besch-style-st-sd35m-lokr-8e-5-bs6-v03
This is a LyCORIS adapter derived from stabilityai/stable-diffusion-3.5-medium.
No validation prompt was used during training.
None
Validation settings
- CFG:
6.0
- CFG Rescale:
0.0
- Steps:
30
- Sampler:
FlowMatchEulerDiscreteScheduler
- Seed:
42
- Resolution:
832x1216
- Skip-layer guidance: skip_guidance_layers=[7, 8, 9],
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: 8
- Training steps: 7980
- Learning rate: 8e-05
- Learning rate schedule: polynomial
- Warmup steps: 798
- Max grad norm: 0.01
- Effective batch size: 6
- Micro-batch size: 6
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: flow-matching (extra parameters=['flux_schedule_auto_shift', 'shift=0.0', 'flux_use_uniform_schedule'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Caption dropout probability: 25.0%
LyCORIS Config:
{
"bypass_mode": true,
"algo": "lokr",
"multiplier": 1.0,
"full_matrix": true,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 4,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"FeedForward": {
"factor": 4
},
"Attention": {
"factor": 2
}
}
}
}
Datasets
BESCH-CROP-SD35M-V03-512
- Repeats: 1
- Total number of images: 101
- Total number of aspect buckets: 3
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-CROP-SD35M-V03-768
- Repeats: 1
- Total number of images: 101
- Total number of aspect buckets: 3
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-CROP-SD35M-V03-1024
- Repeats: 1
- Total number of images: 101
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-MIX-SD35M-V03-512
- Repeats: 3
- Total number of images: 202
- Total number of aspect buckets: 8
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-MIX-SD35M-V03-768
- Repeats: 3
- Total number of images: 202
- Total number of aspect buckets: 2
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-MIX-SD35M-V03-1024
- Repeats: 3
- Total number of images: 201
- Total number of aspect buckets: 14
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-MIX-SD35M-V03-1280
- Repeats: 3
- Total number of images: 199
- Total number of aspect buckets: 2
- Resolution: 1.6384 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-ORIGINAL-SD35M-V03-512
- Repeats: 3
- Total number of images: 68
- Total number of aspect buckets: 4
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-ORIGINAL-SD35M-V03-768
- Repeats: 3
- Total number of images: 68
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-ORIGINAL-SD35M-V03-1024
- Repeats: 3
- Total number of images: 68
- Total number of aspect buckets: 1
- Resolution: 1.048576 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
def download_adapter(repo_id: str):
import os
from huggingface_hub import hf_hub_download
adapter_filename = "pytorch_lora_weights.safetensors"
cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
os.makedirs(path_to_adapter, exist_ok=True)
hf_hub_download(
repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
)
return path_to_adapter_file
model_id = 'stabilityai/stable-diffusion-3.5-medium'
adapter_repo_id = 'gattaplayer/besch-style-st-sd35m-lokr-8e-5-bs6-v03'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()
prompt = "An astronaut is riding a horse through the jungles of Thailand."
negative_prompt = 'blurry, cropped, ugly'
## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
from optimum.quanto import quantize, freeze, qint8
quantize(pipeline.transformer, weights=qint8)
freeze(pipeline.transformer)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=30,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
width=832,
height=1216,
guidance_scale=6.0,
skip_guidance_layers=[7, 8, 9],
).images[0]
image.save("output.png", format="PNG")
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Model tree for gattaplayer/besch-style-st-sd35m-lokr-8e-5-bs6-v03
Base model
stabilityai/stable-diffusion-3.5-medium