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Merge branch 'comfyanonymous:master' into weightedConditionCombine

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This commit is contained in:
FizzleDorf
2023-04-28 20:16:10 -04:00
committed by GitHub
parent a95d6e172b 2ca934f7d4
commit 03a08e86d3
31 changed files with 681 additions and 314 deletions
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65
.ci/nightly/update_windows/update.py
View File

@@ -1,65 +0,0 @@
import pygit2
from datetime import datetime
import sys
def pull(repo, remote_name='origin', branch='master'):
for remote in repo.remotes:
if remote.name == remote_name:
remote.fetch()
remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target
merge_result, _ = repo.merge_analysis(remote_master_id)
# Up to date, do nothing
if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
return
# We can just fastforward
elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:
repo.checkout_tree(repo.get(remote_master_id))
try:
master_ref = repo.lookup_reference('refs/heads/%s' % (branch))
master_ref.set_target(remote_master_id)
except KeyError:
repo.create_branch(branch, repo.get(remote_master_id))
repo.head.set_target(remote_master_id)
elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:
repo.merge(remote_master_id)
if repo.index.conflicts is not None:
for conflict in repo.index.conflicts:
print('Conflicts found in:', conflict[0].path)
raise AssertionError('Conflicts, ahhhhh!!')
user = repo.default_signature
tree = repo.index.write_tree()
commit = repo.create_commit('HEAD',
user,
user,
'Merge!',
tree,
[repo.head.target, remote_master_id])
# We need to do this or git CLI will think we are still merging.
repo.state_cleanup()
else:
raise AssertionError('Unknown merge analysis result')
repo = pygit2.Repository(str(sys.argv[1]))
ident = pygit2.Signature('comfyui', 'comfy@ui')
try:
print("stashing current changes")
repo.stash(ident)
except KeyError:
print("nothing to stash")
backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
print("creating backup branch: {}".format(backup_branch_name))
repo.branches.local.create(backup_branch_name, repo.head.peel())
print("checking out master branch")
branch = repo.lookup_branch('master')
ref = repo.lookup_reference(branch.name)
repo.checkout(ref)
print("pulling latest changes")
pull(repo)
print("Done!")

2
.ci/nightly/update_windows/update_comfyui.bat
View File

@@ -1,2 +0,0 @@
..\python_embeded\python.exe .\update.py ..\ComfyUI\
pause

2
.ci/nightly/update_windows/update_comfyui_and_python_dependencies.bat
View File

@@ -1,3 +1,3 @@
..\python_embeded\python.exe .\update.py ..\ComfyUI\
..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 -r ../ComfyUI/requirements.txt pygit2
..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu118 -r ../ComfyUI/requirements.txt pygit2
pause

27
.ci/nightly/windows_base_files/README_VERY_IMPORTANT.txt
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@@ -1,27 +0,0 @@
HOW TO RUN:
if you have a NVIDIA gpu:
run_nvidia_gpu.bat
To run it in slow CPU mode:
run_cpu.bat
IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
You can download the stable diffusion 1.5 one from: https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt
RECOMMENDED WAY TO UPDATE:
To update the ComfyUI code: update\update_comfyui.bat
To update ComfyUI with the python dependencies:
update\update_comfyui_and_python_dependencies.bat

2
.ci/nightly/windows_base_files/run_cpu.bat
View File

@@ -1,2 +0,0 @@
.\python_embeded\python.exe -s ComfyUI\main.py --cpu --windows-standalone-build
pause

2
.github/workflows/windows_release_cu118_dependencies_2.yml vendored
View File

@@ -17,7 +17,7 @@ jobs:
- shell: bash
run: |
python -m pip wheel --no-cache-dir torch torchvision torchaudio xformers==0.0.19.dev516 --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
python -m pip wheel --no-cache-dir torch torchvision torchaudio xformers --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
python -m pip install --no-cache-dir ./temp_wheel_dir/*
echo installed basic
ls -lah temp_wheel_dir

2
.github/workflows/windows_release_nightly_pytorch.yml vendored
View File

@@ -46,6 +46,8 @@ jobs:
mkdir update
cp -r ComfyUI/.ci/update_windows/* ./update/
cp -r ComfyUI/.ci/windows_base_files/* ./
cp -r ComfyUI/.ci/nightly/update_windows/* ./update/
cp -r ComfyUI/.ci/nightly/windows_base_files/* ./
cd ..

3
README.md
View File

@@ -7,6 +7,8 @@ A powerful and modular stable diffusion GUI and backend.
This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
### [Installing ComfyUI](#installing)
## Features
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
- Fully supports SD1.x and SD2.x
@@ -17,6 +19,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
- Embeddings/Textual inversion
- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
- [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
- Loading full workflows (with seeds) from generated PNG files.
- Saving/Loading workflows as Json files.
- Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.

1
comfy/cli_args.py
View File

@@ -10,6 +10,7 @@ parser.add_argument("--output-directory", type=str, default=None, help="Set the
parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")
parser.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
attn_group = parser.add_mutually_exclusive_group()
attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization instead of the sub-quadratic one. Ignored when xformers is used.")

