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Merge branch 'master' into reroute-shortcut

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melMass 2023-06-12 23:17:28 +02:00
commit 52e3f392af
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6
.gitignore vendored
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@ -9,4 +9,8 @@ custom_nodes/
!custom_nodes/example_node.py.example
extra_model_paths.yaml
/.vs
.idea/
.idea/
venv/
web/extensions/*
!web/extensions/logging.js.example
!web/extensions/core/

18
README.md
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@ -119,12 +119,22 @@ After this you should have everything installed and can proceed to running Comfy
### Others:
[Intel Arc](https://github.com/comfyanonymous/ComfyUI/discussions/476)
#### [Intel Arc](https://github.com/comfyanonymous/ComfyUI/discussions/476)
Mac/MPS: There is basic support in the code but until someone makes some install instruction you are on your own.
#### Apple Mac silicon
Directml: ```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
1. Install pytorch. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide.
1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).
1. Launch ComfyUI by running `python main.py`.
> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
#### DirectML (AMD Cards on Windows)
```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
### I already have another UI for Stable Diffusion installed do I really have to install all of these dependencies?
@ -162,6 +172,8 @@ You can use () to change emphasis of a word or phrase like: (good code:1.2) or (
You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}.
Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`.
To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):
```embedding:embedding_filename.pt```

28
comfy/diffusers_load.py
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@ -4,7 +4,7 @@ import yaml
import folder_paths
from comfy.ldm.util import instantiate_from_config
from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE
from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE, load_checkpoint
import os.path as osp
import re
import torch
@ -84,28 +84,4 @@ def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, emb
# Put together new checkpoint
sd = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
clip = None
vae = None
class WeightsLoader(torch.nn.Module):
pass
w = WeightsLoader()
load_state_dict_to = []
if output_vae:
vae = VAE(scale_factor=scale_factor, config=vae_config)
w.first_stage_model = vae.first_stage_model
load_state_dict_to = [w]
if output_clip:
clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model
load_state_dict_to = [w]
model = instantiate_from_config(config["model"])
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
if fp16:
model = model.half()
return ModelPatcher(model), clip, vae
return load_checkpoint(embedding_directory=embedding_directory, state_dict=sd, config=config)

97
comfy/model_base.py Normal file
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@ -0,0 +1,97 @@
import torch
from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel
from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
import numpy as np
class BaseModel(torch.nn.Module):
def __init__(self, unet_config, v_prediction=False):
super().__init__()
self.register_schedule(given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
self.diffusion_model = UNetModel(**unet_config)
self.v_prediction = v_prediction
if self.v_prediction:
self.parameterization = "v"
else:
self.parameterization = "eps"
if "adm_in_channels" in unet_config:
self.adm_channels = unet_config["adm_in_channels"]
else:
self.adm_channels = 0
print("v_prediction", v_prediction)
print("adm", self.adm_channels)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if given_betas is not None:
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
def apply_model(self, x, t, c_concat=None, c_crossattn=None, c_adm=None, control=None, transformer_options={}):
if c_concat is not None:
xc = torch.cat([x] + c_concat, dim=1)
else:
xc = x
context = torch.cat(c_crossattn, 1)
return self.diffusion_model(xc, t, context=context, y=c_adm, control=control, transformer_options=transformer_options)
def get_dtype(self):
return self.diffusion_model.dtype
def is_adm(self):
return self.adm_channels > 0
class SD21UNCLIP(BaseModel):
def __init__(self, unet_config, noise_aug_config, v_prediction=True):
super().__init__(unet_config, v_prediction)
self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config)
def encode_adm(self, **kwargs):
unclip_conditioning = kwargs.get("unclip_conditioning", None)
device = kwargs["device"]
if unclip_conditioning is not None:
adm_inputs = []
weights = []
noise_aug = []
for unclip_cond in unclip_conditioning:
adm_cond = unclip_cond["clip_vision_output"].image_embeds
weight = unclip_cond["strength"]
noise_augment = unclip_cond["noise_augmentation"]
noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = self.noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device))
adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
weights.append(weight)
noise_aug.append(noise_augment)
adm_inputs.append(adm_out)
if len(noise_aug) > 1:
adm_out = torch.stack(adm_inputs).sum(0)
#TODO: add a way to control this
noise_augment = 0.05
noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = self.noise_augmentor(adm_out[:, :self.noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))
adm_out = torch.cat((c_adm, noise_level_emb), 1)
else:
adm_out = torch.zeros((1, self.adm_channels))
return adm_out
class SDInpaint(BaseModel):
def __init__(self, unet_config, v_prediction=False):
super().__init__(unet_config, v_prediction)
self.concat_keys = ("mask", "masked_image")

