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import streamlit as st
import numpy as np
from PIL import Image
# constants
HF_REPO_NAME_DIFFUSION = 'nostalgebraist/nostalgebraist-autoresponder-diffusion'
model_path_diffusion = 'nostalgebraist-autoresponder-diffusion'
timestep_respacing_sres1 = '20' # '90,60,60,20,20'
timestep_respacing_sres2 = '20' # '250'
DIFFUSION_DEFAULTS = dict(
batch_size=1,
n_samples=1,
clf_free_guidance=False,
clf_free_guidance_sres=False,
guidance_scale=1,
guidance_scale_sres=0,
yield_intermediates=True
)
@st.cache
def setup():
import os, subprocess, sys
os.system("git clone https://github.com/nostalgebraist/improved-diffusion.git && cd improved-diffusion && git fetch origin nbar-space && git checkout nbar-dev && pip install -e .")
os.system("pip install tokenizers x-transformers==0.22.0 axial-positional-embedding")
os.system("pip install einops==0.3.2")
sys.path.append("improved-diffusion")
import improved_diffusion.pipeline
from transformer_utils.util.tfm_utils import get_local_path_from_huggingface_cdn
if not os.path.exists(model_path_diffusion):
model_tar_name = 'model.tar'
model_tar_path = get_local_path_from_huggingface_cdn(
HF_REPO_NAME_DIFFUSION, model_tar_name
)
subprocess.run(f"tar -xf {model_tar_path} && rm {model_tar_path}", shell=True)
checkpoint_path_sres1 = os.path.join(model_path_diffusion, "sres1.pt")
config_path_sres1 = os.path.join(model_path_diffusion, "config_sres1.json")
checkpoint_path_sres2 = os.path.join(model_path_diffusion, "sres2.pt")
config_path_sres2 = os.path.join(model_path_diffusion, "config_sres2.json")
# load
sampling_model_sres1 = improved_diffusion.pipeline.SamplingModel.from_config(
checkpoint_path=checkpoint_path_sres1,
config_path=config_path_sres1,
timestep_respacing=timestep_respacing_sres1
)
sampling_model_sres2 = improved_diffusion.pipeline.SamplingModel.from_config(
checkpoint_path=checkpoint_path_sres2,
config_path=config_path_sres2,
timestep_respacing=timestep_respacing_sres2
)
pipeline = improved_diffusion.pipeline.SamplingPipeline(sampling_model_sres1, sampling_model_sres2)
return pipeline
pipeline = setup()
def handler(text, ts1, ts2, gs1):
# a = np.random.randint(0, 255, (128, 128, 3)).astype(np.uint8)
data = {'text': text[:380], 'guidance_scale': gs1}
args = {k: v for k, v in DIFFUSION_DEFAULTS.items()}
args.update(data)
print(f"running: {args}")
pipeline.base_model.set_timestep_respacing(str(ts1))
pipeline.super_res_model.set_timestep_respacing(str(ts2))
return pipeline.sample(**args)
text = st.text_area('asdf')
if st.button('rweerew'):
for s, xs in handler(text, 20, 20, 0):
pass
# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x)
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