SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Subin  $\mathbf{Kim}^{*,1}$
Kyungmin Lee\*,1
June Suk Choi
Jongheon Jeong
Kihyuk Sohn
Jinwoo Shin
$^{1}$ KAIST
2Google Research
\*{subin-kim, kyungmnlee}@kaist.ac.kr
Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video or 3D scene), achieving consistency across a set of images is challenging. In this paper, we address this challenge with a novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein Variational Gradient Descent (SVGD). Specifically, we propose to consider multiple samples as "particles" in the SVGD update and combine their score functions to distill generative priors over a set of images synchronously. Thus, CSD facilitates the seamless integration of information across 2D images, leading to a consistent visual synthesis across multiple samples. We show the effectiveness of CSD in a variety of editing tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.<sup>1</sup>
Text-to-image diffusion models [1, 2, 3, 4] have been scaled up by using billions of image-text pairs [5, 6] and efficient architectures [7, 8, 9, 4], showing impressive capability in synthesizing high-quality, realistic, and diverse images with the text given as an input. Furthermore, they have branched into various applications, such as image-to-image translation [10, 11, 12, 13, 14, 15, 16], controllable generation [17], or personalization [18, 19]. One of the latest applications in this regard is to translate the capability into other complex modalities, viz., beyond 2D images [20, 21] without modifying diffusion models using modality-specific training data. This paper focuses on the problem of adapting the knowledge of pre-trained text-to-image diffusion models to more complex high-dimensional visual manipulation tasks beyond 2D images without modifying diffusion models using modality-specific training data.
We start from an intuition that many complex visual data, e.g., videos and 3D scenes, are represented as a set of images constrained by modality-specific consistency. For example, a video is a set of frames requiring temporal consistency, and a 3D scene is a set of multi-view frames with view consistency. Unfortunately, image diffusion models do not have a built-in capability to ensure consistency between a set of images for synthesis or editing because their generative sampling process does not take into account the consistency when using the image diffusion model as is. As such, when applying image diffusion models to manipulate these complex data without consistency in consideration, it
Visualizations are available at the website https://subin-kim-cv.github.io/CSD.
Figure 1: Method overview. CSD-Edit enables various visual-to-visual translations with two novel components. First, a new score distillation scheme using Stein variational gradient descent, which considers inter-sample relationships (Section 3.1) to synthesize a set of images while preserving modality-specific consistency constraints. Second, our method edits images with minimal information given from text instruction by subtracting image-conditional noise estimate instead of random noise during score distillation (Section 3.2). By doing so, CSD-Edit is used for text-guided manipulation of various visual domains, e.g., panorama images, videos, and 3D scenes (Section 3.3).
results in a highly incoherent output, as in Figure 2 (Patch-wise Crop), where one can easily identify where images are being stitched. Such behaviors are also reported in video editing, thus, recent works [22, 23, 24, 25] propose to handle video-specific temporal consistency when using the image diffusion model.
Here, we take attention to an alternative approach, Score Distillation Sampling (SDS) [26], which enables the optimization of arbitrary differentiable operators by leveraging the rich generative prior of text-to-image diffusion models. SDS poses generative sampling as an optimization problem by distilling the learned diffusion density scores. While Poole et al. [26] has shown the effectiveness of SDS in generating 3D objects from the text by resorting on Neural Radience Fields [27] priors which inherently suppose coherent geometry in 3D space through density modeling, it has not been studied for consistent visual manipulation of other modalities, where modality-specific consistency constraints should be considered when manipulating.
In this paper, we propose Collaborative Score Distillation (CSD), a simple yet effective method that extends the singular of the text-to-image diffusion model for consistent visual manipulation. The crux of our method is two-fold: first, we establish a generalization of SDS by using Stein variational gradient descent (SVGD), where multiple samples share their knowledge distilled from diffusion models to accomplish inter-sample consistency. Second, we present CSD-Edit, an effective method for consistent visual editing by leveraging CSD with Instruct-Pix2Pix [14], a recently proposed instruction-guided image diffusion model (See Figure 1).
