Title: StyleDrop: Text-to-Image Synthesis of Any Style
Venue: NeurIPS 2023
Accepted: yes
Keywords: text-to-image synthesis

fine-tuning

stylization
Abstract: Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language, and out-of-distribution effects make it hard to synthesize arbitrary image styles, leveraging a specific design pattern, texture or material. In this paper, we introduce *StyleDrop*, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. StyleDrop is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. StyleDrop works by efficiently learning a new style by fine-tuning very few trainable parameters (less than 1\% of total model parameters), and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a *single* image specifying the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. More results are available at our project website: [https://styledrop.github.io](https://styledrop.github.io).

StyleDrop: Text-to-Image Generation in Any Style

Abstract

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distribution effects make it hard to synthesize image styles, that leverage a specific design pattern, texture or material. In this paper, we introduce StyleDrop , a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse [5] convincingly outperforms other methods, including DreamBooth [34] and textual inversion [11] on Imagen [35] or Stable Diffusion [33]. More results are available at our project website: https://styledrop.github.io .

1 Introduction

Text-to-image models trained on large image and text pairs have enabled the creation of rich and diverse images encompassing many genres and themes [2, 5, 33, 35, 43]. The resulting creations have become a sensation, with Midjourney [2] reportedly being the largest Discord server in the world [1]. The styles of famous artists, such as Vincent Van Gogh, might be captured due to the presence of their work in the training data. Moreover, popular styles such as 'anime' or 'steampunk', when added to the input text prompt, may translate to specific visual outputs based on the training data. While many efforts have been put into 'prompt engineering", a wide range of styles are simply hard to describe in text form, due to the nuances of color schemes, illumination and other characteristics. As an example, Van Gogh has paintings in different styles ( e.g ., Fig. 1, top row, rightmost three columns). Thus, a text prompt that simply says 'Van Gogh' may either result in one specific style (selected at random), or in an unpredictable mix of several styles. Neither of these is a desirable outcome.

In this paper, we introduce StyleDrop 3 which allows significantly higher level of stylized text-to-image synthesis, using as few as one image as an example of a given style. Our experiments (Fig. 1) show that StyleDrop achieves unprecedented accuracy and fidelity in stylized image synthesis. StyleDrop is built on a few crucial components: (1) a transformer-based text-to-image generation model [5]; (2) adapter tuning [15]; and (3) iterative training with feedback. For the first component, we find that Muse [5], a transformer modeling a discrete visual token sequence, shows an advantage over diffusion models such as Imagen [35] and Stable Diffusion [33] for learning fine-grained styles from single images. For the second component, we employ adapter tuning [15] to style-tune a large text-to-image transformer efficiently. Specifically, we construct a text input of a style reference image by composing content and style text descriptors to promote content-style disentanglement, which is crucial for compositional image synthesis [37, 32, 41]. Finally, for the third component, we propose an iterative training framework, which trains a new adapter on images sampled from a previously trained adapter. We find that, when trained on a small set of high-quality synthesized images, iterative training effectively alleviates overfitting, a prevalent issue for fine-tuning a text-to-image model on a very few ( e.g ., one) images. We study high-quality sample selection methods using CLIP score ( e.g ., image-text alignment) and human feedback in Sec. 4.4.3, verifying the complementary benefit.

In addition to handling various styles, we extend our approach to customize not only style but also content ( e.g ., the identifying/distinctive features of a given object or subject), leveraging DreamBooth [34]. We propose a novel approach that samples an image of my content in my style from two adapters trained for content and style independently. This compositional approach voids the need to jointly optimize on both content and style images [20, 13] and is therefore very flexible. We show in Fig. 5 that this approach produces compelling results that combines personalized generation respecting both object identity and object style.

We test StyleDrop on Muse on a diverse set of style reference images, as shown in Fig. 1. We compare with other recent methods including DreamBooth [34] and Textual Inversion [11], using Imagen [35] and Stable Diffusion [33] as pre-trained text-to-image backbones. An extensive evaluation based on prompt and style fidelity metrics using CLIP [29] and a user study shows the superiority of StyleDrop to other methods. Please visit our website and Appendix for more results.

2 Related Work

Personalized Text-to-Image Synthesis has been studied to edit images of personal assets by leveraging the power of pre-trained text-to-image models. Textual inversion [11] and Hard prompt made easy (PEZ) [39] find text representations ( e.g ., embedding, token) corresponding to a set of images of an object without changing parameters of the text-to-image model.

DreamBooth [34] fine-tunes an entire text-to-image model on a few images describing the subject of interest. As such, it is more expressive and captures the subject with greater details. Parameterefficient fine-tuning (PEFT) methods, such as LoRA [16] or adapter tuning [15], are adopted to improve its efficiency [3, 27]. Custom diffusion [20] and SVDiff [13] have extended DreamBooth to synthesize multiple subjects simultaneously. Inversion-based Style Transfer [44] presents a one-shot style tuning of text-to-image diffusion models. Unlike these methods built on text-to-image diffusion

models, we build StyleDrop on Muse [5], a generative vision transformer. [11, 39, 20] have shown learning styles with text-to-image diffusion models, but from a handful or a dozen of style reference images, and are limited to painting styles. We demonstrate on a wide variety of visual styles, including 3d rendering, design illustration, and sculpture, using a single style reference image.

Neural Style Transfer (NST). A large body of work [12, 18, 24, 7] has investigated style transfer using deep networks by solving a composite objective of style and content consistency [12]. Recently, [17] has shown that quantizing the latent space leads to improved visual and style fidelity of NST compared to continuous latent spaces. MaskSketch [4] converts sketch images into natural images via structure-guided parallel decoding of a masked image generation model. While both output stylized images, StyleDrop is different from NST in many ways; ours is based on text-to-image models to generate content, whereas NST uses an image to guide content ( e.g ., spatial structure) for synthesis; we use adapters to capture fine-grained visual style properties; we incorporate feedback signals to refine the style from a single input image.

Parameter Efficient Fine Tuning (PEFT) is a new paradigm for fine-tuning of deep learning models by only tuning a much smaller number of parameters, instead of the entire model. These parameters are either subsets of the original trained model, or small number of parameters that are added for the fine-tuning stage. PEFT has been introduced in the context of large language models [15, 23, 16], and then applied to text-to-image diffusion models [35, 33] with LoRA [3] or adapter tuning [27]. Fine-tuning of autoregressive (AR) [10, 43, 21] and non-autoregressive (NAR) [6, 5, 38] generative vision transformers has been studied recently [36], but without the text modality.

