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Merge remote-tracking branch 'upstream/master' into ModelLoader

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This commit is contained in:
d8ahazard 2022-09-27 11:02:41 -05:00
commit 5756d517a6
13 changed files with 258 additions and 34 deletions
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2
.gitignore vendored
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@ -21,3 +21,5 @@ __pycache__
/interrogate
/user.css
/.idea
notification.mp3
/SwinIR

24
javascript/dragdrop.js vendored
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@ -68,13 +68,19 @@ window.addEventListener('paste', e => {
if ( ! isValidImageList( files ) ) {
return;
}
[...gradioApp().querySelectorAll('input[type=file][accept="image/x-png,image/gif,image/jpeg"]')]
.filter(input => !input.matches('.\\!hidden input[type=file]'))
.forEach(input => {
input.files = files;
input.dispatchEvent(new Event('change'))
});
[...gradioApp().querySelectorAll('[data-testid="image"]')]
.filter(imgWrap => !imgWrap.closest('.\\!hidden'))
.forEach(imgWrap => dropReplaceImage( imgWrap, files ));
const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
.filter(el => uiElementIsVisible(el));
if ( ! visibleImageFields.length ) {
return;
}
const firstFreeImageField = visibleImageFields
.filter(el => el.querySelector('input[type=file]'))?.[0];
dropReplaceImage(
firstFreeImageField ?
firstFreeImageField :
visibleImageFields[visibleImageFields.length - 1]
, files );
});

3
javascript/notification.js
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@ -25,6 +25,9 @@ onUiUpdate(function(){
lastHeadImg = headImg;
// play notification sound if available
gradioApp().querySelector('#audio_notification audio')?.play();
if (document.hasFocus()) return;
// Multiple copies of the images are in the DOM when one is selected. Dedup with a Set to get the real number generated.

5
javascript/ui.js
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@ -1,9 +1,8 @@
// various functions for interation with ui.py not large enough to warrant putting them in separate files
function selected_gallery_index(){
var gr = gradioApp()
var buttons = gradioApp().querySelectorAll(".gallery-item")
var button = gr.querySelector(".gallery-item.\\!ring-2")
var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
var result = -1
buttons.forEach(function(v, i){ if(v==button) { result = i } })

57
modules/extras.py
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@ -3,6 +3,9 @@ import os
import numpy as np
from PIL import Image
import torch
import tqdm
from modules import processing, shared, images, devices
from modules.shared import opts
import modules.gfpgan_model
@ -137,3 +140,57 @@ def run_pnginfo(image):
info = f"<div><p>{message}<p></div>"
return '', geninfo, info
def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
def weighted_sum(theta0, theta1, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
# Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
def sigmoid(theta0, theta1, alpha):
alpha = alpha * alpha * (3 - (2 * alpha))
return theta0 + ((theta1 - theta0) * alpha)
if os.path.exists(modelname_0):
model0_filename = modelname_0
modelname_0 = os.path.splitext(os.path.basename(modelname_0))[0]
else:
model0_filename = 'models/' + modelname_0 + '.ckpt'
if os.path.exists(modelname_1):
model1_filename = modelname_1
modelname_1 = os.path.splitext(os.path.basename(modelname_1))[0]
else:
model1_filename = 'models/' + modelname_1 + '.ckpt'
print(f"Loading {model0_filename}...")
model_0 = torch.load(model0_filename, map_location='cpu')
print(f"Loading {model1_filename}...")
model_1 = torch.load(model1_filename, map_location='cpu')
theta_0 = model_0['state_dict']
theta_1 = model_1['state_dict']
theta_funcs = {
"Weighted Sum": weighted_sum,
"Sigmoid": sigmoid,
}
theta_func = theta_funcs[interp_method]
print(f"Merging...")
for key in tqdm.tqdm(theta_0.keys()):
if 'model' in key and key in theta_1:
theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)
for key in theta_1.keys():
if 'model' in key and key not in theta_0:
theta_0[key] = theta_1[key]
output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt'
print(f"Saving to {output_modelname}...")
torch.save(model_0, output_modelname)
print(f"Checkpoint saved.")
return "Checkpoint saved to " + output_modelname