44
comfy/samplers.py
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@ -248,7 +248,7 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
c['transformer_options'] = transformer_options
output = model_function(input_x, timestep_, cond=c).chunk(batch_chunks)
output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
del input_x
model_management.throw_exception_if_processing_interrupted()
@ -460,36 +460,18 @@ def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
uncond[temp[1]] = [o[0], n]
def encode_adm(noise_augmentor, conds, batch_size, device):
def encode_adm(model, conds, batch_size, device):
for t in range(len(conds)):
x = conds[t]
adm_out = None
if 'adm' in x[1]:
adm_inputs = []
weights = []
noise_aug = []
adm_in = x[1]["adm"]
for adm_c in adm_in:
adm_cond = adm_c[0].image_embeds
weight = adm_c[1]
noise_augment = adm_c[2]
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device))
adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
weights.append(weight)
noise_aug.append(noise_augment)
adm_inputs.append(adm_out)
if len(noise_aug) > 1:
adm_out = torch.stack(adm_inputs).sum(0)
#TODO: add a way to control this
noise_augment = 0.05
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))
adm_out = torch.cat((c_adm, noise_level_emb), 1)
adm_out = x[1]["adm"]
else:
adm_out = torch.zeros((1, noise_augmentor.time_embed.dim * 2), device=device)
x[1] = x[1].copy()
x[1]["adm_encoded"] = torch.cat([adm_out] * batch_size)
params = x[1].copy()
adm_out = model.encode_adm(device=device, **params)
if adm_out is not None:
x[1] = x[1].copy()
x[1]["adm_encoded"] = torch.cat([adm_out] * batch_size).to(device)
return conds
@ -591,14 +573,14 @@ class KSampler:
apply_empty_x_to_equal_area(positive, negative, 'control', lambda cond_cnets, x: cond_cnets[x])
apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
if self.model.model.diffusion_model.dtype == torch.float16:
if self.model.get_dtype() == torch.float16:
precision_scope = torch.autocast
else:
precision_scope = contextlib.nullcontext
if hasattr(self.model, 'noise_augmentor'): #unclip
positive = encode_adm(self.model.noise_augmentor, positive, noise.shape[0], self.device)
negative = encode_adm(self.model.noise_augmentor, negative, noise.shape[0], self.device)
if self.model.is_adm():
positive = encode_adm(self.model, positive, noise.shape[0], self.device)
negative = encode_adm(self.model, negative, noise.shape[0], self.device)
extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options}