3 StyleDrop: Style Tuning for Text-to-Image Synthesis

StyleDrop is built on Muse [5], reviewed in Sec. 3.1. There are two key parts. The parameter-efficient fine-tuning of a generative vision transformer (Sec. 3.2) and an iterative training with feedback (Sec. 3.3). Finally, we discuss how to synthesize images from two fine-tuned models in Sec. 3.4.

3.1 Preliminary: Muse [5], a masked Transformer for Text-to-Image Synthesis

Muse [5] is a state-of-the-art text-to-image synthesis model based on the masked generative image transformer, or MaskGIT [6]. It contains two synthesis modules for base image generation ( 256 × 256 ) and super-resolution ( 512 × 512 or 1024 × 1024 ). Each module is composed of a text encoder T , a transformer G , a sampler S , an image encoder E , and decoder D . T maps a text prompt t ∈ T to a continuous embedding space E . G processes a text embedding e ∈ E to generate logits l ∈ L for the visual token sequence. S draws a sequence of visual tokens v ∈ V from logits via iterative decoding [6, 5], which runs a few steps of transformer inference conditioned on the text embeddings e and visual tokens decoded from previous steps. Finally, D maps the sequence of discrete tokens to pixel space I . 4 To summarize, given a text prompt t , an image I is synthesized as follows:

where n ∈ T is a negative prompt, λ is a guidance scale, k is the synthesis step, and l k 's are logits, from which the next set of visual tokens v k +1 's are sampled. We refer to [6, 5] for details on the iterative decoding process. The T5-XXL [30] encoder for T and VQGAN [10, 42] for E and D are used. G is trained on a large (image, text) pairs D using masked visual token modeling loss [6]:

where M is a masking operator that applies masks to the tokens in v i . CE m is a weighted cross-entropy calculated by summing only over the unmasked tokens.

3.2 Parameter-Efficient Fine-Tuning of Text-to-Image Generative Vision Transformers

Nowwepresent a unified framework for parameter-efficient fine-tuning of generative vision transformers. The proposed framework is not limited to a specific model and application, and is easily applied to the fine-tuning of text-to-image ( e.g ., Muse [5], Paella [31], Parti [43], RQ-Transformer [21]) and text-to-video ( e.g ., Phenaki [38], CogVideo [14]) transformers, with a variety of PEFT methods, such as prompt tuning [23], LoRA [16], or adapter tuning [15], as in [36]. Nonetheless, we focus on Muse [5], an NAR text-to-image transformer, using adapter tuning [15].

Self-Attn

Cross-Attn

e

x L

A

θ

Following [36], we are interested in adapting a transformer G , while the rest ( E , D , T ) remain fixed. Let ̂ G : V × E × Θ → L a modified version of a transformer G that takes learnable parameters θ ∈ Θ as an additional input. Here, θ would represent parameters for learnable soft prompts of prompt tuning or weights of adapter tuning. Fig. 2 provides an intuitive description of G with adapter tuning.

̂ Fine-tuning of the transformer ̂ G involves learning of newly introduced parameters θ , while existing parameters of G ( e.g ., parameters of self-attention and cross-attention layers) remain fixed, with the learning objective as follows:

l k = ̂ G ( v k , T ( t ) , θ ) + λ A ( ̂ G ( v k , T ( t ) , θ ) -G ( v k , T ( t )) ) + λ B ( G ( v k , T ( t )) -G ( v k , T ( n )) ) , (4) where λ A controls the level of adaptation to the target distribution by contrasting the two generation distributions, one that is fine-tuned ̂ G ( v k , T ( t ) , θ ) and another that is not G ( v k , T ( t )) , and λ B controls the textual alignment by contrasting the positive ( t ) and negative ( n ) text prompts.

where D tr contains a few (image, text) pairs for fine-tuning. Unlike DreamBooth [34] where the same text prompt is used to represent a set of training images, we use different text prompts for each input image to better disentangle content and style. Once trained, similarly to the procedure in Eq. (2), we synthesize images from the generation distribution of ̂ G ( · , · , θ ) . Specifically, at each decoding step k , we generate logits l k as follows:

3.2.1 Constructing Text Prompts

image

text prompt

'A

cat

in

watercolor painting style

'

Table 1: An example text prompt at training. We construct a text prompt by composing descriptions of content ( e.g ., an object) and style ( e.g ., watercolor painting).

To train θ , we require training data D tr = { ( I i , t i ) } N i =1 composed of (image, text) pairs for style reference. In many scenarios, we may be given only images as a style reference. In such cases, we need to manually append text prompts.

We propose a simple, templated approach to construct text prompts, consisting of the description of a content ( e.g ., object, scene) followed by the phrase describing the style. For example, we use a 'cat' to describe an object in Tab. 1 and append 'watercolor painting' as a style descriptor. Incorporating descriptions of both content and style in the text prompt is critical, as it helps to disentangle the content from style and let learned parameters θ model the style, which is our primary goal. While we find that using a rare token identifier [34] in place of a style descriptor ( e.g ., 'watercolor painting') works as well, having such a descriptive style descriptor provides an extra flexibility of style property editing, which will be shown in Sec. 4.4.2 and Fig. 7.

3.3 Iterative Training with Feedback

While our framework is generic and works well even on small training sets, the generation quality of the style-tuned model from a single image can sometimes be sub-optimal. The text construction method in Sec. 3.2.1 helps the quality, but we still find that overfitting to content is a concern. As in red boxes of Fig. 3 where the same house is rendered in the background, it is hard to perfectly avoid the content leakage. However, we see that many of the rendered images successfully disentangle style from content, as shown in the blue boxes of Fig. 3.

For such a scenario, we leverage this finding of high precision when successful and introduce an iterative training (IT) of StyleDrop using synthesized images by StyleDrop trained at an earlier stage to improve the recall (more disentanglement). We opt for a simple solution: construct a new training

set with a few dozen successful (image, text) pairs ( e.g ., images in blue box of Fig. 3) while using the same objective in Eq. (3). IT results in an immediate improvement with a reduced content leakage, as in Fig. 3 green box. The key question is how to assess the quality of synthesized images.

CLIP score [29] measures the image-text alignment. As such, it could be used to assess the quality of generated images by measuring the CLIP score ( i.e ., cosine similarity of visual and textual CLIP embeddings). We select images with the highest CLIP scores and we call this method an iterative training with CLIP feedback (CF). In our experiments, we find that the CLIP score to assess the quality of synthesized images is an efficient way of improving the recall ( i.e ., textual fidelity) without losing too much style fidelity. On the other hand, CLIP score may not be perfectly aligned with the human intention [22, 40] and would not capture the subtle style property.

