Neta Art XL

Full Report:https://nieta-art.feishu.cn/wiki/PpwqwVDzjiNE5kkUhRtcEsn6nmhFirst release address:Civitai - https://civitai.com/models/410737Liblib - https://www.liblib.art/modelinfo/55b06e35dd724862b3524ff00b069fe8I. OverviewWe 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 guideIn 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 strategiesModel Training is divided into three stages.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.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.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: 5VI. Terms of UseThe model was developed by Nieta Lab : Neta.art Lab - https://civitai.com/user/neta_artCollaborative participants:Euge: https://civitai.com/user/Euge_汤人烂: https://space.bilibili.com/8594480Chenkin: https://civitai.com/user/ChenkinBo Dai: https://daibo.info/Terms of Use:The model was developed from ArtiWaifu and AAM XL using Fair AI Public License 1.0-SDIf you later modify, merge, or develop the model again, you need to open source the subsequent derived model .VII. Summary and OutlookNeta Art DiT follow-up training. Stay tuned and test our product for free: https://neta.art .Bilibili: https://space.bilibili.com/505727005RED: https://www.xiaohongshu.com/user/profile/63f2ebf2000000001001e206Twitter: https://twitter.com/netaart_aiCivitai：https://civitai.com/user/neta_art