Datasets:

Merserk
/

Krea-2-Turbo-Checkpoint-Format-Benchmark

advanced_dinov2_large_image_cosine_to_bf16 float64	advanced_dists float64	advanced_hpsv2_1 float64	advanced_lpips_alex float64	advanced_lpips_vgg float64	advanced_ms_ssim float64	advanced_ocr_expected_phrase_exact_fraction float64	advanced_ocr_expected_phrase_normalized_edit_distance float64	advanced_ocr_mean_confidence float64	advanced_ocr_recognized_text string	advanced_openclip_vith14_image_cosine_to_bf16 float64	advanced_openclip_vith14_prompt_score float64	advanced_pickscore_v1 float64	advanced_pyiqa_brisque float64	advanced_pyiqa_maniqa float64	advanced_pyiqa_musiq float64	advanced_pyiqa_niqe float64	advanced_pyiqa_topiq_nr float64	capture_metadata_path string	cfg float64	checkpoint_filename string	checkpoint_sha256 string	checkpoint_source_url string	core_artifact_channel_clipping_fraction float64	core_artifact_clip_black_fraction float64	core_artifact_clip_white_fraction float64	core_artifact_flat_region_chroma_noise float64	core_artifact_flat_region_gradient_entropy_bits float64	core_artifact_flat_region_near_zero_gradient_fraction float64	core_artifact_gradient_mean float64	core_artifact_gradient_p95 float64	core_artifact_grid_boundary_ratio_16 float64	core_artifact_grid_boundary_ratio_32 float64	core_artifact_grid_boundary_ratio_8 float64	core_artifact_high_frequency_power_ratio float64	core_artifact_laplacian_variance float64	core_artifact_luminance_mean float64	core_artifact_luminance_std float64	core_artifact_saturation_mean float64	core_delta_e_2000_mean float64	core_delta_e_2000_p95 float64	core_edge_mae float64	core_final_latent_cosine float64	core_final_latent_mae float64	core_final_latent_max_abs float64	core_final_latent_relative_l2 float64	core_final_latent_rmse float64	core_luminance_histogram_js float64	core_pixel_cosine float64	core_pixel_mae float64	core_pixel_max_abs float64	core_pixel_mse float64	core_pixel_psnr_db float64	core_pixel_rmse float64	core_ssim_luminance float64	core_ssim_rgb float64	decoded_array_path string	denoise float64	final_latent_path string	format_id string	format_label string	height int64	latency_sampling_seconds float64	latency_step_mean_seconds float64	latency_vae_decode_seconds float64	negative_conditioning string	performance_api_end_to_end_seconds float64	performance_gpu_energy_joules float64	performance_gpu_gpu_utilization_percent_mean float64	performance_gpu_gpu_utilization_percent_peak float64	performance_gpu_memory_clock_mhz_mean float64	performance_gpu_memory_clock_mhz_peak float64	performance_gpu_memory_utilization_percent_mean float64	performance_gpu_memory_utilization_percent_peak float64	performance_gpu_power_w_mean float64	performance_gpu_power_w_peak float64	performance_gpu_samples int64	performance_gpu_sm_clock_mhz_mean float64	performance_gpu_sm_clock_mhz_peak float64	performance_gpu_temperature_c_mean float64	performance_gpu_temperature_c_peak float64	performance_gpu_vram_total_mib_mean float64	