flux-training-pocket-creature-nocrop
This is a LoRA derived from black-forest-labs/FLUX.1-dev.
The main validation prompt used during training was:
a cute pokemon in a city scene
Validation settings
- CFG:
3.5
- CFG Rescale:
0.0
- Steps:
15
- Sampler:
None
- Seed:
42
- Resolution:
512
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: 17
- Training steps: 2700
- Learning rate: 0.0001
- Effective batch size: 6
- Micro-batch size: 6
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Enabled
- LoRA Rank: 32
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
Datasets
flux-pocket-creature-nocrop
- Repeats: 0
- Total number of images: 900
- Total number of aspect buckets: 1
- Resolution: 512 px
- Cropped: False
- Crop style: None
- Crop aspect: None
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'toxicwind/flux-training-pocket-creature-nocrop'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)
prompt = "a cute pokemon in a city scene"
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
num_inference_steps=15,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=512,
height=512,
guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")
- Downloads last month
- 13
Model tree for toxicwind/flux-training-pocket-creature-nocrop
Base model
black-forest-labs/FLUX.1-dev