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optionally scale initial image to fit box defined by width x height

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* This functionality is triggered by the --fit option in the CLI (default
false), and by the "fit" checkbox in the WebGUI (default True)

* In addition, this commit contains a number of whitespace changes to
make the code more readable, as well as an attempt to unify the visual
appearance of info and warning messages.
This commit is contained in:
Lincoln Stein 2022-09-01 00:50:28 -04:00
parent 4b560b50c2
commit 28fe84177e
8 changed files with 232 additions and 182 deletions
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41
ldm/dream/image_util.py
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@ -8,11 +8,10 @@ class InitImageResizer():
def resize(self,width=None,height=None) -> Image: def resize(self,width=None,height=None) -> Image:
""" """
Return a copy of the image resized to width x height. Return a copy of the image resized to fit within
The aspect ratio is maintained, with any excess space a box width x height. The aspect ratio is
filled using black borders (i.e. letterboxed). If maintained. If neither width nor height are provided,
neither width nor height are provided, then returns then returns a copy of the original image. If one or the other is
a copy of the original image. If one or the other is
provided, then the other will be calculated from the provided, then the other will be calculated from the
aspect ratio. aspect ratio.
@ -21,38 +20,34 @@ class InitImageResizer():
""" """
im = self.image im = self.image
if not(width or height): ar = im.width/float(im.height)
return im.copy()
ar = im.width/im.height
# Infer missing values from aspect ratio # Infer missing values from aspect ratio
if not height: # height missing if not(width or height): # both missing
width = im.width
height = im.height
elif not height: # height missing
height = int(width/ar) height = int(width/ar)
if not width: # width missing elif not width: # width missing
width = int(height*ar) width = int(height*ar)
# rw and rh are the resizing width and height for the image # rw and rh are the resizing width and height for the image
# they maintain the aspect ratio, but may not completelyl fill up # they maintain the aspect ratio, but may not completelyl fill up
# the requested destination size # the requested destination size
(rw,rh) = (width,int(width/ar)) if im.width>=im.height else (int(height*ar),width) (rw,rh) = (width,int(width/ar)) if im.width>=im.height else (int(height*ar),height)
#round everything to multiples of 64 #round everything to multiples of 64
width,height,rw,rh = map( width,height,rw,rh = map(
lambda x: x-x%64, (width,height,rw,rh) lambda x: x-x%64, (width,height,rw,rh)
) )
# resize the original image so that it fits inside the dest # no resize necessary, but return a copy
if im.width == width and im.height == height:
return im.copy()
# otherwise resize the original image so that it fits inside the bounding box
resized_image = self.image.resize((rw,rh),resample=Image.Resampling.LANCZOS) resized_image = self.image.resize((rw,rh),resample=Image.Resampling.LANCZOS)
return resized_image
# create new destination image of specified dimensions
# and paste the resized image into it centered appropriately
new_image = Image.new('RGB',(width,height))
new_image.paste(resized_image,((width-rw)//2,(height-rh)//2))
print(f'>> Resized image size to {width}x{height}')
return new_image
def make_grid(image_list, rows=None, cols=None): def make_grid(image_list, rows=None, cols=None):
image_cnt = len(image_list) image_cnt = len(image_list)

2
ldm/dream/pngwriter.py
View File

@ -61,6 +61,8 @@ class PromptFormatter:
switches.append(f'-A{opt.sampler_name or t2i.sampler_name}') switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
if opt.init_img: if opt.init_img:
switches.append(f'-I{opt.init_img}') switches.append(f'-I{opt.init_img}')
if opt.fit:
switches.append(f'--fit')
if opt.strength and opt.init_img is not None: if opt.strength and opt.init_img is not None:
switches.append(f'-f{opt.strength or t2i.strength}') switches.append(f'-f{opt.strength or t2i.strength}')
if opt.gfpgan_strength: if opt.gfpgan_strength:

14
ldm/dream/server.py
View File

@ -70,6 +70,7 @@ class DreamServer(BaseHTTPRequestHandler):
steps = int(post_data['steps']) steps = int(post_data['steps'])
width = int(post_data['width']) width = int(post_data['width'])
height = int(post_data['height']) height = int(post_data['height'])
fit = 'fit' in post_data
cfgscale = float(post_data['cfgscale']) cfgscale = float(post_data['cfgscale'])
sampler_name = post_data['sampler'] sampler_name = post_data['sampler']
gfpgan_strength = float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0 gfpgan_strength = float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0
@ -80,7 +81,7 @@ class DreamServer(BaseHTTPRequestHandler):
seed = self.model.seed if int(post_data['seed']) == -1 else int(post_data['seed']) seed = self.model.seed if int(post_data['seed']) == -1 else int(post_data['seed'])
self.canceled.clear() self.canceled.clear()
print(f"Request to generate with prompt: {prompt}") print(f">> Request to generate with prompt: {prompt}")
# In order to handle upscaled images, the PngWriter needs to maintain state # In order to handle upscaled images, the PngWriter needs to maintain state
# across images generated by each call to prompt2img(), so we define it in # across images generated by each call to prompt2img(), so we define it in
# the outer scope of image_done() # the outer scope of image_done()
@ -177,10 +178,13 @@ class DreamServer(BaseHTTPRequestHandler):
init_img = "./img2img-tmp.png", init_img = "./img2img-tmp.png",
strength = strength, strength = strength,
iterations = iterations, iterations = iterations,
cfg_scale = cfgscale, cfg_scale = cfgscale,
seed = seed, seed = seed,
steps = steps, steps = steps,
sampler_name = sampler_name, sampler_name = sampler_name,
width = width,
height = height,
fit = fit,
gfpgan_strength=gfpgan_strength, gfpgan_strength=gfpgan_strength,
upscale = upscale, upscale = upscale,
step_callback=image_progress, step_callback=image_progress,
@ -192,8 +196,6 @@ class DreamServer(BaseHTTPRequestHandler):
print(f"Canceled.") print(f"Canceled.")
return return
print(f"Prompt generated!")
class ThreadingDreamServer(ThreadingHTTPServer): class ThreadingDreamServer(ThreadingHTTPServer):
def __init__(self, server_address): def __init__(self, server_address):

10
ldm/gfpgan/gfpgan_tools.py
View File

@ -14,7 +14,7 @@ model_path = os.path.join(opt.gfpgan_dir, opt.gfpgan_model_path)
gfpgan_model_exists = os.path.isfile(model_path) gfpgan_model_exists = os.path.isfile(model_path)
def _run_gfpgan(image, strength, prompt, seed, upsampler_scale=4): def _run_gfpgan(image, strength, prompt, seed, upsampler_scale=4):
print(f'\n* GFPGAN - Restoring Faces: {prompt} : seed:{seed}') print(f'>> GFPGAN - Restoring Faces: {prompt} : seed:{seed}')
gfpgan = None gfpgan = None
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning) warnings.filterwarnings('ignore', category=DeprecationWarning)
@ -41,12 +41,12 @@ def _run_gfpgan(image, strength, prompt, seed, upsampler_scale=4):
except Exception: except Exception:
import traceback import traceback
print('Error loading GFPGAN:', file=sys.stderr) print('>> Error loading GFPGAN:', file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
if gfpgan is None: if gfpgan is None:
print( print(
f'GFPGAN not initialized, it must be loaded via the --gfpgan argument' f'>> GFPGAN not initialized, it must be loaded via the --gfpgan argument'
) )
return image return image
@ -129,7 +129,7 @@ def _load_gfpgan_bg_upsampler(bg_upsampler, upsampler_scale, bg_tile=400):
def real_esrgan_upscale(image, strength, upsampler_scale, prompt, seed): def real_esrgan_upscale(image, strength, upsampler_scale, prompt, seed):
print( print(
f'\n* Real-ESRGAN Upscaling: {prompt} : seed:{seed} : scale:{upsampler_scale}x' f'>> Real-ESRGAN Upscaling: {prompt} : seed:{seed} : scale:{upsampler_scale}x'
) )
with warnings.catch_warnings(): with warnings.catch_warnings():
@ -143,7 +143,7 @@ def real_esrgan_upscale(image, strength, upsampler_scale, prompt, seed):
except Exception: except Exception:
import traceback import traceback
print('Error loading Real-ESRGAN:', file=sys.stderr) print('>> Error loading Real-ESRGAN:', file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
output, img_mode = upsampler.enhance( output, img_mode = upsampler.enhance(

