cs.CVOct 14, 2026
SaveBluePixel: Toward Native 4K Agentic Image Editing
Organizations: vivo Mobile Communication Co., Ltd. · Nanjing University · Shanghai Jiao Tong University
Abstract
Instruction-driven image editing has reached native 4K, yet how such editors are trained remains undisclosed, and pretraining one from scratch is beyond most teams. The task itself compounds the difficulty: a single end-to-end editor cannot make precise parametric adjustments, match specialized experts, or interpret the vague instructions users actually write. We argue that 4K editing is a system problem rather than a single-model problem, and present BluePixel, which couples a native-4K editing model with an agent harness; the complete training recipe is disclosed in this paper. The model makes 4K training practical: it turns a 1K-pretrained editor into a native-4K one through progressive VAE adaptation that keeps 4K training as affordable as 1K, absorbs a long tail of vertical editing skills through continual learning, and refines the aesthetic-fidelity trade-off with reinforcement learning. Distillation via source-anchored target projection (SATP) then compresses 50 denoising steps to 4, completing a 4K edit in 8.7 s on a single Kunlunxin P800 XPU while preserving task success rate and background consistency. The harness, BluePixel Agent, makes editing controllable: it decomposes user instructions into specialist and general operations, verifies every result with the same reward model that guided training, and retries within bounded attempts. On VINS-4KEval++ (3,305 native-4K cases), BluePixel ranks second among seven models, behind only GPT Image 2 in non-edited-region consistency.
Figures & tables
Figure 1: BluePixel editing showcase. Diverse 4K editing capabilities including text editing, pose editing, scene recomposition, anime-style expression editing, and low-level image editing. The model preserves fine details faithfully: even small text remains crisp and legible.
Figure 2: Phase 1 vs. final model. The Phase 1 checkpoint produces usable but limited results; after Phase 2 enhancement (progressive SFT, benchmark-driven fine-tuning, continual learning, and reinforcement learning), the final model achieves substantially higher editing quality and detail fidelity.
| Group | Task | Sub-templates | #Templates | #Samples |
| Portrait Beauty | Change Hairstyle | side-part, braids, bangs, curly, …(see Table 9 ) | 26 | 420 |
| Adjust Expression | wink, cool, closed mouth, laugh, smile, smirk, open eyes | 7 | 350 | |
| Retouch Teeth | remove braces, remove stains, align, whiten | 4 | 305 | |
| Adjust Gesture / Pose | stretch, heart-hands, cheek-poke, face-hold, pockets | 5 | 250 | |
| Apply Lens Defect | reflection, smudge | 2 | 168 | |
| Adjust Hair Volume | hairline, density boost | 2 | 84 |
Table 1: The 14 vertical editing tasks used for ACE-LoRA adaptation, spanning portrait beauty and three capability categories. Teeth retouching and clothing-wrinkle removal have no held-out test set.
Figure 3: Agent-assisted editing with supplementary factual context. Top: adding visual effects and dialogue associated with the depicted character. Bottom: adding a plush toy corresponding to the Chinese zodiac year specified in the instruction. Each row shows, left to right: the input image, BluePixel Agent, GPT Image 2, Nano Banana Pro, and Seedream 5.0 Pro.
Figure 4: Filter-style editing comparison. BluePixel Agent vs. GPT Image 2, Nano Banana Pro, and Seedream 5.0 Pro on a filter task requiring professional aesthetic judgment. The three compared baselines produce plausible but aesthetically inconsistent results. BluePixel Agent routes the request to LUT-based (look-up-table) color grading, a specialist editing capability in Table 2 , rather than relying solely on the diffusion model; this produces more accurate and professionally consistent filter effects.
| Tier | Capability | Role |
| Primary editing | BluePixel 4K diffusion editor | Native-4K, open-ended edits to objects, backgrounds, and scene composition |
| Specialist editing | LUT-based color grading | Global color transformations for filter-style edits |
| Tone and lighting effects | Preset-based adjustments to illumination and visual atmosphere | |
| Template-guided editing | Application of predefined styles or scene treatments | |
| Mask-guided local editing | Changes restricted to a selected image region | |
| Hairstyle editing | Hair-region edits guided by a user-provided mask |
Table 2: Skill-based editing capabilities. Representative editing and supporting capabilities of BluePixel Agent, organized by functional tier.