116
comfy/sd.py
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@ -15,8 +15,15 @@ from . import utils
from . import clip_vision
from . import gligen
from . import diffusers_convert
from . import model_base
def load_model_weights(model, sd, verbose=False, load_state_dict_to=[]):
replace_prefix = {"model.diffusion_model.": "diffusion_model."}
for rp in replace_prefix:
replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), sd.keys())))
for x in replace:
sd[x[1]] = sd.pop(x[0])
m, u = model.load_state_dict(sd, strict=False)
k = list(sd.keys())
@ -182,7 +189,7 @@ def model_lora_keys(model, key_map={}):
counter = 0
for b in range(12):
tk = "model.diffusion_model.input_blocks.{}.1".format(b)
tk = "diffusion_model.input_blocks.{}.1".format(b)
up_counter = 0
for c in LORA_UNET_MAP_ATTENTIONS:
k = "{}.{}.weight".format(tk, c)
@ -193,13 +200,13 @@ def model_lora_keys(model, key_map={}):
if up_counter >= 4:
counter += 1
for c in LORA_UNET_MAP_ATTENTIONS:
k = "model.diffusion_model.middle_block.1.{}.weight".format(c)
k = "diffusion_model.middle_block.1.{}.weight".format(c)
if k in sdk:
lora_key = "lora_unet_mid_block_attentions_0_{}".format(LORA_UNET_MAP_ATTENTIONS[c])
key_map[lora_key] = k
counter = 3
for b in range(12):
tk = "model.diffusion_model.output_blocks.{}.1".format(b)
tk = "diffusion_model.output_blocks.{}.1".format(b)
up_counter = 0
for c in LORA_UNET_MAP_ATTENTIONS:
k = "{}.{}.weight".format(tk, c)
@ -223,7 +230,7 @@ def model_lora_keys(model, key_map={}):
ds_counter = 0
counter = 0
for b in range(12):
tk = "model.diffusion_model.input_blocks.{}.0".format(b)
tk = "diffusion_model.input_blocks.{}.0".format(b)
key_in = False
for c in LORA_UNET_MAP_RESNET:
k = "{}.{}.weight".format(tk, c)
@ -242,7 +249,7 @@ def model_lora_keys(model, key_map={}):
counter = 0
for b in range(3):
tk = "model.diffusion_model.middle_block.{}".format(b)
tk = "diffusion_model.middle_block.{}".format(b)
key_in = False
for c in LORA_UNET_MAP_RESNET:
k = "{}.{}.weight".format(tk, c)
@ -256,7 +263,7 @@ def model_lora_keys(model, key_map={}):
counter = 0
us_counter = 0
for b in range(12):
tk = "model.diffusion_model.output_blocks.{}.0".format(b)
tk = "diffusion_model.output_blocks.{}.0".format(b)
key_in = False
for c in LORA_UNET_MAP_RESNET:
k = "{}.{}.weight".format(tk, c)
@ -332,7 +339,7 @@ class ModelPatcher:
patch_list[i] = patch_list[i].to(device)
def model_dtype(self):
return self.model.diffusion_model.dtype
return self.model.get_dtype()
def add_patches(self, patches, strength=1.0):
p = {}
@ -537,6 +544,19 @@ class VAE:
/ 3.0) / 2.0, min=0.0, max=1.0)
return output
def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
pbar = utils.ProgressBar(steps)
encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.device) - 1.).sample() * self.scale_factor
samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples /= 3.0
return samples
def decode(self, samples_in):
model_management.unload_model()
self.first_stage_model = self.first_stage_model.to(self.device)
@ -567,28 +587,29 @@ class VAE:
def encode(self, pixel_samples):
model_management.unload_model()
self.first_stage_model = self.first_stage_model.to(self.device)
pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
samples = self.first_stage_model.encode(2. * pixel_samples - 1.).sample() * self.scale_factor
pixel_samples = pixel_samples.movedim(-1,1)
try:
free_memory = model_management.get_free_memory(self.device)
batch_number = int((free_memory * 0.7) / (2078 * pixel_samples.shape[2] * pixel_samples.shape[3])) #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change.
batch_number = max(1, batch_number)
samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu")
for x in range(0, pixel_samples.shape[0], batch_number):
pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.device)
samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu() * self.scale_factor
except model_management.OOM_EXCEPTION as e:
print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
samples = self.encode_tiled_(pixel_samples)
self.first_stage_model = self.first_stage_model.cpu()
samples = samples.cpu()
return samples
def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
model_management.unload_model()
self.first_stage_model = self.first_stage_model.to(self.device)
pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
pbar = utils.ProgressBar(steps)
samples = utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples /= 3.0
pixel_samples = pixel_samples.movedim(-1,1)
samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
self.first_stage_model = self.first_stage_model.cpu()
samples = samples.cpu()
return samples
def broadcast_image_to(tensor, target_batch_size, batched_number):
@ -764,7 +785,7 @@ def load_controlnet(ckpt_path, model=None):
for x in controlnet_data:
c_m = "control_model."
if x.startswith(c_m):
sd_key = "model.diffusion_model.{}".format(x[len(c_m):])
sd_key = "diffusion_model.{}".format(x[len(c_m):])
if sd_key in model_sd:
cd = controlnet_data[x]
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
@ -931,9 +952,10 @@ def load_gligen(ckpt_path):
model = model.half()
return model
def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=None):
with open(config_path, 'r') as stream:
config = yaml.safe_load(stream)
def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
if config is None:
with open(config_path, 'r') as stream:
config = yaml.safe_load(stream)
model_config_params = config['model']['params']
clip_config = model_config_params['cond_stage_config']
scale_factor = model_config_params['scale_factor']
@ -942,8 +964,19 @@ def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, e
fp16 = False
if "unet_config" in model_config_params:
if "params" in model_config_params["unet_config"]:
if "use_fp16" in model_config_params["unet_config"]["params"]:
fp16 = model_config_params["unet_config"]["params"]["use_fp16"]
unet_config = model_config_params["unet_config"]["params"]
if "use_fp16" in unet_config:
fp16 = unet_config["use_fp16"]
noise_aug_config = None
if "noise_aug_config" in model_config_params:
noise_aug_config = model_config_params["noise_aug_config"]
v_prediction = False
if "parameterization" in model_config_params:
if model_config_params["parameterization"] == "v":
v_prediction = True
clip = None
vae = None
@ -963,9 +996,16 @@ def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, e
w.cond_stage_model = clip.cond_stage_model
load_state_dict_to = [w]
model = instantiate_from_config(config["model"])
sd = utils.load_torch_file(ckpt_path)
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
if config['model']["target"].endswith("LatentInpaintDiffusion"):
model = model_base.SDInpaint(unet_config, v_prediction=v_prediction)
elif config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"):
model = model_base.SD21UNCLIP(unet_config, noise_aug_config["params"], v_prediction=v_prediction)
else:
model = model_base.BaseModel(unet_config, v_prediction=v_prediction)
if state_dict is None:
state_dict = utils.load_torch_file(ckpt_path)
model = load_model_weights(model, state_dict, verbose=False, load_state_dict_to=load_state_dict_to)
if fp16:
model = model.half()
@ -1073,16 +1113,20 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
sd_config["unet_config"] = {"target": "comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel", "params": unet_config}
model_config = {"target": "comfy.ldm.models.diffusion.ddpm.LatentDiffusion", "params": sd_config}
unclip_model = False
inpaint_model = False
if noise_aug_config is not None: #SD2.x unclip model
sd_config["noise_aug_config"] = noise_aug_config
sd_config["image_size"] = 96
sd_config["embedding_dropout"] = 0.25
sd_config["conditioning_key"] = 'crossattn-adm'
unclip_model = True
model_config["target"] = "comfy.ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion"
elif unet_config["in_channels"] > 4: #inpainting model
sd_config["conditioning_key"] = "hybrid"
sd_config["finetune_keys"] = None
model_config["target"] = "comfy.ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
inpaint_model = True
else:
sd_config["conditioning_key"] = "crossattn"
@ -1096,13 +1140,21 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
unet_config["num_classes"] = "sequential"
unet_config["adm_in_channels"] = sd[unclip].shape[1]
v_prediction = False
if unet_config["context_dim"] == 1024 and unet_config["in_channels"] == 4: #only SD2.x non inpainting models are v prediction
k = "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm1.bias"
out = sd[k]
if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
v_prediction = True
sd_config["parameterization"] = 'v'
model = instantiate_from_config(model_config)
if inpaint_model:
model = model_base.SDInpaint(unet_config, v_prediction=v_prediction)
elif unclip_model:
model = model_base.SD21UNCLIP(unet_config, noise_aug_config["params"], v_prediction=v_prediction)
else:
model = model_base.BaseModel(unet_config, v_prediction=v_prediction)
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
if fp16:

