Bara male [Z-image Turbo] (test)
详情
| 模型类型 | LORA |
| 基础模型 | ZImageTurbo |
| 发布时间 | 12/3/2025 |
| 训练词汇 | syj style. 已复制! |
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关于此版本
---
job: "extension"
config:
name: "my_first_lora_v2"
process:
- type: "diffusion_trainer"
training_folder: "D:\\ai_training\\ai-toolkit\\output"
sqlite_db_path: "./aitk_db.db"
device: "cuda"
trigger_word: "syj style"
performance_log_every: 10
network:
type: "lora"
linear: 32
linear_alpha: 32
conv: 16
conv_alpha: 16
lokr_full_rank: true
lokr_factor: -1
network_kwargs:
ignore_if_contains: []
save:
dtype: "bf16"
save_every: 250
max_step_saves_to_keep: 4
save_format: "diffusers"
push_to_hub: false
datasets:
- folder_path: "D:\\ai_training\\ai-toolkit\\datasets/2464453_training_data"
mask_path: null
mask_min_value: 0.1
default_caption: ""
caption_ext: "txt"
caption_dropout_rate: 0.05
cache_latents_to_disk: true
is_reg: false
network_weight: 1
resolution:
- 512
- 768
controls: []
shrink_video_to_frames: true
num_frames: 1
do_i2v: true
flip_x: true
flip_y: false
train:
batch_size: 1
bypass_guidance_embedding: false
steps: 2000
gradient_accumulation: 1
train_unet: true
train_text_encoder: false
gradient_checkpointing: true
noise_scheduler: "flowmatch"
optimizer: "adamw8bit"
timestep_type: "weighted"
content_or_style: "balanced"
optimizer_params:
weight_decay: 0.0001
unload_text_encoder: false
cache_text_embeddings: true
lr: 0.0001
ema_config:
use_ema: false
ema_decay: 0.99
skip_first_sample: false
force_first_sample: false
disable_sampling: false
dtype: "bf16"
diff_output_preservation: false
diff_output_preservation_multiplier: 1
diff_output_preservation_class: "person"
switch_boundary_every: 1
loss_type: "mse"
model:
name_or_path: "Tongyi-MAI/Z-Image-Turbo"
quantize: true
qtype: "uint4"
quantize_te: true
qtype_te: "qfloat8"
arch: "zimage:turbo"
low_vram: true
model_kwargs: {}
layer_offloading: true
layer_offloading_text_encoder_percent: 1
layer_offloading_transformer_percent: 0
assistant_lora_path: "ostris/zimage_turbo_training_adapter/zimage_turbo_training_adapter_v2.safetensors"
sample:
sampler: "flowmatch"
sample_every: 250
width: 1024
height: 1024
samples:
- prompt: ""
- prompt: ""
neg: ""
seed: 42
walk_seed: true
guidance_scale: 1
sample_steps: 8
num_frames: 1
fps: 1
meta:
name: "[name]"
version: "1.0"
模型描述
我为训练添加了大量的毛茸茸图像,但效果并不理想。
由于GPU内存有限,训练分辨率不得不保持较低,并且由于没有时间将标签转换为自然语言,我直接使用标签作为提示。
请不要根据此LoRA的表现来评判基础模型的质量。
这是一个实验性模型,可能会生成不适宜的内容。训练数据来自一位艺术家,因此该模型可能稍后会被下架,仅用于研究和学习目的。
老实说,我只是被建议尝试一下,但结果可能非常糟糕 😛
我仍在暂停更新,将此视为一些简单的实验。
如需更好的效果,请期待经过编辑的Z-image版本和非蒸馏版本。Z-image团队也在考虑进行动漫风格的微调——也许那才是真正的理想之地?


