toxicwind/flux-training-murakami

flux-training-murakami

This is a LoRA derived from black-forest-labs/FLUX.1-dev.

The main validation prompt used during training was:

sprinkled donut with happy heart eyes

Validation settings

• —CFG: 3.5
• —CFG Rescale: 0.0
• —Steps: 15
• —Sampler: None
• —Seed: 42
• —Resolution: 1024

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

<Gallery />

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

• —Training epochs: 32
• —Training steps: 1200
• —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: 64
• —LoRA Alpha: None
• —LoRA Dropout: 0.1
• —LoRA initialisation style: loftq

Datasets

murakami-test

• —Repeats: 0
• —Total number of images: 222
• —Total number of aspect buckets: 1
• —Resolution: 512 px
• —Cropped: True
• —Crop style: center
• —Crop aspect: square

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'toxicwind/flux-training-murakami'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "sprinkled donut with happy heart eyes"


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=1024,
    height=1024,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")