We demonstrate the versatility of our method in various editing applications such as panorama image editing, video editing, and reconstructed 3D scene editing. In editing a panorama image, we show that CSD-Edit obtains spatially consistent image editing by optimizing multiple patches of an image. Also, compared to other methods, our approach achieves a better trade-off between source-target image consistency and instruction fidelity. In video editing experiments, CSD-Edit obtains temporal consistency by taking multiple frames into optimization, resulting in temporal frame-consistent video editing. Furthermore, we apply CSD-Edit to 3D scene editing and generation, by encouraging consistent manipulation and synthesis among multiple views.
2.1 Diffusion models
Generative modeling with diffusion models consists of a forward process  $q$  that gradually adds Gaussian noise to the input  $\mathbf{x}_0 \sim p_{\mathrm{data}}(\mathbf{x})$ , and a reverse process  $p$  which gradually denoises from
Instruct-Pix2Pix, Patch-wise Crop
Spatial Consistency	Instruction Fidelity
Instruct-Pix2Pix Patch-wise Crop	X	✓
Instruct-Pix2Pix Overlapping Patches	✓	X
CSD-Edit (Ours)	✓	✓
Instruct-Pix2Pix + Overlapping Patches,  $\omega_y = 7.5$
Instruct-Pix2Pix + Overlapping Patches,  $\omega_{y} = 15$
CSD-Edit (Ours),  $\omega_{y} = 7.5$
"Turn it into a Van-Gogh style painting"
Figure 2: Panorama image editing. (Top right) Instruct-Pix2Pix [14] on cropped patches results in inconsistent image edits. (Second row) Instruct-Pix2Pix on overlapping patches edits to a consistent image, but less fidelity to the instruction, even with high guidance scale  $\omega_{y}$ . (Third row) CSD-Edit provides consistent image editing with better instruction-fidelity by setting a proper guidance scale.
CSD-Edit (Ours),  $\omega_{y} = 15$
the Gaussian noise  $\mathbf{x}_T\sim \mathcal{N}(\mathbf{0},\mathbf{I})$ . Formally, the forward process  $q(\mathbf{x}_t|\mathbf{x}_0)$  at timestep  $t$  is given by  $q(\mathbf{x}_t|\mathbf{x}_0) = \mathcal{N}(\mathbf{x}_t;\alpha_t\mathbf{x}_0,\sigma_t^2\mathbf{I})$ , where  $\sigma_{t}$  and  $\alpha_{t}^{2} = 1 - \sigma_{t}^{2}$  are pre-defined constants designed for effective modeling [8, 28, 29]. Given enough timesteps, reverse process  $p$  also becomes a Gaussian and the transitions are given by posterior  $q$  with optimal MSE denoiser [30], i.e.,  $p_{\phi}(\mathbf{x}_{t - 1}|\mathbf{x}_t) = \mathcal{N}(\mathbf{x}_{t - 1};\mathbf{x}_t - \hat{\mathbf{x}}_\phi (\mathbf{x}_t;t),\sigma_t^2\mathbf{I})$ , where  $\hat{\mathbf{x}}_{\phi}(\mathbf{x}_{t};t)$  is a learned optimal MSE denoiser. Ho et al. [7] proposed to train an U-Net [31] autoencoder  $\epsilon_{\phi}(\mathbf{x}_{t};t)$  by minimizing following objective:
$$
\mathcal {L} _ {\text {D i f f}} (\phi ; \mathbf {x}) = \mathbb {E} _ {t \sim \mathcal {U} (0, 1), \epsilon \sim \mathcal {N} (\mathbf {0}, \mathbf {I})} [ w (t) \| \epsilon_ {\phi} (\mathbf {x} _ {t}; t) - \epsilon \| _ {2} ^ {2} ], \quad \mathbf {x} _ {t} = \alpha_ {t} \mathbf {x} _ {0} + \sigma_ {t} \epsilon \tag {1}
$$
where  $w(t)$  is a weighting function for each timestep  $t$ . Text-to-image diffusion models [1, 2, 4, 3] are trained by Eq. (1) with  $\epsilon_{\phi}(\mathbf{x}_t; y, t)$  that estimates the noise conditioned on the text prompt  $y$ . To effectively guide the text-conditional generation, Ho et al. [32] proposed classifier-free guidance (CFG), where they jointly train the unconditional and conditional model and interpolate the unconditional and conditional model during the inference, i.e., the noise estimate is given by
$$
\boldsymbol {\epsilon} _ {\phi} ^ {\omega} \left(\mathbf {x} _ {t}; y, t\right) = \boldsymbol {\epsilon} _ {\phi} \left(\mathbf {x} _ {t}; t\right) + \omega_ {y} \left(\boldsymbol {\epsilon} _ {\phi} \left(\mathbf {x} _ {t}; y, t\right) - \boldsymbol {\epsilon} _ {\phi} \left(\mathbf {x} _ {t}; t\right)\right), \tag {2}
$$
where  $\omega_y \geq 0$  is a guidance scale that controls the sample fidelity. Specifically, increasing  $\omega_y$  enhances sample fidelity at the expense of sample diversity. Throughout the paper, we refer  $p_{\phi}^{\omega_y}(\mathbf{x}_t; y, t)$  as conditional distribution of a text  $y$ .
