pip install ikomia
About
Stable diffusion models from Hugging Face.
Run stable diffusion models from Hugging Face.
🚀 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.
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_hf_stable_diffusion", auto_connect=False)
# Run
wf.run()
# Display the image
display(algo.get_output(0).get_image())
☀️ Use with Ikomia Studio
Ikomia Studio offers a friendly UI with the same features as the API.
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If you haven't started using Ikomia Studio yet, download and install it from this page.
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For additional guidance on getting started with Ikomia Studio, check out this blog post.
📝 Set algorithm parameters
- model_name (str) - default 'stabilityai/stable-diffusion-2-base': Name of the stable diffusion model. Other model available:
- CompVis/stable-diffusion-v1-4
- runwayml/stable-diffusion-v1-5
- stabilityai/stable-diffusion-2-base
- stabilityai/stable-diffusion-2
- stabilityai/stable-diffusion-2-1-base
- stabilityai/stable-diffusion-2-1
- stabilityai/stable-diffusion-xl-base-1.0
- prompt (str): Input prompt.
- negative_prompt (str, optional): The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
guidance_scale
is less than1
). - num_inference_steps (int) - default '50': Number of denoising steps (minimum: 1; maximum: 500).
- guidance_scale (float) - default '7.5': Scale for classifier-free guidance (minimum: 1; maximum: 20).
- seed (int) - default '-1': Seed value. '-1' generates a random number between 0 and 191965535.
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_hf_stable_diffusion", auto_connect=False)
algo.set_parameters({
'model_name': 'stabilityai/stable-diffusion-xl-base-1.0',
'prompt': 'Astronaut on Mars during sunset',
'guidance_scale': '7.5',
'negative_prompt': 'low resolution',
'num_inference_steps': '50',
'seed': '1981651'
})
# Run directly on your image
wf.run()
# Display the image
display(algo.get_output(0).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.
import ikomia
from ikomia.dataprocess.workflow import Workflow
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="infer_hf_stable_diffusion", auto_connect=False)
# Run
wf.run()
# Iterate over outputs
for output in algo.get_outputs():
# Print information
print(output)
# Export it to JSON
output.to_json()
Developer
Ikomia
License
MIT License
A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
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