add execution timings to output

change the text output element to HTML
This commit is contained in:
AUTOMATIC 2022-08-24 18:47:23 +03:00
parent 29f7e7ab89
commit 199123e98d
1 changed files with 34 additions and 8 deletions

View File

@ -12,6 +12,8 @@ from contextlib import contextmanager, nullcontext
import mimetypes
import random
import math
import html
import time
import k_diffusion as K
from ldm.util import instantiate_from_config
@ -160,6 +162,11 @@ def save_image(image, path, basename, seed, prompt, extension, info=None, short_
image.save(os.path.join(path, filename), quality=opt.jpeg_quality, pnginfo=pnginfo)
def plaintext_to_html(text):
text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
return text
def load_GFPGAN():
model_name = 'GFPGANv1.3'
model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
@ -331,6 +338,20 @@ def check_prompt_length(prompt, comments):
comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
def wrap_gradio_call(func):
def f(*p1, **p2):
t = time.perf_counter()
res = list(func(*p1, **p2))
elapsed = time.perf_counter() - t
# last item is always HTML
res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
return tuple(res)
return f
def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False):
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
@ -484,7 +505,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, p
del sampler
return output_images, seed, info
return output_images, seed, plaintext_to_html(info)
class Flagging(gr.FlaggingCallback):
@ -529,7 +550,7 @@ class Flagging(gr.FlaggingCallback):
txt2img_interface = gr.Interface(
txt2img,
wrap_gradio_call(txt2img),
inputs=[
gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
@ -547,7 +568,7 @@ txt2img_interface = gr.Interface(
outputs=[
gr.Gallery(label="Images"),
gr.Number(label='Seed'),
gr.Textbox(label="Copy-paste generation parameters"),
gr.HTML(),
],
title="Stable Diffusion Text-to-Image K",
description="Generate images from text with Stable Diffusion (using K-LMS)",
@ -650,14 +671,14 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
del sampler
return output_images, seed, info
return output_images, seed, plaintext_to_html(info)
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
img2img_interface = gr.Interface(
img2img,
wrap_gradio_call(img2img),
inputs=[
gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
@ -677,7 +698,7 @@ img2img_interface = gr.Interface(
outputs=[
gr.Gallery(),
gr.Number(label='Seed'),
gr.Textbox(label="Copy-paste generation parameters"),
gr.HTML(),
],
title="Stable Diffusion Image-to-Image",
description="Generate images from images with Stable Diffusion",
@ -698,7 +719,7 @@ def run_GFPGAN(image, strength):
if strength < 1.0:
res = Image.blend(image, res, strength)
return res
return res, 0, ''
if GFPGAN is not None:
@ -710,6 +731,8 @@ if GFPGAN is not None:
],
outputs=[
gr.Image(label="Result"),
gr.Number(label='Seed', visible=False),
gr.HTML(),
],
title="GFPGAN",
description="Fix faces on images",
@ -719,7 +742,10 @@ if GFPGAN is not None:
demo = gr.TabbedInterface(
interface_list=[x[0] for x in interfaces],
tab_names=[x[1] for x in interfaces],
css=("" if opt.no_progressbar_hiding else css_hide_progressbar)
css=("" if opt.no_progressbar_hiding else css_hide_progressbar) + """
.output-html p {margin: 0 0.5em;}
.performance { font-size: 0.85em; color: #444; }
"""
)
demo.launch()