Human Feedback (HF) is a more direct way of injecting user intention into the quality evaluation of synthetic images. HF is shown to be powerful and effective in LLM fine-tuning with reinforcement learning [28]. In our case, HF could be used to compensate the CLIP score not being able to capture subtle style properties. Empirically, selecting less than a dozen images is enough for IT, and it only takes about 3 minutes per style. As shown in Sec. 4.4.4 and Fig. 9, HF is critical for some applications, such as illustration designs, where capturing subtle differences is important to correctly reflect the designer's intention. Nevertheless, due to human selection bias, style may drift or be reduced.

3.4 Sampling from Two θ 's

There has been an extensive study on personalization of text-to-image diffusion models to synthesize images containing multiple personal assets [20, 26, 13]. In this section, we show how to combine DreamBooth and StyleDrop in a simple manner, thereby enabling personalization of both style and content . Inspired by the idea of diffusion as energy-based models for compositional visual generation [9, 25, 8], we sample from two modified generation distributions, guided by θ s for style and θ c for content, each of which are adapter parameters trained independently on style and content reference images, respectively. Unlike existing works [20, 13], our approach does not require joint training of learnable parameters on multiple concepts, leading to a greater compositional power with pre-trained adapters, which are separately trained on individual subject and style assets.

Our overall sampling procedure follows the iterative decoding of Eq. (1), with differences in how we sample logits at each decoding step. Let t be the text prompt and c be the text prompt without the style descriptor. 5 We compute logits at step k as follows: l k =(1 -γ ) l s k + γ l c k , where

̂ ̂ where γ balances the StyleDrop and DreamBooth - if γ is 0, we get StyleDrop, and DreamBooth if 1. By properly setting γ ( e.g ., 0 . 5 ∼ 0 . 7 ), we get images of my content in my style (see Fig. 5).

4 Experiments

We report results of StyleDrop on a variety of styles and compare with existing methods in Sec. 4.2. In Sec. 4.3 we show results on 'my object in my style' combining the capability of DreamBooth and StyleDrop. Finally, we conduct an ablation study on the design choices of StyleDrop in Sec. 4.4.

4.1 Experimental Setting

To the best of our knowledge, there has not been an extensive study of style-tuning for text-to-image generation models. As such, we suggest a new experimental protocol.

5

3

A

Data collection. We collect a few dozen images of various styles, from watercolor and oil painting, flat illustrations, 3d rendering to sculptures with varying materials. While painting styles have been a major focus for neural style transfer research [12, 7], we go beyond and include a more diverse set of visual styles in our experiments. We provide image sources in Tab. S1 and attribute their ownership.

Model configuration. As in Sec. 3.2, we base StyleDrop on Muse [5] using adapter tuning [15, 36]. For all experiments, we update adapter weights for 1000 steps using Adam optimizer [19] with a learning rate of 0 . 00003 . Unless otherwise stated, we use 'StyleDrop' to denote the second round model trained on as many as 10 synthetic images with human feedback, as in Sec. 3.3. Nevertheless, to mitigate confusion, we append 'HF' (human feedback), 'CF' (CLIP feedback) or 'R1' (first round model) to StyleDrop whenever there needs a clarity. More training details are in Appendix B.1.

Evaluation. We report quantitative metrics based on CLIP [29] that measures the style consistency and textual alignment. In addition, we conduct the user preference study to assess style consistency and textual alignment. Appendix B.2 summarizes details on the human evaluation protocol.

4.2 StyleDrop Results

Fig. 1 shows results of our default approach on the 18 different style images that we collected, for the same text prompt. We see that StyleDrop is able to capture nuances of texture, shading, and structure across a wide range of styles, significantly better than previous approaches, enabling significantly more control over style than previously possible. Fig. 4 shows synthesized images of StyleDrop using 3 different style reference images. For comparison, we also present results of (b) DreamBooth [34] on Imagen [35], (c) a LoRA implementation of DreamBooth [34, 3, 16] and (d) textual inversion [11], both on Stable Diffusion [33]. 6,7 More results are available in Figs. S9 to S14.

For baselines, we follow instructions from the respective papers and open-source implementations, but with a few modifications. For example, instead of using a rare token ( e.g ., 'a watermelon slice in [V*] style'), we use the style descriptor ( e.g ., 'a watermelon slice in 3d rendering style'), similarly to StyleDrop. We train DreamBooth on Imagen for 300 steps after performing grid-search. This is less than 1000 steps recommended in [34], but is chosen to alleviate overfitting to image content and to better capture style. For LoRA DreamBooth on Stable Diffusion, we train for 400 steps with learning rates of 0 . 0002 for UNet and 0 . 000005 for CLIP. We do not adopt the iterative training for baselines in Fig. 4. StyleDrop results without iterative training are in Sec. 4.4.3. It is clear from Fig. 4 that StyleDrop on Muse convincingly outperforms other methods that are geared towards solving subject-driven personalization of text-to-image synthesis using diffusion models.

We see that style-tuning on Stable Diffusion with LoRA DreamBooth (Fig. 4(c)) and textual inversion (Fig. 4(d)) show poor style consistency to reference images. While DreamBooth on Imagen (Fig. 4(b)) improves over those on Stable Diffusion, it still lacks the style consistency over StyleDrop on Muse across text prompts and style references. It is interesting to see such a difference as both Muse [5] and Imagen [35] are trained on the same set of image/text pairs using the same text encoder (T5-XXL [30]). We provide an ablation study to understand where the difference comes from in Sec. 4.4.1.

4.2.1 Quantitative Results

For quantitative evaluation, we synthesize images from a subset of Parti prompts [43]. This includes 190 text prompts of basic text compositions, while removing some categories such as abstract, arts, people or world knowledge. We test on 6 style reference images from Fig. 1. 8

CLIP scores. We employ two metrics using CLIP [29], ( Text ) and ( Style ) scores. For Text score, we measure the cosine similarity between image and text embeddings. For Style score, we measure the cosine similarity between embeddings of style reference and synthesized images. We generate 8 images per prompt for 190 text prompts, 1520 images in total. While we desire high scores, these metrics are not perfect. For example, Style score can easily get to 1 . 0 if mode collapses.

