Name: Mecha Girl - Mechabare | メカ娘 ・メカバレ
Rating: 5 (615 reviews)
Author: motimalu

About 160 Civitai nodes for ComfyUI, image, video, audio, text, and training, with built-in model browsing.

Civitai Nodes for ComfyUI

<h1 id="mecha-girl-mechabare-or-xdiytybug">Mecha Girl - Mechabare | メカ娘 ・メカバレ</h1>Trained on depictions of Exposed mechanical, robotic, or cybernetic body parts, systems, innards, etc of a cyborg, android, or other robotic characters.ℹ️ LoRA work best when applied to the base models on which they are trained. Please read the About This Version on the appropriate base models and workflow/training information.Trigger:<pre><code>mechabare</code></pre>Recommended Tags:<pre><code>android
cyberpunk
cyborg
damaged
mecha musume
mechanical parts
mechanical arms
mechanical legs
mechanical eyes
mechanical ears
mechanical hands
mechanical horns
mechanical tail
mechanical wings
robot joints
ribs
science fiction
skeleton
spine</code></pre>Training images rated for sensitivity, <code>nsfw</code> or <code>explicit</code>

Trained on Anima Base 1Updated dataset with a mix of natural language and tag captionsPartitioned dataset and trained at multi-res 512, 768, 1024, 1280, 1536Training config:<pre><code># trained using diffusion-pipe commit b0aa4f1e03169f3280c8518d37570a448420f8be
# NCCL_P2P_DISABLE="1" NCCL_IB_DISABLE="1" NCCL_CUMEM_ENABLE="0" deepspeed --num_gpus=1 train.py --deepspeed --config anima-lora.toml --i_know_what_i_am_doing

output_dir = '/mnt/d/anima/training_output/anima-base-1-mechabare-v3'

dataset = 'dataset-anima-mechabare.toml'

# training settings
epochs = 2
# Per-resolution batch sizes
micro_batch_size_per_gpu = [[512, 64], [768, 32], [1024, 32], [1280, 24], [1536, 16]]
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1
warmup_steps = 50
lr_scheduler = 'cosine'

# misc settings
save_every_n_epochs = 1
activation_checkpointing = true

partition_method = 'parameters'

save_dtype = 'bfloat16'
caching_batch_size = 1
map_num_proc = 8
steps_per_print = 1
compile = true

[model]
type = 'anima'
transformer_path = '/mnt/c/workspace/models/diffusion_models/anima-base-v1.0.safetensors'
vae_path = '/mnt/c/workspace/models/vae/qwen_image_vae.safetensors'
llm_path = '/mnt/c/workspace/models/text_encoders/qwen_3_06b_base.safetensors'
dtype = 'bfloat16'
#cache_text_embeddings = false
llm_adapter_lr = 4e-7
#timestep_sample_method = 'uniform'
flux_shift = true
multiscale_loss_weight = 0.5
sigmoid_scale = 1.3

[adapter]
type = 'lora'
rank = 32
dtype = 'bfloat16'

[optimizer]
type = 'adamw_optimi'
lr = 4e-5
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-8</code></pre><pre><code>resolutions = [512, 768, 1024, 1280, 1536]

enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 9

# 348 total images

# images_mechabare\1536x1536\captions.json with 21 entries.
[[directory]]
path = '/mnt/d/training_data/images_mechabare/1536x1536'
resolutions = [512, 1024, 1280, 1536]

# images_mechabare\1280x1280\captions.json with 41 entries.
[[directory]]
path = '/mnt/d/training_data/images_mechabare/1280x1280'
resolutions = [512, 1024, 1280]

# images_mechabare\1024x1024\captions.json with 217 entries.
[[directory]]
path = '/mnt/d/training_data/images_mechabare/1024x1024'
resolutions = [512, 768, 1024]

# images_mechabare\768x768\captions.json with 69 entries.
[[directory]]
path = '/mnt/d/training_data/images_mechabare/768x768'
resolutions = [512, 768]</code></pre>

v3.0 [anima-base-1]

v2.0 [anima-preview]

Trained on <a target="_blank" rel="ugc" href="https://civitai.com/models/795765/illustrious-xl">Illustrious XL 0.1</a><ul><li>manually rated quality modifiers <code>masterpiece</code> <code>best quality</code> <code>very aesthetic</code></li><li>tagged <code>nsfw</code> <code>explicit</code> with <a target="_blank" rel="ugc" href="https://github.com/deepghs/imgutils">imgutils </a>anime_rating</li></ul>Previews are generated in ComfyUI with Upscaling and FaceDetailer<ul><li>Full workflow in image metadata, somehow not picked up by CivitAI this time</li></ul>

v1.0 [Illustrious]

