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import gradio as gr | |
from transformers import pipeline | |
import torch | |
from diffusers import StableDiffusionPipeline | |
def get_completion(prompt,negative_prompt,steps,guidance,width,height): | |
return pipeline(prompt=prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=guidance, negative_prompt=negative_prompt)['sample'][0] | |
def generate(prompt,negative_prompt,steps,guidance,width,height): | |
output = get_completion(prompt=prompt, height=height, width=width, steps=steps, guidance=guidance, negative_prompt=negative_prompt) | |
return output | |
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) | |
with gr.Blocks() as demo: | |
gr.Markdown("# Image Generation with Stable Diffusion") | |
with gr.Row(): | |
with gr.Column(scale=4): | |
prompt = gr.Textbox(label="Your prompt") #Give prompt some real estate | |
with gr.Column(scale=1, min_width=50): | |
btn = gr.Button("Submit") #Submit button side by side! | |
with gr.Accordion("Advanced options", open=False): #Let's hide the advanced options! | |
negative_prompt = gr.Textbox(label="Negative prompt") | |
with gr.Row(): | |
with gr.Column(): | |
steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25, | |
info="In many steps will the denoiser denoise the image?") | |
guidance = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7, | |
info="Controls how much the text prompt influences the result") | |
with gr.Column(): | |
width = gr.Slider(label="Width", minimum=64, maximum=512, step=64, value=512) | |
height = gr.Slider(label="Height", minimum=64, maximum=512, step=64, value=512) | |
output = gr.Image(label="Result") #Move the output up too | |
btn.click(fn=generate, inputs=[prompt,negative_prompt,steps,guidance,width,height], outputs=[output]) | |
demo.launch() |