Stability_AI Image generation

Prompt

• Beach blue checked dress for frmale adult

Code

	# Commented out IPython magic to ensure Python compatibility.
	#@title <a name="Step 1"><font color="#FFFFFF">1. Install the Stability SDK package.</font></a>
	# %pip install stability-sdk
	#@title <a name="Step 2"><font color="#FFFFFF">2. Set up our environment variables and API Key.</font></a>
	import getpass, os
	# Our Host URL should not be prepended with "https" nor should it have a trailing slash.
	os.environ['STABILITY_HOST'] = 'grpc.stability.ai:443'
	# Sign up for an account at the following link to get an API Key. https://beta.dreamstudio.ai/membership
	# Click on the following link once you have created an account to be taken to your API Key. Paste it below when prompted after running the cell. https://beta.dreamstudio.ai/membership?tab=apiKeys
	os.environ['STABILITY_KEY'] = 'Enter your API Key'
	#os.environ['STABILITY_KEY'] = getpass.getpass('Enter your API Key')
	#@title <a name="Step 3"><font color="#FFFFFF">3. Import additional dependencies and establish our connection to the API.</font></a>
	import io
	import os
	import warnings
	from IPython.display import display
	from PIL import Image
	from stability_sdk import client
	import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
	# Set up our connection to the API.
	stability_api = client.StabilityInference(
	key=os.environ['STABILITY_KEY'], # API Key reference.
	verbose=True, # Print debug messages.
	engine="stable-diffusion-v1-5", # Set the engine to use for generation.
	# Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0 stable-inpainting-v1-0 stable-inpainting-512-v2-0
	)
	#@title <a name="Step 4"><font color="#FFFFFF">4. Set up initial generation parameters, display image on generation, and safety warning for if the adult content classifier is tripped.</font></a>
	# Set up our initial generation parameters.
	answers = stability_api.generate(
	prompt="Beach blue checked dress for frmale adult",
	seed=992446758, # If a seed is provided, the resulting generated image will be deterministic.
	# What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
	# Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook.
	steps=30, # Amount of inference steps performed on image generation. Defaults to 30.
	cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt.
	# Setting this value higher increases the strength in which it tries to match your prompt.
	# Defaults to 7.0 if not specified.
	width=512, # Generation width, defaults to 512 if not included.
	height=512, # Generation height, defaults to 512 if not included.
	samples=1, # Number of images to generate, defaults to 1 if not included.
	sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with.
	# Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
	# (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m)
	)
	# Set up our warning to print to the console if the adult content classifier is tripped.
	# If adult content classifier is not tripped, save generated images.
	for resp in answers:
	for artifact in resp.artifacts:
	if artifact.finish_reason == generation.FILTER:
	warnings.warn(
	"Your request activated the API's safety filters and could not be processed."
	"Please modify the prompt and try again.")
	if artifact.type == generation.ARTIFACT_IMAGE:
	img = Image.open(io.BytesIO(artifact.binary))
	display(img)
	img.save("stability_AI_results.jpg")

view raw stabilit_ai_image_generation.py hosted with ❤ by GitHub

Results

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