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import gradio as gr
import numpy as np
import torch
from PIL import Image
from diffusers import StableDiffusionPipeline
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id).to('cpu')
def infer(prompt, negative, steps, scale, seed):
generator = torch.Generator(device='cpu').manual_seed(seed)
img = pipe(
prompt,
height=512,
width=512,
num_inference_steps=steps,
guidance_scale=scale,
negative_prompt = negative,
generator=generator,
).images
return img
block = gr.Blocks()
with block:
with gr.Group():
with gr.Box():
with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
with gr.Column():
text = gr.Textbox(
label="Enter your prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
negative = gr.Textbox(
label="Enter your negative prompt",
show_label=False,
placeholder="Enter a negative prompt",
elem_id="negative-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),
)
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style(columns=(1, 2), height="auto")
with gr.Row(elem_id="advanced-options"):
samples = gr.Slider(label="Images", minimum=1, maximum=1, value=1, step=1, interactive=False)
steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=12, step=1, interactive=True)
scale = gr.Slider(label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1, interactive=True)
seed = gr.Slider(label="Random seed",minimum=0,maximum=2147483647,step=1,randomize=True,interactive=True)
btn.click(infer, inputs=[text, negative, steps, scale, seed], outputs=[gallery])
block.launch(show_api=False) |