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Disra/anime-lora-test-05
anime-lora-test-05 is a text-to-image model from Disra. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as other.
This is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev.
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.safetensors105 MB · 75%
From the Hugging Face model README
This is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev.
The main validation prompt used during training was:
anime style digital art of a girl with long black hair and purple eyes wearing an unbuttoned white shirt that shows off her medium breasts, cleavage, and purple bra. She is also wearing black pleated skirt and is has a hand on her breasts while she looks up at the camera with a seductive pose.
Base flux - no lora - are on top, with the lora are on the bottom ( same promt and seed ) The last 2 grids have the same prompt, except the last one is 1.5x strength.

3.50.020None891024x1024Note: 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.
import torch
from diffusers import DiffusionPipeline
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Disra/anime-lora-test-05'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)
prompt = "anime style digital art of a girl with long black hair and purple eyes wearing an unbuttoned white shirt that shows off her medium breasts, cleavage, and purple bra. She is also wearing black pleated skirt and is has a hand on her breasts while she looks up at the camera with a seductive pose."
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=3.5,
).images[0]
image.save("output.png", format="PNG")