StyleDrop results in competitive Text scores to Muse ( e.g ., 0 . 323 vs 0 . 322 of StyleDrop (HF)) while achieving significantly higher Style scores ( e.g ., 0 . 556 vs 0 . 694 of StyleDrop (HF)), implying that synthesized images by StyleDrop are consistent in style with style reference images, without losing text-to-image generation capability. For the 6 styles we test, we see a light mode collapse from the first round of StyleDrop, resulting in a slightly reduced Text score. Iterative training (IT) improves the Text score, which is aligned with our motivation. As a trade-off, however, they show reduced Style scores over Round 1 models, as they are trained on synthetic images and styles may have been drifted due to a selection bias.

DreamBooth on Imagen falls short of StyleDrop in Style score ( 0 . 644 vs 0 . 694 of HF). We note that the increment in Style score for DreamBooth on Imagen is less significant ( 0 . 569 → 0 . 644 ) than StyleDrop on Muse ( 0 . 556 → 0 . 694 ). We think that the fine-tuning for style on Muse is more effective than that on Imagen. We revisit this in Sec. 4.4.1.

Human evaluation. We formulate 3 binary comparison tasks for user preference among StyleDrop (R1), StyleDrop with different feedback signals, and DreamBooth on Imagen. Users are asked to select their preferred result in terms of style and text fidelity between images generated from two

(a) A

teapot

in

3d rendering style

(b) A teapot in

3d rendering style

(c) A

teapot

in

3d rendering style

(a) A

teapot

in

watercolor painting style

(b) A teapot in

watercolor painting style

(c) A

teapot

in

watercolor painting style

(a) A

vase

in

3d rendering style

(b) A vase in

3d rendering style

(c) A

vase

in

3d rendering style

(a) A

vase

in

watercolor painting style

(b) A vase in

watercolor painting style

(c) A

vase

in

watercolor painting style

(a) A

dog

in

3d rendering style

(b) A dog in

3d rendering style

(c) A

dog

in

3d rendering style

(a) A

dog

in

watercolor painting style

(b) A dog in

watercolor painting style

(c) A

dog

in

watercolor painting style

different models ( i.e ., an A/B test), while given a style reference image and the text prompt. Details on the study is in Appendix B.2. Results are in Tab. 2 (top). Compared to DreamBooth on Imagen, images by StyleDrop are significantly more preferred by users in Style score. The user study also shows style drifting more clearly when comparing StyleDrop (R1) and StyleDrop IT either by HF or CF. Between HF and CF, HF retains better Style and CLIP retained better Text . Overall, we find that CLIP scores are a good proxy to the user study.

4.3 My Object in My Style

We show in Fig. 5 synthesized images by sampling from two personalized generation distributions, one for an object and another for the style, as described in Sec. 3.4. To learn object adapters, we use 5 ∼ 6 images per object. 9 Style adapters from Sec. 4.2 are used without any modification. The value of γ (to balance the contribution of object and style adapters) is chosen in the range 0 . 5 -0 . 7 . We show synthesized images from (a) object adapter only ( i.e ., DreamBooth), (b) style adapter only ( i.e ., StyleDrop), and (c) both object and style adapters. We see from Fig. 5(a) that text prompts are not sufficient to generate images with styles we desire. From Fig. 5(b), though StyleDrop gets style correct, it generates objects that are inconsistent with reference subjects. The proposed sampling method from two distributions successfully captures both my object and my style , as in Fig. 5(c).

4.4 Ablations

We conduct ablations to better understand StyleDrop. In Sec. 4.4.1 we compare the behavior of the Imagen and Muse models. In Sec. 4.4.2 we highlight the importance of a style descriptor. In Sec. 4.4.3, we compare choices of feedback signals for iterative training. In Sec. 4.4.4, we show to what extent StyleDrop learns distinctive styles properties from reference images.

4.4.1 Comparative Study of DreamBooth on Imagen and StyleDrop on Muse

We see in Sec. 4.2 that StyleDrop on Muse convincingly outperforms DreamBooth on Imagen. To better understand where the difference comes from, we conduct some control experiments.

Impact of training text prompt. We note that both experiments in Sec. 4.2 are carried out using the proposed descriptive style descriptors. To understand the contribution of fine-tuning , rather than the prompt engineering, we conduct control experiments, one with a rare token as in [34] ( i.e ., 'A flower in [V*] style') and another with descriptive style prompt.

Muse

+

: '[V*]'

-

: ' '

+

: 'melting golden 3d rendering'

-

: ' '

Muse

+

: 'golden 3d rendering'

-

: 'melting'

Muse

+

: '[V*]'

-

: 'melting'

Muse

(a)

(b)

(c)

(d)

Style reference

Imagen

G

Imagen

G

Imagen

G

Imagen

(a)

“[V*]"

(b)

"[V*]"

(c)

“melting golden3d rendering”

(d)

“melting golden 3d rendering

G

Results on Muse are in Fig. 7. Comparing (a) and (c), we do not find a substantial change, suggesting that the style can be learned via an adapter tuning without too much help of a text prior. On the other hand, as seen in Fig. 6, comparing (a) and (c) with Imagen as a backbone model, we see a notable difference. For example, 'melting' property only appears for some images synthesized from a model trained with the descriptive style descriptor. This suggests that the learning capability of fine-tuning on Imagen may not be as powerful as that of Muse, when given only a few training images.

Data efficiency. Next, we study whether the quality of the fine-tuning on Imagen could be improved with more training data. In this study, we train a DreamBooth on Imagen using 10 human selected, synthetic images from StyleDrop on Muse. Results are in Fig. 6. Two models are trained with (b) a rare token and (d) a descriptive style descriptor. We see that the style consistency improves a lot when comparing (c) and (d) of Fig. 6, both in terms melting and golden properties. However, when using a rare token, we do not see any notable improvement from (a) to (b). This suggests that the superiority of StyleDrop on Muse may be coming from its extraordinary fine-tuning data efficiency.

4.4.2 Style Property Edit with Concept Disentanglement

We show in Sec. 4.4.1 that StyleDrop on Muse is able to learn the style using a rare token identifier. Then, what is the benefit of descriptive style descriptor? We argue that not all styles are described in a single word and the user may want to learn style properties selectively. For example, the style of an image in Fig. 7 may be written as a composite of 'melting', 'golden', and '3d rendering', but the user may want to learn its 'golden 3d rendering' style without 'melting'.

We show that such a style property edit can be naturally done with a descriptive style descriptor. As in Fig. 7(d), learning with a descriptive style descriptor provides an extra knob to edit a style by omitting certain words ( e.g ., 'melting') from the style descriptor at synthesis. This clearly shows the benefit of descriptive style descriptors in disentangling visual concepts and creating a new style based on an existing one. This is less amenable when trained with the rare token, as in Fig. 7(b).

