Name: Frieren フリーレン - 葬送のフリーレン
Rating: 5 (1005 reviews)
Author: motimalu

sexual situations

physical violence

disturbing

male nudity

weapon violence

female swimwear or underwear

male swimwear or underwear

graphic female nudity

corpses

wide hips

peeing

emaciated bodies

blowjob

self injury

extremist

hate speech

diapers

urine

revealing clothes

sexual activity

sexual intent

undressed

male underwear

female swimwear

genitals

female underwear

thick thighs

breasts out

strapless leotard

vore

breast out

one breast out

huge breasts

gigantic breasts

huge butt

covered nipples

hair over breasts

no panties

sitting on face

anal

dildo riding

downblouse

oral

porn

futanari

hentai

nude

lingerie

nsfw

sexy

hanging

hate symbols

nazi party

partial nudity

white supremacy

adult toys

graphic male nudity

illustrated explicit nudity

nudity

graphic violence or gore

pg-13

convenient censoring

oral invitation

exposed female nipple

female nudity

suggestive

child on child

incest

scat

latex clothing

swimwear

bukkake

fellatio

cumshot

implied fellatio

eat_cum

cumdrip

cum in pussy

cum on face

after fellatio

cum on hair

cum on body

cum on tongue

cum on hands

cum in mouth

triple fellatio

autofellatio

fucked silly

cum on pussy

pov fellatio

<h1 id="frieren-9myhpc2i1">Frieren フリーレン - 葬送のフリーレン</h1>This LoRA is intended to generate images of the character Frieren in a high quality aesthetic illustration style.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.Metadata is included in all uploaded files, you can drag the generated videos into ComfyUI to use the embedded workflows.For other characters, see: <a target="_blank" rel="ugc" href="https://civitai.com/collections/847637">Sousou no Frieren Collection</a><hr /><h3 id="license-orjwpxwg3">License</h3><a target="_blank" rel="ugc" href="https://freedevproject.org/faipl-1.0-sd/">Fair AI Public License 1.0-SD</a>

[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> with a dataset including image and video<ul><li>22 images, 9 videos</li></ul></li><li>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 fp8 14B <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_e4m3fn.safetensors">wan2.1_t2v_14B_fp8_e4m3fn.safetensors</a></li></ul></li><li>Image previews generated with modified <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>Setting the frame length to 1</li><li>Adding Upscaling</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></li></ul>

v1.0 [wan-t2v-14b]

Trained with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">https://github.com/tdrussell/diffusion-pipe</a>Training data is a combination of:<ul><li>Images used from other versions this model card</li><li>Images extracted as keyframes from several videos</li><li>Short video clips ~40 frames each</li></ul>Training configs:dataset.toml<pre><code># Aspect ratio bucketing settings
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7

[[directory]] # IMAGES
# Path to the directory containing images and their corresponding caption files.
path = '/mnt/d/huanvideo/training_data/images'
num_repeats = 5
resolutions = [1024]
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 = [256] # Set video resolution to 256 (e.g., 244p).
frame_buckets = [33, 49, 81] # Define frame buckets for videos.</code></pre>config.toml<pre><code># Dataset config file.
output_dir = '/mnt/d/huanvideo/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 = 15
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 = 'hunyuan-video'

transformer_path = '/mnt/d/huanvideo/models/diffusion_models/hunyuan_video_720_cfgdistill_fp8_e4m3fn.safetensors'
vae_path = '/mnt/d/huanvideo/models/vae/hunyuan_video_vae_bf16.safetensors'
llm_path = '/mnt/d/huanvideo/models/llm'
clip_path = '/mnt/d/huanvideo/models/clip'

dtype = 'bfloat16'
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 [hunyuan]

<ul><li>Experimental version, first try of the Civitai LoRA trainer for Flux</li><li>Future Flux training planned to be done locally with kohya_ss</li><li>Unmodified v2.0 dataset trained on Flux (Danbooru style captions)</li><li>Previews for this version are made with ComfyUI, couldn't get the metadata to save for showing in Civitai<ul><li>Tried the on-site generator for one post with a sample prompt <a target="_blank" rel="ugc" href="https://civitai.com/images/24588380">here</a></li><li>Will use Forge for previews soon (when development for Flux settles)</li></ul></li></ul>

Type	LoRA
Stats	585 0 367
Reviews	Very Positive (76)
Published	Mar 8, 2025
Base Model	Wan Video 14B t2v
Training	Steps: 1,900 Epochs: 50
Usage Tips	Strength: 1
Trigger Words	frieren, green eyes, elf, pointy ears, twintails, white hair, white capelet
Hash	AutoV2 B1D3B99282