me1
This is a LoRA derived from sayakpaul/FLUX.1-merged.
The main validation prompt used during training was:
a photo of a person
Validation settings
- CFG:
7.5
- CFG Rescale:
0.0
- Steps:
20
- Sampler:
None
- Seed:
42
- Resolution:
1024x1024
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: 11
- Training steps: 1944
- Learning rate: 0.0005
- Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: bf16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LoRA Rank: 32
- LoRA Alpha: 32.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
Datasets
meme1
- Repeats: 0
- Total number of images: 162
- Total number of aspect buckets: 1
- Resolution: 1024 px
- Cropped: False
- Crop style: None
- Crop aspect: None
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'sayakpaul/FLUX.1-merged'
adapter_id = 'ambientocclusion/me1'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)
prompt = "a photo of a person"
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=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=1024,
height=1024,
guidance_scale=7.5,
).images[0]
image.save("output.png", format="PNG")
- Downloads last month
- 6
Model tree for ambientocclusion/me1
Base model
sayakpaul/FLUX.1-merged