CivArchive
    Neta Art XL - v2.0
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    Full Report:

    https://nieta-art.feishu.cn/wiki/PpwqwVDzjiNE5kkUhRtcEsn6nmh

    First release address:

    Civitai - https://civarchive.com/models/410737

    Liblib - https://www.liblib.art/modelinfo/55b06e35dd724862b3524ff00b069fe8

    I. Overview

    • We have updated Neta Art XL V2.0. Comparing to version 1.0, we have optimized the character's posture dynamics, further strengthened hand stability, and are very good at telling stories with atmospheric epic scenes.

    • Main motivation:

      • In V1.0, the default poses generated by our characters are relatively fixed, while the new version enhances the sense of dynamics, diverse character camera expressions, and more precise prompt control.

      • On the basis of increasing more stylistic diversity, more stable anatomy has been maintained, especially the probability of having five fingers (instead of four or six) on the hands is now higher!

      • Further training methods for issues such as Rectified Flow and Noise Offset have been better deployed. The current picture has a strong sense of epic, and can now show very bright and dark scenery (as shown in the figure below).

    {

    "prompt": "1girl, full moon, moonlight shadow, cinematic lighting, battle field, fighting armies, castle, warriors around, waving, looking at viewer, Heterochromatic pupil, sitting on a cliff, very dark, epic scene, inferno, cowboy shot, depressed, angel wings, solo, white long hair, wings bangs, beautiful color, amazing quality,",

    "negative_prompt": "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, rating: sensitive, low contrast, signature, flexible deformity, abstract, low contrast, ",

    "resolution": "1344 x 768",

    "guidance_scale": 8,

    "num_inference_steps": 28,

    "sampler": "Euler a",

    "use_lora": null,

    "use_upscaler": {

    "upscale_method": "bislerp",

    "upscaler_strength": 0.65,

    "upscale_by": 1.5,

    "upscale_cfg": 11,

    "new_resolution": "2016 x 1152"

    }

    }

    Prompt guide

    In Neta V2, we re-trained based on ArtiWaifu Diffusion V1.0 (AWA1). Therefore, we used the prompt order summarized in ArtiWaifu:

    Tag order: art style ( by xxx ) - > character ( 1 frieren (sousou no frieren) ) - > race (elf) - > composition (cowboy shot) - > painting style ( impasto ) - > theme (fantasy theme) - > main environment (in the forest, at day) - > background (gradient background) - > action (sitting on ground) - > expression (expressionless) - > main characteristics (white hair) - > other characteristics (Twintails, green eyes, parted lips) - > clothing (wearing a white dress) - > clothing accessories (frills) - > other items (holding a magic wand) - > secondary environment (grass, sunshine) - > aesthetics ( beautiful color , detailed ) - > quality ( best quality) - > secondary description (birds, clouds, butterflies)

    V. Training strategies

    Model Training is divided into three stages.

    1. Rough practice: Teach models basic knowledge and concepts of the second dimension. Learning Rate: Constants 1e-5 (unet), 7.5e-6 (text encoder) Equivalent batch size: 96 Optimizer: AdaFactor (False relative_step, scale_parameter and warmup_init) Noise-free offset Time step sampling: LogitNormal (ln3,1) Minimum signal to noise ratio: 5 Note: Since the basic model has most of the prior knowledge, this stage is skipped in actual training.

    1. Concept Reinforcement: This stage teaches and reinforces all the concepts that the model needs to learn, including artists and characters with less data. The core strategy is data weighting - increasing the weight of data with less data, that is, the number of times it is repeatedly trained in a single epoch, and supplemented with other strategies to help concept learning, such as randomly removing core features of characters. The training parameters for the concept reinforcement stage are exactly the same as those for rough practice.

