besch-style-st-sd3.5-lora-adamw-1e-5-bs2-v01-1050
This is a standard PEFT LoRA derived from stabilityai/stable-diffusion-3.5-large.
No validation prompt was used during training.
None
Validation settings
- CFG:
5.5
- CFG Rescale:
0.0
- Steps:
20
- Sampler:
None
- Seed:
42
- 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: 46
- Training steps: 10500
- Learning rate: 1e-05
- Max grad norm: 0.01
- Effective batch size: 2
- Micro-batch size: 2
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: No
- Xformers: Not used
- LoRA Rank: 64
- LoRA Alpha: 64.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
Datasets
BESCH-SD35-V01-512
- Repeats: 1
- Total number of images: 70
- Total number of aspect buckets: 6
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-SD35-V01-768
- Repeats: 1
- Total number of images: 70
- Total number of aspect buckets: 6
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
BESCH-SD35-V01-1024
- Repeats: 1
- Total number of images: 70
- Total number of aspect buckets: 6
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: closest
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'stabilityai/stable-diffusion-3.5-large'
adapter_id = 'gattaplayer/besch-style-st-sd3.5-lora-adamw-1e-5-bs2-v01-1050'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)
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=5.5,
).images[0]
image.save("output.png", format="PNG")