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bghira / simpletuner-lora
Model's Last Updated: October 05 2025
text-to-image
Introduction of simpletuner-lora
Model Details of simpletuner-lora
simpletuner-lora
This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev .
The main validation prompt used during training was:
A photo-realistic image of a cat
Validation settings
-
CFG:
3.0 -
CFG Rescale:
0.0 -
Steps:
20 -
Sampler:
None -
Seed:
42 -
Resolution:
1776x512
Note: The validation settings are not necessarily the same as the training settings .
You can find some example images in the following gallery:
- Prompt
- unconditional (blank prompt)
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- a comic strip of garfield, by jim davis. the first panel has garfield saying Help!. the second panel has garfield saying My clungus is leaking! and the third panel has Odie saying uh oh!
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- a comic strip by jim davis, showcasing odie in his full demonic form while garfield cowers in the background
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- a picture of garfield in walmart, shopping amongst the real people
- Negative Prompt
- blurry, cropped, ugly
- Prompt
- A photo-realistic image of a cat
- Negative Prompt
- blurry, cropped, ugly
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 2
- Training steps: 6500
- Learning rate: 6e-05
-
Effective batch size: 6
- Micro-batch size: 2
- Gradient accumulation steps: 1
- Number of GPUs: 3
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: optimi-lion
- Precision: bf16
- Quantised: Yes: fp8-quanto
- Xformers: Not used
- LyCORIS Config:
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 16,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 16
},
"FeedForward": {
"factor": 8
}
}
}
}
Datasets
garfield
- Repeats: 0
- Total number of images: ~2211
- Total number of aspect buckets: 1
- Resolution: 512 px
- Cropped: False
- Crop style: None
- Crop aspect: None
comicstrips-512
- Repeats: 0
- Total number of images: ~1419
- Total number of aspect buckets: 22
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
comicstrips-768
- Repeats: 0
- Total number of images: ~1419
- Total number of aspect buckets: 22
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
comicstrips-1024
- Repeats: 0
- Total number of images: ~1419
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
comicstrips-2048
- Repeats: 0
- Total number of images: ~1419
- Total number of aspect buckets: 22
- Resolution: 4.194304 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
Inference
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "A photo-realistic image of a cat"
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=1776,
height=512,
guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")
Runs of bghira simpletuner-lora on huggingface.co
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More Information About simpletuner-lora huggingface.co Model
Url of simpletuner-lora
simpletuner-lora huggingface.co Url
Provider of simpletuner-lora huggingface.co
Other API from bghira
Updated:August 16 2024
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