4.4.3 Iterative Training with Different Feedback Signals

We study how different feedback signals affects the performance of StyleDrop. We compare three feedback signals, including human, CLIP, and random. For CLIP and random signals, we synthesize

64 images per prompt from 30 text prompts and select one image per prompt. For human, we select 10 images from the same pool, which takes about 3 minutes per style. See Appendix B.2 for details.

Qualitative results are in Fig. 8. We observe that some images in (a) from a Round 1 model show a mix of banana or bottle with a human. Such concept leakage is alleviated with IT, though we still see a banana with arms and legs with Random strategy. The reduction in concept leakage could be verified with the Text score, achieving (a) 0 . 303 , (b) 0 . 322 , (c) 0 . 339 , and (d) 0 . 328 . On the other hand, Style score, (a) 0 . 560 , (b) 0 . 567 , (c) 0 . 542 , and (d) 0 . 549 , could be misleading in this case, as we compute the visual similarity to the style reference image, favoring a content leakage. Between CLIP and human feedback, we see a clear trade-off between text and style fidelity from quantitative metrics.

4.4.4 Fine-Grained Style Control with User Intention

(a) flat color

(b) flat color with offset

(c) gradation

(d) sharp corner

FES

Moreover, human feedback is more critical when trying to capture subtle style properties. In this study, we conduct experiments on four images in Fig. 9 inside orange boxes, created by the same designer with varying style properties, such as color offset (Fig. 9(b)), gradation (Fig. 9(c)), and sharp corners (Fig. 9(d)). We train two more rounds of StyleDrop with human feedback. We use the same style descriptor of 'minimal flat illustration style' to make sure the same text prior is given to all experiments. As in Fig. 9, style properties such as color offset, gradation, and corner shape are captured correctly. This suggests that StyleDrop offers the control of fine-grained style variations.

5 Conclusion

We have presented StyleDrop, a novel approach to enable synthesis of any style through the use of a few user-provided images of that style and a text description. Built on Muse [5] using adapter tuning [15], StyleDrop achieves remarkable style consistency at text-to-image synthesis. Training StyleDrop is efficient both in the number of learnable parameters ( e.g ., < 1%) and the number of style samples ( e.g ., 1) required.

Limitations. Visual styles are of course even more diverse than what is possible to explore in our paper. More study with a well-defined system of visual styles, including, but not limited to, the formal attributes ( e.g ., use of color, composition, shading), media ( e.g ., line drawing, etching, oil painting), history and era ( e.g ., Renaissance painting, medieval mosaics, Art Deco), and style of art ( e.g ., Cubism, Minimalism, Pop Art), would broaden the scope. While we show in part the superiority of a generative vision transformer to diffusion models at few-shot transfer learning, it is by no means conclusive. We leave an in-depth study among text-to-image generation models as a future work.

Societal impact. As illustrated in Fig. 4, StyleDrop could be used to improve the productivity and creativity of art directors and graphic designers when generating various visual assets in their own style. StyleDrop makes it easy to reproduce many personalized visual assets from as little as one seed image. We recognize potential pitfalls such as the ability to copy individual artists' styles without their consent, and urge the responsible use of our technology.

Acknowledgement. We thank Varun Jampani, Jason Baldridge, Forrester Cole, José Lezama, Steven Hickson, Kfir Aberman for their valuable feedback on our manuscript.

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A Image Attributions

We provide the links to images used for style references (please click through on each number).

B Experiments

B.1 Details on Model Training

B.1.1 Adapter Architecture

We apply an adapter at every layer of transformer. Specifically, following [15], we apply two adapters for each layer, one after the cross-attention block, and another after the MLP block. An example code explaining how to apply an adapter to the output of an attention layer and how to generate adapter weights are in Fig. S1. All up weights ( wu ) are initialized with zeros, and down weights ( wd ) are initialized from truncated normal distribution with standard deviation of 0 . 02 .

We note that adapter weights are generated in a parameter-efficient way via weight sharing across transformer layers. This is triggered by setting is_shared to True , and the total number of parameters would be reduced roughly by the number of transformer layers. The number of parameters of adapter weights are given in Tab. S3. While we use these settings on all experiments, one can easily reduce the number of parameters by setting is_shared to True for Base (Round 2) and Super-res fine-tuning without loss in quality.

B.1.2 Hyperparameters

We provide in Tab. S3 hyperparameters for optimizer, adapter architecture, and synthesis. Note that we use the batch size of 8, 1 per core of TPU v3, but StyleDrop can be also optimized on a single GPU ( e.g ., A100) with batch size of 1. We find that learning rate higher than 0 . 00003 for the base model often results in overfitting to content of a style reference image. Learning rate lower than 0 . 00003 for the base model leads to slower convergence and we suggest to increase the number of train steps in such a case.

B.1.3 Style Descriptors

We provide full description on descriptive style descriptors for images used in our experiments in Tab. S4. As discussed in Sec. 4.4.1 and Sec. 4.4.2, StyleDrop works well without descriptive style descriptors, but they add additional capability such as style editing.

B.2 Details on Human Evaluation

In this section, we provide more details on the user preference study discussed in Sec. 4.2.1. 3 binary comparison tasks are conducted between DreamBooth on Imagen and StyleDrop (Round 1), StyleDrop (Round 1) and StyleDrop (IT human), StyleDrop (IT human) and StyleDrop (IT CLIP). 300 queries (50 queries per style from 6 styles, as shown in Fig. S8 are uploaded. The same query is asked to 5 raters independently to mitigate the human selection bias and variance. In total, we collect 4500 answers.

We show in Fig. S2 the screenshot of the interface. We provide an instruction, examples, and task, composed of a reference image, text prompt, and two images that raters are asked to compare.

Instructions.

Task: Given a reference image and two machine-generated output images, select which machine-generated output better matches the style of the reference image.

Review this definition of a style: style (from Merriam-Webster): A particular manner or technique by which something is done, created or performed. Then choose either Image A, Image B, or Cannot Determine / Both Equally.

Next, review the reference text. Select which generated output is best described by the reference text. If you're again not sure, select Cannot Determine / Both Equally.

Questions.