2
comfy/sd1_clip.py
View File

@ -82,6 +82,8 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
next_new_token += 1
else:
print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1])
while len(tokens_temp) < len(x):
tokens_temp += [self.empty_tokens[0][-1]]
out_tokens += [tokens_temp]
if len(embedding_weights) > 0:

9
execution.py
View File

@ -728,9 +728,14 @@ class PromptQueue:
return True
return False
def get_history(self):
def get_history(self, prompt_id=None):
with self.mutex:
return copy.deepcopy(self.history)
if prompt_id is None:
return copy.deepcopy(self.history)
elif prompt_id in self.history:
return {prompt_id: copy.deepcopy(self.history[prompt_id])}
else:
return {}
def wipe_history(self):
with self.mutex:

30
nodes.py
View File

@ -623,11 +623,11 @@ class unCLIPConditioning:
c = []
for t in conditioning:
o = t[1].copy()
x = (clip_vision_output, strength, noise_augmentation)
if "adm" in o:
o["adm"] = o["adm"][:] + [x]
x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}
if "unclip_conditioning" in o:
o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x]
else:
o["adm"] = [x]
o["unclip_conditioning"] = [x]
n = [t[0], o]
c.append(n)
return (c, )
@ -1192,6 +1192,26 @@ class ImageScale:
s = s.movedim(1,-1)
return (s,)
class ImageScaleBy:
upscale_methods = ["nearest-exact", "bilinear", "area"]
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
"scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image/upscaling"
def upscale(self, image, upscale_method, scale_by):
samples = image.movedim(-1,1)
width = round(samples.shape[3] * scale_by)
height = round(samples.shape[2] * scale_by)
s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
s = s.movedim(1,-1)
return (s,)
class ImageInvert:
@classmethod
@ -1290,6 +1310,7 @@ NODE_CLASS_MAPPINGS = {
"LoadImage": LoadImage,
"LoadImageMask": LoadImageMask,
"ImageScale": ImageScale,
"ImageScaleBy": ImageScaleBy,
"ImageInvert": ImageInvert,
"ImagePadForOutpaint": ImagePadForOutpaint,
"ConditioningAverage ": ConditioningAverage ,
@ -1371,6 +1392,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"LoadImage": "Load Image",
"LoadImageMask": "Load Image (as Mask)",
"ImageScale": "Upscale Image",
"ImageScaleBy": "Upscale Image By",
"ImageUpscaleWithModel": "Upscale Image (using Model)",
"ImageInvert": "Invert Image",
"ImagePadForOutpaint": "Pad Image for Outpainting",

164
script_examples/websockets_api_example.py Normal file
View File

@ -0,0 +1,164 @@
#This is an example that uses the websockets api to know when a prompt execution is done
#Once the prompt execution is done it downloads the images using the /history endpoint
import websocket
import uuid
import json
import urllib.request
import urllib.parse
server_address = "127.0.0.1:8188"
client_id = str(uuid.uuid4())
def queue_prompt(prompt):
p = {"prompt": prompt, "client_id": client_id}
data = json.dumps(p).encode('utf-8')
req = urllib.request.Request("http://{}/prompt".format(server_address), data=data)
return json.loads(urllib.request.urlopen(req).read())
def get_image(filename, subfolder, folder_type):
data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
url_values = urllib.parse.urlencode(data)
with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
return response.read()
def get_history(prompt_id):
with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
return json.loads(response.read())
def get_images(ws, prompt):
prompt_id = queue_prompt(prompt)['prompt_id']
output_images = {}
while True:
out = ws.recv()
if isinstance(out, str):
message = json.loads(out)
if message['type'] == 'executing':
data = message['data']
if data['node'] is None and data['prompt_id'] == prompt_id:
break #Execution is done
else:
continue #previews are binary data
history = get_history(prompt_id)[prompt_id]
for o in history['outputs']:
for node_id in history['outputs']:
node_output = history['outputs'][node_id]
if 'images' in node_output:
images_output = []
for image in node_output['images']:
image_data = get_image(image['filename'], image['subfolder'], image['type'])
images_output.append(image_data)
output_images[node_id] = images_output
return output_images
prompt_text = """
{
"3": {
"class_type": "KSampler",
"inputs": {
"cfg": 8,
"denoise": 1,
"latent_image": [
"5",
0
],
"model": [
"4",
0
],
"negative": [
"7",
0
],
"positive": [
"6",
0
],
"sampler_name": "euler",
"scheduler": "normal",
"seed": 8566257,
"steps": 20
}
},
"4": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "v1-5-pruned-emaonly.ckpt"
}
},
"5": {
"class_type": "EmptyLatentImage",
"inputs": {
"batch_size": 1,
"height": 512,
"width": 512
}
},
"6": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"4",
1
],
"text": "masterpiece best quality girl"
}
},
"7": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"4",
1
],
"text": "bad hands"
}
},
"8": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
}
},
"9": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
}
}
}
"""
prompt = json.loads(prompt_text)
#set the text prompt for our positive CLIPTextEncode
prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
#set the seed for our KSampler node
prompt["3"]["inputs"]["seed"] = 5
ws = websocket.WebSocket()
ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
images = get_images(ws, prompt)
#Commented out code to display the output images:
# for node_id in images:
# for image_data in images[node_id]:
# from PIL import Image
# import io
# image = Image.open(io.BytesIO(image_data))
# image.show()

