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Merge branch 'master' of https://github.com/BlenderNeko/ComfyUI

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BlenderNeko 2023-04-28 10:58:58 +02:00
commit 805e043bce
8 changed files with 67 additions and 37 deletions
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6
comfy/extra_samplers/uni_pc.py
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@ -712,7 +712,7 @@ class UniPC:
def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform',
method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
atol=0.0078, rtol=0.05, corrector=False, atol=0.0078, rtol=0.05, corrector=False, callback=None
): ):
t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
t_T = self.noise_schedule.T if t_start is None else t_start t_T = self.noise_schedule.T if t_start is None else t_start
@ -766,6 +766,8 @@ class UniPC:
if model_x is None: if model_x is None:
model_x = self.model_fn(x, vec_t) model_x = self.model_fn(x, vec_t)
model_prev_list[-1] = model_x model_prev_list[-1] = model_x
if callback is not None:
callback(step_index, model_prev_list[-1], x)
else: else:
raise NotImplementedError() raise NotImplementedError()
if denoise_to_zero: if denoise_to_zero:
@ -877,7 +879,7 @@ def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, ex
order = min(3, len(timesteps) - 1) order = min(3, len(timesteps) - 1)
uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant) uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant)
x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True) x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback)
if not to_zero: if not to_zero:
x /= ns.marginal_alpha(timesteps[-1]) x /= ns.marginal_alpha(timesteps[-1])
return x return x

4
comfy/sample.py
View File

@ -56,7 +56,7 @@ def cleanup_additional_models(models):
for m in models: for m in models:
m.cleanup() m.cleanup()
def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None): def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None):
device = comfy.model_management.get_torch_device() device = comfy.model_management.get_torch_device()
if noise_mask is not None: if noise_mask is not None:
@ -76,7 +76,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas) samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback)
samples = samples.cpu() samples = samples.cpu()
cleanup_additional_models(models) cleanup_additional_models(models)

22
comfy/samplers.py
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@ -462,7 +462,7 @@ class KSampler:
self.sigmas = sigmas[-(steps + 1):] self.sigmas = sigmas[-(steps + 1):]
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None): def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None):
if sigmas is None: if sigmas is None:
sigmas = self.sigmas sigmas = self.sigmas
sigma_min = self.sigma_min sigma_min = self.sigma_min
@ -527,9 +527,9 @@ class KSampler:
with precision_scope(model_management.get_autocast_device(self.device)): with precision_scope(model_management.get_autocast_device(self.device)):
if self.sampler == "uni_pc": if self.sampler == "uni_pc":
samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask) samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, callback=callback)
elif self.sampler == "uni_pc_bh2": elif self.sampler == "uni_pc_bh2":
samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, variant='bh2') samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2')
elif self.sampler == "ddim": elif self.sampler == "ddim":
timesteps = [] timesteps = []
for s in range(sigmas.shape[0]): for s in range(sigmas.shape[0]):
@ -537,6 +537,11 @@ class KSampler:
noise_mask = None noise_mask = None
if denoise_mask is not None: if denoise_mask is not None:
noise_mask = 1.0 - denoise_mask noise_mask = 1.0 - denoise_mask
ddim_callback = None
if callback is not None:
ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None)
sampler = DDIMSampler(self.model, device=self.device) sampler = DDIMSampler(self.model, device=self.device)
sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False) sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False)
z_enc = sampler.stochastic_encode(latent_image, torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(self.device), noise=noise, max_denoise=max_denoise) z_enc = sampler.stochastic_encode(latent_image, torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(self.device), noise=noise, max_denoise=max_denoise)
@ -550,6 +555,7 @@ class KSampler:
eta=0.0, eta=0.0,
x_T=z_enc, x_T=z_enc,
x0=latent_image, x0=latent_image,
img_callback=ddim_callback,
denoise_function=sampling_function, denoise_function=sampling_function,
extra_args=extra_args, extra_args=extra_args,
mask=noise_mask, mask=noise_mask,
@ -563,13 +569,17 @@ class KSampler:
noise = noise * sigmas[0] noise = noise * sigmas[0]
k_callback = None
if callback is not None:
k_callback = lambda x: callback(x["i"], x["denoised"], x["x"])
if latent_image is not None: if latent_image is not None:
noise += latent_image noise += latent_image
if self.sampler == "dpm_fast": if self.sampler == "dpm_fast":
samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args=extra_args) samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args=extra_args, callback=k_callback)
elif self.sampler == "dpm_adaptive": elif self.sampler == "dpm_adaptive":
samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args=extra_args) samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback)
else: else:
samples = getattr(k_diffusion_sampling, "sample_{}".format(self.sampler))(self.model_k, noise, sigmas, extra_args=extra_args) samples = getattr(k_diffusion_sampling, "sample_{}".format(self.sampler))(self.model_k, noise, sigmas, extra_args=extra_args, callback=k_callback)
return samples.to(torch.float32) return samples.to(torch.float32)

5
comfy_extras/chainner_models/architecture/block.py
View File

@ -4,7 +4,10 @@
from __future__ import annotations from __future__ import annotations
from collections import OrderedDict from collections import OrderedDict
from typing import Literal try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
import torch import torch
import torch.nn as nn import torch.nn as nn