Which Machine-Generated Image best matches the style of the reference image? Image A, Image B, Cannot Determine / Both Equally

Which Machine-Generated Image is best described by the reference text? Image A, Image B, Cannot Determine / Both Equally

Additional Analysis. We provide an additional analysis on the user preference study results. Note that the numbers reported in Tab. 2 are based on the majority voting and claimed tie only if two models received the same number of votes. To provide a full picture, we draw a diagram that shows individual vote counts in Fig. S3. We find that there are more 'tie' counts, but confirm overall a consistent trend with the results by the majority vote in Tab. 2.

B.3 Extended Ablation Study

B.3.1 Classifier-Free Guidance

We conduct an ablation study on classifier-free guidance (CFG) parameters, λ A and λ B, of Eq. (4). They play different roles: λ A controls the level of style adaptation and λ B controls the text prompt fidelity. We conduct two sets of experiments, one with the StyleDrop Round 1 model and another with the model trained with a human feedback (IT, human).

StyleDrop (Round 1) model. In this study, we use StyleDrop (Round 1) model, which is trained on a single style reference image.

λ A with fixed λ B. Firstly, we vary λ A while fixing λ B to 5 . 0 . Results are in Fig. S4a. When λ A =0 . 0 , we find synthesized images having less faithful styles to the style reference images. As we increase λ A, we see the style of synthesized images getting more consistent. However, when λ A becomes too large ( e.g ., 5 . 0 ) the style factor dominates the sampling process, making the content of synthesized images collapsed to that of the style reference image and hard to make it follow the text condition.

λ B with fixed λ A. Subsequently, we investigate the impact of λ B, while fixing λ A =2 . 0 . Results are in Fig. S4c. When λ B =0 . 0 , we see that synthesized images being collapsed to

Table S4: Text prompts used for experiments in Fig. 1. We construct a text prompt by composing descriptions of a content ( e.g ., object) and style ( e.g ., watercolor painting).

image

text prompt

image

text prompt

'A house in

watercolor painting style'

'A cat in

watercolor painting style'

'Flowers in

watercolor painting style'

'A village in

oil painting style'

'A village in

line drawing style'

'A portrait of a person

wearing a hat in

oil painting style'

'A person drowning

into the phone in

cartoon line drawing style'

'A woman walking a dog in

flat cartoon illustration style'

'A woman working on a laptop

in flat cartoon illustration style'

'A Christmas tree in

sticker style'

'A wave in

abstract rainbow colored

flowing smoke wave design'

'A mushroom

in glowing style'

'Slices of watermelon and

clouds in the background in

3d rendering style'

'A thumbs up in

glowing 3d rendering style'

'A woman in

3d rendering style'

'A bear in

kid crayon drawing style'

'A flower in

melting golden

3d rendering style'

'A Viking face with beard in

wooden sculpture'

家

the style reference image without being too much text controlled. Increasing λ B improves the text fidelity, but eventually override the learned style to a more generic, text-guided style of Muse model.

Overall, we verify that λ A and λ B play roles as intended to control the style adaptation and the text prompt conditioning. Nonetheless, these two parameters impact each other and we find that fixing λ B =5 . 0 and control λ A to trade-off the style and text fidelity is sufficient for most cases.

StyleDrop IT (human) model. Next, we use StyleDrop (IT, human), which is trained on synthetic images manually selected with a human feedback. We find that the model becomes less sensitive to the change of guidance scales λ A or λ B and more robust across values in terms of content disentanglement and style consistency.

B.3.2 Visual Comparison to Muse Baseline

In addition to the Tab. 2, we provide a comparison of StyleDrop to the Muse baseline, i.e ., generating stylized images through only a text prompting. We show results in Fig. S5. As we see, images generated by Muse are in more generic styles, while images by StyleDrop follow the style of reference images more closely.

Instructions

Task:Given areference image and two machine-generated output images,select which machine-generated output better matches the style of the reference image.

Review this definition of a style:

style (from Merriam-Webster):A particular manner or technique by which something is done, created or performed.

Then choose either Image A,Image B,or Cannot Determine/Both Equally.

Next, review the reference text. Select which generated output is best described by the reference text.

If you'reagainnotsure,selectCannotDetermine/BothEqually.

ViewSome Examples:

reference image.

image that best matches that technique.

Example C: Given a 3D rendered image of an octopus, the style is the use of a particular type of 3D rendering. Choose the generated image that best matches that type of 3D rendering.

For thereference text,choose which generated output isbest described by the text.

Forexampleiftherferencetextsnoctopusnawatercolorstylechoose whichexamplemoreaccuratelydisplaysanctopusnwatercolorstylewithoutregardforthereferencemage.

Task

Reference Image

Machine-Generated Image A

Machine-GeneratedImageB

Selection

Which Machine-Generated Image best matches the

style of thereference image?

Image A

ImageB

Cannot Determine /Both Equally

ReferenceText:A dolphin,animals, in melting gold 3d

rendering style

Which Machine-Generated Imageis best described by

the reference text?

Image A

ImageB

Cannot Determine/Both Equally

B.3.3 Quantitative Results on Descriptive Text Prompts

In this section, we ablate the importance of descriptive text prompts. To this end, we compare two one-shot fine-tuning methods on Muse, first, using a descriptive text prompt (a.k.a. StyleDrop, as in Sec. 3.2.1), and second, using a rare token (a.k.a. DreamBooth), for style descriptors. We measure the Text and Style CLIP scores as in Tab. 2. We observe higher scores on both Text (0.313 vs 0.308) and Style (0.705 vs 0.654) with StyleDrop on Muse than DreamBooth on Muse.

B.3.4 Robustness of Human Feedback

StyleDrop involves an iterative training with human feedback and the performance may vary across different users. In this section, we conduct experiments of iterative training based on feedback from 5 different human subjects and see the robustness of our method. Experiments are done on 6 styles considered in Tab. 2. We obtain Style score of 0 . 687 on average with the standard deviation of 0 . 011 and Text score of 0 . 325 on average with the standard deviation of 0 . 003 . We find that there is a clear variance based on the human subject and their quality of feedback, but the standard deviation is low.

(a) StyleDrop Round 1 (SDRP, R1) vs. DreamBooth on Imagen.

(b) StyleDrop Round 1 (SDRP, R1) vs. StyleDrop IT (human) (SDRP, IT).

B.4 Extended Baseline Comparison

In addition to Sec. 4.2 and Fig. 4, we provide additional qualitative comparison with hard prompt made easy (PEZ) [39] in Fig. S15. We do not find PEZ to be better than any of the compared methods including StyleDrop and DreamBooth.