5
server.py
View File

@ -372,6 +372,11 @@ class PromptServer():
async def get_history(request):
return web.json_response(self.prompt_queue.get_history())
@routes.get("/history/{prompt_id}")
async def get_history(request):
prompt_id = request.match_info.get("prompt_id", None)
return web.json_response(self.prompt_queue.get_history(prompt_id=prompt_id))
@routes.get("/queue")
async def get_queue(request):
queue_info = {}

226
web/extensions/core/contextMenuFilter.js
View File

@ -1,132 +1,138 @@
import { app } from "/scripts/app.js";
import {app} from "/scripts/app.js";
// Adds filtering to combo context menus
const id = "Comfy.ContextMenuFilter";
app.registerExtension({
name: id,
const ext = {
name: "Comfy.ContextMenuFilter",
init() {
const ctxMenu = LiteGraph.ContextMenu;
LiteGraph.ContextMenu = function (values, options) {
const ctx = ctxMenu.call(this, values, options);
// If we are a dark menu (only used for combo boxes) then add a filter input
if (options?.className === "dark" && values?.length > 10) {
const filter = document.createElement("input");
Object.assign(filter.style, {
width: "calc(100% - 10px)",
border: "0",
boxSizing: "border-box",
background: "#333",
border: "1px solid #999",
margin: "0 0 5px 5px",
color: "#fff",
});
filter.classList.add("comfy-context-menu-filter");
filter.placeholder = "Filter list";
this.root.prepend(filter);
let selectedIndex = 0;
let items = this.root.querySelectorAll(".litemenu-entry");
let itemCount = items.length;
let selectedItem;
const items = Array.from(this.root.querySelectorAll(".litemenu-entry"));
let displayedItems = [...items];
let itemCount = displayedItems.length;
// Apply highlighting to the selected item
function updateSelected() {
if (selectedItem) {
selectedItem.style.setProperty("background-color", "");
selectedItem.style.setProperty("color", "");
}
selectedItem = items[selectedIndex];
if (selectedItem) {
selectedItem.style.setProperty("background-color", "#ccc", "important");
selectedItem.style.setProperty("color", "#000", "important");
}
}
// We must request an animation frame for the current node of the active canvas to update.
requestAnimationFrame(() => {
const currentNode = LGraphCanvas.active_canvas.current_node;
const clickedComboValue = currentNode.widgets
.filter(w => w.type === "combo" && w.options.values.length === values.length)
.find(w => w.options.values.every((v, i) => v === values[i]))
.value;
const positionList = () => {
const rect = this.root.getBoundingClientRect();
// If the top is off screen then shift the element with scaling applied
if (rect.top < 0) {
const scale = 1 - this.root.getBoundingClientRect().height / this.root.clientHeight;
const shift = (this.root.clientHeight * scale) / 2;
this.root.style.top = -shift + "px";
}
}
updateSelected();
// Arrow up/down to select items
filter.addEventListener("keydown", (e) => {
if (e.key === "ArrowUp") {
if (selectedIndex === 0) {
selectedIndex = itemCount - 1;
} else {
selectedIndex--;
}
updateSelected();
e.preventDefault();
} else if (e.key === "ArrowDown") {
if (selectedIndex === itemCount - 1) {
selectedIndex = 0;
} else {
selectedIndex++;
}
updateSelected();
e.preventDefault();
} else if ((selectedItem && e.key === "Enter") || e.keyCode === 13 || e.keyCode === 10) {
selectedItem.click();
} else if(e.key === "Escape") {
this.close();
}
});
filter.addEventListener("input", () => {
// Hide all items that dont match our filter
const term = filter.value.toLocaleLowerCase();
items = this.root.querySelectorAll(".litemenu-entry");
// When filtering recompute which items are visible for arrow up/down
// Try and maintain selection
let visibleItems = [];
for (const item of items) {