6
comfy/extra_samplers/uni_pc.py
View File

@@ -712,7 +712,7 @@ class UniPC:
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',
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_T = self.noise_schedule.T if t_start is None else t_start
@@ -766,6 +766,8 @@ class UniPC:
if model_x is None:
model_x = self.model_fn(x, vec_t)
model_prev_list[-1] = model_x
if callback is not None:
callback(step_index, model_prev_list[-1], x)
else:
raise NotImplementedError()
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)
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:
x /= ns.marginal_alpha(timesteps[-1])
return x

70
comfy/ldm/modules/attention.py
View File

@@ -163,13 +163,17 @@ class CrossAttentionBirchSan(nn.Module):
nn.Dropout(dropout)
)
def forward(self, x, context=None, mask=None):
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
query = self.to_q(x)
context = default(context, x)
key = self.to_k(context)
if value is not None:
value = self.to_v(value)
else:
value = self.to_v(context)
del context, x
query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
@@ -256,12 +260,16 @@ class CrossAttentionDoggettx(nn.Module):
nn.Dropout(dropout)
)
def forward(self, x, context=None, mask=None):
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
k_in = self.to_k(context)
if value is not None:
v_in = self.to_v(value)
del value
else:
v_in = self.to_v(context)
del context, x
@@ -350,12 +358,16 @@ class CrossAttention(nn.Module):
nn.Dropout(dropout)
)
def forward(self, x, context=None, mask=None):
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
if value is not None:
v = self.to_v(value)
del value
else:
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
@@ -402,10 +414,14 @@ class MemoryEfficientCrossAttention(nn.Module):
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, mask=None):
def forward(self, x, context=None, value=None, mask=None):
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
if value is not None:
v = self.to_v(value)
del value
else:
v = self.to_v(context)
b, _, _ = q.shape
@@ -447,19 +463,19 @@ class CrossAttentionPytorch(nn.Module):
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, mask=None):
def forward(self, x, context=None, value=None, mask=None):
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
if value is not None:
v = self.to_v(value)
del value
else:
v = self.to_v(context)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.contiguous(),
lambda t: t.view(b, -1, self.heads, self.dim_head).transpose(1, 2),
(q, k, v),
)
@@ -468,10 +484,7 @@ class CrossAttentionPytorch(nn.Module):
if exists(mask):
raise NotImplementedError
out = (
out.unsqueeze(0)
.reshape(b, self.heads, out.shape[1], self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], self.heads * self.dim_head)
out.transpose(1, 2).reshape(b, -1, self.heads * self.dim_head)
)
return self.to_out(out)
@@ -519,11 +532,25 @@ class BasicTransformerBlock(nn.Module):
transformer_patches = {}
n = self.norm1(x)
if self.disable_self_attn:
context_attn1 = context
else:
context_attn1 = None
value_attn1 = None
if "attn1_patch" in transformer_patches:
patch = transformer_patches["attn1_patch"]
if context_attn1 is None:
context_attn1 = n
value_attn1 = context_attn1
for p in patch:
n, context_attn1, value_attn1 = p(current_index, n, context_attn1, value_attn1)
if "tomesd" in transformer_options:
m, u = tomesd.get_functions(x, transformer_options["tomesd"]["ratio"], transformer_options["original_shape"])
n = u(self.attn1(m(n), context=context if self.disable_self_attn else None))
n = u(self.attn1(m(n), context=context_attn1, value=value_attn1))
else:
n = self.attn1(n, context=context if self.disable_self_attn else None)
n = self.attn1(n, context=context_attn1, value=value_attn1)
x += n
if "middle_patch" in transformer_patches:
@@ -532,7 +559,16 @@ class BasicTransformerBlock(nn.Module):
x = p(current_index, x)
n = self.norm2(x)
n = self.attn2(n, context=context)
context_attn2 = context
value_attn2 = None
if "attn2_patch" in transformer_patches:
patch = transformer_patches["attn2_patch"]
value_attn2 = context_attn2
for p in patch:
n, context_attn2, value_attn2 = p(current_index, n, context_attn2, value_attn2)
n = self.attn2(n, context=context_attn2, value=value_attn2)
x += n
x = self.ff(self.norm3(x)) + x

36
comfy/model_management.py
View File

@@ -20,6 +20,18 @@ total_vram_available_mb = -1
accelerate_enabled = False
xpu_available = False
directml_enabled = False
if args.directml is not None:
import torch_directml
directml_enabled = True
device_index = args.directml
if device_index < 0:
directml_device = torch_directml.device()
else:
directml_device = torch_directml.device(device_index)
print("Using directml with device:", torch_directml.device_name(device_index))
# torch_directml.disable_tiled_resources(True)
try:
import torch
try:
@@ -133,6 +145,7 @@ def unload_model():
#never unload models from GPU on high vram
if vram_state != VRAMState.HIGH_VRAM:
current_loaded_model.model.cpu()
current_loaded_model.model_patches_to("cpu")
current_loaded_model.unpatch_model()
current_loaded_model = None
@@ -156,6 +169,8 @@ def load_model_gpu(model):
except Exception as e:
model.unpatch_model()
raise e
model.model_patches_to(get_torch_device())
current_loaded_model = model
if vram_state == VRAMState.CPU:
pass
@@ -214,6 +229,10 @@ def unload_if_low_vram(model):
def get_torch_device():
global xpu_available
global directml_enabled
if directml_enabled:
global directml_device
return directml_device
if vram_state == VRAMState.MPS:
return torch.device("mps")
if vram_state == VRAMState.CPU:
@@ -231,8 +250,14 @@ def get_autocast_device(dev):
def xformers_enabled():
global xpu_available
global directml_enabled
if vram_state == VRAMState.CPU:
return False
if xpu_available:
return False
if directml_enabled:
return False
return XFORMERS_IS_AVAILABLE
@@ -248,6 +273,7 @@ def pytorch_attention_enabled():
def get_free_memory(dev=None, torch_free_too=False):
global xpu_available
global directml_enabled
if dev is None:
dev = get_torch_device()
@@ -255,7 +281,10 @@ def get_free_memory(dev=None, torch_free_too=False):
mem_free_total = psutil.virtual_memory().available
mem_free_torch = mem_free_total
else:
if xpu_available:
if directml_enabled:
mem_free_total = 1024 * 1024 * 1024 #TODO
mem_free_torch = mem_free_total
elif xpu_available:
mem_free_total = torch.xpu.get_device_properties(dev).total_memory - torch.xpu.memory_allocated(dev)
mem_free_torch = mem_free_total
else:
@@ -290,9 +319,14 @@ def mps_mode():
def should_use_fp16():
global xpu_available
global directml_enabled
if FORCE_FP32:
return False
if directml_enabled:
return False
if cpu_mode() or mps_mode() or xpu_available:
return False #TODO ?

