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Merge branch 'master' of github.com:comfyanonymous/ComfyUI

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doctorpangloss 2024-05-07 13:57:53 -07:00
commit 3a64e04a93
8 changed files with 175 additions and 100 deletions
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49
README.md
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@ -448,29 +448,32 @@ The default installation includes a fast latent preview method that's low-resolu
## Keyboard Shortcuts ## Keyboard Shortcuts
| Keybind | Explanation | | Keybind | Explanation |
|---------------------------|--------------------------------------------------------------------------------------------------------------------| |------------------------------------|--------------------------------------------------------------------------------------------------------------------|
| Ctrl + Enter | Queue up current graph for generation | | Ctrl + Enter | Queue up current graph for generation |
| Ctrl + Shift + Enter | Queue up current graph as first for generation | | Ctrl + Shift + Enter | Queue up current graph as first for generation |
| Ctrl + Z/Ctrl + Y | Undo/Redo | | Ctrl + Z/Ctrl + Y | Undo/Redo |
| Ctrl + S | Save workflow | | Ctrl + S | Save workflow |
| Ctrl + O | Load workflow | | Ctrl + O | Load workflow |
| Ctrl + A | Select all nodes | | Ctrl + A | Select all nodes |
| Alt + C | Collapse/uncollapse selected nodes | | Alt + C | Collapse/uncollapse selected nodes |
| Ctrl + M | Mute/unmute selected nodes | | Ctrl + M | Mute/unmute selected nodes |
| Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) | | Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |
| Delete/Backspace | Delete selected nodes | | Delete/Backspace | Delete selected nodes |
| Ctrl + Delete/Backspace | Delete the current graph | | Ctrl + Delete/Backspace | Delete the current graph |
| Space | Move the canvas around when held and moving the cursor | | Space | Move the canvas around when held and moving the cursor |
| Ctrl/Shift + Click | Add clicked node to selection | | Ctrl/Shift + Click | Add clicked node to selection |
| Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) | | Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |
| Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) | | Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
| Shift + Drag | Move multiple selected nodes at the same time | | Shift + Drag | Move multiple selected nodes at the same time |
| Ctrl + D | Load default graph | | Ctrl + D | Load default graph |
| Q | Toggle visibility of the queue | | Alt + `+` | Canvas Zoom in |
| H | Toggle visibility of history | | Alt + `-` | Canvas Zoom out |
| R | Refresh graph | | Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out |
| Double-Click LMB | Open node quick search palette | | Q | Toggle visibility of the queue |
| H | Toggle visibility of history |
| R | Refresh graph |
| Double-Click LMB | Open node quick search palette |
Ctrl can also be replaced with Cmd instead for macOS users Ctrl can also be replaced with Cmd instead for macOS users

15
comfy/node_helpers.py
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@ -1,3 +1,4 @@
from PIL import Image, ImageFile, UnidentifiedImageError
def conditioning_set_values(conditioning, values={}): def conditioning_set_values(conditioning, values={}):
c = [] c = []
@ -8,3 +9,17 @@ def conditioning_set_values(conditioning, values={}):
c.append(n) c.append(n)
return c return c
def open_image(path):
prev_value = None
try:
img = Image.open(path)
except (UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445
prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
ImageFile.LOAD_TRUNCATED_IMAGES = True
img = Image.open(path)
finally:
if prev_value is not None:
ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
return img

13
comfy/nodes/base_nodes.py
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@ -7,7 +7,7 @@ import math
import random import random
import logging import logging
from PIL import Image, ImageOps, ImageSequence from PIL import Image, ImageOps, ImageSequence, ImageFile
from PIL.PngImagePlugin import PngInfo from PIL.PngImagePlugin import PngInfo
from natsort import natsorted from natsort import natsorted
from pkg_resources import resource_filename from pkg_resources import resource_filename
@ -1460,9 +1460,18 @@ class LoadImage:
_, ext = os.path.splitext(image) _, ext = os.path.splitext(image)
if ext == ".exr": if ext == ".exr":
return load_exr(image_path, srgb=False) return load_exr(image_path, srgb=False)
with open_image(image_path) as img: with node_helpers.open_image(image_path)(image_path) as img:
for i in ImageSequence.Iterator(img): for i in ImageSequence.Iterator(img):
prev_value = None
try:
i = ImageOps.exif_transpose(i) i = ImageOps.exif_transpose(i)
except OSError:
prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
ImageFile.LOAD_TRUNCATED_IMAGES = True
i = ImageOps.exif_transpose(i)
finally:
if prev_value is not None:
ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
if i.mode == 'I': if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255)) i = i.point(lambda i: i * (1 / 255))
image = i.convert("RGB") image = i.convert("RGB")