performance_gpu_vram_total_mib_peak float64	performance_gpu_vram_used_mib_mean float64	performance_gpu_vram_used_mib_peak float64	performance_pre_and_post_sampling_seconds float64	performance_sampling_seconds float64	performance_system_comfy_process_tree_rss_bytes_mean float64	performance_system_comfy_process_tree_rss_bytes_peak float64	performance_system_samples int64	performance_system_system_cpu_percent_mean float64	performance_system_system_cpu_percent_peak float64	performance_system_system_ram_available_bytes_mean float64	performance_system_system_ram_available_bytes_peak float64	performance_system_system_ram_used_bytes_mean float64	performance_system_system_ram_used_bytes_peak float64	performance_vae_decode_seconds float64	prompt string	prompt_category string	prompt_id string	publisher_repository string	publisher_revision string	replicate int64	run_id string	sampler string	scheduler string	seed int64	steps int64	text_encoder string	trajectory_path string	vae string	width int64
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0.999038	0.017882	0.33374	0.028193	0.052512	0.976266	null	null	null	null	0.98997	36.713615	23.034338	8.300232	0.453473	74.44664	3.649186	0.598456	raw/fp8_scaled/p01_portrait__r0__fp8_scaled/metadata.json	1	krea2_turbo_fp8_scaled.safetensors	eb4dd8c612cfd10f64f25b057e6e6bbcb5737c94a7372177e456dbf7579502f1	null	0.010065	0.007658	0	0.001284	6.155295	0.059116	0.013369	0.044811	1.005418	1.014791	0.990053	0.00157	0.00106	0.375432	0.274957	0.085597	1.113954	3.236207	0.015707	0.995725	0.052724	2.457048	0.092413	0.098358	0.001509	0.999022	0.011474	0.546875	0.000431	33.653743	0.020764	0.968921	0.969013	raw/fp8_scaled/p01_portrait__r0__fp8_scaled/decoded_float32.npy	1	raw/fp8_scaled/p01_portrait__r0__fp8_scaled/final_latent_float32.npy	fp8_scaled	FP8 Scaled	1,024	19.51815	2.439769	0.342476	zero_positive_conditioning	20.847685	3,029.831627	94.069149	100	8,751	8,751	45.553191	75	145.753883	152.13	188	2,711.010638	2,730	70.356383	73	16,380	16,380	13,926.574468	14,395	0.987059	19.51815	2,385,670,579.744681	2,423,832,576	188	9.737234	59.8	39,098,776,641.3617	39,129,534,464	11,540,693,950.638298	11,601,420,288	0.342476	Ultra-realistic close-up editorial portrait of an elderly woman by a north-facing window, natural unretouched skin with visible pores and fine wrinkles, individual silver-gray hairs and eyelashes, wet catchlights in both eyes, a dark indigo wool coat with clearly visible fabric weave, soft neutral background bokeh, res...	portrait_microdetail	p01_portrait	Comfy-Org/Krea-2	8038ce89b91b042141541ad0fa51b985ca262c5f	0	p01_portrait__r0__fp8_scaled	euler	simple	5,756,281,479,356,970,000	8	qwen3vl_4b_bf16.safetensors	raw/fp8_scaled/p01_portrait__r0__fp8_scaled/trajectory.npz	qwen_image_vae.safetensors	1,024