83
comfy/sample.py Normal file
View File

@@ -0,0 +1,83 @@
import torch
import comfy.model_management
import comfy.samplers
import math
def prepare_noise(latent_image, seed, skip=0):
"""
creates random noise given a latent image and a seed.
optional arg skip can be used to skip and discard x number of noise generations for a given seed
"""
generator = torch.manual_seed(seed)
for _ in range(skip):
noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
return noise
def prepare_mask(noise_mask, shape, device):
"""ensures noise mask is of proper dimensions"""
noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
noise_mask = noise_mask.round()
noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
if noise_mask.shape[0] < shape[0]:
noise_mask = noise_mask.repeat(math.ceil(shape[0] / noise_mask.shape[0]), 1, 1, 1)[:shape[0]]
noise_mask = noise_mask.to(device)
return noise_mask
def broadcast_cond(cond, batch, device):
"""broadcasts conditioning to the batch size"""
copy = []
for p in cond:
t = p[0]
if t.shape[0] < batch:
t = torch.cat([t] * batch)
t = t.to(device)
copy += [[t] + p[1:]]
return copy
def get_models_from_cond(cond, model_type):
models = []
for c in cond:
if model_type in c[1]:
models += [c[1][model_type]]
return models
def load_additional_models(positive, negative):
"""loads additional models in positive and negative conditioning"""
control_nets = get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")
gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen")
gligen = [x[1] for x in gligen]
models = control_nets + gligen
comfy.model_management.load_controlnet_gpu(models)
return models
def cleanup_additional_models(models):
"""cleanup additional models that were loaded"""
for m in models:
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, callback=None):
device = comfy.model_management.get_torch_device()
if noise_mask is not None:
noise_mask = prepare_mask(noise_mask, noise.shape, device)
real_model = None
comfy.model_management.load_model_gpu(model)
real_model = model.model
noise = noise.to(device)
latent_image = latent_image.to(device)
positive_copy = broadcast_cond(positive, noise.shape[0], device)
negative_copy = broadcast_cond(negative, noise.shape[0], device)
models = load_additional_models(positive, negative)
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, callback=callback)
samples = samples.cpu()
cleanup_additional_models(models)
return samples

62
comfy/samplers.py
View File

@@ -7,23 +7,6 @@ from comfy import model_management
from .ldm.models.diffusion.ddim import DDIMSampler
from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
class CFGDenoiser(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.inner_model = model
def forward(self, x, sigma, uncond, cond, cond_scale):
if len(uncond[0]) == len(cond[0]) and x.shape[0] * x.shape[2] * x.shape[3] < (96 * 96): #TODO check memory instead
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
else:
cond = self.inner_model(x, sigma, cond=cond)
uncond = self.inner_model(x, sigma, cond=uncond)
return uncond + (cond - uncond) * cond_scale
#The main sampling function shared by all the samplers
#Returns predicted noise
def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}):
@@ -214,6 +197,14 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
transformer_options = model_options['transformer_options'].copy()
if patches is not None:
if "patches" in transformer_options:
cur_patches = transformer_options["patches"].copy()
for p in patches:
if p in cur_patches:
cur_patches[p] = cur_patches[p] + patches[p]
else:
cur_patches[p] = patches[p]
else:
transformer_options["patches"] = patches
c['transformer_options'] = transformer_options
@@ -438,7 +429,7 @@ class KSampler:
self.denoise = denoise
self.model_options = model_options
def _calculate_sigmas(self, steps):
def calculate_sigmas(self, steps):
sigmas = None
discard_penultimate_sigma = False
@@ -447,13 +438,13 @@ class KSampler:
discard_penultimate_sigma = True
if self.scheduler == "karras":
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max, device=self.device)
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max)
elif self.scheduler == "normal":
sigmas = self.model_wrap.get_sigmas(steps).to(self.device)
sigmas = self.model_wrap.get_sigmas(steps)
elif self.scheduler == "simple":
sigmas = simple_scheduler(self.model_wrap, steps).to(self.device)
sigmas = simple_scheduler(self.model_wrap, steps)
elif self.scheduler == "ddim_uniform":
sigmas = ddim_scheduler(self.model_wrap, steps).to(self.device)
sigmas = ddim_scheduler(self.model_wrap, steps)
else:
print("error invalid scheduler", self.scheduler)
@@ -464,14 +455,15 @@ class KSampler:
def set_steps(self, steps, denoise=None):
self.steps = steps
if denoise is None or denoise > 0.9999:
self.sigmas = self._calculate_sigmas(steps)
self.sigmas = self.calculate_sigmas(steps).to(self.device)
else:
new_steps = int(steps/denoise)
sigmas = self._calculate_sigmas(new_steps)
sigmas = self.calculate_sigmas(new_steps).to(self.device)
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):
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:
sigmas = self.sigmas
sigma_min = self.sigma_min
@@ -535,9 +527,9 @@ class KSampler:
with precision_scope(model_management.get_autocast_device(self.device)):
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":
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":
timesteps = []
for s in range(sigmas.shape[0]):
@@ -545,6 +537,11 @@ class KSampler:
noise_mask = None
if denoise_mask is not None:
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.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)
@@ -558,6 +555,7 @@ class KSampler:
eta=0.0,
x_T=z_enc,
x0=latent_image,
img_callback=ddim_callback,
denoise_function=sampling_function,
extra_args=extra_args,
mask=noise_mask,
@@ -571,13 +569,17 @@ class KSampler:
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:
noise += latent_image
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":
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:
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)