84
comfy/sd.py
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@ -14,7 +14,6 @@ from . import utils
from . import clip_vision from . import clip_vision
from . import gligen from . import gligen
from . import diffusers_convert from . import diffusers_convert
from . import model_base
from . import model_detection from . import model_detection
from . import sd1_clip from . import sd1_clip
@ -22,9 +21,9 @@ from . import sd2_clip
from . import sdxl_clip from . import sdxl_clip
from . import model_patcher from . import model_patcher
from . import model_sampling
from . import lora from . import lora
from .t2i_adapter import adapter from .t2i_adapter import adapter
from . import supported_models_base
from .taesd import taesd from .taesd import taesd
def load_model_weights(model, sd): def load_model_weights(model, sd):
@ -418,6 +417,8 @@ def load_gligen(ckpt_path):
return model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) return model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.")
model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True)
#TODO: this function is a mess and should be removed eventually #TODO: this function is a mess and should be removed eventually
if config is None: if config is None:
with open(config_path, 'r') as stream: with open(config_path, 'r') as stream:
@ -425,81 +426,20 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
model_config_params = config['model']['params'] model_config_params = config['model']['params']
clip_config = model_config_params['cond_stage_config'] clip_config = model_config_params['cond_stage_config']
scale_factor = model_config_params['scale_factor'] scale_factor = model_config_params['scale_factor']
vae_config = model_config_params['first_stage_config']
fp16 = False
if "unet_config" in model_config_params:
if "params" in model_config_params["unet_config"]:
unet_config = model_config_params["unet_config"]["params"]
if "use_fp16" in unet_config:
fp16 = unet_config.pop("use_fp16")
if fp16:
unet_config["dtype"] = torch.float16
noise_aug_config = None
if "noise_aug_config" in model_config_params:
noise_aug_config = model_config_params["noise_aug_config"]
model_type = model_base.ModelType.EPS
if "parameterization" in model_config_params: if "parameterization" in model_config_params:
if model_config_params["parameterization"] == "v": if model_config_params["parameterization"] == "v":
model_type = model_base.ModelType.V_PREDICTION m = model.clone()
class ModelSamplingAdvanced(model_sampling.ModelSamplingDiscrete, model_sampling.V_PREDICTION):
pass
m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config))
model = m
clip = None layer_idx = clip_config.get("params", {}).get("layer_idx", None)
vae = None if layer_idx is not None:
clip.clip_layer(layer_idx)
class WeightsLoader(torch.nn.Module): return (model, clip, vae)
pass
if state_dict is None:
state_dict = utils.load_torch_file(ckpt_path)
class EmptyClass:
pass
model_config = supported_models_base.BASE({})
from . import latent_formats
model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor)
model_config.unet_config = model_detection.convert_config(unet_config)
if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"):
model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type)
else:
model = model_base.BaseModel(model_config, model_type=model_type)
if config['model']["target"].endswith("LatentInpaintDiffusion"):
model.set_inpaint()
if fp16:
model = model.half()
offload_device = model_management.unet_offload_device()
model = model.to(offload_device)
model.load_model_weights(state_dict, "model.diffusion_model.")
if output_vae:
vae_sd = utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True)
vae = VAE(sd=vae_sd, config=vae_config)
if output_clip:
w = WeightsLoader()
clip_target = EmptyClass()
clip_target.params = clip_config.get("params", {})
if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"):
clip_target.clip = sd2_clip.SD2ClipModel
clip_target.tokenizer = sd2_clip.SD2Tokenizer
clip = CLIP(clip_target, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model.clip_h
elif clip_config["target"].endswith("FrozenCLIPEmbedder"):
clip_target.clip = sd1_clip.SD1ClipModel
clip_target.tokenizer = sd1_clip.SD1Tokenizer
clip = CLIP(clip_target, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model.clip_l
load_clip_weights(w, state_dict)
return (model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True): def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
sd = utils.load_torch_file(ckpt_path) sd = utils.load_torch_file(ckpt_path)