    2. Refinement: This stage uses data with quality ratings of best and amazing to continue fine-tuning the model, freezes the text encoder, enables label randomization algorithm, adds noise offset of 0.0357, and uniformly samples time steps during training. Learning Rate: Constants 2e-6 (unet), 1e-6 (text encoder) Equivalent batch size: 48 Optimizer: AdaFactor (False relative_step, scale_parameter and warmup_init) Noise offset: 0.0357 Time step sampling: uniform distribution (same as normal training) Minimum signal to noise ratio: 5

    VI. Terms of Use

    VII. Summary and Outlook

    Neta Art DiT follow-up training. Stay tuned and test our product for free: https://neta.art .

    Bilibili: https://space.bilibili.com/505727005

    RED: https://www.xiaohongshu.com/user/profile/63f2ebf2000000001001e206

    Twitter: https://twitter.com/netaart_ai

    Civitai:https://civarchive.com/user/neta_art

    Description

    Full report:

    https://nieta-art.feishu.cn/wiki/PpwqwVDzjiNE5kkUhRtcEsn6nmh

    First release address:

    Civitai - https://civitai.com/models/410737

    Liblib - https://www.liblib.art/modelinfo/55b06e35dd724862b3524ff00b069fe8

    I. Overview

    • We have updated Neta Art XL V2.0. Comparing to version 1.0, we have optimized the character's posture dynamics, further strengthened hand stability, and are very good at telling stories with atmospheric epic scenes.

    • Main motivation:

      • In V1.0, the default poses generated by our characters are relatively fixed, while the new version enhances the sense of dynamics, diverse character camera expressions, and more precise prompt control.

      • On the basis of increasing more stylistic diversity, more stable anatomy has been maintained, especially the probability of having five fingers (instead of four or six) on the hands is now higher!

      • Further training methods for issues such as Rectified Flow and Noise Offset have been better deployed. The current picture has a strong sense of epic, and can now show very bright and dark scenery (as shown in the figure below).

    {

    "prompt": "1girl, full moon, moonlight shadow, cinematic lighting, battle field, fighting armies, castle, warriors around, waving, looking at viewer, Heterochromatic pupil, sitting on a cliff, very dark, epic scene, inferno, cowboy shot, depressed, angel wings, solo, white long hair, wings bangs, beautiful color, amazing quality,",

    "negative_prompt": "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, rating: sensitive, low contrast, signature, flexible deformity, abstract, low contrast, ",

    "resolution": "1344 x 768",

    "guidance_scale": 8,

    "num_inference_steps": 28,

    "sampler": "Euler a",

    "use_lora": null,

    "use_upscaler": {

    "upscale_method": "bislerp",

    "upscaler_strength": 0.65,

    "upscale_by": 1.5,

    "upscale_cfg": 11,

    "new_resolution": "2016 x 1152"

    }

    }

    Tip guide

    In Neta V2, we re-trained based on ArtiWaifu Diffusion V1.0 (AWA1). Therefore, we used the prompt order summarized in ArtiWaifu:

    Tag order: art style ( by xxx ) - > character ( 1 frieren (sousou no frieren) ) - > race (elf) - > composition (cowboy shot) - > painting style ( impasto ) - > theme (fantasy theme) - > main environment (in the forest, at day) - > background (gradient background) - > action (sitting on ground) - > expression (expressionless) - > main characteristics (white hair) - > other characteristics (Twintails, green eyes, parted lips) - > clothing (wearing a white dress) - > clothing accessories (frills) - > other items (holding a magic wand) - > secondary environment (grass, sunshine) - > aesthetics ( beautiful color , detailed ) - > quality ( best quality) - > secondary description (birds, clouds, butterflies)

    [{

    "prompt": "(oil-pasto:0.6), half-pasto, anime coloring, 1girl, raiden shogun\(genshin_impact\), genshin_impact, solo, smile, sitting beside window, indoor, classroom, hand rest, japanese school uniform,"

    },

    {

    "prompt": "(line sketch, (black and white, monochrome:1.2), draft, 1girl, solo, march 7th\(honkai: star rail\),honkai: star rail, cowboy shot, medium shot, smile, standing, hand rest,"

