metadata
license: other
base_model: black-forest-labs/FLUX.1-dev
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- lora
- template:sd-lora
inference: true
widget:
- text: unconditional (blank prompt)
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_0_0.png
- text: >-
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!
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: >-
a comic strip by jim davis, showcasing odie in his full demonic form while
garfield cowers in the background
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: a picture of garfield in walmart, shopping amongst the real people
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: A photo-realistic image of a cat
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
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: 2000
- Learning rate: 0.0001
- 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: 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: 2206
- Total number of aspect buckets: 4
- Resolution: 512 px
- Cropped: False
- Crop style: None
- Crop aspect: None
Inference
import argparse
import torch
from helpers.models.flux.pipeline import FluxPipeline as DiffusionPipeline
from lycoris import create_lycoris_from_weights
from huggingface_hub import hf_hub_download
def generate_image(pipeline, prompt, output_file, num_inference_steps, width, height, guidance_scale, seed, device):
# Set device
pipeline.to(device)
# Generate image
generator = torch.Generator(device=device).manual_seed(seed)
image = pipeline(
prompt=prompt,
num_inference_steps=num_inference_steps,
generator=generator,
width=width,
height=height,
guidance_scale=guidance_scale,
).images[0]
# Save image
output_file = "output.png"
image.save(output_file, format="PNG")
print(f"Image saved as {output_file}")
def main():
parser = argparse.ArgumentParser(description="Generate images using a custom diffusion pipeline with LoRA weights.")
parser.add_argument("--model_id", type=str, default='black-forest-labs/FLUX.1-dev', help="Model ID from Hugging Face Hub.")
parser.add_argument("--adapter_id", type=str, default='pytorch_lora_weights.safetensors', help="LoRA weights file.")
parser.add_argument("--lora_scale", type=float, default=1.0, help="Scale for LoRA weights.")
parser.add_argument("--output_file", type=str, default="output.png", help="Output file name for the generated image.")
parser.add_argument("--num_inference_steps", type=int, default=30, help="Number of inference steps.")
parser.add_argument("--guidance_scale", type=float, default=3.5, help="Guidance scale for the generation.")
parser.add_argument("--seed", type=int, default=1641421826, help="Random seed for reproducibility.")
parser.add_argument("--device", type=str, default='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu', help="Device to run the model on.")
args = parser.parse_args()
# Load model and weights
hf_hub_download(repo_id="terminusresearch/flux-lokr-garfield-nomask", filename=args.adapter_id, local_dir="./")
pipeline = DiffusionPipeline.from_pretrained(args.model_id, torch_dtype=torch.bfloat16)
# Apply LoRA weights
wrapper, _ = create_lycoris_from_weights(args.lora_scale, args.adapter_id, pipeline.transformer)
wrapper.merge_to()
print("Model loaded successfully. Ready to generate images.")
while True:
user_input = input("Enter a prompt or 'quit' to exit: ")
if user_input.lower() == 'quit':
break
# Check for resolution command
if user_input.startswith("resolution:"):
resolution = user_input.split(":")[1]
width, height = map(int, resolution.split("x"))
print(f"Resolution set to {width}x{height}")
continue
prompt = user_input
output_file = args.output_file.replace(".png", f"_{prompt.replace(' ', '_')}.png")
# Use default or previously set resolution
width = locals().get('width', 1024)
height = locals().get('height', 1024)
generate_image(
pipeline=pipeline,
prompt=prompt,
output_file=output_file,
num_inference_steps=args.num_inference_steps,
width=width,
height=height,
guidance_scale=args.guidance_scale,
seed=args.seed,
device=args.device
)
if __name__ == "__main__":
main()