const visible = !term || item.textContent.toLocaleLowerCase().includes(term);
if (visible) {
item.style.display = "block";
if (item === selectedItem) {
selectedIndex = visibleItems.length;
}
visibleItems.push(item);
} else {
item.style.display = "none";
if (item === selectedItem) {
selectedIndex = 0;
}
}
}
items = visibleItems;
let selectedIndex = values.findIndex(v => v === clickedComboValue);
let selectedItem = displayedItems?.[selectedIndex];
updateSelected();
// If we have an event then we can try and position the list under the source
if (options.event) {
let top = options.event.clientY - 10;
const bodyRect = document.body.getBoundingClientRect();
const rootRect = this.root.getBoundingClientRect();
if (bodyRect.height && top > bodyRect.height - rootRect.height - 10) {
top = Math.max(0, bodyRect.height - rootRect.height - 10);
}
this.root.style.top = top + "px";
positionList();
// Apply highlighting to the selected item
function updateSelected() {
selectedItem?.style.setProperty("background-color", "");
selectedItem?.style.setProperty("color", "");
selectedItem = displayedItems[selectedIndex];
selectedItem?.style.setProperty("background-color", "#ccc", "important");
selectedItem?.style.setProperty("color", "#000", "important");
}
});
requestAnimationFrame(() => {
// Focus the filter box when opening
filter.focus();
const positionList = () => {
const rect = this.root.getBoundingClientRect();
positionList();
});
// If the top is off-screen then shift the element with scaling applied
if (rect.top < 0) {
const scale = 1 - this.root.getBoundingClientRect().height / this.root.clientHeight;
const shift = (this.root.clientHeight * scale) / 2;
this.root.style.top = -shift + "px";
}
}
// Arrow up/down to select items
filter.addEventListener("keydown", (event) => {
switch (event.key) {
case "ArrowUp":
event.preventDefault();
if (selectedIndex === 0) {
selectedIndex = itemCount - 1;
} else {
selectedIndex--;
}
updateSelected();
break;
case "ArrowRight":
event.preventDefault();
selectedIndex = itemCount - 1;
updateSelected();
break;
case "ArrowDown":
event.preventDefault();
if (selectedIndex === itemCount - 1) {
selectedIndex = 0;
} else {
selectedIndex++;
}
updateSelected();
break;
case "ArrowLeft":
event.preventDefault();
selectedIndex = 0;
updateSelected();
break;
case "Enter":
selectedItem?.click();
break;
case "Escape":
this.close();
break;
}
});
filter.addEventListener("input", () => {
// Hide all items that don't match our filter
const term = filter.value.toLocaleLowerCase();
// When filtering, recompute which items are visible for arrow up/down and maintain selection.
displayedItems = items.filter(item => {
const isVisible = !term || item.textContent.toLocaleLowerCase().includes(term);
item.style.display = isVisible ? "block" : "none";
return isVisible;
});
selectedIndex = 0;
if (displayedItems.includes(selectedItem)) {
selectedIndex = displayedItems.findIndex(d => d === selectedItem);
}
itemCount = displayedItems.length;
updateSelected();
// If we have an event then we can try and position the list under the source
if (options.event) {
let top = options.event.clientY - 10;
const bodyRect = document.body.getBoundingClientRect();
const rootRect = this.root.getBoundingClientRect();
if (bodyRect.height && top > bodyRect.height - rootRect.height - 10) {
top = Math.max(0, bodyRect.height - rootRect.height - 10);
}
this.root.style.top = top + "px";
positionList();
}
});
requestAnimationFrame(() => {
// Focus the filter box when opening
filter.focus();
positionList();
});
})
}
return ctx;
@ -134,4 +140,6 @@ app.registerExtension({
LiteGraph.ContextMenu.prototype = ctxMenu.prototype;
},
});
}
app.registerExtension(ext);