    },

    {

    "prompt": "gufeng style, 2.5d, impasto, old withered vines and ancient trees, a murder of crows descending into darkness, a small bridge over gently flowing water, quaint houses nestled by the river, best quality, masterpiece, highres,"

    },

    {

    "prompt": "(2.5d:1.4),(pseudo-impasto:1.2),best quality, masterpiece, ultra-detailed, illustration, detailed light,an extremely delicate and beautiful, incredibly_absurdres, <lora:sdxl_zzz_all_v2:1>,1girl, nicole_demara, solo,crop_top, hair_ribbon, two_side_up, black_ribbon, tube_top, midriff, hairclip, bare_shoulders, black_shorts, cleavage, long_sleeves, cowboy shot, standing, medim shot,"

    },

    {

    "prompt": "pixel art, 1girl, solo, kar98k\(girls' frontline\),girls' frontline, cowboy shot, standing, medim shot,"

    }]

    Negative prompt: ( worst quality : 1.3), low quality , lowres , messy , abstract, ugly, disfigured, bad anatomy, draft, deformed hands, fused fingers, signature, text, multi views

    Sampler parameters: Eular a is recommended as default for 28 + inferences.

    Neta Art XL 2.0 still supports a very wide range of CFGs (3-20). But this time we have re-aligned the standard CFG of the model to the normal range of 7-9 for everyone to switch experiments.

    II. New style recommendations

    We have updated some style activation words and optimized the training. To avoid too much confusion with the original meaning of these words, it is now recommended to add a style suffix when activating.

    Classic anime

    (Activation word: anime coloring)

    Optimize thick impasto

    (Activating word: oil-pasto style)

    Semi-thick impasto

    (Activation word: half-impasto style)

    Heavy training 2.5d

    (Activation word: 2.5d style)

    Neta Art XL 2.0 updates a number of special artists, activated using the by xxx tag.

    by chi4

    by hitomio16

    by ikky

    by ciloranko

    by yomu

    You can refer to https://civitai.com/models/435207/artiwaifu-diffusion to find more supported artists

    III. Better role dynamics

    1girl, jack-o' challenge

    1girl, upside-down

    1boy, harry potter, magic wand, amazing quality

    1girl, yoimiya\(genshin impact\), by as109, by antifreeze3, by sho \(sho lwlw\), by junny

    Neta Art XL V2.0

    Neta Art XL V1.0

    Original question

    Incoordination of limbs

    Not distinguishing hands and feet

    Scenes are more templated

    V1's stylization is not obvious and more like a fixed character illustration, while V2's characters are very vivid and lively

    IV. Epic scenes

    1girl, amazing quality, ultra wide shot of Gal Gadot Wonder Woman, holding Mjölnir Hammer, fighting in THOR movie, action-packed, ultra-wide, battle scene, intense, high-quality digital camera, epic cinematic shot, high-resolution digital image, sharp focus, panchromatic film,

    super dark, extreme dark, fireflies, unicorn, forest, twilight

    Trump, at a public gathering, on stage, flags over head, giving a speech, people screaming, amazing quality

    Depth of field in battle scenes

    Atmosphere of extremely dark scenes

    Dramatic tension

    V. Training strategies

    Model Training is divided into three stages.

    1. Rough practice: Teach models basic knowledge and concepts of the second dimension. Learning Rate: Constants 1e-5 (unet), 7.5e-6 (text encoder) Equivalent batch size: 96 Optimizer: AdaFactor (False relative_step, scale_parameter and warmup_init) Noise-free offset Time step sampling: LogitNormal (ln3,1) Minimum signal to noise ratio: 5 Note: Since the basic model has most of the prior knowledge, this stage is skipped in actual training.

