nodes/TinyTerra-ComfyUI_tinyterraNodes
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SDXL Support and Bislerp added

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TinyTerra
2023-07-28 23:38:41 +10:00
parent c6c6f92e4b
commit 70acba66e6
3 changed files with 102 additions and 43 deletions
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adv_encode.py
+2 -2
View File
@@ -212,7 +212,7 @@ def advanced_encode_from_tokens(tokenized, token_normalization, weight_interpret
return weighted_emb, pooled
else:
return weighted_emb, pooled_base
return [[weighted_emb,{}]], None
return weighted_emb, None
def encode_token_weights_g(model, token_weight_pairs):
return model.clip_g.encode_token_weights(token_weight_pairs)
@@ -287,4 +287,4 @@ def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpret
repeat_l = int((embs_g.shape[1] / gcd_num) * embs_l.shape[1])
repeat_g = int((embs_l.shape[1] / gcd_num) * embs_g.shape[1])
return prepareXL(embs_l.expand((-1,repeat_l,-1)), embs_g.expand((-1,repeat_g,-1)), pooled, clip_balance)
return prepareXL(embs_l.expand((-1,repeat_l,-1)), embs_g.expand((-1,repeat_g,-1)), pooled, clip_balance)
js/ttNinterface.js
+59 -7
View File
@@ -281,6 +281,44 @@ app.registerExtension({
return false;
};
LGraphCanvas.ttNlinkStyleBorder = function(value, options, e, menu, node) {
new LiteGraph.ContextMenu(
[false, true],
{ event: e, callback: inner_clicked, parentMenu: menu, node: node }
);
function inner_clicked(v) {
if (!node) {
return;
}
localStorage.setItem('Comfy.Settings.ttN.links_render_border', JSON.stringify(v));
app.canvas.render_connections_border = v;
}
return false;
};
LGraphCanvas.ttNlinkStyleShadow = function(value, options, e, menu, node) {
new LiteGraph.ContextMenu(
[false, true],
{ event: e, callback: inner_clicked, parentMenu: menu, node: node }
);
function inner_clicked(v) {
if (!node) {
return;
}
localStorage.setItem('Comfy.Settings.ttN.links_render_shadow', JSON.stringify(v));
app.canvas.render_connections_shadows = v;
}
return false;
};
LGraphCanvas.ttNsetDefaultBGColor = function(value, options, e, menu, node) {
if (!node) {
throw "no node for color";
@@ -353,6 +391,22 @@ app.registerExtension({
);
return options;
};
LGraphCanvas.prototype.ttNupdateRenderSettings = function (app) {
console.log('i made it')
let customLinkType = Number(localStorage.getItem('Comfy.Settings.ttN.links_render_mode'));
if (customLinkType !== undefined) {app.canvas.links_render_mode = customLinkType}
let showLinkBorder = Number(localStorage.getItem('Comfy.Settings.ttN.links_render_border'));
if (showLinkBorder !== undefined) {app.canvas.render_connections_border = showLinkBorder}
let showLinkShadow = Number(localStorage.getItem('Comfy.Settings.ttN.links_render_shadow'));
if (showLinkShadow) {app.canvas.render_connections_shadows = showLinkShadow}
var customLinkColors = JSON.parse(localStorage.getItem('Comfy.Settings.ttN.customLinkColors')) || {};
Object.assign(app.canvas.default_connection_color_byType, customLinkColors);
Object.assign(LGraphCanvas.link_type_colors, customLinkColors);
}
},
beforeRegisterNodeDef(nodeType, nodeData, app) {
@@ -379,6 +433,8 @@ app.registerExtension({
}
menu_info.push({ content: "Slot Type Color (ttN)", slot: slot, callback: () => { LGraphCanvas.prototype.ttNsetSlotTypeColor(slot) } });
menu_info.push({ content: "Show Link Border (ttN)", has_submenu: true, slot: slot, callback: LGraphCanvas.ttNlinkStyleBorder });
menu_info.push({ content: "Show Link Shadow (ttN)", has_submenu: true, slot: slot, callback: LGraphCanvas.ttNlinkStyleShadow });
menu_info.push({ content: "Link Style (ttN)", has_submenu: true, slot: slot, callback: LGraphCanvas.ttNonShowLinkStyles });
return menu_info;
@@ -386,19 +442,15 @@ app.registerExtension({
},
setup() {
let customLinkType = Number(localStorage.getItem('Comfy.Settings.ttN.links_render_mode'));
if (customLinkType) {app.canvas.links_render_mode = customLinkType}
var customLinkColors = JSON.parse(localStorage.getItem('Comfy.Settings.ttN.customLinkColors')) || {};
Object.assign(app.canvas.default_connection_color_byType, customLinkColors);
Object.assign(LGraphCanvas.link_type_colors, customLinkColors);
LGraphCanvas.prototype.ttNupdateRenderSettings(app);
},
nodeCreated(node) {
let defaultBGColor = JSON.parse(localStorage.getItem('Comfy.Settings.ttN.defaultBGColor'));
if (defaultBGColor) {LGraphCanvas.prototype.ttNdefaultBGcolor(node, defaultBGColor)};
},
loadedGraphNode(node, app) {
LGraphCanvas.prototype.ttNupdateRenderSettings(app);
let defaultBGColor = JSON.parse(localStorage.getItem('Comfy.Settings.ttN.defaultBGColor'));
if (defaultBGColor) {LGraphCanvas.prototype.ttNdefaultBGcolor(node, defaultBGColor)};
},
tinyterraNodes.py
+41 -34
View File
@@ -19,11 +19,11 @@ from torch import Tensor
