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사용자 LoRA

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Style - Chen bin/鬼针草 [Anima]

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Style - Chen bin/鬼针草 [Anima] - AI Model cover image
업로드 날짜
2026. 7. 28. 오후 10:33
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트리거 단어

@4x0style

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설명

Trigger word@4x0styleRecommended strength is 0.6 - 1.0V4What's different from v3: pantyhose texture.That was the one thing v3 weaker — the actual material: the fine mesh structure, the individual threads, the way the knit lines catch light across the fabric. This version goes after that specifically.The approach is experimental NaViT native-resolution training: images are trained at their original resolution (up to 3000×4000) instead of being downscaled into fixed buckets. Fine fabric structure survives instead of getting smeared away in the downscale and WAN VAE— which is exactly what was killing the texture before.This also means the LoRA holds up at large inference resolutions, and that's not a separate feature — it's the same goal. More pixels means more room for the weave to actually render. The samples were generated at 1920×1920, and I'd recommend generating at high resolution to get the most out of this version.Recommendation to Anima Lora Trainer I am working on /ᐠ ̷ ̷𝅒 ̷‸ ̷𝅒 ̷ ᐟ\ノhttps://github.com/WalkingMeatAxolotl/AnimaLoraStudiotransformer_path: ~ vae_path: ~ text_encoder_path: ~ t5_tokenizer_path: ~ data_dir: ~ resolution:

  • 1024 aspect_ratio_limit: 2.0 reg_data_dir: ~ reg_caption: null reg_weight: 0.5 shuffle_caption: true keep_tokens: 1 flip_augment: true tag_dropout: 0.0 prefer_json: true caption_comfy_encoding: true cache_latents: true vae_cache_batch_size: 0 navit_packing: true navit_token_budget: 16384 navit_max_images_per_pack: 0 navit_text_trim_padding: false navit_pack_strategy: next_fit navit_pack_ffd_window: 256 navit_drop_last: false navit_native_resolution: true navit_native_over_budget: downscale cache_encode_tiled: true cache_encode_tile_px: 1024 cache_encode_tile_overlap: 128 cache_encode_max_pixels: 0 lora_type: lora lora_rank: 32 lora_alpha: 32.0 lora_dora: false lora_rs: false lora_dropout: 0.0 lora_rank_dropout: 0.0 lora_module_dropout: 0.05 lora_reg_dims: null epochs: 40 max_steps: 0 batch_size: 2 grad_checkpoint: true grad_accum: 2 learning_rate: 1.0 lr_scheduler: none optimizer_type: prodigy_plus_schedulefree ppsf_d_coef: 3.0 ppsf_prodigy_steps: 0 ppsf_beta1: 0.9 ppsf_beta2: 0.99 ppsf_split_groups: true ppsf_split_groups_mean: false ppsf_use_speed: false ppsf_fused_back_pass: false ppsf_use_stableadamw: true weight_decay: 0.0 kv_trim: false vae_tiling: auto noise_enhancement_type: none timestep_sampling: uniform timestep_schedule_shift: 0.7 timestep_shift_resolution_aware: true infonoise_enabled: false loss_type: mse loss_weighting: none leap_enabled: false sra_enabled: false grad_clip_max_norm: 0.0 mixed_precision: bf16 attention_backend: xformers num_workers: 0 output_dir: ~ output_name: ~ save_every_epochs: 2 save_every_steps: 0 save_state_every_epochs: 0 save_state_every_steps: 500 seed: 42 resume_lora: null resume_state: null

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