Instruction-based image editing by Instruct-Pix2Pix. Recently, many works have demonstrated the capability of diffusion models in editing or stylizing images [10, 13, 11, 12, 14]. Among them, Brooks et al. [14] proposed Instruct-Pix2Pix, where they finetuned Stable Diffusion [4] model with the source image, text instruction, (edited) target image (edited by Prompt-to-Prompt [12]) triplets to enable instruction-based editing of an image. Then during the inference, Instruct-Pix2Pix starts from a source image and conducts diffusion sampling by the diffusion model that takes instruction  $y$ . In specific, given the source image  $\tilde{\mathbf{x}}$  and instruction  $y$ , the noise estimate at time  $t$  is given by
$$
\begin{array}{l} \boldsymbol {\epsilon} _ {\phi} ^ {\omega_ {s}, \omega_ {y}} (\mathbf {x} _ {t}; \tilde {\mathbf {x}}, y, t) = \boldsymbol {\epsilon} _ {\phi} (\mathbf {x} _ {t}; t) + \omega_ {s} \left(\boldsymbol {\epsilon} _ {\phi} (\mathbf {x} _ {t}; \tilde {\mathbf {x}}, t) - \boldsymbol {\epsilon} _ {\phi} (\mathbf {x} _ {t}; t)\right) \\ + \omega_ {y} \left(\epsilon_ {\phi} \left(\mathbf {x} _ {t}; \tilde {\mathbf {x}}, y, t\right) - \epsilon_ {\phi} \left(\mathbf {x} _ {t}; \tilde {\mathbf {x}}, t\right)\right), \tag {3} \\ \end{array}
$$
where  $\omega_{y} \geq 0$  is the CFG parameter for text instruction as in Eq. (2) and  $\omega_{s} \geq 0$  is an additional CFG parameter that controls the fidelity to the source image  $\tilde{\mathbf{x}}$ .
2.2 Score distillation sampling
Poole et al. [26] proposed Score Distillation Sampling (SDS), an alternative sample generation method by distilling the rich knowledge of text-to-image diffusion models. SDS allows optimization of any differentiable image generator, e.g., Neural Radiance Fields [27] or the image space itself.
Figure 3: Video editing. Qualitative results on the lucia video in DAVIS 2017 [33]. CSD shows frame-wise consistent editing providing coherent content across video frames e.g., consistent color and background without changes in person. Compared to Gen-1 [21], a video editing method trained on a large video dataset, CSD-Edit shows high-quality video editing results reflecting given prompts.