55
execution.py
View File

@ -149,40 +149,44 @@ def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item
is_changed_old = '' is_changed_old = ''
is_changed = '' is_changed = ''
to_delete = False
if hasattr(class_def, 'IS_CHANGED'): if hasattr(class_def, 'IS_CHANGED'):
if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]: if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
is_changed_old = old_prompt[unique_id]['is_changed'] is_changed_old = old_prompt[unique_id]['is_changed']
if 'is_changed' not in prompt[unique_id]: if 'is_changed' not in prompt[unique_id]:
input_data_all = get_input_data(inputs, class_def, unique_id, outputs) input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
if input_data_all is not None: if input_data_all is not None:
is_changed = class_def.IS_CHANGED(**input_data_all) try:
prompt[unique_id]['is_changed'] = is_changed is_changed = class_def.IS_CHANGED(**input_data_all)
prompt[unique_id]['is_changed'] = is_changed
except:
to_delete = True
else: else:
is_changed = prompt[unique_id]['is_changed'] is_changed = prompt[unique_id]['is_changed']
if unique_id not in outputs: if unique_id not in outputs:
return True return True
to_delete = False if not to_delete:
if is_changed != is_changed_old: if is_changed != is_changed_old:
to_delete = True to_delete = True
elif unique_id not in old_prompt: elif unique_id not in old_prompt:
to_delete = True to_delete = True
elif inputs == old_prompt[unique_id]['inputs']: elif inputs == old_prompt[unique_id]['inputs']:
for x in inputs: for x in inputs:
input_data = inputs[x] input_data = inputs[x]
if isinstance(input_data, list): if isinstance(input_data, list):
input_unique_id = input_data[0] input_unique_id = input_data[0]
output_index = input_data[1] output_index = input_data[1]
if input_unique_id in outputs: if input_unique_id in outputs:
to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id) to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id)
else: else:
to_delete = True to_delete = True
if to_delete: if to_delete:
break break
else: else:
to_delete = True to_delete = True
if to_delete: if to_delete:
d = outputs.pop(unique_id) d = outputs.pop(unique_id)
@ -204,6 +208,15 @@ class PromptExecutor:
self.server.client_id = None self.server.client_id = None
with torch.inference_mode(): with torch.inference_mode():
#delete cached outputs if nodes don't exist for them
to_delete = []
for o in self.outputs:
if o not in prompt:
to_delete += [o]
for o in to_delete:
d = self.outputs.pop(o)
del d
for x in prompt: for x in prompt:
recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x) recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)

2
notebooks/comfyui_colab.ipynb
View File

@ -47,7 +47,7 @@
" !git pull\n", " !git pull\n",
"\n", "\n",
"!echo -= Install dependencies =-\n", "!echo -= Install dependencies =-\n",
"!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118" "!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117"
] ]
}, },
{ {

6
web/lib/litegraph.core.js
View File

@ -9953,11 +9953,11 @@ LGraphNode.prototype.executeAction = function(action)
} }
break; break;
case "slider": case "slider":
var range = w.options.max - w.options.min; var old_value = w.value;
var nvalue = Math.clamp((x - 15) / (widget_width - 30), 0, 1); var nvalue = Math.clamp((x - 15) / (widget_width - 30), 0, 1);
if(w.options.read_only) break; if(w.options.read_only) break;
w.value = w.options.min + (w.options.max - w.options.min) * nvalue; w.value = w.options.min + (w.options.max - w.options.min) * nvalue;
if (w.callback) { if (old_value != w.value) {
setTimeout(function() { setTimeout(function() {
inner_value_change(w, w.value); inner_value_change(w, w.value);
}, 20); }, 20);
@ -10044,7 +10044,7 @@ LGraphNode.prototype.executeAction = function(action)
if (event.click_time < 200 && delta == 0) { if (event.click_time < 200 && delta == 0) {
this.prompt("Value",w.value,function(v) { this.prompt("Value",w.value,function(v) {
// check if v is a valid equation or a number // check if v is a valid equation or a number
if (/^[0-9+\-*/()\s]+$/.test(v)) { if (/^[0-9+\-*/()\s]+|\d+\.\d+$/.test(v)) {
try {//solve the equation if possible try {//solve the equation if possible
v = eval(v); v = eval(v);
} catch (e) { } } catch (e) { }

4
web/scripts/widgets.js
View File

@ -136,9 +136,11 @@ function addMultilineWidget(node, name, opts, app) {
left: `${t.a * margin + t.e}px`, left: `${t.a * margin + t.e}px`,
top: `${t.d * (y + widgetHeight - margin - 3) + t.f}px`, top: `${t.d * (y + widgetHeight - margin - 3) + t.f}px`,
width: `${(widgetWidth - margin * 2 - 3) * t.a}px`, width: `${(widgetWidth - margin * 2 - 3) * t.a}px`,
background: (!node.color)?'':node.color,
height: `${(this.parent.inputHeight - margin * 2 - 4) * t.d}px`, height: `${(this.parent.inputHeight - margin * 2 - 4) * t.d}px`,
position: "absolute", position: "absolute",
zIndex: 1, color: (!node.color)?'':'white',
zIndex: app.graph._nodes.indexOf(node),
fontSize: `${t.d * 10.0}px`, fontSize: `${t.d * 10.0}px`,
}); });
this.inputEl.hidden = !visible; this.inputEl.hidden = !visible;