app.py

import gradio as gr 
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation


theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_sm,
    font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
)

def infer(prompt, api_key):
    stability_api = client.StabilityInference(
    key=api_key, # API Key reference.
    verbose=True, # Print debug messages.
    engine="stable-diffusion-xl-beta-v2-2-2", # 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
)
    answers = stability_api.generate(
    prompt=prompt,
    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)
)
    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))
    
    return img

with gr.Blocks(theme = theme) as demo:
    gr.Markdown("# Stable Diffusion XL")
    #gr.Markdown('<p> For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/RamAnanth1/stable-diffusion-xl?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>')
    api_key_input = gr.Textbox(type = "password", label = "Enter your StabilityAI API key here")
    text = gr.Textbox(label="Enter your prompt",
                        show_label=True,
                        max_lines=1,
                        placeholder="Enter your prompt",
                        elem_id="prompt-text-input",
                    ).style(
                        border=(True, False, True, True),
                        rounded=(True, False, False, True),
                        container=False,
                    )
    btn = gr.Button("Generate image").style(
                    margin=False,
                    rounded=(False, True, True, False),
                    full_width=False,
                )

    gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(grid=[2], height="auto")

    btn.click(infer, inputs=[text, api_key_input], outputs=[gallery])

    
    
demo.launch()