[WAN 14B] LoRA<ul><li>Trained with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a> on <a target="_blank" rel="ugc" href="https://huggingface.co/Wan-AI/Wan2.1-T2V-14B">Wan2.1-T2V-14B</a> </li><li>Seems usable for both text-to-video and image-to-video workflows with Wan</li><li>Text to Video previews generated with <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/wan/#text-to-video">ComfyUI_examples/wan/#text-to-video</a><ul><li>Loading the LoRA with LoraLoaderModelOnly node and using <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_14B_fp8_scaled.safetensors">wan2.1_t2v_14B_fp8_scaled.safetensors</a></li></ul></li><li>Image to Video previews generated with <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/wan/#image-to-video">ComfyUI_examples/wan/#image-to-video</a><ul><li>Using image generations from the IL/Noobai versions of this model card</li></ul></li></ul>

v1.0 [wan-t2v-14b]

[WAN 1.3B] LoRA<ul><li>Trained with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a> on <a target="_blank" rel="ugc" href="https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B">Wan2.1-T2V-1.3B</a></li><li>Text to Video previews generated with <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/wan/#text-to-video">ComfyUI_examples/wan/#text-to-video</a><ul><li>Loading the LoRA with LoraLoaderModelOnly node and using the fp16 1.3B <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_1.3B_fp16.safetensors">wan2.1_t2v_1.3B_fp16.safetensors</a></li></ul></li></ul>dataset.toml<pre><code># Resolution settings.
resolutions = [512]

# Aspect ratio bucketing settings
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7

# Frame buckets (1 is for images)
frame_buckets = [1]

[[directory]] # IMAGES
# Path to the directory containing images and their corresponding caption files.
path = '/mnt/d/huanvideo/training_data/images'
num_repeats = 5
resolutions = [720]
frame_buckets = [1] # Use 1 frame for images.

[[directory]] # VIDEOS
# Path to the directory containing videos and their corresponding caption files.
path = '/mnt/d/huanvideo/training_data/videos'
num_repeats = 5
resolutions = [512]
frame_buckets = [30, 33, 38, 50, 56, 80]</code></pre>config.toml<pre><code># Dataset config file.
output_dir = '/mnt/d/wan/training_output'
dataset = 'dataset.toml'

# Training settings
epochs = 50
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 4
gradient_clipping = 1.0
warmup_steps = 100

# eval settings
eval_every_n_epochs = 5
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

# misc settings
save_every_n_epochs = 5
checkpoint_every_n_minutes = 30
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 1
steps_per_print = 1
video_clip_mode = 'single_middle'

[model]
type = 'wan'
ckpt_path = '../Wan2.1-T2V-1.3B'
dtype = 'bfloat16'
# You can use fp8 for the transformer when training LoRA.
transformer_dtype = 'float8'
timestep_sample_method = 'logit_normal'

[adapter]
type = 'lora'
rank = 32
dtype = 'bfloat16'

[optimizer]
type = 'adamw_optimi'
lr = 5e-5
betas = [0.9, 0.99]
weight_decay = 0.02
eps = 1e-8</code></pre>

v1.0 [wan-t2v-1.3b]

Trained on <a target="_blank" rel="ugc" href="https://civitai.com/models/833294/noobai-xl-nai-xl?modelVersionId=1190596">NoobAI-XL (NAI-XL) V-Pred 1.0-Version</a><ul><li>manually rated quality modifiers <code>masterpiece</code> <code>best quality</code> <code>very aesthetic</code></li><li>tagged <code>nsfw</code> <code>explicit</code> with <a target="_blank" rel="ugc" href="https://github.com/deepghs/imgutils">imgutils </a>anime_rating</li></ul>Previews are generated in ComfyUI with DynamicThresholding (CFG-Fix) Integrated enabled, settings:<pre><code>dynthres_enabled: True, dynthres_mimic_scale: 7, dynthres_threshold_percentile: 1, dynthres_mimic_mode: Half Cosine Down, dynthres_mimic_scale_min: 1, dynthres_cfg_mode: Half Cosine Down, dynthres_cfg_scale_min: 3, dynthres_sched_val: 1, dynthres_separate_feature_channels: enable, dynthres_scaling_startpoint: ZERO, dynthres_variability_measure: STD, dynthres_interpolate_phi: 1</code></pre>

Mecha Girl - Mechabare | メカ娘・メカバレ

Mecha Girl - Mechabare | メカ娘・メカバレ

Mecha Girl - Mechabare | メカ娘 ・メカバレ

Mecha Girl - Mechabare | メカ娘 ・メカバレ

Mecha Girl - Mechabare | メカ娘・メカバレ

Mecha Girl - Mechabare | メカ娘・メカバレ