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| import gradio as gr | |
| from stability_sdk import client | |
| import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation | |
| from PIL import Image | |
| 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() |