23
comfy/sd.py
View File

@@ -254,6 +254,29 @@ class ModelPatcher:
def set_model_sampler_cfg_function(self, sampler_cfg_function):
self.model_options["sampler_cfg_function"] = sampler_cfg_function
def set_model_patch(self, patch, name):
to = self.model_options["transformer_options"]
if "patches" not in to:
to["patches"] = {}
to["patches"][name] = to["patches"].get(name, []) + [patch]
def set_model_attn1_patch(self, patch):
self.set_model_patch(patch, "attn1_patch")
def set_model_attn2_patch(self, patch):
self.set_model_patch(patch, "attn2_patch")
def model_patches_to(self, device):
to = self.model_options["transformer_options"]
if "patches" in to:
patches = to["patches"]
for name in patches:
patch_list = patches[name]
for i in range(len(patch_list)):
if hasattr(patch_list[i], "to"):
patch_list[i] = patch_list[i].to(device)
def model_dtype(self):
return self.model.diffusion_model.dtype

5
comfy/utils.py
View File

@@ -1,9 +1,12 @@
import torch
def load_torch_file(ckpt):
def load_torch_file(ckpt, safe_load=False):
if ckpt.lower().endswith(".safetensors"):
import safetensors.torch
sd = safetensors.torch.load_file(ckpt, device="cpu")
else:
if safe_load:
pl_sd = torch.load(ckpt, map_location="cpu", weights_only=True)
else:
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:

3
comfy_extras/chainner_models/architecture/block.py
View File

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

109
comfy_extras/nodes_hypernetwork.py Normal file
View File

@@ -0,0 +1,109 @@
import comfy.utils
import folder_paths
import torch
def load_hypernetwork_patch(path, strength):
sd = comfy.utils.load_torch_file(path, safe_load=True)
activation_func = sd.get('activation_func', 'linear')
is_layer_norm = sd.get('is_layer_norm', False)
use_dropout = sd.get('use_dropout', False)
activate_output = sd.get('activate_output', False)
last_layer_dropout = sd.get('last_layer_dropout', False)
valid_activation = {
"linear": torch.nn.Identity,
"relu": torch.nn.ReLU,
"leakyrelu": torch.nn.LeakyReLU,
"elu": torch.nn.ELU,
"swish": torch.nn.Hardswish,
"tanh": torch.nn.Tanh,
"sigmoid": torch.nn.Sigmoid,
}
if activation_func not in valid_activation:
print("Unsupported Hypernetwork format, if you report it I might implement it.", path, " ", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)
return None
out = {}
for d in sd:
try:
dim = int(d)
except:
continue
output = []
for index in [0, 1]:
attn_weights = sd[dim][index]
keys = attn_weights.keys()
linears = filter(lambda a: a.endswith(".weight"), keys)
linears = list(map(lambda a: a[:-len(".weight")], linears))
layers = []
for i in range(len(linears)):
lin_name = linears[i]
last_layer = (i == (len(linears) - 1))
penultimate_layer = (i == (len(linears) - 2))
lin_weight = attn_weights['{}.weight'.format(lin_name)]
lin_bias = attn_weights['{}.bias'.format(lin_name)]
layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0])
layer.load_state_dict({"weight": lin_weight, "bias": lin_bias})
layers.append(layer)
if activation_func != "linear":
if (not last_layer) or (activate_output):
layers.append(valid_activation[activation_func]())
if is_layer_norm:
layers.append(torch.nn.LayerNorm(lin_weight.shape[0]))
if use_dropout:
if (not last_layer) and (not penultimate_layer or last_layer_dropout):
layers.append(torch.nn.Dropout(p=0.3))
output.append(torch.nn.Sequential(*layers))
out[dim] = torch.nn.ModuleList(output)
class hypernetwork_patch:
def __init__(self, hypernet, strength):
self.hypernet = hypernet
self.strength = strength
def __call__(self, current_index, q, k, v):
dim = k.shape[-1]
if dim in self.hypernet:
hn = self.hypernet[dim]
k = k + hn[0](k) * self.strength
v = v + hn[1](v) * self.strength
return q, k, v
def to(self, device):
for d in self.hypernet.keys():
self.hypernet[d] = self.hypernet[d].to(device)
return self
return hypernetwork_patch(out, strength)
class HypernetworkLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ),
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "load_hypernetwork"
CATEGORY = "loaders"
def load_hypernetwork(self, model, hypernetwork_name, strength):
hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name)
model_hypernetwork = model.clone()
patch = load_hypernetwork_patch(hypernetwork_path, strength)
if patch is not None:
model_hypernetwork.set_model_attn1_patch(patch)
model_hypernetwork.set_model_attn2_patch(patch)
return (model_hypernetwork,)
NODE_CLASS_MAPPINGS = {
"HypernetworkLoader": HypernetworkLoader
}

