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import os
import io
import IPython.display
from PIL import Image
import base64
from diffusers import DiffusionPipeline
hf_api_key = "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"
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
def get_completion(prompt):
return pipeline(prompt).images[0]
import gradio as gr
#A helper function to convert the PIL image to base64
#so you can send it to the API
#A helper function to convert the PIL image to base64
# so you can send it to the API
def base64_to_pil(img_base64):
base64_decoded = base64.b64decode(img_base64)
byte_stream = io.BytesIO(base64_decoded)
pil_image = Image.open(byte_stream)
return pil_image
def generate(prompt, negative_prompt, steps, guidance, width, height):
params = {
"negative_prompt": negative_prompt,
"num_inference_steps": steps,
"guidance_scale": guidance,
"width": width,
"height": height
}
output = get_completion(prompt, params)
pil_image = base64_to_pil(output)
return pil_image
gr.close_all()
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])
gr.close_all()
demo.launch() |