0.988772	0.044684	0.326172	0.100479	0.138737	0.910895	null	null	null	null	0.980985	36.488029	23.013006	8.693176	0.447646	74.42804	3.731901	0.592159	raw/gguf_q4_k_m/p01_portrait__r0__gguf_q4_k_m/metadata.json	1	krea2_turbo-Q4_K_M.gguf	bc12f539de7a7a6ddf9bac17ec6e5bfecd42c3517190f491131d8464a1034b40	https://huggingface.co/vantagewithai/Krea-2-Turbo-GGUF/resolve/main/krea2_turbo-Q4_K_M.gguf	0.01017	0.007316	0	0.001296	6.18891	0.05661	0.012998	0.043899	1.001941	1.00072	0.992709	0.00151	0.000985	0.366369	0.271628	0.080218	2.678032	9.08988	0.027503	0.978936	0.119248	3.79078	0.205346	0.218557	0.003417	0.993545	0.027671	0.635742	0.002756	25.596931	0.052499	0.908655	0.908144	raw/gguf_q4_k_m/p01_portrait__r0__gguf_q4_k_m/decoded_float32.npy	1	raw/gguf_q4_k_m/p01_portrait__r0__gguf_q4_k_m/final_latent_float32.npy	gguf_q4_k_m	GGUF Q4_K_M	1,024	31.345965	3.918246	0.341916	zero_positive_conditioning	32.624999	4,500.240898	96.357143	100	8,751	8,751	59.989796	80	138.160816	142.52	294	2,730	2,730	69.037415	71	16,380	16,380	13,646.70068	13,691	0.937118	31.345965	2,413,946,183.401361	2,445,586,432	294	10.282653	59.8	40,144,792,513.30612	40,169,885,696	10,494,678,078.693878	10,586,525,696	0.341916	Ultra-realistic close-up editorial portrait of an elderly woman by a north-facing window, natural unretouched skin with visible pores and fine wrinkles, individual silver-gray hairs and eyelashes, wet catchlights in both eyes, a dark indigo wool coat with clearly visible fabric weave, soft neutral background bokeh, res...	portrait_microdetail	p01_portrait	vantagewithai/Krea-2-Turbo-GGUF	6658a30c67045ec72799333eb42f3bf10b3d107a	0	p01_portrait__r0__gguf_q4_k_m	euler	simple	5,756,281,479,356,970,000	8	qwen3vl_4b_bf16.safetensors	raw/gguf_q4_k_m/p01_portrait__r0__gguf_q4_k_m/trajectory.npz	qwen_image_vae.safetensors	1,024
0.99893	0.010604	0.33252	0.022579	0.041567	0.982037	null	null	null	null	0.996715	37.087936	22.977488	8.306824	0.447411	74.526772	3.521729	0.597592	raw/gguf_q8_0/p01_portrait__r0__gguf_q8_0/metadata.json	1	krea2_turbo-Q8_0.gguf	1fa2da08a7a708827c2100d0af41c8371d79efd9f7c5490c23ebc65408536490	https://huggingface.co/vantagewithai/Krea-2-Turbo-GGUF/resolve/main/krea2_turbo-Q8_0.gguf	0.010856	0.008533	0	0.001279	6.149518	0.061733	0.013425	0.045265	1.003755	1.005874	0.989061	0.001616	0.00107	0.372564	0.273066	0.083498	0.912724	2.500021	0.010752	0.995356	0.041272	3.57852	0.09633	0.102527	0.001319	0.998461	0.009202	0.652344	0.000656	31.829114	0.025618	0.97782	0.978022	raw/gguf_q8_0/p01_portrait__r0__gguf_q8_0/decoded_float32.npy	1	raw/gguf_q8_0/p01_portrait__r0__gguf_q8_0/final_latent_float32.npy	gguf_q8_0	GGUF Q8_0	1,024	27.486215	3.435777	0.831068	zero_positive_conditioning	29.15262	3,970.769888	94.339695	100	8,751	8,751	51.721374	66	136.693168	145.71	262	2,717.919847	2,745	69.194656	72	16,380	16,380	14,119.335878	14,267	0.835336	27.486215	2,526,917,168.669201	5,867,495,424	263	7.791255	59.6	39,200,669,929.61217	39,385,686,016	11,438,800,662.387833	14,777,761,792	0.831068	Ultra-realistic close-up editorial portrait of an elderly woman by a north-facing window, natural unretouched skin with visible pores and fine wrinkles, individual silver-gray hairs and eyelashes, wet catchlights in both eyes, a dark indigo wool coat with clearly visible fabric weave, soft neutral background bokeh, res...	portrait_microdetail	p01_portrait	vantagewithai/Krea-2-Turbo-GGUF	3ddc3b8a70209d9da1e327701ab96b15bafc4d28	0	p01_portrait__r0__gguf_q8_0	euler	simple	5,756,281,479,356,970,000	8	qwen3vl_4b_bf16.safetensors	raw/gguf_q8_0/p01_portrait__r0__gguf_q8_0/trajectory.npz	qwen_image_vae.safetensors	1,024