173
ldm/simplet2i.py
View File

@ -194,29 +194,29 @@ class T2I:
return self.prompt2png(prompt, outdir, **kwargs) return self.prompt2png(prompt, outdir, **kwargs)
def prompt2image( def prompt2image(
self, self,
# these are common # these are common
prompt, prompt,
iterations=None, iterations = None,
steps=None, steps = None,
seed=None, seed = None,
cfg_scale=None, cfg_scale = None,
ddim_eta=None, ddim_eta = None,
skip_normalize=False, skip_normalize = False,
image_callback=None, image_callback = None,
step_callback=None, step_callback = None,
width=None, width = None,
height=None, height = None,
# these are specific to img2img # these are specific to img2img
init_img=None, init_img = None,
strength=None, fit = False,
gfpgan_strength=0, strength = None,
save_original=False, gfpgan_strength= 0,
upscale=None, save_original = False,
variants=None, upscale = None,
sampler_name=None, sampler_name = None,
log_tokenization=False, log_tokenization= False,
**args, **args,
): # eat up additional cruft ): # eat up additional cruft
""" """
ldm.prompt2image() is the common entry point for txt2img() and img2img() ldm.prompt2image() is the common entry point for txt2img() and img2img()
@ -232,7 +232,6 @@ class T2I:
strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
gfpgan_strength // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely gfpgan_strength // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely
ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image) ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
variants // if >0, the 1st generated image will be passed back to img2img to generate the requested number of variants
step_callback // a function or method that will be called each step step_callback // a function or method that will be called each step
image_callback // a function or method that will be called each time an image is generated image_callback // a function or method that will be called each time an image is generated
@ -251,14 +250,14 @@ class T2I:
to create the requested output directory, select a unique informative name for each image, and to create the requested output directory, select a unique informative name for each image, and
write the prompt into the PNG metadata. write the prompt into the PNG metadata.
""" """
steps = steps or self.steps steps = steps or self.steps
seed = seed or self.seed seed = seed or self.seed
width = width or self.width width = width or self.width
height = height or self.height height = height or self.height
cfg_scale = cfg_scale or self.cfg_scale cfg_scale = cfg_scale or self.cfg_scale
ddim_eta = ddim_eta or self.ddim_eta ddim_eta = ddim_eta or self.ddim_eta
iterations = iterations or self.iterations iterations = iterations or self.iterations
strength = strength or self.strength strength = strength or self.strength
self.log_tokenization = log_tokenization self.log_tokenization = log_tokenization
model = ( model = (
@ -269,9 +268,7 @@ class T2I:
0.0 <= strength <= 1.0 0.0 <= strength <= 1.0
), 'can only work with strength in [0.0, 1.0]' ), 'can only work with strength in [0.0, 1.0]'
if not(width == self.width and height == self.height): width, height, _ = self._resolution_check(width, height, log=True)
width, height, _ = self._resolution_check(width, height, log=True)
scope = autocast if self.precision == 'autocast' else nullcontext scope = autocast if self.precision == 'autocast' else nullcontext
if sampler_name and (sampler_name != self.sampler_name): if sampler_name and (sampler_name != self.sampler_name):
@ -295,6 +292,7 @@ class T2I:
init_img=init_img, init_img=init_img,
width=width, width=width,
height=height, height=height,
fit=fit,
strength=strength, strength=strength,
callback=step_callback, callback=step_callback,
) )
@ -312,7 +310,7 @@ class T2I:
) )
with scope(self.device.type), self.model.ema_scope(): with scope(self.device.type), self.model.ema_scope():
for n in trange(iterations, desc='Generating'): for n in trange(iterations, desc='>> Generating'):
seed_everything(seed) seed_everything(seed)
image = next(images_iterator) image = next(images_iterator)
results.append([image, seed]) results.append([image, seed])
@ -365,12 +363,12 @@ class T2I:
print('Are you sure your system has an adequate NVIDIA GPU?') print('Are you sure your system has an adequate NVIDIA GPU?')
toc = time.time() toc = time.time()
print('Usage stats:') print('>> Usage stats:')
print( print(
f' {len(results)} image(s) generated in', '%4.2fs' % (toc - tic) f'>> {len(results)} image(s) generated in', '%4.2fs' % (toc - tic)
) )
print( print(
f' Max VRAM used for this generation:', f'>> Max VRAM used for this generation:',
'%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9), '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
) )
@ -379,7 +377,7 @@ class T2I:
self.session_peakmem, torch.cuda.max_memory_allocated() self.session_peakmem, torch.cuda.max_memory_allocated()
) )
print( print(
f' Max VRAM used since script start: ', f'>> Max VRAM used since script start: ',
'%4.2fG' % (self.session_peakmem / 1e9), '%4.2fG' % (self.session_peakmem / 1e9),
) )
return results return results
@ -425,18 +423,19 @@ class T2I:
@torch.no_grad() @torch.no_grad()
def _img2img( def _img2img(
self, self,
prompt, prompt,
precision_scope, precision_scope,
steps, steps,
cfg_scale, cfg_scale,
ddim_eta, ddim_eta,
skip_normalize, skip_normalize,
init_img, init_img,
width, width,
height, height,