18
modules/processing.py
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@ -78,7 +78,14 @@ class StableDiffusionProcessing:
self.paste_to = None
self.color_corrections = None
self.denoising_strength: float = 0
self.ddim_eta = opts.ddim_eta
self.ddim_discretize = opts.ddim_discretize
self.s_churn = opts.s_churn
self.s_tmin = opts.s_tmin
self.s_tmax = float('inf') # not representable as a standard ui option
self.s_noise = opts.s_noise
if not seed_enable_extras:
self.subseed = -1
self.subseed_strength = 0
@ -117,6 +124,13 @@ class Processed:
self.extra_generation_params = p.extra_generation_params
self.index_of_first_image = index_of_first_image
self.ddim_eta = p.ddim_eta
self.ddim_discretize = p.ddim_discretize
self.s_churn = p.s_churn
self.s_tmin = p.s_tmin
self.s_tmax = p.s_tmax
self.s_noise = p.s_noise
self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
@ -406,7 +420,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
index_of_first_image = 1
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p)
images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
devices.torch_gc()
return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)

28
modules/sd_samplers.py
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@ -37,6 +37,11 @@ samplers = [
]
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
sampler_extra_params = {
'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
}
def setup_img2img_steps(p, steps=None):
if opts.img2img_fix_steps or steps is not None:
@ -120,9 +125,9 @@ class VanillaStableDiffusionSampler:
# existing code fails with cetain step counts, like 9
try:
self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
except Exception:
self.sampler.make_schedule(ddim_num_steps=steps+1, verbose=False)
self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
@ -149,9 +154,9 @@ class VanillaStableDiffusionSampler:
# existing code fails with cetin step counts, like 9
try:
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
except Exception:
samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
return samples_ddim
@ -224,6 +229,7 @@ class KDiffusionSampler:
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
self.funcname = funcname
self.func = getattr(k_diffusion.sampling, self.funcname)
self.extra_params = sampler_extra_params.get(funcname,[])
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
self.sampler_noises = None
self.sampler_noise_index = 0
@ -269,7 +275,12 @@ class KDiffusionSampler:
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
extra_params_kwargs = {}
for val in self.extra_params:
if hasattr(p,val):
extra_params_kwargs[val] = getattr(p,val)
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
steps = steps or p.steps
@ -286,7 +297,12 @@ class KDiffusionSampler:
if self.sampler_noises is not None:
k_diffusion.sampling.torch = TorchHijack(self)
samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
extra_params_kwargs = {}
for val in self.extra_params:
if hasattr(p,val):
extra_params_kwargs[val] = getattr(p,val)
samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
return samples

10
modules/shared.py
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@ -76,7 +76,7 @@ class State:
job = ""
job_no = 0
job_count = 0
job_timestamp = 0
job_timestamp = '0'
sampling_step = 0
sampling_steps = 0
current_latent = None
@ -90,6 +90,7 @@ class State:
self.job_no += 1
self.sampling_step = 0
self.current_image_sampling_step = 0
def get_job_timestamp(self):
return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
@ -229,6 +230,13 @@ options_templates.update(options_section(('ui', "User interface"), {
"js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"ddim_eta": OptionInfo(0.0, "DDIM eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
}))
class Options:
data = None

40
modules/ui.py
View File

@ -49,6 +49,7 @@ sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
css_hide_progressbar = """
.wrap .m-12 svg { display:none!important; }
.wrap .m-12::before { content:"Loading..." }
.progress-bar { display:none!important; }
.meta-text { display:none!important; }
"""
@ -393,7 +394,7 @@ def setup_progressbar(progressbar, preview, id_part):
)
def create_ui(txt2img, img2img, run_extras, run_pnginfo):
def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
dummy_component = gr.Label(visible=False)
@ -564,13 +565,13 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
with gr.TabItem('Inpaint', id='inpaint'):
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False)
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False)
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
with gr.Row():
mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask")
mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
@ -853,6 +854,33 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
outputs=[html, generation_info, html2],
)
with gr.Blocks() as modelmerger_interface:
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>/models</b> directory.</p>")
modelname_0 = gr.Textbox(elem_id="modelmerger_modelname_0", label="Model Name (to)")
modelname_1 = gr.Textbox(elem_id="modelmerger_modelname_1", label="Model Name (from)")
interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid"], value="Weighted Sum", label="Interpolation Method")
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
submit = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
with gr.Column(variant='panel'):
submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
submit.click(
fn=run_modelmerger,
inputs=[
modelname_0,
modelname_1,
interp_method,
interp_amount
],
outputs=[
submit_result,
]
)
def create_setting_component(key):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
@ -950,6 +978,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
(img2img_interface, "img2img", "img2img"),
(extras_interface, "Extras", "extras"),
(pnginfo_interface, "PNG Info", "pnginfo"),
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
(settings_interface, "Settings", "settings"),
]
@ -970,6 +999,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
for interface, label, ifid in interfaces:
with gr.TabItem(label, id=ifid):
interface.render()
if os.path.exists(os.path.join(script_path, "notification.mp3")):
audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
settings_submit.click(