0.986836	0.089103	0.327148	0.170043	0.247364	0.83253	null	null	null	null	0.968098	37.915047	22.896713	10.81488	0.419709	73.02179	4.1787	0.587464	raw/int4_convrot/p01_portrait__r0__int4_convrot/metadata.json	1	krea2_turbo_int4_convrot.safetensors	4c74e8fc00d121aeb4f75cdf066b70d33f6a59e36f143b0bfd53d6467ffc13bd	https://huggingface.co/Winnougan/INT4-Convrot-Comfy-Models/resolve/main/krea2_turbo-int4_convrot.safetensors	0.013982	0.009968	0	0.001331	6.112275	0.074764	0.012181	0.040798	1.000938	1.012105	0.993817	0.001414	0.000807	0.36374	0.275022	0.081886	4.543243	14.58322	0.042604	0.96438	0.191248	3.920254	0.27075	0.288169	0.007161	0.986985	0.045012	0.730469	0.005519	22.581535	0.074289	0.844425	0.84603	raw/int4_convrot/p01_portrait__r0__int4_convrot/decoded_float32.npy	1	raw/int4_convrot/p01_portrait__r0__int4_convrot/final_latent_float32.npy	int4_convrot	INT4 ConvRot W4A4	1,024	9.540272	1.192534	0.350047	zero_positive_conditioning	10.892805	1,443.868354	91.717172	100	8,751	8,751	65.20202	76	132.75303	143.92	99	2,727.272727	2,730	68.20202	71	16,380	16,380	13,869.10101	14,619	1.002486	9.540272	2,416,029,571.878788	2,454,241,280	99	15.173737	61.9	39,579,404,722.42424	39,622,000,640	11,060,065,869.575758	11,142,533,120	0.350047	Ultra-realistic close-up editorial portrait of an elderly woman by a north-facing window, natural unretouched skin with visible pores and fine wrinkles, individual silver-gray hairs and eyelashes, wet catchlights in both eyes, a dark indigo wool coat with clearly visible fabric weave, soft neutral background bokeh, res...	portrait_microdetail	p01_portrait	Winnougan/INT4-Convrot-Comfy-Models	f01ad8da8c799f1da5ffe92f82e2a75a51dd7447	0	p01_portrait__r0__int4_convrot	euler	simple	5,756,281,479,356,970,000	8	qwen3vl_4b_bf16.safetensors	raw/int4_convrot/p01_portrait__r0__int4_convrot/trajectory.npz	qwen_image_vae.safetensors	1,024
0.999443	0.005563	0.333252	0.010271	0.021212	0.992292	null	null	null	null	0.997939	37.190372	23.073093	8.701355	0.450577	74.470329	3.459733	0.596188	raw/int8_convrot/p01_portrait__r0__int8_convrot/metadata.json	1	krea2_turbo_int8_convrot.safetensors	8e4eeda70dd5037ab1ba2bef6b417f9f901e26093117cf397f741fc1fdaaf3f1	null	0.011157	0.008945	0	0.001272	6.152777	0.062565	0.013348	0.044878	1.002423	1.003855	0.989651	0.001575	0.001027	0.372072	0.273855	0.083866	0.634125	1.646248	0.007952	0.99803	0.02523	2.897759	0.06279	0.06683	0.000924	0.99912	0.00629	0.610352	0.000375	34.255986	0.019373	0.989724	0.989617	raw/int8_convrot/p01_portrait__r0__int8_convrot/decoded_float32.npy	1	