In Task 2, you try to look inside the generative process. What can you say about it? Select one: a. The generative process appears slightly chaotic and is not immediately clear for a human to interpret. b. The model starts with the image it came up with which is first in black and white. It then gradually colours the image one bit at a time. c. The model drafts things similarly as a human would. It starts by sketching out outlines and then fills in the details.
In Task 2, you try to look inside the generative process. What can you say about it? Select one: a. The generative process appears slightly chaotic and is not immediately clear for a human to interpret. b. The model starts with the image it came up with which is first in black and white. It then gradually colours the image one bit at a time. c. The model drafts things similarly as a human would. It starts by sketching out outlines and then fills in the details.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
In Task 2, you try to look inside the generative process. What can you say about it?
Select one:
a.
The generative process appears slightly chaotic and is not immediately clear for a human to interpret.
b.
The model starts with the image it came up with which is first in black and white. It then gradually colours the image one bit at a time.
c.
The model drafts things similarly as a human would. It starts by sketching out outlines and then fills in the details.
![Task 2: Images from different inference steps
We can inspect what the generated images look like at different number of inference steps. An inference step involves a forward pass of the the text prompt and
the current image passed as input to Stable Diffusion. The first inference step involves inputting an image with random pixels together with the written text
prompt to obtain the generated image. The subsequent inference steps then uses the last generated image as input together with the text prompt to refine the
generated image.
Given a text prompt, we will generate images at different inference steps and inspect the resulting output images by running the cell below.
Be patient. Generating these images will take several minutes with 30 seconds per inference step. You can try reducing the number of steps that you
want to use.
In [] # The prompt is the text you input to the model, edit below
prompt = "a photo of an astronaut riding a horse on mars"
# The different number of inference steps to test
inference_steps
[1, 2, 4, 8, 16, 25]
images []
for num_inference_steps in inference_steps:
# Generate image from the prompt for the given number of inference steps
image = generate_image_from_text_prompt (pipe, prompt,
images. append (image)
num_inference_steps=num_inference_steps,
seed=seed)
Plot the generated images to inspect what they look like at different inference steps by running the cell below.
In [ ]: plot_images_and_inference_steps (images, inference_steps, rows=2, cols=3)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F61ea897e-d5ca-4ed8-a188-9b1557795693%2Ffd9528ed-cbb4-4ebd-a183-93432a837225%2F3it6x22p_processed.png&w=3840&q=75)
Transcribed Image Text:Task 2: Images from different inference steps
We can inspect what the generated images look like at different number of inference steps. An inference step involves a forward pass of the the text prompt and
the current image passed as input to Stable Diffusion. The first inference step involves inputting an image with random pixels together with the written text
prompt to obtain the generated image. The subsequent inference steps then uses the last generated image as input together with the text prompt to refine the
generated image.
Given a text prompt, we will generate images at different inference steps and inspect the resulting output images by running the cell below.
Be patient. Generating these images will take several minutes with 30 seconds per inference step. You can try reducing the number of steps that you
want to use.
In [] # The prompt is the text you input to the model, edit below
prompt = "a photo of an astronaut riding a horse on mars"
# The different number of inference steps to test
inference_steps
[1, 2, 4, 8, 16, 25]
images []
for num_inference_steps in inference_steps:
# Generate image from the prompt for the given number of inference steps
image = generate_image_from_text_prompt (pipe, prompt,
images. append (image)
num_inference_steps=num_inference_steps,
seed=seed)
Plot the generated images to inspect what they look like at different inference steps by running the cell below.
In [ ]: plot_images_and_inference_steps (images, inference_steps, rows=2, cols=3)
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