pytorch_lora_weights.safetensors

Prompt
A young woman with smile, wearing a purple hat.

Negative Prompt
worst quality, low quality, bad anatomy, watermark, text, blurry, cartoon, unreal

Model description

This model is a fine-tuned version of the Stable Diffusion architecture, leveraging the Low-Rank Adaptation (LoRA) technique. It has been trained using the CelebA-HQ and FFHQ datasets, both renowned for their high-quality images of human faces.

Training Details:

Base Model: Stable Diffusion
Adaptation Technique: Low-Rank Adaptation (LoRA)
Datasets: CelebA-HQ (30,000 images), FFHQ (70,000 images)
Resolution: resolution : 512*512 fine-tuning for detailed facial synthesis

Example Usages:


import torch
from diffusers import StableDiffusionPipeline,UNet2DConditionModel

pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("cuda")

pipeline.load_lora_weights("phil329/face_lora_sd15", weight_name="pytorch_lora_weights.safetensors")

NEGATIVE_PROMPT = "worst quality, low quality, bad anatomy, watermark, text, blurry, cartoon, unreal"
text = 'A young woman with smile, wearing a purple hat.'

lora_image = pipeline(text,negative_prompt=NEGATIVE_PROMPT).images[0]

display(lora_image)

Results

We use four prompts as follows:

'A young woman with smile, wearing a purple hat.'
'A middle-aged man,beard ,attractive'
'A girl with long blonde hair'
'An young man with curry hair'

The negative prompt are the same as the example codes. All the results are randomly generated and not cherry-picked.

If the generation effect is not good, try adding a negative prompt, or try different prompts and seeds.

Download model

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.