λ

A

= 0.0

λ

A

= 0.2

λ

A

= 0.5

λ

A

= 1.0

λ

A

= 1.5

λ

A

= 2.0

λ

A

= 5.0

Style reference

Style reference

λ

A

= 0.0

λ

A

= 0.2

λ

A

= 0.5

λ

A

= 1.0

λ

A

= 1.5

λ

A

= 2.0

λ

A

= 5.0

Style reference

λ

B

= 0.0

λ

B

= 1.0

λ

B

= 2.0

λ

B

= 5.0

λ

B

= 10.0

λ

B

= 20.0

λ

B

= 50.0

Stylereference

入=0.0

入=1.0

入=2.0

入=5.0

入= 10.0

入= 20.0

入=50.0

(d) Varying

λ

B

while fixing

λ

A

=2

.

0

on StyleDrop (HF) model.

Muse

StyleDrops

'3d rendering style'

Style

Reference

Muse

StyleDrops

'watercolor painting style'

Style

Reference

Muse

StyleDrop

Style

Reference

Muse

StyleDrop

Style

Reference

Muse

StyleDrop

Style

Reference

Muse

StyleDrops

'oil painting style'

Style

Reference

'flat cartoon illustration style'

'kid crayon drawing style'

'line drawing style'

A

M

4

G

G

G

G

T

G

G

G!

G

G

G

G

G

G

(c) '

The letter 'G'

'

盒

(c) '

A friendly robot

'

(a) source

(b) source

(c) source

(d) source

(e) source

(f) source

(a) source

(b) A baby penguin, animals

(c) A moose, animals

(d) A towel, artifacts

(e) An espresso machine, artifacts

(f) An avocado, produce and plants

60

(a) source

(b) A baby penguin, animals

(c) A moose, animals

(d) A towel, artifacts

(e) An espresso machine, artifacts

(f) An avocado, produce and plants

881

CD

(g) A crown, artifacts

(a) source

(b) A baby penguin, animals

(c) A moose, animals

(d) A towel, artifacts

(e) An espresso machine, artifacts

(f) An avocado, produce and plants

(a) source

(b) A baby penguin, animals

(c) A moose, animals

(d) A towel, artifacts

(e) An espresso machine, artifacts

(f) An avocado, produce and plants

ACROWN

(g) A crown, artifacts

G

G

G

G

1

C

3

Peer reviews:
comments: SUMMARY:
This paper proposes a new method that conducts the image synthesis following a new style using a text-to-image model. The method is called StyleDrop, which works by efficiently learning a new style by fine-tuning few parameters, and improving the quality via iterative training with either human or automated feedback. StyleDrop is able to give impressive results even when the users supplies only a single image. Experiments show the effectiveness of the proposed method.

STRENGTHS:
The experimental results, especially the user study results, demonstrate the effects of the proposed approach. The visual effects are good.

WEAKNESSES:
1.	This method needs iterative training with different feedbacks. Thus, I wonder whether the proposed strategy will sacrifice the finetuning efficiency even the parameter number to finetune is small. I want to see such comparisons.

2.	The strategy of adding trainable parameters in Fig.2 is similar to the ControlNet [A]. This method should be led into the comparison.

[A] Adding Conditional Control to Text-to-Image Diffusion Models, arxiv 2023

3.	Different human feedback may lead to different results which may depends on the professionalism of the users. I wonder the robustness of the proposed method towards different persons. Authors could provide the results with iterative feedback with different persons.

QUESTIONS:
Please see the weakness section and answer these questions.

LIMITATIONS:
This paper could provide the interactive experimental results with different persons and reference images.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2023-06-28