44
execution.py
View File

@@ -40,15 +40,13 @@ def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_da
input_data_all[x] = unique_id
return input_data_all
def recursive_execute(server, prompt, outputs, current_item, extra_data={}):
def recursive_execute(server, prompt, outputs, current_item, extra_data, executed):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
if unique_id in outputs:
return []
executed = []
return
for x in inputs:
input_data = inputs[x]
@@ -57,7 +55,7 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data={}):
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id not in outputs:
executed += recursive_execute(server, prompt, outputs, input_unique_id, extra_data)
recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed)
input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
if server.client_id is not None:
@@ -72,7 +70,7 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data={}):
server.send_sync("executed", { "node": unique_id, "output": outputs[unique_id]["ui"] }, server.client_id)
if "result" in outputs[unique_id]:
outputs[unique_id] = outputs[unique_id]["result"]
return executed + [unique_id]
executed.add(unique_id)
def recursive_will_execute(prompt, outputs, current_item):
unique_id = current_item
@@ -99,21 +97,25 @@ def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item
is_changed_old = ''
is_changed = ''
to_delete = False
if hasattr(class_def, 'IS_CHANGED'):
if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
is_changed_old = old_prompt[unique_id]['is_changed']
if 'is_changed' not in prompt[unique_id]:
input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
if input_data_all is not None:
try:
is_changed = class_def.IS_CHANGED(**input_data_all)
prompt[unique_id]['is_changed'] = is_changed
except:
to_delete = True
else:
is_changed = prompt[unique_id]['is_changed']
if unique_id not in outputs:
return True
to_delete = False
if not to_delete:
if is_changed != is_changed_old:
to_delete = True
elif unique_id not in old_prompt:
@@ -154,11 +156,20 @@ class PromptExecutor:
self.server.client_id = None
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:
recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)
current_outputs = set(self.outputs.keys())
executed = []
executed = set()
try:
to_execute = []
for x in prompt:
@@ -181,12 +192,12 @@ class PromptExecutor:
except:
valid = False
if valid:
executed += recursive_execute(self.server, prompt, self.outputs, x, extra_data)
recursive_execute(self.server, prompt, self.outputs, x, extra_data, executed)
except Exception as e:
print(traceback.format_exc())
to_delete = []
for o in self.outputs:
if o not in current_outputs:
if (o not in current_outputs) and (o not in executed):
to_delete += [o]
if o in self.old_prompt:
d = self.old_prompt.pop(o)
@@ -194,11 +205,9 @@ class PromptExecutor:
for o in to_delete:
d = self.outputs.pop(o)
del d
else:
executed = set(executed)
finally:
for x in executed:
self.old_prompt[x] = copy.deepcopy(prompt[x])
finally:
self.server.last_node_id = None
if self.server.client_id is not None:
self.server.send_sync("executing", { "node": None }, self.server.client_id)
@@ -249,6 +258,12 @@ def validate_inputs(prompt, item):
if "max" in info[1] and val > info[1]["max"]:
return (False, "Value bigger than max. {}, {}".format(class_type, x))
if hasattr(obj_class, "VALIDATE_INPUTS"):
input_data_all = get_input_data(inputs, obj_class, unique_id)
ret = obj_class.VALIDATE_INPUTS(**input_data_all)
if ret != True:
return (False, "{}, {}".format(class_type, ret))
else:
if isinstance(type_input, list):
if val not in type_input:
return (False, "Value not in list. {}, {}: {} not in {}".format(class_type, x, val, type_input))
@@ -273,7 +288,8 @@ def validate_prompt(prompt):
m = validate_inputs(prompt, o)
valid = m[0]
reason = m[1]
except:
except Exception as e:
print(traceback.format_exc())
valid = False
reason = "Parsing error"

1
extra_model_paths.yaml.example
View File

@@ -13,6 +13,7 @@ a111:
models/ESRGAN
models/SwinIR
embeddings: embeddings
hypernetworks: models/hypernetworks
controlnet: models/ControlNet
#other_ui:

41
folder_paths.py
View File

@@ -32,6 +32,7 @@ folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_m
folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], [])
folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
@@ -68,6 +69,46 @@ def get_directory_by_type(type_name):
return None
# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format
# otherwise use default_path as base_dir
def annotated_filepath(name):
if name.endswith("[output]"):
base_dir = get_output_directory()
name = name[:-9]
elif name.endswith("[input]"):
base_dir = get_input_directory()
name = name[:-8]
elif name.endswith("[temp]"):
base_dir = get_temp_directory()
name = name[:-7]
else:
return name, None
return name, base_dir
def get_annotated_filepath(name, default_dir=None):
name, base_dir = annotated_filepath(name)
if base_dir is None:
if default_dir is not None:
base_dir = default_dir
else:
base_dir = get_input_directory() # fallback path
return os.path.join(base_dir, name)
def exists_annotated_filepath(name):
name, base_dir = annotated_filepath(name)
if base_dir is None:
base_dir = get_input_directory() # fallback path
filepath = os.path.join(base_dir, name)
return os.path.exists(filepath)
def add_model_folder_path(folder_name, full_folder_path):
global folder_names_and_paths
if folder_name in folder_names_and_paths:

0
models/hypernetworks/put_hypernetworks_here Normal file
View File

116
nodes.py
View File

@@ -16,6 +16,7 @@ sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "co
import comfy.diffusers_convert
import comfy.samplers
import comfy.sample
import comfy.sd
import comfy.utils
@@ -203,24 +204,24 @@ class VAEEncodeForInpaint:
def encode(self, vae, pixels, mask):
x = (pixels.shape[1] // 64) * 64
y = (pixels.shape[2] // 64) * 64
mask = torch.nn.functional.interpolate(mask[None,None,], size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")[0][0]
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
pixels = pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
pixels = pixels[:,:x,:y,:]
mask = mask[:x,:y]
mask = mask[:,:,:x,:y]
#grow mask by a few pixels to keep things seamless in latent space
kernel_tensor = torch.ones((1, 1, 6, 6))
mask_erosion = torch.clamp(torch.nn.functional.conv2d((mask.round())[None], kernel_tensor, padding=3), 0, 1)
m = (1.0 - mask.round())
mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=3), 0, 1)
m = (1.0 - mask.round()).squeeze(1)
for i in range(3):
pixels[:,:,:,i] -= 0.5
pixels[:,:,:,i] *= m
pixels[:,:,:,i] += 0.5
t = vae.encode(pixels)
return ({"samples":t, "noise_mask": (mask_erosion[0][:x,:y].round())}, )
return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
class CheckpointLoader:
@classmethod
@@ -771,79 +772,23 @@ class SetLatentNoiseMask:
s["noise_mask"] = mask
return (s,)
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"]
noise_mask = None
device = comfy.model_management.get_torch_device()
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_index = 0
if "batch_index" in latent:
batch_index = latent["batch_index"]
generator = torch.manual_seed(seed)
for i in range(batch_index):
noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
skip = latent["batch_index"] if "batch_index" in latent else 0
noise = comfy.sample.prepare_noise(latent_image, seed, skip)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent['noise_mask']
noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")
noise_mask = noise_mask.round()
noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
noise_mask = torch.cat([noise_mask] * noise.shape[0])
noise_mask = noise_mask.to(device)
real_model = None
comfy.model_management.load_model_gpu(model)
real_model = model.model
noise = noise.to(device)
latent_image = latent_image.to(device)
positive_copy = []
negative_copy = []
control_nets = []
def get_models(cond):
models = []
for c in cond:
if 'control' in c[1]:
models += [c[1]['control']]
if 'gligen' in c[1]:
models += [c[1]['gligen'][1]]
return models
for p in positive:
t = p[0]
if t.shape[0] < noise.shape[0]:
t = torch.cat([t] * noise.shape[0])
t = t.to(device)
positive_copy += [[t] + p[1:]]
for n in negative:
t = n[0]
if t.shape[0] < noise.shape[0]:
t = torch.cat([t] * noise.shape[0])
t = t.to(device)
negative_copy += [[t] + n[1:]]
models = get_models(positive) + get_models(negative)
comfy.model_management.load_controlnet_gpu(models)
if sampler_name in comfy.samplers.KSampler.SAMPLERS:
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
else:
#other samplers
pass
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)
samples = samples.cpu()
for m in models:
m.cleanup()
noise_mask = latent["noise_mask"]
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask)
out = latent.copy()
out["samples"] = samples
return (out, )
@@ -1006,8 +951,7 @@ class LoadImage:
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image"
def load_image(self, image):
input_dir = folder_paths.get_input_directory()
image_path = os.path.join(input_dir, image)
image_path = folder_paths.get_annotated_filepath(image)
i = Image.open(image_path)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
@@ -1021,20 +965,27 @@ class LoadImage:
@classmethod
def IS_CHANGED(s, image):
input_dir = folder_paths.get_input_directory()
image_path = os.path.join(input_dir, image)
image_path = folder_paths.get_annotated_filepath(image)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image):
if not folder_paths.exists_annotated_filepath(image):
return "Invalid image file: {}".format(image)
return True
class LoadImageMask:
_color_channels = ["alpha", "red", "green", "blue"]
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
return {"required":
{"image": (sorted(os.listdir(input_dir)), ),
"channel": (["alpha", "red", "green", "blue"], ),}
"channel": (s._color_channels, ),}
}
CATEGORY = "mask"
@@ -1042,8 +993,7 @@ class LoadImageMask:
RETURN_TYPES = ("MASK",)
FUNCTION = "load_image"
def load_image(self, image, channel):
input_dir = folder_paths.get_input_directory()
image_path = os.path.join(input_dir, image)
image_path = folder_paths.get_annotated_filepath(image)
i = Image.open(image_path)
if i.getbands() != ("R", "G", "B", "A"):
i = i.convert("RGBA")
@@ -1060,13 +1010,22 @@ class LoadImageMask:
@classmethod
def IS_CHANGED(s, image, channel):
input_dir = folder_paths.get_input_directory()
image_path = os.path.join(input_dir, image)
image_path = folder_paths.get_annotated_filepath(image)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image, channel):
if not folder_paths.exists_annotated_filepath(image):
return "Invalid image file: {}".format(image)
if channel not in s._color_channels:
return "Invalid color channel: {}".format(channel)
return True
class ImageScale:
upscale_methods = ["nearest-exact", "bilinear", "area"]
crop_methods = ["disabled", "center"]
@@ -1302,6 +1261,7 @@ def load_custom_nodes():
def init_custom_nodes():
load_custom_nodes()
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_hypernetwork.py"))
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))

