infer_pulid

Run PuLID, a tuning-free ID customization approach. It's is an ip-adapter alike method to restore facial identity. It uses insightface embedding, CLIP embedding and SDXL-Lightning for inferences in 4 steps.

Note: This algorithm is using about 19 GB of VRAM

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_pulid", auto_connect=True)

# Run on your image  
wf.run_on(url="https://images.pexels.com/photos/4927360/pexels-photo-4927360.jpeg?cs=srgb&dl=pexels-anntarazevich-4927360.jpg&fm=jpg&w=1280&h=1920")

# Inpect your result
display(algo.get_output(0).get_image())

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

• If you haven't started using Ikomia Studio yet, download and install it from this page.
• For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

• prompt (str): Text prompt to guide the image generation.
• negative_prompt (str, optional) - default 'flaws in the eyes, flaws in the face, flaws, lowres, non-HDRi, low quality, worst quality,' 'artifacts noise, text, watermark, glitch, deformed, mutated, ugly, disfigured, hands, ' 'low resolution, partially rendered objects, deformed or partially rendered eyes, ' 'deformed, deformed eyeballs, cross-eyed, blurry'. The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
• num_inference_steps (int) - default '4': Number of denoising steps.
• guidance_scale (float) - default '1.2': Stable diffusion Scale for classifier-free guidance. Recommended between [1, 1.5]. 1 will be faster.
• guidance_scale_id (float) - default '0.8': ID guidance scale. Recommended between [0, 5].
• seed (int) - default '-1': Seed value. '-1' generates a random number between 0 and 1919655350.
• num_images_per_prompt (int) - default '1': Number of generated images.
• mode (str) - default 'fidelity': Mode of the image generation 'fidelity' or 'extremely style'. We don't see much of a difference between the two.
• width (int) - default '1024': Output width. If not divisible by 8 it will be automatically modified to a multiple of 8.
• height (int) - default '1024': Output height. If not divisible by 8 it will be automatically modified to a multiple of 8.
• id_mix (bool) - default 'False': If you want to mix two ID image, please turn this on, otherwise, turn this off.

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Set main image
wf.set_image_input(
    url="https://images.pexels.com/photos/4927360/pexels-photo-4927360.jpeg?cs=srgb&dl=pexels-anntarazevich-4927360.jpg&fm=jpg&w=1280&h=1920",
    index=0
)

# Set additional image(s) [optional]
wf.set_image_input(
    url="https://images.pexels.com/photos/4927361/pexels-photo-4927361.jpeg?cs=srgb&dl=pexels-anntarazevich-4927361.jpg&fm=jpg&w=1280&h=1920",
    index=1
)

wf.set_image_input(
    url="https://images.pexels.com/photos/4927359/pexels-photo-4927359.jpeg?cs=srgb&dl=pexels-anntarazevich-4927359.jpg&fm=jpg&w=1280&h=1920",
    index=2
)

wf.set_image_input(
    url="https://images.pexels.com/photos/4927358/pexels-photo-4927358.jpeg?cs=srgb&dl=pexels-anntarazevich-4927358.jpg&fm=jpg&w=1280&h=1920",
    index=3
)

# Add algorithm
algo = wf.add_task(name="infer_pulid", auto_connect=False)

# Connect inputs
wf.connect_tasks(wf.root(), algo, [(0,0), (1,1), (2,2), (3,3)])

# Set parameters
algo.set_parameters({
    'prompt': 'portrait, color, cinematic, in garden, soft light, detailed face, wonderwoman costum, golden boomerang tiara, short hair',
    'guidance_scale': '1.2',
    'guidance_scale_id': '0.8',
    'num_inference_steps': '4',
    'seed': '-1',
    'width': '1024',
    'height': '1024',
    'mode': 'fidelity',
    'num_images_per_prompt':'2',
    'mix_id' : 'False'
    })

# Run your workflow
wf.run()

# Inpect your result
display(algo.get_output(0).get_image())
display(algo.get_output(1).get_image())

🔍 Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_pulid", auto_connect=True)

# Run on your image  
wf.run_on(url="https://images.pexels.com/photos/4927360/pexels-photo-4927360.jpeg?cs=srgb&dl=pexels-anntarazevich-4927360.jpg&fm=jpg&w=1280&h=1920")

# Iterate over outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()

⏩ Troubleshooting

If you encounter issues while installing Insightface on Windows, please follow this guide.