46
comfy/web/extensions/core/maskeditor.js
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@ -164,6 +164,41 @@ class MaskEditorDialog extends ComfyDialog {
return divElement; return divElement;
} }
createOpacitySlider(self, name, callback) {
const divElement = document.createElement('div');
divElement.id = "maskeditor-opacity-slider";
divElement.style.cssFloat = "left";
divElement.style.fontFamily = "sans-serif";
divElement.style.marginRight = "4px";
divElement.style.color = "var(--input-text)";
divElement.style.backgroundColor = "var(--comfy-input-bg)";
divElement.style.borderRadius = "8px";
divElement.style.borderColor = "var(--border-color)";
divElement.style.borderStyle = "solid";
divElement.style.fontSize = "15px";
divElement.style.height = "21px";
divElement.style.padding = "1px 6px";
divElement.style.display = "flex";
divElement.style.position = "relative";
divElement.style.top = "2px";
divElement.style.pointerEvents = "auto";
self.opacity_slider_input = document.createElement('input');
self.opacity_slider_input.setAttribute('type', 'range');
self.opacity_slider_input.setAttribute('min', '0.1');
self.opacity_slider_input.setAttribute('max', '1.0');
self.opacity_slider_input.setAttribute('step', '0.01')
self.opacity_slider_input.setAttribute('value', '0.7');
const labelElement = document.createElement("label");
labelElement.textContent = name;
divElement.appendChild(labelElement);
divElement.appendChild(self.opacity_slider_input);
self.opacity_slider_input.addEventListener("input", callback);
return divElement;
}
setlayout(imgCanvas, maskCanvas) { setlayout(imgCanvas, maskCanvas) {
const self = this; const self = this;
@ -203,6 +238,13 @@ class MaskEditorDialog extends ComfyDialog {
self.updateBrushPreview(self, null, null); self.updateBrushPreview(self, null, null);
}); });
this.brush_opacity_slider = this.createOpacitySlider(self, "Opacity", (event) => {
self.brush_opacity = event.target.value;
if (self.brush_color_mode !== "negative") {
self.maskCanvas.style.opacity = self.brush_opacity;
}
});
this.colorButton = this.createLeftButton(this.getColorButtonText(), () => { this.colorButton = this.createLeftButton(this.getColorButtonText(), () => {
if (self.brush_color_mode === "black") { if (self.brush_color_mode === "black") {
self.brush_color_mode = "white"; self.brush_color_mode = "white";
@ -237,6 +279,7 @@ class MaskEditorDialog extends ComfyDialog {
bottom_panel.appendChild(this.saveButton); bottom_panel.appendChild(this.saveButton);
bottom_panel.appendChild(cancelButton); bottom_panel.appendChild(cancelButton);
bottom_panel.appendChild(this.brush_size_slider); bottom_panel.appendChild(this.brush_size_slider);
bottom_panel.appendChild(this.brush_opacity_slider);
bottom_panel.appendChild(this.colorButton); bottom_panel.appendChild(this.colorButton);
imgCanvas.style.position = "absolute"; imgCanvas.style.position = "absolute";
@ -472,7 +515,7 @@ class MaskEditorDialog extends ComfyDialog {
else { else {
return { return {
mixBlendMode: "initial", mixBlendMode: "initial",
opacity: "0.7", opacity: this.brush_opacity,
}; };
} }
} }
@ -538,6 +581,7 @@ class MaskEditorDialog extends ComfyDialog {
this.maskCtx.putImageData(maskData, 0, 0); this.maskCtx.putImageData(maskData, 0, 0);
} }
brush_opacity = 0.7;
brush_size = 10; brush_size = 10;
brush_color_mode = "black"; brush_color_mode = "black";
drawing_mode = false; drawing_mode = false;