"Make it as a painting of Claude Monet"
Formally, let  $\mathbf{x} = g(\theta)$  be an image rendered by a differentiable generator  $g$  with parameter  $\theta$ , then SDS minimizes density distillation loss [34] which is a variational inference via minimizing KL divergence between the posterior of  $\mathbf{x} = g(\theta)$  and the text-conditional density  $p_{\phi}^{\omega}$ :
$$
\min  _ {\theta} \mathcal {L} _ {\text {D i s t i l l}} (\theta ; \mathbf {x} = g (\theta)) = \mathbb {E} _ {t, \epsilon} \left[ \alpha_ {t} / \sigma_ {t} D _ {\mathrm {K L}} \left(q \left(\mathbf {x} _ {t} \mid \mathbf {x} = g (\theta)\right) \| p _ {\phi} ^ {\omega} \left(\mathbf {x} _ {t}; y, t\right)\right) \right], \tag {4}
$$
where  $\mathbf{x}_t = \alpha_t\mathbf{x} + \sigma_t\boldsymbol{\epsilon}$  with  $\mathbf{x} = g(\theta)$  and  $\boldsymbol{\epsilon} \sim \mathcal{N}(\mathbf{0},\mathbf{I})$ . They derive SDS by differentiating Eq. 4 with respect to generator parameter  $\theta$ , but omitting the U-Net Jacobian term due to its poor performance and computationally inefficient. The SDS gradient update is given as follows:
$$
\nabla_ {\theta} \mathcal {L} _ {\mathrm {S D S}} (\theta ; \mathbf {x} = g (\theta)) = \mathbb {E} _ {t, \epsilon} \left[ w (t) \left(\epsilon_ {\phi} ^ {\omega} \left(\mathbf {x} _ {t}; y, t\right) - \epsilon\right) \frac {\partial \mathbf {x}}{\partial \theta} \right]. \tag {5}
$$
In its implementation, we randomly sample timestep from uniformly distributed interval  $\mathcal{U}[t_{\mathrm{min}}, t_{\mathrm{max}}]$ . The range of timesteps  $t_{\mathrm{min}}$  and  $t_{\mathrm{max}}$  are chosen to sample from not too small or large noise levels, and the guidance scales are chosen to be larger than those used for image generation.
2.3 Stein variational gradient descent
The original motivation of Stein variational gradient descent (SVGD) [35] is to solve a variational inference problem, where the goal is to approximate a target distribution from a simpler distribution by minimizing KL divergence. Formally, suppose  $p$  is a target distribution with a known score function  $\nabla_{\mathbf{x}}\log p(\mathbf{x})$  that we aim to approximate, and  $q(\mathbf{x})$  is a known source distribution. Liu and Wang [35] showed that the steepest descent of KL divergence between  $q$  and  $p$  is given as follows:
$$
\mathbb {E} _ {q (\mathbf {x})} \left[ \mathbf {f} (\mathbf {x}) ^ {\top} \nabla_ {\mathbf {x}} \log p (\mathbf {x}) + \operatorname {T r} \left(\nabla_ {\mathbf {x}} \mathbf {f} (\mathbf {x})\right) \right], \tag {6}
$$
where  $\mathbf{f}:\mathbb{R}^D\to \mathbb{R}^D$  is any smooth vector function that satisfies  $\lim_{\| \mathbf{x}\| \to \infty}p(\mathbf{x})\mathbf{f}(\mathbf{x}) = 0$ . Remark that Eq. (6) becomes zero if we replace  $q(\mathbf{x})$  with  $p(\mathbf{x})$  in the expectation term, which is known as
Rnrnnnne nnnnne
Original NeRF
(a) "What if he were an anime character?"
Figure 4: 3D NeRF scene editing. Visualizing novel-views of edited Fangzhou NeRF scene [37]. CSD-Edit leads to high-quality editing of 3D scenes and better preserves semantics of source scenes, e.g., obtains sharp facial details (left) and makes him smile without giving beard (right).
(b) "Make him smile"
Stein's identity [36]. Here, the choice of the critic  $\mathbf{f}$  is crucial in its convergence and computational tractability. To that end, Liu and Wang [35] proposed to constrain  $\mathbf{f}$  in the Reproducing Kernel Hilbert Space (RKHS) which yields a closed-form solution. Specifically, given a positive definite kernel  $k: \mathbb{R}^D \times \mathbb{R}^D \to \mathbb{R}^+$ , Stein variational gradient descent provides the greedy directions as follows:
$$
\mathbf {x} \leftarrow \mathbf {x} - \eta \Delta \mathbf {x}, \quad \Delta \mathbf {x} = \mathbb {E} _ {q \left(\mathbf {x} ^ {\prime}\right)} \left[ k \left(\mathbf {x}, \mathbf {x} ^ {\prime}\right) \nabla_ {\mathbf {x} ^ {\prime}} \log p \left(\mathbf {x} ^ {\prime}\right) + \nabla_ {\mathbf {x} ^ {\prime}} k \left(\mathbf {x}, \mathbf {x} ^ {\prime}\right) \right], \tag {7}
$$
with small step size  $\eta > 0$ . The SVGD update in Eq. (7) consists of two terms that play different roles: the first term moves the particles towards the high-density region of target density  $p(\mathbf{x})$ , where the direction is smoothed by kernels of other particles. The second term acts as a repulsive force that prevents the mode collapse of particles. One can choose different kernel functions, while we resort to standard Radial Basis Function (RBF) kernel  $k(\mathbf{x}, \mathbf{x}^{\prime}) = \exp \left(-\frac{1}{h}\|\mathbf{x} - \mathbf{x}^{\prime}\|_{2}^{2}\right)$  with bandwidth  $h > 0$ .