raw/int8_convrot/p01_portrait__r0__int8_convrot/final_latent_float32.npy	int8_convrot	INT8 ConvRot	1,024	12.41274	1.551593	0.343104	zero_positive_conditioning	13.605664	1,953.689655	92.707317	100	8,751	8,751	68.772358	80	144.440163	151.66	123	2,705.243902	2,745	69.577236	72	16,380	16,380	14,037.536585	14,811	0.849819	12.41274	2,418,758,198.113821	2,455,592,960	123	12.219512	59.8	38,942,058,479.349594	38,966,693,888	11,697,412,112.650406	11,735,945,216	0.343104	Ultra-realistic close-up editorial portrait of an elderly woman by a north-facing window, natural unretouched skin with visible pores and fine wrinkles, individual silver-gray hairs and eyelashes, wet catchlights in both eyes, a dark indigo wool coat with clearly visible fabric weave, soft neutral background bokeh, res...	portrait_microdetail	p01_portrait	Comfy-Org/Krea-2	8038ce89b91b042141541ad0fa51b985ca262c5f	0	p01_portrait__r0__int8_convrot	euler	simple	5,756,281,479,356,970,000	8	qwen3vl_4b_bf16.safetensors	raw/int8_convrot/p01_portrait__r0__int8_convrot/trajectory.npz	qwen_image_vae.safetensors	1,024
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0.973147	0.011618	0.284668	0.023431	0.034064	0.975487	null	null	null	null	0.997229	34.452751	22.382786	25.924011	0.385779	69.28183	4.376736	0.502374	raw/int8_convrot/p02_hands_group__r1__int8_convrot/metadata.json	1	krea2_turbo_int8_convrot.safetensors	8e4eeda70dd5037ab1ba2bef6b417f9f901e26093117cf397f741fc1fdaaf3f1	null	0.06906	0.049832	0.000002	0.000983	5.217629	0.246693	0.016011	0.061187	0.98364	0.980747	0.985533	0.001658	0.001181	0.246525	0.251284	0.159547	1.215912	4.04953	0.019975	0.994751	0.049879	3.572098	0.102573	0.116686	0.00054	0.994966	0.011345	0.935547	0.00129	28.895508	0.035911	0.962855	0.963102	raw/int8_convrot/p02_hands_group__r1__int8_convrot/decoded_float32.npy	1	raw/int8_convrot/p02_hands_group__r1__int8_convrot/final_latent_float32.npy	int8_convrot	INT8 ConvRot	1,024	12.393452	1.549182	0.34263	zero_positive_conditioning	13.605954	1,919.40051	92.650407	100	8,751	8,751	67.219512	78	141.729431	151.86	123	2,706.097561	2,730	69.252033	72	16,380	16,380	14,028.170732	14,811	0.869872	12.393452	2,429,779,102.178862	2,468,610,048	123	12.858537	64.3	38,223,343,049.88618	38,258,204,672	12,416,127,542.113821	12,479,762,432	0.34263	Candid documentary photograph of exactly four adult friends seated around a small round wooden table, each person exchanging a different small object with another person, all eight hands fully visible, natural overlapping arms and fingers, ceramic cups and folded paper on the table, warm side lighting, realistic anatom...	anatomy_occlusion_counting	p02_hands_group	Comfy-Org/Krea-2	8038ce89b91b042141541ad0fa51b985ca262c5f	1	p02_hands_group__r1__int8_convrot	euler	simple	8,824,944,874,703,836,000	8	qwen3vl_4b_bf16.safetensors	raw/int8_convrot/p02_hands_group__r1__int8_convrot/trajectory.npz	qwen_image_vae.safetensors	1,024
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Krea 2 Turbo ComfyUI Format Fidelity Benchmark

