infer_kandinsky_2_img2img
About
Kandinsky 2.2 image-to-image diffusion model.
Kandinsky 2.2 image-to-image is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder.
Note: This algorithm requires 10GB GPU RAM
🚀 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 Workflowfrom ikomia.utils.displayIO import display# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name = "infer_kandinsky_2_img2img", auto_connect=False)# Run directly on your imagewf.run_on(url="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")# Display the imagedisplay(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 'kandinsky-community/kandinsky-2-2-decoder': Name of the latent diffusion model.
- prompt (str) - default 'portrait of a young women, blue eyes, cinematic' : Text prompt to guide the image generation .
- negative_prompt (str, optional) - default 'low quality, bad quality': The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
guidance_scale
is less than1
). - prior_num_inference_steps (int) - default '25': Number of denoising steps of the prior model (CLIP).
- prior_guidance_scale (float) - default '4.0': Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality. (minimum: 1; maximum: 20).
- num_inference_steps (int) - default '100': The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
- guidance_scale (float) - default '1.0': Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality. (minimum: 1; maximum: 20).
- strength (int) - default '0.2': Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image will be used as a starting point, adding more noise to it the larger the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in num_inference_steps. A value of 1, therefore, essentially ignores image.
- height (int) - default '768: The height in pixels of the generated image.
- width (int) - default '768: The width in pixels of the generated image.
- seed (int) - default '-1': Seed value. '-1' generates a random number between 0 and 191965535.
from ikomia.dataprocess.workflow import Workflowfrom ikomia.utils.displayIO import display# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name = "infer_kandinsky_2_img2img", auto_connect=True)algo.set_parameters({'model_name': 'kandinsky-community/kandinsky-2-2-decoder','prompt': 'A fantasy landscape, Cinematic lighting','negative_prompt': 'low quality, bad quality','prior_num_inference_steps': '25','prior_guidance_scale': '4.0','num_inference_steps': '100','guidance_scale': '4.0','strength': '0.4','seed': '1231689','width': '768','height': '512',})# Runwf.run_on(url="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")# Display the imagedisplay(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.
from ikomia.dataprocess.workflow import Workflow# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name="infer_kandinsky_2_img2img", auto_connect=True)# Run on your imagewf.run_on(url="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")# Iterate over outputsfor output in algo.get_outputs():# Print informationprint(output)# Export it to JSONoutput.to_json()
Developer
Ikomia
License
Apache License 2.0
A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
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