sd35-training-loona
This is a LyCORIS adapter derived from sd3/unknown-model.
The main validation prompt used during training was:
A figurine of a character with green hair, wearing a white shirt, a black vest, and a gray cap, sitting with one hand on their knee and the other hand making a peace sign. The character is wearing a blue pendant and has a gold bracelet. In the background, there are green plants and a tree branch.
Validation settings
- CFG:
5.5 - CFG Rescale:
0.0 - Steps:
20 - Sampler:
None - Seed:
42 - Resolution:
1024x1024
Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
- Prompt
- unconditional (blank prompt)
- Negative Prompt
- '
- Prompt
- A moment during a track relay race where one runner is passing the baton to his teammate; spectators can be seen in the stands, cheering.
- Negative Prompt
- '
- Prompt
- An anime character is racing down the street while wearing cat ears, a red dress, black shoes, and wearing aviator style sunglasses.
- Negative Prompt
- '
- Prompt
- Loona stands on a dark, rocky cliff under a stormy sky, her sharp features silhouetted by flashes of lightning. The wind tousles her black-and-white fur as her bright red eyes glow intensely. The storm seems to mirror her fierce and brooding expression, creating a powerful, haunting scene. In the background, jagged mountains and ominous clouds stretch into the distance, amplifying the dark, hellish atmosphere.
- Negative Prompt
- '
- Prompt
- Loona lounges in the IMP office, scrolling her phone with a bored expression amidst the cluttered chaos.
- Negative Prompt
- '
- Prompt
- Reimu stands in a serene, autumn-colored forest at sunset, surrounded by a cascade of red and orange leaves. She holds her gohei staff gracefully, her red and white shrine maiden outfit fluttering gently in the breeze. Her expression is calm but vigilant, as if she’s guarding the peaceful forest. The soft, warm light of the setting sun filters through the trees, casting an ethereal glow around her, enhancing the mystical aura of the scene.
- Negative Prompt
- '
- Prompt
- Reimu enjoys a festival, holding dango with a smile as fireworks light up the night sky.
- Negative Prompt
- '
- Prompt
- A figurine of a character with green hair, wearing a white shirt, a black vest, and a gray cap, sitting with one hand on their knee and the other hand making a peace sign. The character is wearing a blue pendant and has a gold bracelet. In the background, there are green plants and a tree branch.
- Negative Prompt
- '
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 0
- Training steps: 28000
- Learning rate: 5e-05
- Max grad norm: 0.01
- Effective batch size: 5
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 5
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LyCORIS Config:
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 1000000,
"linear_alpha": 1,
"factor": 1,
"full_matrix": true,
"apply_preset": {
"target_module": [
"Linear",
"JointTransformerBlock"
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"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.29.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.29.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.29.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.29.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.30.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.30.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.30.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.30.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.31.norm1*": {
"algo": "lokr",
"factor": 16,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.31.norm1_context*": {
"algo": "lokr",
"factor": 16,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.31.ff*": {
"algo": "lokr",
"factor": 1,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.31.*": {
"algo": "lokr",
"factor": 1,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.33.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.33.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.33.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.33.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.34.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.34.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.34.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.34.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.35.norm1*": {
"algo": "lokr",
"factor": 16,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.35.norm1_context*": {
"algo": "lokr",
"factor": 16,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.35.ff*": {
"algo": "lokr",
"factor": 1,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.35.*": {
"algo": "lokr",
"factor": 1,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.36.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.36.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.36.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.36.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.37.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.37.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.37.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.37.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
}
},
"use_fnmatch": true
}
}
Datasets
default_dataset_arb
- Repeats: 9999
- Total number of images: ~105
- Total number of aspect buckets: 11
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
default_dataset_arb2
- Repeats: 9999
- Total number of images: ~3295
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
default_dataset_arb3
- Repeats: 9999
- Total number of images: ~3305
- Total number of aspect buckets: 16
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
default_dataset
- Repeats: 9999
- Total number of images: ~70
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_512
- Repeats: 9999
- Total number of images: ~45
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_640
- Repeats: 9999
- Total number of images: ~70
- Total number of aspect buckets: 1
- Resolution: 0.4096 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_768
- Repeats: 9999
- Total number of images: ~45
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_896
- Repeats: 9999
- Total number of images: ~45
- Total number of aspect buckets: 1
- Resolution: 0.802816 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_uncaptioned
- Repeats: 9999
- Total number of images: ~3260
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_uncaptioned_512
- Repeats: 9999
- Total number of images: ~3140
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art
- Repeats: 9999
- Total number of images: ~2485
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art_512
- Repeats: 9999
- Total number of images: ~3195
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art_640
- Repeats: 9999
- Total number of images: ~3115
- Total number of aspect buckets: 1
- Resolution: 0.4096 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art_768
- Repeats: 9999
- Total number of images: ~2990
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art_896
- Repeats: 9999
- Total number of images: ~2790
- Total number of aspect buckets: 1
- Resolution: 0.802816 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = '/home/user/storage/models/sd35_merged_236000'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "A figurine of a character with green hair, wearing a white shirt, a black vest, and a gray cap, sitting with one hand on their knee and the other hand making a peace sign. The character is wearing a blue pendant and has a gold bracelet. In the background, there are green plants and a tree branch."
negative_prompt = ''
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1024,
height=1024,
guidance_scale=5.5,
).images[0]
image.save("output.png", format="PNG")
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