2
notebooks/comfyui_colab.ipynb
View File

@@ -47,7 +47,7 @@
" !git pull\n",
"\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"
]
},
{

13
server.py
View File

@@ -112,14 +112,21 @@ class PromptServer():
@routes.post("/upload/image")
async def upload_image(request):
post = await request.post()
image = post.get("image")
if post.get("type") is None:
upload_dir = folder_paths.get_input_directory()
elif post.get("type") == "input":
upload_dir = folder_paths.get_input_directory()
elif post.get("type") == "temp":
upload_dir = folder_paths.get_temp_directory()
elif post.get("type") == "output":
upload_dir = folder_paths.get_output_directory()
if not os.path.exists(upload_dir):
os.makedirs(upload_dir)
post = await request.post()
image = post.get("image")
if image and image.file:
filename = image.filename
if not filename:

32
web/extensions/core/editAttention.js
View File

@@ -89,24 +89,17 @@ app.registerExtension({
end = nearestEnclosure.end;
selectedText = inputField.value.substring(start, end);
} else {
// Select the current word, find the start and end of the word (first space before and after)
const wordStart = inputField.value.substring(0, start).lastIndexOf(" ") + 1;
const wordEnd = inputField.value.substring(end).indexOf(" ");
// If there is no space after the word, select to the end of the string
if (wordEnd === -1) {
end = inputField.value.length;
} else {
end += wordEnd;
}
start = wordStart;
// Select the current word, find the start and end of the word
const delimiters = " .,\\/!?%^*;:{}=-_`~()\r\n\t";
// Remove all punctuation at the end and beginning of the word
while (inputField.value[start].match(/[.,\/#!$%\^&\*;:{}=\-_`~()]/)) {
start++;
while (!delimiters.includes(inputField.value[start - 1]) && start > 0) {
start--;
}
while (inputField.value[end - 1].match(/[.,\/#!$%\^&\*;:{}=\-_`~()]/)) {
end--;
while (!delimiters.includes(inputField.value[end]) && end < inputField.value.length) {
end++;
}
selectedText = inputField.value.substring(start, end);
if (!selectedText) return;
}
@@ -135,8 +128,13 @@ app.registerExtension({
// Increment the weight
const weightDelta = event.key === "ArrowUp" ? delta : -delta;
const updatedText = selectedText.replace(/(.*:)(\d+(\.\d+)?)(.*)/, (match, prefix, weight, _, suffix) => {
return prefix + incrementWeight(weight, weightDelta) + suffix;
const updatedText = selectedText.replace(/\((.*):(\d+(?:\.\d+)?)\)/, (match, text, weight) => {
weight = incrementWeight(weight, weightDelta);
if (weight == 1) {
return text;
} else {
return `(${text}:${weight})`;
}
});
inputField.setRangeText(updatedText, start, end, "select");

75
web/extensions/core/slotDefaults.js
View File

@@ -1,21 +1,72 @@
import { app } from "/scripts/app.js";
import { ComfyWidgets } from "/scripts/widgets.js";
// Adds defaults for quickly adding nodes with middle click on the input/output
app.registerExtension({
name: "Comfy.SlotDefaults",
suggestionsNumber: null,
init() {
LiteGraph.middle_click_slot_add_default_node = true;
LiteGraph.slot_types_default_in = {
MODEL: "CheckpointLoaderSimple",
LATENT: "EmptyLatentImage",
VAE: "VAELoader",
};
LiteGraph.slot_types_default_out = {
LATENT: "VAEDecode",
IMAGE: "SaveImage",
CLIP: "CLIPTextEncode",
};
this.suggestionsNumber = app.ui.settings.addSetting({
id: "Comfy.NodeSuggestions.number",
name: "number of nodes suggestions",
type: "slider",
attrs: {
min: 1,
max: 100,
step: 1,
},
defaultValue: 5,
onChange: (newVal, oldVal) => {
this.setDefaults(newVal);
}
});
},
slot_types_default_out: {},
slot_types_default_in: {},
async beforeRegisterNodeDef(nodeType, nodeData, app) {
var nodeId = nodeData.name;
var inputs = [];
inputs = nodeData["input"]["required"]; //only show required inputs to reduce the mess also not logical to create node with optional inputs
for (const inputKey in inputs) {
var input = (inputs[inputKey]);
if (typeof input[0] !== "string") continue;
var type = input[0]
if (type in ComfyWidgets) {
var customProperties = input[1]
if (!(customProperties?.forceInput)) continue; //ignore widgets that don't force input
}
if (!(type in this.slot_types_default_out)) {
this.slot_types_default_out[type] = ["Reroute"];
}
if (this.slot_types_default_out[type].includes(nodeId)) continue;
this.slot_types_default_out[type].push(nodeId);
}
var outputs = nodeData["output"];
for (const key in outputs) {
var type = outputs[key];
if (!(type in this.slot_types_default_in)) {
this.slot_types_default_in[type] = ["Reroute"];// ["Reroute", "Primitive"]; primitive doesn't always work :'()
}
this.slot_types_default_in[type].push(nodeId);
}
var maxNum = this.suggestionsNumber.value;
this.setDefaults(maxNum);
},
setDefaults(maxNum) {
LiteGraph.slot_types_default_out = {};
LiteGraph.slot_types_default_in = {};
for (const type in this.slot_types_default_out) {
LiteGraph.slot_types_default_out[type] = this.slot_types_default_out[type].slice(0, maxNum);
}
for (const type in this.slot_types_default_in) {
LiteGraph.slot_types_default_in[type] = this.slot_types_default_in[type].slice(0, maxNum);
}
}
});

