cs7150 / app.py
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demo launch error fix
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from threading import Lock
import math
import os
import random
from diffusers import StableDiffusionPipeline
from diffusers.models.attention import get_global_heat_map, clear_heat_maps
from matplotlib import pyplot as plt
import gradio as gr
import torch
import torch.nn.functional as F
import spacy
if not os.environ.get('NO_DOWNLOAD_SPACY'):
spacy.cli.download('en_core_web_sm')
model_id = "CompVis/stable-diffusion-v1-4"
# Changed
device = 'cuda' if torch.cuda.is_available() else 'cpu'
gen = torch.Generator(device=device) #changed device = 'cuda'
gen.manual_seed(12758672)
orig_state = gen.get_state()
use_auth_token= "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"
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=use_auth_token).to(device)
lock = Lock()
nlp = spacy.load('en_core_web_sm')
def expand_m(m, n: int = 1, o=512, mode='bicubic'):
m = m.unsqueeze(0).unsqueeze(0) / n
m = F.interpolate(m.float().detach(), size=(o, o), mode='bicubic', align_corners=False)
m = (m - m.min()) / (m.max() - m.min() + 1e-8)
m = m.cpu().detach()
return m
@torch.no_grad()
def predict(prompt, inf_steps, threshold):
global lock
with torch.cuda.amp.autocast(), lock:
try:
plt.close('all')
except:
pass
gen.set_state(orig_state.clone())
clear_heat_maps()
out = pipe(prompt, guidance_scale=7.5, height=512, width=512, do_intermediates=False, generator=gen,
num_inference_steps=int(inf_steps))
heat_maps = get_global_heat_map()
with torch.cuda.amp.autocast(dtype=torch.float32):
m = 0
n = 0
w = ''
w_idx = 0
fig, ax = plt.subplots()
ax.imshow(out.images[0].cpu().float().detach().permute(1, 2, 0).numpy())
ax.set_xticks([])
ax.set_yticks([])
fig1, axs1 = plt.subplots(math.ceil(len(out.words) / 4), 4) # , figsize=(20, 20))
fig2, axs2 = plt.subplots(math.ceil(len(out.words) / 4), 4) # , figsize=(20, 20))
for idx in range(len(out.words) + 1):
if idx == 0:
continue
word = out.words[idx - 1]
m += heat_maps[idx]
n += 1
w += word
if '</w>' not in word:
continue
else:
mplot = expand_m(m, n)
spotlit_im = out.images[0].cpu().float().detach()
w = w.replace('</w>', '')
spotlit_im2 = torch.cat((spotlit_im, (1 - mplot.squeeze(0)).pow(1)), dim=0)
if len(out.words) <= 4:
a1 = axs1[w_idx % 4]
a2 = axs2[w_idx % 4]
else:
a1 = axs1[w_idx // 4, w_idx % 4]
a2 = axs2[w_idx // 4, w_idx % 4]
a1.set_xticks([])
a1.set_yticks([])
a1.imshow(mplot.squeeze().numpy(), cmap='jet')
a1.imshow(spotlit_im2.permute(1, 2, 0).numpy())
a1.set_title(w)
mask = torch.ones_like(mplot)
mask[mplot < threshold * mplot.max()] = 0
im2 = spotlit_im * mask.squeeze(0)
a2.set_xticks([])
a2.set_yticks([])
a2.imshow(im2.permute(1, 2, 0).numpy())
a2.set_title(w)
m = 0
n = 0
w_idx += 1
w = ''
for idx in range(w_idx, len(axs1.flatten())):
fig1.delaxes(axs1.flatten()[idx])
fig2.delaxes(axs2.flatten()[idx])
return fig, fig1, fig2
def set_prompt(prompt):
return prompt
with gr.Blocks() as demo:
md = '''# DAAM: Attention Maps for Interpreting Stable Diffusion
Check out the paper: [What the DAAM: Interpreting Stable Diffusion Using Cross Attention](http://arxiv.org/abs/2210.04885). Note that, due to server costs, this demo will transition to HuggingFace Spaces on 2022-10-20.
'''
gr.Markdown(md)
with gr.Row():
with gr.Column():
dropdown = gr.Dropdown([
'A monkey wearing a halloween costume',
'A smiling, red cat chewing gum',
# 'Doing research at Comcast Applied AI labs',
# 'Professor Jimmy Lin from the University of Waterloo',
# 'Yann Lecun teaching machine learning on a chalkboard',
# 'A cat eating cake for her birthday',
# 'Steak and dollars on a plate',
# 'A fox, a dog, and a wolf in a field'
], label='Examples', value='An angry, bald man doing research')
text = gr.Textbox(label='Prompt', value='An angry, bald man doing research')
slider1 = gr.Slider(15, 35, value=25, interactive=True, step=1, label='Inference steps')
slider2 = gr.Slider(0, 1.0, value=0.4, interactive=True, step=0.05, label='Threshold (tau)')
submit_btn = gr.Button('Submit')
with gr.Tab('Original Image'):
p0 = gr.Plot()
with gr.Tab('Soft DAAM Maps'):
p1 = gr.Plot()
with gr.Tab('Hard DAAM Maps'):
p2 = gr.Plot()
submit_btn.click(fn=predict, inputs=[text, slider1, slider2], outputs=[p0, p1, p2])
dropdown.change(set_prompt, dropdown, text)
dropdown.update()
# ADDED PART
# import portpicker
# port = portpicker.pick_unused_port()
# select_ip = "0.0.0.0:"+str(port)
# print("Port: ", port)
# from IPython.display import Javascript
# def show_port(port, height=400):
# display(Javascript("""
# (async ()=>{
# fm = document.createElement('iframe')
# fm.src = await google.colab.kernel.proxyPort(%s)
# fm.width = '95%%'
# fm.height = '%d'
# fm.frameBorder = 0
# document.body.append(fm)
# })();
# """ % (port, height)))
# get_ipython().system_raw(f'python3 -m http.server {port} &')
# show_port(port)
###
demo.launch()
# demo.launch(server_name='0.0.0.0', server_port=port)