40
comfy/web/scripts/app.js
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@ -953,6 +953,12 @@ export class ComfyApp {
const origProcessMouseDown = LGraphCanvas.prototype.processMouseDown; const origProcessMouseDown = LGraphCanvas.prototype.processMouseDown;
LGraphCanvas.prototype.processMouseDown = function(e) { LGraphCanvas.prototype.processMouseDown = function(e) {
// prepare for ctrl+shift drag: zoom start
if(e.ctrlKey && e.shiftKey && e.buttons) {
self.zoom_drag_start = [e.x, e.y, this.ds.scale];
return;
}
const res = origProcessMouseDown.apply(this, arguments); const res = origProcessMouseDown.apply(this, arguments);
this.selected_group_moving = false; this.selected_group_moving = false;
@ -973,6 +979,26 @@ export class ComfyApp {
const origProcessMouseMove = LGraphCanvas.prototype.processMouseMove; const origProcessMouseMove = LGraphCanvas.prototype.processMouseMove;
LGraphCanvas.prototype.processMouseMove = function(e) { LGraphCanvas.prototype.processMouseMove = function(e) {
// handle ctrl+shift drag
if(e.ctrlKey && e.shiftKey && self.zoom_drag_start) {
// stop canvas zoom action
if(!e.buttons) {
self.zoom_drag_start = null;
return;
}
// calculate delta
let deltaY = e.y - self.zoom_drag_start[1];
let startScale = self.zoom_drag_start[2];
let scale = startScale - deltaY/100;
this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]);
this.graph.change();
return;
}
const orig_selected_group = this.selected_group; const orig_selected_group = this.selected_group;
if (this.selected_group && !this.selected_group_resizing && !this.selected_group_moving) { if (this.selected_group && !this.selected_group_resizing && !this.selected_group_moving) {
@ -1059,6 +1085,20 @@ export class ComfyApp {
// Trigger onPaste // Trigger onPaste
return true; return true;
} }
if((e.key === '+') && e.altKey) {
block_default = true;
let scale = this.ds.scale * 1.1;
this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]);
this.graph.change();
}
if((e.key === '-') && e.altKey) {
block_default = true;
let scale = this.ds.scale * 1 / 1.1;
this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]);
this.graph.change();
}
} }
this.graph.change(); this.graph.change();

23
comfy_extras/nodes/nodes_custom_sampler.py
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@ -141,6 +141,7 @@ class SplitSigmas:
} }
} }
RETURN_TYPES = ("SIGMAS","SIGMAS") RETURN_TYPES = ("SIGMAS","SIGMAS")
RETURN_NAMES = ("high_sigmas", "low_sigmas")
CATEGORY = "sampling/custom_sampling/sigmas" CATEGORY = "sampling/custom_sampling/sigmas"
FUNCTION = "get_sigmas" FUNCTION = "get_sigmas"
@ -150,6 +151,27 @@ class SplitSigmas:
sigmas2 = sigmas[step:] sigmas2 = sigmas[step:]
return (sigmas1, sigmas2) return (sigmas1, sigmas2)
class SplitSigmasDenoise:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sigmas": ("SIGMAS", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS","SIGMAS")
RETURN_NAMES = ("high_sigmas", "low_sigmas")
CATEGORY = "sampling/custom_sampling/sigmas"
FUNCTION = "get_sigmas"
def get_sigmas(self, sigmas, denoise):
steps = max(sigmas.shape[-1] - 1, 0)
total_steps = round(steps * denoise)
sigmas1 = sigmas[:-(total_steps)]
sigmas2 = sigmas[-(total_steps + 1):]
return (sigmas1, sigmas2)
class FlipSigmas: class FlipSigmas:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -623,6 +645,7 @@ NODE_CLASS_MAPPINGS = {
"SamplerDPMPP_SDE": SamplerDPMPP_SDE, "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
"SamplerDPMAdaptative": SamplerDPMAdaptative, "SamplerDPMAdaptative": SamplerDPMAdaptative,
"SplitSigmas": SplitSigmas, "SplitSigmas": SplitSigmas,
"SplitSigmasDenoise": SplitSigmasDenoise,
"FlipSigmas": FlipSigmas, "FlipSigmas": FlipSigmas,
"CFGGuider": CFGGuider, "CFGGuider": CFGGuider,

5
comfy_extras/nodes/nodes_model_downscale.py
View File

@ -20,8 +20,9 @@ class PatchModelAddDownscale:
CATEGORY = "_for_testing" CATEGORY = "_for_testing"
def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
sigma_start = model.model.model_sampling.percent_to_sigma(start_percent) model_sampling = model.get_model_object("model_sampling")
sigma_end = model.model.model_sampling.percent_to_sigma(end_percent) sigma_start = model_sampling.percent_to_sigma(start_percent)
sigma_end = model_sampling.percent_to_sigma(end_percent)
def input_block_patch(h, transformer_options): def input_block_patch(h, transformer_options):
if transformer_options["block"][1] == block_number: if transformer_options["block"][1] == block_number: