Stability AI - reimagine your home's potential

gistfile1.txt

import os
import requests
import io
import random
import time
import warnings
from PIL import Image
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation

os.environ["STABILITY_HOST"] = "grpc.stability.ai:443"

# Sign up for an account at the following link to get an API Key.
# https://platform.stability.ai/

# Click on the following link once you have created an account to be taken to your API Key.
# https://platform.stability.ai/account/keys

# Paste your API Key below.

os.environ["STABILITY_KEY"] = "sk-XYZ"

# 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-xl-1024-v1-0",  # Set the engine to use for generation.
    # Check out the following link for a list of available engines: https://platform.stability.ai/docs/features/api-parameters#engine
)

# make sure the out directory exists
if not os.path.exists("./out"):
    os.makedirs("./out")

# URL of the image you want to download
image_url = "https://c.pxhere.com/photos/bd/fb/bed_cot_old_historically_vintage_kl_den_burg-1175321.jpg!d"

# Download the image
response = requests.get(image_url)

if response.status_code == 200:

    # If download is successful, use the image content directly in the files parameter
    image = Image.open(io.BytesIO(response.content))

    # random seed value
    seed_value = int(time.time())
    seed = random.seed(seed_value)

    answers2 = stability_api.generate(
        prompt="""Transform the interior design of the image provided to embody a rustic aesthetic. 
        Focus on incorporating elements such as natural wood textures, exposed stone, warm earthy 
        tones, and vintage furnishings to create a cozy, inviting space. Emphasize simplicity 
        and the beauty of the outdoors with handcrafted objects and textiles that suggest a 
        connection to nature. The goal is to create a serene, homey atmosphere that blends 
        traditional charm with the comfort of rural living --ar 3:2.""",
        init_image=image,  # Assign our previously generated img as our Initial Image for transformation.
        start_schedule=0.6,  # Set the strength of our prompt in relation to our initial image.
        seed=seed,  # If attempting to transform an image that was previously generated with our API,
        # initial images benefit from having their own distinct seed rather than using the seed of the original image generation.
        steps=50,  # 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=1024,  # Generation width, defaults to 512 if not included.
        height=1024,  # Generation height, defaults to 512 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, k_dpmpp_sde)
    )

    for resp in answers2:
        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:
                global img2
                img2 = Image.open(io.BytesIO(artifact.binary))
                img2.save(
                    f"./out/img2img_{str(artifact.seed)}.jpg"
                )  # Save our generated image with its seed number as the filename and the img2img suffix so that we know this is our transformed image.