In this section, we introduce Collaborative Score Distillation (CSD) for consistent synthesis and editing of multiple samples. We first derive a collaborative score distillation method using Stein variational gradient descent (Section 3.1) and propose an effective image editing method using CSD, i.e., CSD-Edit, that leads to coherent editing of multiple images with instruction (Section 3.2). Lastly, we present various applications of CSD-Edit in editing panorama images, videos, and 3D scenes (Section 3.3).
3.1 Collaborative score distillation
Suppose a set of parameters  $\{\theta_i\}_{i=1}^N$  that generates images  $\mathbf{x}^{(i)} = g(\theta_i)$ . Similar to SDS, our goal is to update each  $\theta_i$  by distilling the smoothed densities from the diffusion model by minimizing KL divergence in Eq. (4). On the contrary, CSD solves Eq. (4) using SVGD demonstrated in Section 2.3 so that each  $\theta_i$  can be updated in sync with updates of other parameters in the set  $\{\theta_i\}_{i=1}^N$ . At each update, CSD samples  $t \sim \mathcal{U}(t_{\min}, t_{\max})$  and  $\epsilon \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$ , and update each  $\theta_i$  as follows:
$$
\nabla_ {\theta_ {i}} \mathcal {L} _ {\mathrm {C S D}} (\theta_ {i}) = \frac {w (t)}{N} \sum_ {j = 1} ^ {N} \left(k \left(\mathbf {x} _ {t} ^ {(j)}, \mathbf {x} _ {t} ^ {(i)}\right) \left(\boldsymbol {\epsilon} _ {\phi} ^ {\omega} \left(\mathbf {x} _ {t} ^ {(j)}; y, t\right) - \boldsymbol {\epsilon}\right) + \nabla_ {\mathbf {x} _ {t} ^ {(j)}} k \left(\mathbf {x} _ {t} ^ {(j)}, \mathbf {x} _ {t} ^ {(i)}\right)\right) \frac {\partial \mathbf {x} ^ {(i)}}{\partial \theta_ {i}}, \tag {8}
$$
for each  $i = 1,2,\dots ,N$ . We refer to Appendix A for full derivation. Note CSD is equivalent to SDS in Eq. (5) when  $N = 1$ , showing that CSD is a generalization of SDS to multiple samples. As the pairwise kernel values are multiplied by the noise prediction term, each parameter update on  $\theta_{i}$  is affected by other parameters, i.e., the scores are mixed with importance weights according to the affinity among samples. The more similar samples tend to exchange more score updates, while different samples tend to interchange the score information less. The gradient of the kernels acts as a repulsive force that prevents the mode collapse of samples. Moreover, we note that Eq. (8) does not
make any assumption on the relation between  $\theta_{i}$ 's or their order besides them being a set of images to be synthesized coherently with each other. As such, CSD is also applicable to arbitrary image generators, as well as text-to-3D synthesis in DreamFusion [26], which we compare in Section 4.4.
3.2 Instruction-guided editing by collaborative score distillation
In this section, we introduce an instruction-guided visual editing method using Collaborative Score Distillation (CSD-Edit). Given source images  $\tilde{\mathbf{x}}^{(i)} = g(\tilde{\theta}_i)$  with parameters  $\tilde{\theta}_i$ , we optimize new target parameters  $\{\theta_i\}_{i=1}^N$  with  $\mathbf{x}^{(i)} = g(\theta_i)$  such that 1) each  $\mathbf{x}^{(i)}$  follows the instruction prompt, 2) preserves the semantics of source images as much as possible, and 3) the obtained images are consistent with each other. To this end, we update each parameter  $\theta_i$ , initialized with  $\tilde{\theta}_i$ , using CSD with noise estimate  $\epsilon_{\phi}^{\omega_y,\omega_s}$  of Instruct-Pix2Pix [14]. However, this approach often results in blurred outputs, leading to the loss of details of the source image (see Figure 6). This is because the score distillation term subtracts random noise  $\epsilon$ , which perturbs the undesirable details of source images.