Figure 5: The 4K set concentrates on three task types , while the 1K set is more evenly distributed: task type proportions for the 1K and 4K training datasets across the eight unified task types.
Figure 6: Main qualitative results on VINS-4KEval++. Each two-row block shows one instruction: compound attribute editing, facial-expression editing, or text restyling. Columns, left to right: the input image, BluePixel, GPT Image 2, Nano Banana Pro, Nano Banana 2.0, Seedream 5.0 Lite, Wan 2.7 Pro, and Kling.
Figure 7: Qualitative comparison on CIE-Bench [ 36 ] . BluePixel vs. Seedream 5.0 on a causal-relation edit and a refocus edit (left), and a virtual try-on (right). BluePixel renders each requested outcome — the badger’s bite, the legible refocused spine text, and the garment transfer at the original framing — whereas Seedream 5.0 shows an intact potato, unchanged spine text, and a full-body shot.
Figure 8: Effect of the agent harness. Three editing instructions (facial expression, text restyling, and mask repair) executed by the BluePixel backbone alone (middle) and by BluePixel Agent (right). The agent produces the requested expression, renders the target word correctly, and fully repairs the cracked mask, while the backbone under-modifies or corrupts these details.
| Seed | Overall | Success (%) | vs. Nano Banana Pro |
|---|---|---|---|
| 2026 | 8.653 | 63.8 | +0.052 |
| 42 | 8.651 | 64.0 | +0.050 |
| 1337 | 8.621 | 63.2 | +0.020 |
| 1 | 8.619 | 63.4 | +0.018 |
| 3407 | 8.619 | 62.4 | +0.018 |
| 777 | 8.615 | 63.0 | +0.014 |
Table 4: Per-seed results on the 2,513 shared VINS-4KEval++ cases (BluePixReward judge, prompt rewriting), ranked by Overall. is the Overall margin over Nano Banana Pro.
| Task type | Overall | Success (%) |
|---|---|---|
| Add object or element | 9.203 | 79.3 |
| Remove object or element | 8.994 | 72.9 |
| Edit people/faces/character | 8.931 | 74.0 |
| Multi-step (compound) | 8.769 | 62.6 |
| Modify existing object | 8.663 | 62.0 |
| Alter background/texture/style | 8.579 | 56.9 |
Table 5: Per-task-type results on the full VINS-4KEval++ benchmark (8-seed mean, BluePixReward judge).
Figure 10: ACE-MOPD qualitative visualization: hairstyle manipulation. The leftmost column shows the source image; subsequent columns correspond to six target hairstyles: wind-swept long hair under warm backlight, wind-swept long hair, braids, a short pixie cut, loose waves, and a high ponytail. The edits change hair structure and appearance while retaining subject identity, apparel, pose, and scene layout.
Figure 11: ACE-MOPD qualitative visualization: global image-effect rendering. The leftmost column shows the source image; subsequent columns apply chromatic aberration, directional motion blur, radial zoom blur, vignetting, fisheye distortion, a DV-camera effect, and chromatic noise, respectively. The source image’s semantic content and scene structure are retained under these global appearance transformations.
Figure 12: ACE-MOPD qualitative visualization: facial-expression manipulation. The leftmost column shows the source image; subsequent columns depict a surprised expression, a subtle smile, a wink, and a broad smile. The transformation is localized to the face while preserving identity cues, hairstyle, apparel, viewpoint, and background.
Figure 13: ACE-MOPD qualitative visualization: human pose manipulation. The leftmost column shows the source image; subsequent columns show a relaxed standing pose, a cheek-heart gesture, a head tilt, a peace sign, and a raised-arms pose. The model changes the articulated body configuration while maintaining subject identity, clothing, full-body framing, and the outdoor scene.