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

@@ -9953,11 +9953,11 @@ LGraphNode.prototype.executeAction = function(action)
}
break;
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);
if(w.options.read_only) break;
w.value = w.options.min + (w.options.max - w.options.min) * nvalue;
if (w.callback) {
if (old_value != w.value) {
setTimeout(function() {
inner_value_change(w, w.value);
}, 20);
@@ -10044,7 +10044,7 @@ LGraphNode.prototype.executeAction = function(action)
if (event.click_time < 200 && delta == 0) {
this.prompt("Value",w.value,function(v) {
// 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
v = eval(v);
} catch (e) { }

4
web/scripts/api.js
View File

@@ -35,7 +35,7 @@ class ComfyApi extends EventTarget {
}
let opened = false;
let existingSession = sessionStorage["Comfy.SessionId"] || "";
let existingSession = window.name;
if (existingSession) {
existingSession = "?clientId=" + existingSession;
}
@@ -75,7 +75,7 @@ class ComfyApi extends EventTarget {
case "status":
if (msg.data.sid) {
this.clientId = msg.data.sid;
sessionStorage["Comfy.SessionId"] = this.clientId;
window.name = this.clientId;
}
this.dispatchEvent(new CustomEvent("status", { detail: msg.data.status }));
break;

83
web/scripts/app.js
View File

@@ -20,6 +20,12 @@ export class ComfyApp {
*/
#processingQueue = false;
/**
* Content Clipboard
* @type {serialized node object}
*/
static clipspace = null;
constructor() {
this.ui = new ComfyUI(this);
@@ -130,6 +136,83 @@ export class ComfyApp {
);
}
}
options.push(
{
content: "Copy (Clipspace)",
callback: (obj) => {
var widgets = null;
if(this.widgets) {
widgets = this.widgets.map(({ type, name, value }) => ({ type, name, value }));
}
let img = new Image();
var imgs = undefined;
if(this.imgs != undefined) {
img.src = this.imgs[0].src;
imgs = [img];
}
ComfyApp.clipspace = {
'widgets': widgets,
'imgs': imgs,
'original_imgs': imgs,
'images': this.images
};
}
});
if(ComfyApp.clipspace != null) {
options.push(
{
content: "Paste (Clipspace)",
callback: () => {
if(ComfyApp.clipspace != null) {
if(ComfyApp.clipspace.widgets != null && this.widgets != null) {
ComfyApp.clipspace.widgets.forEach(({ type, name, value }) => {
const prop = Object.values(this.widgets).find(obj => obj.type === type && obj.name === name);
if (prop) {
prop.callback(value);
}
});
}
// image paste
if(ComfyApp.clipspace.imgs != undefined && this.imgs != undefined && this.widgets != null) {
var filename = "";
if(this.images && ComfyApp.clipspace.images) {
this.images = ComfyApp.clipspace.images;
}
if(ComfyApp.clipspace.images != undefined) {
const clip_image = ComfyApp.clipspace.images[0];
if(clip_image.subfolder != '')
filename = `${clip_image.subfolder}/`;
filename += `${clip_image.filename} [${clip_image.type}]`;
}
else if(ComfyApp.clipspace.widgets != undefined) {
const index_in_clip = ComfyApp.clipspace.widgets.findIndex(obj => obj.name === 'image');
if(index_in_clip >= 0) {
filename = `${ComfyApp.clipspace.widgets[index_in_clip].value}`;
}
}
const index = this.widgets.findIndex(obj => obj.name === 'image');
if(index >= 0 && filename != "" && ComfyApp.clipspace.imgs != undefined) {
this.imgs = ComfyApp.clipspace.imgs;
this.widgets[index].value = filename;
if(this.widgets_values != undefined) {
this.widgets_values[index] = filename;
}
}
}
this.trigger('changed');
}
}
}
);
}
};
}

7
web/scripts/widgets.js
View File

@@ -136,9 +136,11 @@ function addMultilineWidget(node, name, opts, app) {
left: `${t.a * margin + t.e}px`,
top: `${t.d * (y + widgetHeight - margin - 3) + t.f}px`,
width: `${(widgetWidth - margin * 2 - 3) * t.a}px`,
background: (!node.color)?'':node.color,
height: `${(this.parent.inputHeight - margin * 2 - 4) * t.d}px`,
position: "absolute",
zIndex: 1,
color: (!node.color)?'':'white',
zIndex: app.graph._nodes.indexOf(node),
fontSize: `${t.d * 10.0}px`,
});
this.inputEl.hidden = !visible;
@@ -270,6 +272,9 @@ export const ComfyWidgets = {
app.graph.setDirtyCanvas(true);
};
img.src = `/view?filename=${name}&type=input`;
if ((node.size[1] - node.imageOffset) < 100) {
node.size[1] = 250 + node.imageOffset;
}
}
// Add our own callback to the combo widget to render an image when it changes