9
web/extensions/core/dynamicPrompts.js
View File

@ -3,6 +3,13 @@ import { app } from "../../scripts/app.js";
// Allows for simple dynamic prompt replacement
// Inputs in the format {a|b} will have a random value of a or b chosen when the prompt is queued.
/*
* Strips C-style line and block comments from a string
*/
function stripComments(str) {
return str.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g,'');
}
app.registerExtension({
name: "Comfy.DynamicPrompts",
nodeCreated(node) {
@ -15,7 +22,7 @@ app.registerExtension({
for (const widget of widgets) {
// Override the serialization of the value to resolve dynamic prompts for all widgets supporting it in this node
widget.serializeValue = (workflowNode, widgetIndex) => {
let prompt = widget.value;
let prompt = stripComments(widget.value);
while (prompt.replace("\\{", "").includes("{") && prompt.replace("\\}", "").includes("}")) {
const startIndex = prompt.replace("\\{", "00").indexOf("{");
const endIndex = prompt.replace("\\}", "00").indexOf("}");

17
web/extensions/core/widgetInputs.js
View File

@ -200,8 +200,23 @@ app.registerExtension({
applyToGraph() {
if (!this.outputs[0].links?.length) return;
function get_links(node) {
let links = [];
for (const l of node.outputs[0].links) {
const linkInfo = app.graph.links[l];
const n = node.graph.getNodeById(linkInfo.target_id);
if (n.type == "Reroute") {
links = links.concat(get_links(n));
} else {
links.push(l);
}
}
return links;
}
let links = get_links(this);
// For each output link copy our value over the original widget value
for (const l of this.outputs[0].links) {
for (const l of links) {
const linkInfo = app.graph.links[l];
const node = this.graph.getNodeById(linkInfo.target_id);
const input = node.inputs[linkInfo.target_slot];

15
web/style.css
View File

@ -50,7 +50,7 @@ body {
padding: 30px 30px 10px 30px;
background-color: var(--comfy-menu-bg); /* Modal background */
color: var(--error-text);
box-shadow: 0px 0px 20px #888888;
box-shadow: 0 0 20px #888888;
border-radius: 10px;
top: 50%;
left: 50%;
@ -84,7 +84,7 @@ body {
font-size: 15px;
position: absolute;
top: 50%;
right: 0%;
right: 0;
text-align: center;
z-index: 100;
width: 170px;
@ -252,7 +252,7 @@ button.comfy-queue-btn {
bottom: 0 !important;
left: auto !important;
right: 0 !important;
border-radius: 0px;
border-radius: 0;
}
.comfy-menu span.drag-handle {
visibility:hidden
@ -291,7 +291,7 @@ button.comfy-queue-btn {
.litegraph .dialog {
z-index: 1;
font-family: Arial;
font-family: Arial, sans-serif;
}
.litegraph .litemenu-entry.has_submenu {
@ -330,6 +330,13 @@ button.comfy-queue-btn {
color: var(--input-text) !important;
}
.comfy-context-menu-filter {
box-sizing: border-box;
border: 1px solid #999;
margin: 0 0 5px 5px;
width: calc(100% - 10px);
}
/* Search box */
.litegraph.litesearchbox {