strength, fit,
callback, # Currently not implemented for img2img strength,
callback, # Currently not implemented for img2img
): ):
""" """
An infinite iterator of images from the prompt and the initial image An infinite iterator of images from the prompt and the initial image
@ -445,13 +444,13 @@ class T2I:
# PLMS sampler not supported yet, so ignore previous sampler # PLMS sampler not supported yet, so ignore previous sampler
if self.sampler_name != 'ddim': if self.sampler_name != 'ddim':
print( print(
f"sampler '{self.sampler_name}' is not yet supported. Using DDIM sampler" f">> sampler '{self.sampler_name}' is not yet supported. Using DDIM sampler"
) )
sampler = DDIMSampler(self.model, device=self.device) sampler = DDIMSampler(self.model, device=self.device)
else: else:
sampler = self.sampler sampler = self.sampler
init_image = self._load_img(init_img, width, height).to(self.device) init_image = self._load_img(init_img, width, height,fit).to(self.device)
with precision_scope(self.device.type): with precision_scope(self.device.type):
init_latent = self.model.get_first_stage_encoding( init_latent = self.model.get_first_stage_encoding(
self.model.encode_first_stage(init_image) self.model.encode_first_stage(init_image)
@ -581,7 +580,7 @@ class T2I:
print(msg) print(msg)
def _load_model_from_config(self, config, ckpt): def _load_model_from_config(self, config, ckpt):
print(f'Loading model from {ckpt}') print(f'>> Loading model from {ckpt}')
pl_sd = torch.load(ckpt, map_location='cpu') pl_sd = torch.load(ckpt, map_location='cpu')
# if "global_step" in pl_sd: # if "global_step" in pl_sd:
# print(f"Global Step: {pl_sd['global_step']}") # print(f"Global Step: {pl_sd['global_step']}")
@ -596,41 +595,63 @@ class T2I:
) )
else: else:
print( print(
'Using half precision math. Call with --full_precision to use more accurate but VRAM-intensive full precision.' '>> Using half precision math. Call with --full_precision to use more accurate but VRAM-intensive full precision.'
) )
model.half() model.half()
return model return model
def _load_img(self, path, width, height): def _load_img(self, path, width, height, fit=False):
print(f'image path = {path}, cwd = {os.getcwd()}')
with Image.open(path) as img: with Image.open(path) as img:
image = img.convert('RGB') image = img.convert('RGB')
print( print(
f'loaded input image of size {image.width}x{image.height} from {path}') f'>> loaded input image of size {image.width}x{image.height} from {path}'
)
from ldm.dream.image_util import InitImageResizer # The logic here is:
if width == self.width and height == self.height: # 1. If "fit" is true, then the image will be fit into the bounding box defined
new_image_width, new_image_height, resize_needed = self._resolution_check( # by width and height. It will do this in a way that preserves the init image's
image.width, image.height) # aspect ratio while preventing letterboxing. This means that if there is
# leftover horizontal space after rescaling the image to fit in the bounding box,
# the generated image's width will be reduced to the rescaled init image's width.
# Similarly for the vertical space.
# 2. Otherwise, if "fit" is false, then the image will be scaled, preserving its
# aspect ratio, to the nearest multiple of 64. Large images may generate an
# unexpected OOM error.
if fit:
image = self._fit_image(image,(width,height))
else: else:
if height == self.height: image = self._squeeze_image(image)
new_image_width, new_image_height, resize_needed = self._resolution_check(
width, image.height)
if width == self.width:
new_image_width, new_image_height, resize_needed = self._resolution_check(
image.width, height)
else:
image = InitImageResizer(image).resize(width, height)
resize_needed=False
if resize_needed:
image = InitImageResizer(image).resize(
new_image_width, new_image_height)
image = np.array(image).astype(np.float32) / 255.0 image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2) image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image) image = torch.from_numpy(image)
return 2.0 * image - 1.0 return 2.0 * image - 1.0
def _squeeze_image(self,image):
x,y,resize_needed = self._resolution_check(image.width,image.height)
if resize_needed:
return InitImageResizer(image).resize(x,y)
return image
def _fit_image(self,image,max_dimensions):
w,h = max_dimensions
print(
f'>> image will be resized to fit inside a box {w}x{h} in size.'
)
if image.width > image.height:
h = None # by setting h to none, we tell InitImageResizer to fit into the width and calculate height
elif image.height > image.width:
w = None # ditto for w
else:
pass
image = InitImageResizer(image).resize(w,h) # note that InitImageResizer does the multiple of 64 truncation internally
print(
f'>> after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}'
)
return image
# TO DO: Move this and related weighted subprompt code into its own module.
def _split_weighted_subprompts(text, skip_normalize=False): def _split_weighted_subprompts(text, skip_normalize=False):
""" """
grabs all text up to the first occurrence of ':' grabs all text up to the first occurrence of ':'
@ -698,6 +719,6 @@ class T2I:
f'>> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}' f'>> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}'
) )
height = h height = h
width = w width = w
resize_needed = True resize_needed = True
return width, height, resize_needed return width, height, resize_needed

