import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("dtthanh/vlu_logo")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Van Lang University Logo
LoRA Adapter for black-forest-labs/FLUX.1-dev trained on 15 pictures of Van Lang University Logo with ai-toolkit
Trigger words
You should use VLU to trigger the image generation. The trigger phrase `VLU Logo with 'Van Lang University' text printed on [material].
Model Details
Some Examples
.jpg){kind=logo}
.jpg){kind=logo}
.jpg){kind=logo}
.png){kind=logo}
.png){kind=logo}
.png){kind=logo}
.png){kind=logo}
.png){kind=logo}
.png){kind=logo}
Usage
With diffusers package
Note: FLUX uses ~70GBvram when loaded directly with diffusers
Note: Recommended to load at ~70% scale for best results
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
pipeline.load_lora_weights('dtthanh/vlu_logo', weight_name='lora.safetensors')
pipeline.to("cuda")
prompt = "a photo of a female student with red polo on that there is VLU Logo with 'Van Lang University' text printed. backgound is a class."
out = pipeline(
prompt=prompt,
guidance_scale=3.5,
num_inference_steps=20,
cross_attention_kwargs={"scale": 0.7}
).images[0]
out.save("vlulogo.png")
Additional Details
Please see base model page black-forest-labs/FLUX.1-dev for all details on appropriate usage, licensing, and more.
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Base model
black-forest-labs/FLUX.1-dev