malgosia-st-sd3.5-lokr-adamw-1e-6-bs4-v03
This is a LyCORIS adapter derived from stabilityai/stable-diffusion-3.5-large.
No validation prompt was used during training.
None
Validation settings
- CFG:
4.5
- CFG Rescale:
0.0
- Steps:
20
- Sampler:
None
- Seed:
420
- Resolution:
832x1216
Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 24
- Training steps: 11000
- Learning rate: 1e-06
- Max grad norm: 0.01
- Effective batch size: 4
- Micro-batch size: 4
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LyCORIS Config:
{
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"multiplier": 1.0,
"linear_dim": 1000000,
"linear_alpha": 1,
"factor": 1,
"full_matrix": true,
"apply_preset": {
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"transformer_blocks.31.ff*": {
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},
"transformer_blocks.32.norm1*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.norm1_context*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
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},
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},
"transformer_blocks.32.*": {
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}
Datasets
MALGOSIA-SD35-V03-512
- Repeats: 1
- Total number of images: 285
- Total number of aspect buckets: 2
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
MALGOSIA-SD35-V03-768
- Repeats: 1
- Total number of images: 235
- Total number of aspect buckets: 4
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
MALGOSIA-SD35-V03-1024
- Repeats: 1
- Total number of images: 125
- Total number of aspect buckets: 7
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
MALGOSIA-RAFRAF-SD35-V03-512
- Repeats: 2
- Total number of images: 30
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
MALGOSIA-RAFRAF-SD35-V03-768
- Repeats: 2
- Total number of images: 30
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
MALGOSIA-RAFRAF-SD35-V03-1024
- Repeats: 2
- Total number of images: 30
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
MALGOSIA-PINTEREST-SD35-V03-512
- Repeats: 1
- Total number of images: 56
- Total number of aspect buckets: 7
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'stabilityai/stable-diffusion-3.5-large'
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 = "An astronaut is riding a horse through the jungles of Thailand."
negative_prompt = 'blurry, cropped, ugly'
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=832,
height=1216,
guidance_scale=4.5,
).images[0]
image.save("output.png", format="PNG")
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Model tree for gattaplayer/malgosia-st-sd3.5-lokr-adamw-1e-6-bs4-v03
Base model
stabilityai/stable-diffusion-3.5-large