9
scripts/dream.py
View File

@ -88,7 +88,7 @@ def main():
tic = time.time() tic = time.time()
t2i.load_model() t2i.load_model()
print( print(
f'model loaded in', '%4.2fs' % (time.time() - tic) f'>> model loaded in', '%4.2fs' % (time.time() - tic)
) )
if not infile: if not infile:
@ -483,6 +483,13 @@ def create_cmd_parser():
type=str, type=str,
help='Path to input image for img2img mode (supersedes width and height)', help='Path to input image for img2img mode (supersedes width and height)',
) )
parser.add_argument(
'-T',
'-fit',
'--fit',
action='store_true',
help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
)
parser.add_argument( parser.add_argument(
'-f', '-f',
'--strength', '--strength',

21
static/dream_web/index.css
View File

@ -8,13 +8,15 @@
margin-top: 20vh; margin-top: 20vh;
margin-left: auto; margin-left: auto;
margin-right: auto; margin-right: auto;
max-width: 800px; max-width: 1024px;
text-align: center; text-align: center;
} }
fieldset { fieldset {
border: none; border: none;
} }
div {
padding: 10px 10px 10px 10px;
}
#fieldset-search { #fieldset-search {
display: flex; display: flex;
} }
@ -78,3 +80,18 @@ label {
cursor: pointer; cursor: pointer;
color: red; color: red;
} }
#txt2img {
background-color: #DCDCDC;
}
#img2img {
background-color: #F5F5F5;
}
#gfpgan {
background-color: #DCDCDC;
}
#progress-section {
background-color: #F5F5F5;
}
#about {
background-color: #DCDCDC;
}