    1. Concept Reinforcement: This stage teaches and reinforces all the concepts that the model needs to learn, including artists and characters with less data. The core strategy is data weighting - increasing the weight of data with less data, that is, the number of times it is repeatedly trained in a single epoch, and supplemented with other strategies to help concept learning, such as randomly removing core features of characters. The training parameters for the concept reinforcement stage are exactly the same as those for rough practice.

    2. Refinement: This stage uses data with quality ratings of best and amazing to continue fine-tuning the model, freezes the text encoder, enables label randomization algorithm, adds noise offset of 0.0357, and uniformly samples time steps during training. Learning Rate: Constants 2e-6 (unet), 1e-6 (text encoder) Equivalent batch size: 48 Optimizer: AdaFactor (False relative_step, scale_parameter and warmup_init) Noise offset: 0.0357 Time step sampling: uniform distribution (same as normal training) Minimum signal to noise ratio: 5

    VI. Terms of Use

    VI. Summary and Outlook

    Neta Art DiT follow-up training. Stay tuned and test our product for free: http://nieta.art .

    Bilibili: https://space.bilibili.com/505727005

    RED: https://www.xiaohongshu.com/user/profile/63f2ebf2000000001001e206

    Twitter: https://twitter.com/netaart_ai

    Civitai:https://civitai.com/user/neta_art

    FAQ

    Comments (11)

    Euge_Jul 23, 2024· 8 reactions
    CivitAI

    This is by far the best model I have ever worked on.
    We've used a lot of innovative training techniques that work really well, and we're excited to share our work with the community.

    bruh5877394Jul 24, 2024· 1 reaction
    CivitAI

    This model is no joke bruh. If you like NAI, copy your prompts over to this one and see (v2 not 1). It doesn't to poses nearly as well (or I just suck at prompting) but alot of artist combos work

    chachamaruOJul 26, 2024
    CivitAI

    构图没ArtiWaifu Diffusion激进,但比awd稳定一点,而且awd练的lora似乎能通用

    Story_MakerJul 26, 2024· 2 reactions
    CivitAI

    模型很棒,背景能画的很好、手也是很好。但高清修复是个痛点,和artiwaifu一样不把降噪开到0.6左右就会修复不完整留下很多噪点,但是0.6的强度都能改变原图了,很多时候原图是好的高清修复反而修烂了。

    KewtieAug 10, 2024
    CivitAI

    please shuffle tags

    needing to prompt in a specific order makes the model unnecessarily rigid

    GOOKLEAug 16, 2024
    CivitAI

    我们有机会得到flux版本吗

    bullseyetrollAug 19, 2024· 2 reactions
    CivitAI

    原來更新到這..

    yae6547393Aug 25, 2024
    CivitAI

    Btw if you guys do plan on making neta 3, please improve poses and ability to hold stuff (like sword poses...)

    valazorAug 27, 2024· 2 reactions
    CivitAI

    Best anime model right now. It's like artiwaifu except more consistent.

    Lacks a bit in terms of NSFW, but for SFW/slightly-NSFW images this is on par with NAI v3 (maybe even better)

    bc732Sep 2, 2024· 2 reactions
    CivitAI

    This has quickly become my favorite model for anime. I keep trying others and coming back.

    What really impresses me is how well it does with differential inpainting. I just need one or two words in the positive prompt and it gives me what I want.

    Thank you!

    EbenezerDanglewoodSep 26, 2024· 3 reactions
    CivitAI

    This model has some interesting quirks. A couple tips:

    1. CFG works very differently from most other models. Crank it very high to add more detail. Like, absurdly high. It doesn't even start to burn until over 20.
    2. Hires with latent upscaler drastically improves results. Non-latent upscale does little to nothing.

    Checkpoint
    SDXL 1.0

    Details

    Downloads
    1,783
    Platform
    CivitAI
    Platform Status
    Deleted
    Created
    7/21/2024
    Updated
    8/9/2025
    Deleted
    8/9/2025

    Available On (1 platform)

    Same model published on other platforms. May have additional downloads or version variants.