| Method | Add | Adjust | Extract | Replace | Remove | Background | Style | Hybrid (Compose) | Action | Overall |
|---|---|---|---|---|---|---|---|---|---|---|
| GPT Image 2 | 4.85 | 4.82 | 4.07 | 4.92 | 4.75 | 4.88 | 4.96 | 4.47 | 4.85 | 4.73 |
| Qwen-Image-Edit-2511 | 4.54 | 4.57 | 4.13 | 4.70 | 4.46 | 4.36 | 4.89 | 4.16 | 4.81 | 4.51 |
| Nano Banana 2.0 | 4.56 | 4.41 | 4.04 | 4.62 | 4.74 | 4.54 | 4.87 | 4.01 | 4.81 | 4.51 |
| Seedream 5.0 Lite | 4.93 | 4.69 | 3.01 | 4.41 | 4.45 | 4.65 | 4.93 | 3.91 | 4.82 | 4.42 |
| Emu3.5 | 4.61 | 4.32 | 3.96 | 4.84 | 4.58 | 4.35 | 4.79 | 3.69 | 4.57 | 4.41 |
| Nano Banana Pro | 4.44 | 4.62 | 3.42 | 4.60 | 4.63 | 4.32 | 4.97 | 3.64 | 4.69 | 4.37 |
Table 6: BluePixel ranks first on Hybrid and Action, ties for first on Add, and remains competitive overall. Main results on ImgEdit-Bench: each cell is the average score (1–5); Overall is the average across the nine task categories. Superscripts distinguish the reporting source for models with multiple reported scores: a FireRed-Image-Edit, b LongCat-Image, c HiDream-O1-Image, d Boogu-Image-0.1.
| Method | Instr. Adh. | Nat. | Detail |
|---|---|---|---|
| Nano Banana 2.0 | 4.68 | 4.79 | 4.74 |
| Seedream 4.0 | 4.60 | 4.79 | 4.70 |
| Seedream 5.0 Lite | 4.57 | 4.65 | 4.61 |
| FLUX.1 Kontext [Dev] + VINS [ 11 ] | 4.23 | 4.70 | 4.47 |
| FLUX.1 Kontext [Dev] | 4.22 | 4.60 | 4.41 |
| OmniGen2 | 4.14 | 4.54 | 4.34 |
Table 7: Quantitative comparison on the VINS-4KEval benchmark [ 11 ] (509 tasks, 4K). BluePixel matches the naturalness of FLUX.1 Kontext [Dev] + VINS while exceeding it on instruction adherence and detail. ImgEditJudge scores each row on instruction following (Instr. Adh.) and naturalness (Nat.); Detail is their average. BluePixel is the final model after the full training recipe. Per-column best in bold, second best underlined.
| Method | Overall | IF | NC | VQ | Success (%) |
|---|---|---|---|---|---|
| GPT Image 2 [ 39 ] | 9.15 | 9.22 | 9.34 | 9.42 | 74.8 |
| Nano Banana Pro [ 17 ] | 8.60 | 8.80 | 8.72 | 9.12 | 59.4 |
| Nano Banana 2.0 [ 17 ] | 8.56 | 8.81 | 8.63 | 9.08 | 57.9 |
| Wan 2.7 Pro [ 1 ] | 8.56 | 8.72 | 8.77 | 9.04 | 59.5 |
| Seedream 5.0 Lite [ 8 ] | 8.44 | 8.95 | 8.53 | 8.81 | 51.8 |
| Kling [ 28 ] | 7.90 | 8.01 | 8.37 | 8.55 | 45.4 |
Table 8: BluePixel ranks second on VINS-4KEval++ behind GPT Image 2, with the second-best non-edited-region consistency: BluePixReward scores on the 2,513 cases shared by all models. BluePixel reports the mean of an eight-seed run with prompt rewriting (per-seed detail in Table 4 ); each external model has one output per case. Scores are on a 0–10 scale, with higher values indicating better performance. Success is the percentage of outputs judged successful by the same evaluator.
| # | Template | # | Template | # | Template |
|---|---|---|---|---|---|
| 1 | side-part | 2 | braids | 3 | bangs |
| 4 | half-up princess | 5 | half-up bun | 6 | curly |
| 7 | glowing strands | 8 | flying hair | 9 | short wavy |
| 10 | layered perm | 11 | long wavy | 12 | latin curl |
| 13 | side bangs | 14 | japanese short curly | 15 | japanese bangs |
| 16 | straight | 17 | sakura | 18 | french bangs |
Table 9: Full list of hairstyle sub-templates for the Change Hairstyle task.
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