140
static/dream_web/index.html
View File

@ -14,78 +14,84 @@
<h2 id="header">Stable Diffusion Dream Server</h2> <h2 id="header">Stable Diffusion Dream Server</h2>
<form id="generate-form" method="post" action="#"> <form id="generate-form" method="post" action="#">
<fieldset id="fieldset-search"> <div id="txt2img">
<input type="text" id="prompt" name="prompt"> <fieldset id="fieldset-search">
<input type="submit" id="submit" value="Generate"> <input type="text" id="prompt" name="prompt">
</fieldset> <input type="submit" id="submit" value="Generate">
<fieldset id="fieldset-config"> </fieldset>
<label for="iterations">Images to generate:</label> <fieldset id="fieldset-config">
<input value="1" type="number" id="iterations" name="iterations" size="4"> <label for="iterations">Images to generate:</label>
<label for="steps">Steps:</label> <input value="1" type="number" id="iterations" name="iterations" size="4">
<input value="50" type="number" id="steps" name="steps"> <label for="steps">Steps:</label>
<label for="cfgscale">Cfg Scale:</label> <input value="50" type="number" id="steps" name="steps">
<input value="7.5" type="number" id="cfgscale" name="cfgscale" step="any"> <label for="cfgscale">Cfg Scale:</label>
<label for="sampler">Sampler:</label> <input value="7.5" type="number" id="cfgscale" name="cfgscale" step="any">
<select id="sampler" name="sampler" value="k_lms"> <label for="sampler">Sampler:</label>
<option value="ddim">DDIM</option> <select id="sampler" name="sampler" value="k_lms">
<option value="plms">PLMS</option> <option value="ddim">DDIM</option>
<option value="k_lms" selected>KLMS</option> <option value="plms">PLMS</option>
<option value="k_dpm_2">KDPM_2</option> <option value="k_lms" selected>KLMS</option>
<option value="k_dpm_2_a">KDPM_2A</option> <option value="k_dpm_2">KDPM_2</option>
<option value="k_euler">KEULER</option> <option value="k_dpm_2_a">KDPM_2A</option>
<option value="k_euler_a">KEULER_A</option> <option value="k_euler">KEULER</option>
<option value="k_heun">KHEUN</option> <option value="k_euler_a">KEULER_A</option>
</select> <option value="k_heun">KHEUN</option>
<br> </select>
<label title="Set to multiple of 64" for="width">Width:</label> <br>
<select id="width" name="width" value="512"> <label title="Set to multiple of 64" for="width">Width:</label>
<option value="64">64</option> <option value="128">128</option> <select id="width" name="width" value="512">
<option value="192">192</option> <option value="256">256</option> <option value="64">64</option> <option value="128">128</option>
<option value="320">320</option> <option value="384">384</option> <option value="192">192</option> <option value="256">256</option>
<option value="448">448</option> <option value="512" selected>512</option> <option value="320">320</option> <option value="384">384</option>
<option value="576">576</option> <option value="640">640</option> <option value="448">448</option> <option value="512" selected>512</option>
<option value="704">704</option> <option value="768">768</option> <option value="576">576</option> <option value="640">640</option>
<option value="832">832</option> <option value="896">896</option> <option value="704">704</option> <option value="768">768</option>
<option value="960">960</option> <option value="1024">1024</option> <option value="832">832</option> <option value="896">896</option>
</select> <option value="960">960</option> <option value="1024">1024</option>
<label title="Set to multiple of 64" for="height">Height:</label> </select>
<select id="height" name="height" value="512"> <label title="Set to multiple of 64" for="height">Height:</label>
<option value="64">64</option> <option value="128">128</option> <select id="height" name="height" value="512">
<option value="192">192</option> <option value="256">256</option> <option value="64">64</option> <option value="128">128</option>
<option value="320">320</option> <option value="384">384</option> <option value="192">192</option> <option value="256">256</option>