We handle this issue by adjusting the noise prediction term that enhances the consistency between source and target images. Subtracting a random noise  $\epsilon$  in Eq. (5) when computing the gradient is a crucial factor, which helps optimization by reducing the variance of a gradient. Therefore, we amend the optimization by changing the random noise into a better baseline function. Since our goal is to edit an image with only minimal information given text instructions, we set the baseline by the image-conditional noise estimate  $\epsilon_{\phi}^{\omega_s}$  of the Instruct-Pix2Pix model without giving text instructions on the source image. To be specific, our CSD-Edit is given as follows:
$$
\nabla_ {\theta_ {i}} \mathcal {L} _ {\text {C S D - E d i t}} \left(\theta_ {i}\right) = \frac {w (t)}{N} \sum_ {j = 1} ^ {N} \left(k \left(\mathbf {x} _ {t} ^ {(j)}, \mathbf {x} _ {t} ^ {(i)}\right) \Delta \boldsymbol {\mathcal {E}} _ {t} ^ {(i)} + \nabla_ {\mathbf {x} _ {t} ^ {(j)}} k \left(\mathbf {x} _ {t} ^ {(j)}, \mathbf {x} _ {t} ^ {(i)}\right)\right) \frac {\partial \mathbf {x} ^ {(i)}}{\partial \theta_ {i}}, \tag {9}
$$
$$
\Delta \mathcal {E} _ {t} ^ {(i)} = \epsilon_ {\phi} ^ {\omega_ {y}, \omega_ {s}} (\mathbf {x} _ {t} ^ {(i)}; \tilde {\mathbf {x}}, y, t) - \epsilon_ {\phi} ^ {\omega_ {s}} (\tilde {\mathbf {x}} _ {t} ^ {(i)}; \tilde {\mathbf {x}}, t).
$$
In Section 4.4, we validate our findings on the effect of baseline noise on image editing performance. We notice that CSD-Edit presents an alternative way to utilize Instruct-Pix2Pix in image-editing without any finetuning of diffusion models, by posing an optimization problem.
3.3 CSD-Edit for various complex visual domains
Panorama image editing. Diffusion models are usually trained on a fixed resolution (e.g.,  $512 \times 512$  for Stable Diffusion [4]), thus when editing a panorama image (i.e., an image with a large aspect ratio), the editing quality significantly degrades. Otherwise, one can crop an image into smaller patches and apply image editing on each patch. However this results in spatially inconsistent images (see Figure 2, Patch-wise Crop, Appendix D). To that end, we propose to apply CSD-Edit on patches to obtain spatially consistent editing of an image, while preserving the semantics of the source image. Following [38], we sample patches of size  $512 \times 512$  that overlap using small stride and apply CSD-Edit on the latent space of Stable Diffusion [4]. Since we allow overlapping, some pixels might be updated more frequently. Thus, we normalize the gradient of each pixel by counting the appearance. Remark that one can give different instructions on the different regions of an image while maintaining consistency (See Appendix C for details).
Video editing. Editing a video with an instruction should satisfy the following: 1) temporal consistency between frames such that the degree of changes compared to the source video should be consistent across frames, 2) ensuring that desired edits in each edited frame are in line with the given prompts while preserving the original structure of source video, and 3) maintaining the sample quality in each frame after editing. To meet these requirements, we randomly sample a batch of frames and update them with CSD-Edit to achieve temporal consistency between frames.
3D scene editing. We consider editing a 3D scene reconstructed by a Neural Radiance Field (NeRF) [27], which represents volumetric 3D scenes using 2D images. To edit reconstructed 3D NeRF scenes, it is straightforward to update the training views with edited views and finetune the NeRF with edited views. Here, the multi-view consistency between edited views should be considered since inconsistencies between edits across multiple viewpoints lead to blurry and undesirable artifacts, hindering the optimization of NeRF. To mitigate this, Haque et al. [39] proposed
Figure 5: Panorama image editing. Comparison of CSD-Edit with baselines at different guidance scales  $\omega_{y} \in \{3.0, 5.0, 7.5, 10.0\}$ .