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a method to fine-tune a pre-trained large text-to-image model using a few images that provide an artistic style, so that the fine-tuned model can generate images that follow the provided artistic style. The proposed method is a combination of Muse [1], a transformer-based text-to-image generation model, and adapter tuning [2], a method to fine-tune a large text-conditioned transformer efficiently. The paper additionally proposes iterative training, i.e., using CLIP or human feedback to select a few good generated images and use them to train the proposed method for a second round or more rounds, to prevent leaked content from the style reference images. The method shows improved generation results over prior works such as DreamBooth [3]. The method can also be combined with DreamBooth [3] to generate images of a user-specified content with a user-specified style, while both the content and the style are provided as images.\n\n[1] Muse: Text-to-image generation via masked generative transformers. arXiv preprint, 2023.\n[2] Parameter-efficient transfer learning for nlp. International Conference on Machine Learning, 2019.\n[3] DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation. CVPR, 2023.\n\nSTRENGTHS:\n- The paper is well-written. The preliminary works are introduced in the paper so I can understand the technical details even though I am not familiar with this field. The figure illustrations are also very helpful for me to quickly grasp the ideas behind this work.\n\n- The qualitative results are astonishing.\n\n- The comparison experiments and ablation studies are extensive with detailed analysis.\n\n- The proposed method could potentially enable many applications, especially when coupled with DreamBooth to provide extra user control.\n\nWEAKNESSES:\nI do not find any significant weaknesses. Minor weaknesses are described below.\n\n- I am not familiar with this field, therefore it is hard for me to comment on the novelty of this method. To me, it looks like a system built by combining Muse [1] and adapter tuning [2], with an optional iterative training step. It shows great results, but according to Figure 4 (b), the results of prior work DreamBooth are not bad at all, so I guess the performance improvement is marginal?\n\n- One possible weakness is the training time. I do not find any mention of training time in the paper, so I assume it is going to take a long time to train a model. Therefore, the iterative training steps that require human feedback is going to be very inconvenient. The proposed method is unlikely to support interactive artistic design in real applications.\n\n- I think the quantitative results may not be very informative. According to Table 2, the \"Round 1\" model without iterative training consistently outperforms other methods in terms of visual style alignment. The authors explained the possible reasons, but still, this can only mean one of two things: either the iterative training is unnecessary and the \"Round 1\" model without iterative training outperforms the full model, or the quantitative results are not informative and it is unclear whether the proposed method performs better than prior works such as DreamBooth.\n\nQUESTIONS:\n1. What is the training time for each round?\n\n2. Any comments on the uninformative quantitative results?\n\nLIMITATIONS:\nLimitations and societal impact are discussed in the paper. They look fair.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTo synthesize arbitrary image styles, this paper proposed a StyleDrop method that enables an image synthesis with the style of a given image, using text-to-image models, e.g., Muse and Imagen. StyleDrop consists of three crucial components: 1) a transformer-based text-to-image generation model using Muse, 2) an adapter tuning depended on [32]; and 3) an iterative training with CLIP and Human feedbacks. Extensive results demonstrate the effectiveness of the proposed StyleDrop method, and show that StyleDrop on Muse is better than the other related methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion.\n\nSTRENGTHS:\n1.  The main strength is that the extensive visual results are impressive and show the nuances and details of a user-provided style.\n\n2. This paper proposed an incremental method to synthesize arbitrary image styles as it uses CLIP and human feedbacks to train a new adapter in the Muse model for the stylized text-to-image synthesis.\n\n3. This paper is well-written and is easy to follow.\n\nWEAKNESSES:\n1. Since the proposed StyleDrop model is based on Muse, the reviewer thinks the Muse should be as a baseline. However, their visual comparison is missing expect the CLIP scores in Table 2. This is unnormal. Please show some visual results.\n\n2. Why does this paper construct the form of Eq. (4)? How about the following forms?\n\n  1) l_k=\\hat{G}(v_k,T(t),theta)+lambda_A(\\hat{G}(v_k,T(t),theta)- G(v_k,T(n)))\n  2) l_k=\\hat{G}(v_k,T(t),theta)+lambda_A(\\hat{G}(v_k,T(t),theta)-0.5*(G(v_k,T(t))+G(v_k,T(n))))\n\nIt sees that there is only one hyper-parameter in the above two forms. \n\n3. Overall, the framework is unclear although Fig. 2 shows the improved architecture. As the StyleDrop is an improved Muse model, please draw the overall pipeline so that readers and reviewers can better understand this work and the difference between StyleDrop and Muse.\n\nQUESTIONS:\nPlease see the weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2023-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper works on personalized text-to-image synthesis. Given reference style images (even a single one), the model can adapt the style to the synthesized images. There are several key components in the papers including: using a transformer-based t2i model rather than a diffusion-based one; adding an adaptor into the model for training; incorporating human-in-the-loop feedback for selecting useful training data during iterative learning. Extensive details with experiments have been included to validate the effectiveness of the proposed method.\n\nSTRENGTHS:\n+ The problem of adapting a t2i model based on a single style image is interesting and worth studying. It should also be valuable to this venue.\n+ The proposed method is well-motivated, and the components seem effective based on the experiments.\n+ The extensive supplementary and limitation discussion is appreciated, especially for the interface for collecting human feedback.\n+ The paper generally writes well and easy-to-follow.\n+ The results look great.\n\nWEAKNESSES:\nOverall, I am kind of on the supportive side but with several questions/concerns.\n\n+ Reproducibility: It would be great if more context on reproducibility is provided. Will the code be released? If the code cannot be released in a complete way, how the reproducibility be achieved?\n+ Generability: The paper argues that using MUSE - a transformer-based method is more reasonable, but the proposed components such as the adaptor and human feedback can be used in other models such as diffusion. What will the performance be if all these proposed components are used on the other frameworks?\n+  How large effect the negative prompt will be, and it would be great if there are some specific examples or statistics that can be shown.\n+ It's still not clear to me why the model only captures the style rather than the content. is this influenced by the human-selected images? Can the method be directly adapted to learning concept/content just like dream-booth? If not, may I know what factors let the model fail to capture the content/semantic info of the references?\n+ In L120, it is saying \"we use different text prompts for each input image to better disentangle ...\". I am not sure whether I understand this sentence or not. Besides, is there any ablation study related to this claim?\n+ In L161, how many images are used for annotation? When compare with the baseline, especially with single reference images, are the human feedback also incorporated? if that's the case, the experiment may be unfair, as though those images are generated by the model itself, incorporating human feedback should also be considered as labeling/more reference data.\n+ How robustness of the model is unclear, how many annotated images are necessary to maintain the high-quality. Will the number vary a lot for different cases? Besides, the metrics are only computed on a few of styles which may not be enough or conclusive. More discussions should be added here to evaluate the paper fairly rather than simply saying they are the future works.\n\nQUESTIONS:\nPlease see the comments above.\n\nLIMITATIONS:\nThe limitation has been studied but is not that convincing. It would be great to show some specific failure cases in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes StyleDrop to perform text-to-image generation following the style of a reference image. StyleDrop finetunes Muse [1] via parameter-efficient fine-tuning (PEFT) using the style reference images and their corresponding text prompts. To better finetune Muse, this paper proposed (1) a templated approach to construct the text prompts for style reference images, (2) iterative training with feedback (model-based or human-based). This paper also tried to combine DreamBooth and StyleDrop, yeilding personalization of both style and content. \n\nRef:\n[1] Huiwen Chang et al. Muse: Text-to-image generation via masked generative transformers.\n\nSTRENGTHS:\n(1) StyleDrop is able to produce high-quality and visually pleasing synthesis results, even when the style images and the content text prompt are highly unrelated.\n\n(2) This paper presents a well-established system which leverages both a large model (Muse) and human feedback to handle this task.\n\n(3) The overall presentation is satisfying, and the paper is easy to read.\n\nWEAKNESSES:\n(1) There is no significant technical contribution in this paper. Section 3.2.1, 3.3, and 3.4 are more like engineering tricks to me. The task of style tuning text-to-image models is also not new, given the existence of DreamBooth, Custom Diffusion, etc. \n\n(2) In the data-collection section (Line 185-188), what is the criterion of selecting styles? The authors did not make it clear. A skeptical view is that the authors could select the styles that favor their method.\n\n(3) Some important hyper-parameter and ablation studies are missing. For example, \n\n(a) how the number of style reference images will affect the model performance? (I assume that StyleDrop is not overfitting to just one style.) \n\n(b) When comparing StyleDrop on MUSE to DreamBooth on Imagen/ Stable Diffusion, how much performance gain is from MUSE? Can DreamBooth be applied in Muse? It seems unfair to compare StyleDrop on Muse vs DreamBooth on Imagen/Stable Diffusion.\n\nQUESTIONS:\n(1) How many style reference images are needed to finetune Muse? In Line 127, the number of $N$ seems not clear. Is $N$ the number of images in Table S1? How the number $N$ will affect the model performance seems not discussed.\n\n(2) When a new style reference image is added, how to finetune StyleDrop to fit it? Using only this style image or all style images so far?\n\nLIMITATIONS:\nThe authors did not show any failure cases of StyleDrop. In Line 321-327, they mentioned the visual styles are more diverse than what is possible to explore in the paper. It is necessary to conduct some experiments to show what StyleDrop is capable of and not capable of. I don’t think StyleDrop can always generate good results for any given style references.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-07-27"}