View file src/colab/from_imagination_to_reality_in_real_time_unveiling_the_power_of_sdxl.py - Download
# -*- coding: utf-8 -*-
"""From Imagination to Reality in Real-Time: Unveiling the Power of SDXL.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/16jFPljOgEynJKqVZfG0tlh_a_ZXdV-sh
This notebook is inspired by this page written by Nashit Budhwani :
https://medium.com/@nashitnoorali78/from-imagination-to-reality-in-real-time-unveiling-the-power-of-sdxl-80f259ba4e86
NOTE : This notebook may be stuck on "Fetching 30 files: 0%". This problem can be solved by creating a secret named HF_TOKEN (click on the key icon on the left) with any value (not necessarily a valid HuggingFace token). When you run the notebook, it asks if you want to grant access to the HF_TOKEN secret. Then DON'T GRANT ACCESS BUT CANCEL TO DENY ACCESS. (It's strange but it works like this.)
# Real-Time Image Generation with SDXL: Redefining Speed and Interactivity
In addition to the previously mentioned features, SDXL boasts impressive real-time image generation capabilities. This groundbreaking functionality allows users to witness the image come to life as they fine-tune the textual prompts and adjust various parameters. This interactive experience is made possible by:
* SDXL Turbo: This optimized version of SDXL significantly reduces the time required to generate images, often achieving results in a single sampling step. This allows for real-time feedback and iterative refinement.
* Latent Space Editing: Unlike traditional image editing tools that manipulate individual pixels, SDXL operates in the “latent space,” where abstract representations of the image reside. This allows for fine-grained control over specific image features without compromising overall image quality.
* RefineNet: This additional neural network works in tandem with the base SDXL model to enhance the details and sharpness of the final image, particularly when using real-time editing.
## Benefits of Real-Time Image Generation
* Enhanced Creativity and Exploration: Artists and designers can experiment with different ideas and iterate quickly, leading to more creative and innovative outputs.
* Improved Learning and Experimentation: Users can gain a deeper understanding of the image generation process and explore the impact of various parameters in real-time.
* Accessibility and Broader Applications: Real-time capabilities open up new possibilities for using SDXL in interactive applications, educational tools, and even live performances.
## Generating Images with SDXL-turbo
Diffusers
"""
!pip install diffusers transformers accelerate --upgrade
"""## Text-to-image:
SDXL-Turbo does not make use of guidance_scale or negative_prompt, we disable it with guidance_scale=0.0. Preferably, the model generates images of size 512x512 but higher image sizes work as well. A single step is enough to generate high quality images.
"""
from diffusers import AutoPipelineForText2Image
import torch
from IPython.display import display
pipe_t2i = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16").to("cuda")
torch.manual_seed(14)
prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
image = pipe_t2i(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
display(image)
torch.manual_seed(14)
prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"
image = pipe_t2i(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
display(image)
"""## Image-to-image:
When using SDXL-Turbo for image-to-image generation, make sure that num_inference_steps * strength is larger or equal to 1. The image-to-image pipeline will run for int(num_inference_steps * strength) steps, e.g. 0.5 * 2.0 = 1 step in our example below.
"""
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
pipe_i2i = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float32, variant="fp16")
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png").resize((512, 512))
display(init_image)
torch.manual_seed(14)
prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"
image = pipe_i2i(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0]
display(image)
"""## The Future of Real-Time Image Generation
The potential of SDXL’s real-time capabilities is vast. We can expect to see further advancements in areas such as:
* Interactive storytelling and game development: Imagine creating and experiencing narratives that unfold in real-time based on your choices and actions.
* Augmented reality and virtual reality experiences: SDXL can be used to generate dynamic and interactive environments that respond to user input in real-time.
* Collaborative art creation and design: Artists and designers can work together in real-time to co-create and refine images, fostering collaboration and innovation.
As these technologies mature, we can expect real-time image generation to play an increasingly significant role in diverse sectors, blurring the lines between imagination and reality.
## Conclusion
SDXL is a revolutionary AI tool that pushes the boundaries of text-to-image generation. Its impressive capabilities, open-source nature, and real-time functionalities offer immense potential for creative professionals, researchers, and anyone interested in exploring the intersection of AI and art. By understanding the inner workings and potential of SDXL, we can better utilize this powerful tool and contribute to shaping the future of intelligent image creation.
## Reference Links
* Paper: https://arxiv.org/abs/2112.10752
"""
!python --version
!for p in diffusers accelerate torch ipython; do pip list | grep "^$p[ \t]"; done
"""Python 3.10.12
diffusers 0.32.1
accelerate 1.2.1
torch 2.5.1+cu121
ipython
"""