reviews: 
Comments: This paper presents DragonDiffusion - a novel framework to enable drag-style image editing with diffusion models.
To this end, new techniques are presented, including (1) using an energy function to guide the editing and (2) a memory bank for editing consistency.
Qualitative and quantitative experiments show the merits of DragonDiffusion.
Strengths: - The idea of enabling more precise and interactive image editing with diffusion models is an attractive topic. I believe that this work will attract both academic and community interest.
- Sufficient experiments have been conducted to compare with DragGAN-related methods, showing the merits of the methods (e.g., DragGAN can not edit based on a reference image).
- The ablation study demonstrates the effectiveness of the framework design.
- Generally, the paper is easy to follow.
Weaknesses: 1. My primary concern about this paper is **whether ICLR is a suitable venue**.  
I believe this paper would be more fitting for a Computer Vision conference (e.g., CVPR, ICCV, ECCV, SIGGRAPH).  
While I don't intend to downplay the contribution of this paper (in fact, I appreciate it), I find it challenging to identify a precise description for this paper within the context of ICLR.  
Perhaps, "representation learning for application in Computer Vision", but given that there is no "representation learning" happening, I am not sure. Thus, my initial rating is "marginally below the acceptance threshold".

1. My other complaints are mainly about *writing* (but it is not the main reasons for my decision). Authors can use it to improve paper' clarity.
- In Abstract, "... they often lack the ability to precisely edit the generated or real images." -> I think this should tone down to "interactively" as "precisely" might have a broad meaning (e.g., precise in terms of pose, shape, etc.). In a broader meaning of precision, I see existing works can also achieve "precise" image editing (e.g., ControlNet [1]).
- Introduction, first paragraph: (Similar to above) While I see DragonDiffusion has clearly advanced in interactive image editing, I think it'd be more comprehensive to mention seminal works aiming to perform more precise image editing (e.g., [1-4]... you name it). Alternatively, authors can briefly discuss these works in Section 2.3.
- Section 2.3, "InstructPix2Pix retrain diffusion models.." -> "InstructPix2Pix finetunes diffusion models..."
- Section 2.3, "However, text-guided image editing is coarse." Could you add a sentence explaining why "coarse" is a bad thing?
- Section 2 though Section 3.1 all use $x_{T}$, then suddenly Section 3.2 uses $z_{T}$. As far as I understand, the authors intend to use Latent Diffusion (Stable Diffusion), which is $z_{T}$. Then, could authors revise Section 2.1 (and other related parts in Section 2-3), so it is made sure that we have mentioned $z_{T}$ before?

**Reference**:  
[1] Zhang et al., Adding Conditional Control to Text-to-Image Diffusion Models, ICCV, 2023.  
[2] Couairon et al., DiffEdit: Diffusion-based semantic image editing with mask guidance, ICLR, 2023.  
[3] Nguyen et al., Visual Instruction Inversion: Image Editing via Visual Prompting, NeurIPS, 2023.  
[4] Epstein et al., Diffusion Self-Guidance for Controllable Image Generation, NeurIPS, 2023.
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 3 good