21
script.js
View File

@ -39,3 +39,24 @@ document.addEventListener("DOMContentLoaded", function() {
});
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
});
/**
* checks that a UI element is not in another hidden element or tab content
*/
function uiElementIsVisible(el) {
let isVisible = !el.closest('.\\!hidden');
if ( ! isVisible ) {
return false;
}
while( isVisible = el.closest('.tabitem')?.style.display !== 'none' ) {
if ( ! isVisible ) {
return false;
} else if ( el.parentElement ) {
el = el.parentElement
} else {
break;
}
}
return isVisible;
}

68
scripts/img2imgalt.py
View File

@ -59,7 +59,55 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
return x / x.std()
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt"])
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])
# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
x = p.init_latent
s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(shared.sd_model)
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
for i in trange(1, len(sigmas)):
shared.state.sampling_step += 1
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
if i == 1:
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
else:
t = dnw.sigma_to_t(sigma_in)
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
if i == 1:
d = (x - denoised) / (2 * sigmas[i])
else:
d = (x - denoised) / sigmas[i - 1]
dt = sigmas[i] - sigmas[i - 1]
x = x + d * dt
sd_samplers.store_latent(x)
# This shouldn't be necessary, but solved some VRAM issues
del x_in, sigma_in, cond_in, c_out, c_in, t,
del eps, denoised_uncond, denoised_cond, denoised, d, dt
shared.state.nextjob()
return x / sigmas[-1]
class Script(scripts.Script):
@ -78,9 +126,10 @@ class Script(scripts.Script):
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
return [original_prompt, original_negative_prompt, cfg, st, randomness]
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness):
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
p.batch_size = 1
p.batch_count = 1
@ -88,7 +137,10 @@ class Script(scripts.Script):
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
lat = (p.init_latent.cpu().numpy() * 10).astype(int)
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt and self.cache.original_negative_prompt == original_negative_prompt
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
and self.cache.original_prompt == original_prompt \
and self.cache.original_negative_prompt == original_negative_prompt \
and self.cache.sigma_adjustment == sigma_adjustment
same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
if same_everything:
@ -97,8 +149,11 @@ class Script(scripts.Script):
shared.state.job_count += 1
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt)
if sigma_adjustment:
rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
else:
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
@ -121,6 +176,7 @@ class Script(scripts.Script):
p.extra_generation_params["Decode CFG scale"] = cfg
p.extra_generation_params["Decode steps"] = st
p.extra_generation_params["Randomness"] = randomness
p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
processed = processing.process_images(p)

13
scripts/xy_grid.py
View File

@ -2,6 +2,7 @@ from collections import namedtuple
from copy import copy
import random
from PIL import Image
import numpy as np
import modules.scripts as scripts
@ -86,7 +87,12 @@ axis_options = [
AxisOption("Prompt S/R", str, apply_prompt, format_value),
AxisOption("Sampler", str, apply_sampler, format_value),
AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
AxisOption("DDIM Eta", float, apply_field("ddim_eta"), format_value_add_label),
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),# as it is now all AxisOptionImg2Img items must go after AxisOption ones
]
@ -108,7 +114,10 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
if first_pocessed is None:
first_pocessed = processed
res.append(processed.images[0])
try:
res.append(processed.images[0])
except:
res.append(Image.new(res[0].mode, res[0].size))
grid = images.image_grid(res, rows=len(ys))
if draw_legend:

3
webui.py
View File

@ -85,7 +85,8 @@ def webui():
txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
run_pnginfo=modules.extras.run_pnginfo
run_pnginfo=modules.extras.run_pnginfo,
run_modelmerger=modules.extras.run_modelmerger
)
demo.launch(