<option value="448">448</option> <option value="512" selected>512</option> <option value="320">320</option> <option value="384">384</option>
<option value="576">576</option> <option value="640">640</option> <option value="448">448</option> <option value="512" selected>512</option>
<option value="704">704</option> <option value="768">768</option> <option value="576">576</option> <option value="640">640</option>
<option value="832">832</option> <option value="896">896</option> <option value="704">704</option> <option value="768">768</option>
<option value="960">960</option> <option value="1024">1024</option> <option value="832">832</option> <option value="896">896</option>
</select> <option value="960">960</option> <option value="1024">1024</option>
<label title="Set to -1 for random seed" for="seed">Seed:</label> </select>
<input value="-1" type="number" id="seed" name="seed"> <label title="Set to -1 for random seed" for="seed">Seed:</label>
<button type="button" id="reset-seed">&olarr;</button> <input value="-1" type="number" id="seed" name="seed">
<br> <button type="button" id="reset-seed">&olarr;</button>
<input type="checkbox" name="progress_images" id="progress_images">
<label for="progress_images">Display in-progress images (slows down generation):</label>
<button type="button" id="reset-all">Reset to Defaults</button>
</div>
<div id="img2img">
<label title="Upload an image to use img2img" for="initimg">Initial image:</label>
<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
<br>
<label for="strength">Img2Img Strength:</label> <label for="strength">Img2Img Strength:</label>
<input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1"> <input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
<label title="Upload an image to use img2img" for="initimg">Init:</label> <input type="checkbox" id="fit" name="fit" checked>
<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png"> <label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height:</label>
<button type="button" id="reset-all">Reset to Defaults</button> </div>
<br> <div id="gfpgan">
<label for="progress_images">Display in-progress images (slows down generation):</label> <label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
<input type="checkbox" name="progress_images" id="progress_images"> <input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05">
<div id="gfpgan"> <label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
<label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label> <select id="upscale_level" name="upscale_level" value="">
<input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05"> <option value="" selected>None</option>
<label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label> <option value="2">2x</option>
<select id="upscale_level" name="upscale_level" value=""> <option value="4">4x</option>
<option value="" selected>None</option> </select>
<option value="2">2x</option> <label title="Strength of the esrgan (upscaling) algorithm." for="upscale_strength">Upscale Strength:</label>
<option value="4">4x</option> <input value="0.75" min="0" max="1" type="number" id="upscale_strength" name="upscale_strength" step="0.05">
</select> </div>
<label title="Strength of the esrgan (upscaling) algorithm." for="upscale_strength">Upscale Strength:</label>
<input value="0.75" min="0" max="1" type="number" id="upscale_strength" name="upscale_strength" step="0.05">
</div>
</fieldset> </fieldset>
</form> </form>
<div id="about">For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a></div> <div id="about">For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a></div>
<br>
<div id="progress-section"> <div id="progress-section">
<progress id="progress-bar" value="0" max="1"></progress> <progress id="progress-bar" value="0" max="1"></progress>
<span id="cancel-button" title="Cancel">&#10006;</span> <span id="cancel-button" title="Cancel">&#10006;</span>