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import streamlit as st
import cv2 as cv
import time
import torch
from diffusers import StableDiffusionPipeline
# "stabilityai/stable-diffusion-2-1-base"
# "CompVis/stable-diffusion-v1-4"
def create_model(loc = "stabilityai/stable-diffusion-2-1-base", mch = 'cpu'):
pipe = StableDiffusionPipeline.from_pretrained(loc)
pipe = pipe.to(mch)
return pipe
t2i = st.title("""
Txt2Img
###### `CLICK "Create_Update_Model"` :
- `FIRST RUN OF THE CODE`
- `CHANGING MODEL`""")
the_type = st.selectbox("Model",("stabilityai/stable-diffusion-2-1-base",
"CompVis/stable-diffusion-v1-4"))
create = st.button("Create The Model")
if create:
st.session_state.t2m_mod = create_model(loc=the_type)
prom = st.text_input("# Prompt",'')
c1,c2,c3 = st.columns([1,1,3])
c4,c5 = st.columns(2)
with c1:
bu_1 = st.text_input("Seed",'999')
with c2:
bu_2 = st.text_input("Steps",'12')
with c3:
bu_3 = st.text_input("Number of Images",'1')
with c4:
sl_1 = st.slider("Width",128,1024,512,8)
with c5:
sl_2 = st.slider("hight",128,1024,512,8)
create = st.button("Imagine")
if create:
generator = torch.Generator("cpu").manual_seed(int(bu_1))
model = st.session_state.t2m_mod
img = model(prom, width=int(sl_1), height=int(sl_2), num_inference_steps=int(bu_2), generator=generator).images[0]
# st.image(img)
if int(bu_3) == 1 :
generator = torch.Generator("cpu").manual_seed(int(bu_1))
model = st.session_state.t2m_mod
IMG = model(prom, width=int(sl_1), height=int(sl_2),
num_inference_steps=int(bu_2),
generator=generator).images[0]
st.image(IMG)
else :
generator = torch.Generator("cpu").manual_seed(int(bu_1))
PROMS = [prom]*int(bu_3)
model = st.session_state.t2m_mod
IMGS = model(prom, width=int(sl_1), height=int(sl_2),
num_inference_steps=int(bu_2),
generator=generator).images
IMGS = np.hstack(IMGS)
st.image(IMGS) |