Recent text-to-image (T2I) diffusion models achieve remarkable realism, yet faithful prompt-image alignment remains challenging, particularly for complex prompts with multiple objects, relations, and fine-grained attributes. Existing training-free inference-time scaling methods rely on fixed iteration budgets that cannot adapt to prompt difficulty, while reflection-tuned models require carefully curated reflection datasets and extensive joint fine-tuning of diffusion and vision-language models, often overfitting to reflection paths data and lacking transferability across models. We introduce RAISE (Requirement-Adaptive Self-Improving Evolution), a training-free, requirement-driven evolutionary framework for adaptive T2I generation. RAISE formulates image generation as a requirement-driven adaptive scaling process, evolving a population of candidates at inference time through a diverse set of refinement actions-including prompt rewriting, noise resampling, and instructional editing. Each generation is verified against a structured checklist of requirements, enabling the system to dynamically identify unsatisfied items and allocate further computation only where needed. This achieves adaptive test-time scaling that aligns computational effort with semantic query complexity. On GenEval and DrawBench, RAISE attains state-of-the-art alignment (0.94 overall GenEval) while incurring fewer generated samples (reduced by 30-40%) and VLM calls (reduced by 80%) than prior scaling and reflection-tuned baselines, demonstrating efficient, generalizable, and model-agnostic multi-round self-improvement. Code is available at https://github.com/LiyaoJiang1998/RAISE.
Figures & tables
Figure 2: Framework overview. RAISE employs diverse mutational refinement actions concurrently—including prompt rewriting, noise resampling, and instructional editing—to evolve candidates in each round. It operates as a multi-agent system composed of an analyzer, rewriter, and verifier: 1) Analyzer performs requirement analysis by extracting a structured and detailed checklist of prompt requirements based on user prompt and previous verification results; 2) Rewriter refines T2I generation prompts or produces image editing instructions to address unsatisfied requirements; 3) Verifier evaluates generated candidates via structured tool-grounded verification.
Methods
Avg.
Avg.
GenEval Score
#Samples
#Calls
Overall
Single
Two
Count-
Colors
Posit-
Attribute
Generated
VLM
Object
Object
ing
ion
Binding
Diffusion Models (More Models in Supplementary)
FLUX.1-dev [ 19 ]
1
0
0.67
0.99
0.81
0.75
0.80
0.21
0.48
SD3.5 Large [ 9 ]
1
0
0.71
0.98
0.89
0.73
0.83
0.34
0.47
SANA-1.5 4.8B [ 46 ]
1
0
0.72
0.99
0.85
0.77
0.87
0.34
0.54
Table 1: Quantitative comparison on GenEval [ 11 ] . The best and second results are bolded and underlined , respectively; category-best methods are also bolded . “Avg. #Samples Generated” and “Avg. #Calls VLM” indicate efficiency.
Figure 3: Visual comparison on GenEval [ 11 ] and DrawBench [ 36 ] . RAISE improves prompt-image alignment on challenging prompts.
Figure 4: Pareto frontier and scaling performance. RAISE (red) achieves the highest GenEval [ 11 ] score with 41.9% fewer samples (18.6 vs. 32) and 88.6% fewer VLM calls (7.3 vs. 64). Unlike baselines that plateau or fail to improve with additional computation, RAISE maintains a strong performance–efficiency Pareto frontier and continues to improve as more samples are generated.
Methods
Efficiency
Prompt– Image Alignment
Perceptual Quality
Avg.
Avg.
VQA-
Image-
HPS-
#Samples
#Calls
Score
Reward
v2
Generated
VLM
[ 22 ]
[ 48 ]
[ 42 ]
FLUX.1-dev [ 19 ]
1
0
0.778
1.06
0.298
+ T2I-Copilot [ 5 ]
3.9
7.9
0.822
0.97
0.300
+ T2I-Copilot [ 5 ]
6.6
13.1
0.820
0.96
0.299
Table 2: Quantitative comparison on the DrawBench [ 36 ] . The best result in each column is bolded , the second-best is underlined . The top-performing setting for each method is also bolded .
Methods
Overall
Colors
Position
Attribute
Binding
FLUX.1-dev [ 19 ]
0.67
0.80
0.21
0.48
+ RAISE (1 round)
0.89
0.95
0.69
0.79
+ RAISE (2 rounds)
0.92
0.97
0.78
0.83
+ RAISE ( ≤ 3 rounds)
0.93
0.98
0.82
0.86
+ RAISE ( ≤ 4 rounds)
0.94
0.98
0.83
0.87
Table 3: Ablation Studies on the GenEval [ 11 ] benchmark. The best result in each column is bolded , the second-best is underlined .
Methods
GenEval Score
Max. #Samples Allowed
Avg. #Samples Generated
Max. #Calls VLM
Avg. #Calls VLM
FLUX.1-dev [ 19 ]
0.67
1
1
0
0
+ T2I-Copilot [ 5 ]
0.75
8
4.3
17
8.6
+ ReflectionFlow [ 50 ]
0.86
8
8
16
16
+ RAISE (1 round)
0.89
8
8
3
3
+ T2I-Copilot [ 5 ]
0.75
16
6.9
33
13.8
+ ReflectionFlow [ 50 ]
0.90
16
16
32
32
Table 6: Efficiency comparison on GenEval [ 11 ] . RAISE consistently achieves the highest GenEval score across budgets (Max #Samples = 8, 16, 32). At 32 samples, it requires 41.9% fewer samples generated and 88.6% fewer VLM calls on average than the second-best method.
Methods
Avg.
Avg.
GenEval Score
#Samples
#Calls
Overall
Single
Two
Count-
Colors
Posit-
Attribute
Generated
VLM
Object
Object
ing
ion
Binding
Diffusion Models
PixArt- α [ 6 ]
1
0
0.48
0.98
0.50
0.44
0.80
0.08
0.07
Emu3-Gen [ 40 ]
1
0
0.54
0.98
0.71
0.34
0.81
0.17
0.21
SDXL [ 29 ]
1
0
0.55
0.98
0.74
0.39
0.85
0.15
0.23
Table 7: Quantitative comparison of RAISE on the GenEval benchmark [ 11 ] against diffusion models, unified multimodal models, training-free inference-time scaling, and training-based reflection tuning methods. The best and second results are bolded and underlined , respectively; category-best methods are also bolded . “Avg. #Samples Generated” and “Avg. #Calls VLM” indicate efficiency, and the last row shows RAISE ’s adaptive allocation of more computation to harder categories such as “Colors”, “Position”, and “Attribute Binding”.
Figure 5: Visualization of the multi-round evolutionary refinement path in RAISE (prompt: “a photo of a bear above a clock”). This figure illustrates how RAISE explores the search space through evolutionary multi-action refinements such as prompt rewriting, resampling, and instructional editing to adaptively improve prompt–image alignment. Across successive rounds, the system evaluates requirement satisfaction and allocates additional refinements only where needed, demonstrating requirement-driven adaptive scaling.
Text-to-image generation creates images from natural-language prompts using generative models, with research focused on fidelity, control, efficiency, and safety.