This release is a paired, deterministic comparison of eight Krea 2 Turbo checkpoint formats in ComfyUI: BF16, FP8 Scaled, INT8 ConvRot, MXFP8, NVFP4, INT4 ConvRot W4A4, GGUF Q8_0, and GGUF Q4_K_M. It contains 240 scored 1024×1024 images, saved float32 decoded tensors and final latents, every denoising trajectory, raw metric tables, telemetry, statistical comparisons, and reproduction code.

Main result

BF16 is the highest-fidelity reference because it is the unquantized published checkpoint.
INT8 ConvRot narrowly leads the preregistered LPIPS-Alex endpoint. GGUF Q8_0 is statistically tied with it on that endpoint (Holm-adjusted permutation p=0.9067) and leads INT8 on DISTS, DINO similarity, and reconstructed-weight SNR.
GGUF Q8_0 is the highest-fidelity of the three added formats.
GGUF Q4_K_M is a storage-quality compromise: 6.972 GiB and much closer to BF16 than INT4 ConvRot, but slow on this Ada GPU under ComfyUI-GGUF dequantized execution.
INT4 ConvRot W4A4 is the smallest checkpoint and fastest of the three additions on this RTX 4060 Ti, but has the largest measured fidelity loss.

No weighted composite score is used. See the full technical report and tables/decision_table.csv.

Important performance limitation

The test GPU was an NVIDIA GeForce RTX 4060 Ti 16 GB (SM 8.9). MXFP8 and NVFP4 native fast matrix multiplication requires SM 10.0 in the tested runtime. GGUF Q8_0 and Q4_K_M use the pinned ComfyUI-GGUF on-demand dequantized matrix-multiplication path. These Ada timings must not be projected to native Blackwell execution.

The original five formats and the three additions were measured in separate sessions. Five unscored BF16 and INT8 ConvRot bridge repeats quantify drift. INT8 crossed the preregistered 5% drift threshold, so exact old-versus-new timing comparisons remain caveated; fidelity comparisons remain paired to the same saved BF16 prompt/seed outputs.

Dataset organization

data/train/metadata.jsonl is the Hugging Face ImageFolder index. Each row links an image to its prompt, seed, format, checkpoint provenance, raw scientific artifacts, and flattened metric values.
raw/ contains decoded_float32.npy, final_latent_float32.npy, trajectory.npz, and capture metadata.json for every scored run.
metrics/ contains raw per-image, per-parameter, paired-statistics, summary, trajectory, latency, and performance tables.
comparison_sheets/ contains eight-format sheets, BF16-relative difference maps, and automatically selected detail crops.
telemetry/ contains scored GPU/system telemetry plus the BF16/INT8 bridge telemetry.
provenance/ contains sanitized environment, model, run, and release manifests.
reproduction/ contains the ComfyUI workflows, custom capture node, benchmark driver, analyzers, model downloader, and exact instructions.

Load the image table locally:

from datasets import load_dataset

dataset = load_dataset("Merserk/Krea-2-Turbo-Checkpoint-Format-Benchmark", split="train")
print(dataset.num_rows)  # 240

Fixed inference controls

15 prompts × 2 deterministic seeds × 8 checkpoint formats = 240 scored images
1024×1024, batch size 1
8 steps, CFG 1.0, Euler sampler, simple scheduler, denoise 1.0
Shared qwen3vl_4b_bf16.safetensors text encoder and qwen_image_vae.safetensors VAE
Zeroed positive conditioning used as negative conditioning
No prompt rewriting, LoRAs, previews, upscaling, or post-processing
Identical initial-noise SHA-256 for every eight-format prompt/seed group

Reproduce or verify

Follow reproduction/README.md. A complete rerun downloads about 104 GiB of upstream model files and should have at least 150 GiB free for models, captures, analysis caches, and reports. The published benchmark was tested on Windows 11 with a 16 GB NVIDIA GPU; lower-memory configurations are not certified.

Validate this downloaded release:

python scripts/validate_release.py --root . --full

Upload a locally rebuilt copy:

hf auth login
python scripts/upload_to_huggingface.py OWNER/DATASET --root .

Licenses and responsible use

Generated images, data, and reports are released under CC BY 4.0. Benchmark scripts are MIT licensed. Model weights are not redistributed and remain governed by the Krea 2 Community License. Krea states that it does not claim intellectual-property rights over user-generated outputs, while users remain responsible for their prompts, outputs, and downstream use. Review the official Krea 2 Turbo model card and license before downloading or running the checkpoints.

The prompt suite avoids requests for real people, explicit content, unlawful activity, or protected logos. Generated images can still contain model errors, stereotypes, malformed text, or unintended resemblance. This benchmark evaluates checkpoint fidelity on one controlled campaign; it is not a universal ranking of human aesthetic preference or all hardware.

Citation

See CITATION.cff. The project attribution name is Krea 2 Turbo Formats Benchmark Contributors.

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