting malicious generation. Consequently, we anticipate that our work will contribute to this domain. In forthcoming iterations of our method, we intend to introduce the NSFW (Not Safe for Work) test for detecting possible malicious generations. Through rigorous experimentation and analysis, our objective is to enhance comprehension of image generation techniques and alleviate their potential misuse.