from pathlib import Path
import comfy.model_management
from PIL.PngImagePlugin import PngInfo
from .adv_encode import advanced_encode
from PIL import Image, ImageDraw, ImageFont
from comfy.sd import ModelPatcher, CLIP, VAE
from typing import Dict, List, Optional, Tuple, Union
from comfy_extras.chainner_models import model_loading
from .adv_encode import advanced_encode, advanced_encode_XL
class CC:
CLEAN = '\33[0m'
@@ -342,24 +342,18 @@ def enforce_mul_of_64(d):
return int(d)
def upscale(samples, upscale_method, factor, crop):
s = samples.copy()
x = samples["samples"].shape[3]
y = samples["samples"].shape[2]
def upscale(samples, upscale_method, scale_by, crop):
s = samples.copy()
width = enforce_mul_of_64(round(samples["samples"].shape[3] * scale_by))
height = enforce_mul_of_64(round(samples["samples"].shape[2] * scale_by))
new_x = int(x * factor)
new_y = int(y * factor)
if (new_x > MAX_RESOLUTION):
new_x = MAX_RESOLUTION
if (new_y > MAX_RESOLUTION):
new_y = MAX_RESOLUTION
s["samples"] = comfy.utils.common_upscale(
samples["samples"], enforce_mul_of_64(
new_x), enforce_mul_of_64(new_y), upscale_method, crop
)
return (s,)
if (width > MAX_RESOLUTION):
width = MAX_RESOLUTION
if (height > MAX_RESOLUTION):
height = MAX_RESOLUTION
s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, crop)
return (s,)
def tensor2pil(image: torch.Tensor) -> Image.Image:
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
@@ -495,7 +489,7 @@ def save_images(self, images, preview_prefix, save_prefix, image_output, prompt=
#---------------------------------------------------------------ttN/pipe START----------------------------------------------------------------------#
class ttN_TSC_pipeLoader:
version = '1.0.0'
version = '1.0.1'
@classmethod
def INPUT_TYPES(cls):
return {"required": {
@@ -517,11 +511,11 @@ class ttN_TSC_pipeLoader:
"positive": ("STRING", {"default": "Positive","multiline": True}),
"positive_token_normalization": (["none", "mean", "length", "length+mean"],),
"positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],),
"positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"negative": ("STRING", {"default": "Negative", "multiline": True}),
"negative_token_normalization": (["none", "mean", "length", "length+mean"],),
"negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],),
"negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
@@ -577,8 +571,11 @@ class ttN_TSC_pipeLoader:
clip = clip.clone()
clip.clip_layer(clip_skip)
positive_embeddings_final, pooled = advanced_encode(clip, positive, positive_token_normalization, positive_weight_interpretation, w_max=1.0)
negative_embeddings_final, pooled = advanced_encode(clip, negative, negative_token_normalization, negative_weight_interpretation, w_max=1.0)
positive_embeddings_final, positive_pooled = advanced_encode(clip, positive, positive_token_normalization, positive_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
positive_embeddings_final = [[positive_embeddings_final, {"pooled_output": positive_pooled}]]
negative_embeddings_final, negative_pooled = advanced_encode(clip, negative, negative_token_normalization, negative_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
negative_embeddings_final = [[negative_embeddings_final, {"pooled_output": negative_pooled}]]
image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
pipe = {"vars": {"model": model,
@@ -611,11 +608,17 @@ class ttN_TSC_pipeLoader:
"lora3_model_strength": lora3_model_strength,
"lora3_clip_strength": lora3_clip_strength,
"positive": positive,
"positive_l": None,
"positive_g": None,
"positive_token_normalization": positive_token_normalization,
"positive_weight_interpretation": positive_weight_interpretation,
"positive_balance": None,
"negative": negative,
"negative_l": None,
"negative_g": None,
"negative_token_normalization": negative_token_normalization,
"negative_weight_interpretation": negative_weight_interpretation,
"negative_balance": None,
"empty_latent_width": empty_latent_width,
"empty_latent_height": empty_latent_height,
"batch_size": batch_size,
@@ -627,9 +630,9 @@ class ttN_TSC_pipeLoader:
return (pipe, model, positive_embeddings_final, negative_embeddings_final, samples, vae, clip, seed)
class ttN_TSC_pipeKSampler:
version = '1.0.1'
version = '1.0.2'
empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0)))
upscale_methods = ["None", "nearest-exact", "bilinear", "area"]
upscale_methods = ["None", "nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
crop_methods = ["disabled", "center"]
def __init__(self):
@@ -961,9 +964,12 @@ class ttN_TSC_pipeKSampler:
clip = clip.clone()
clip.clip_layer(plot_image_vars['clip_skip'])
positive, pooled = advanced_encode(clip, plot_image_vars['positive'], plot_image_vars['positive_token_normalization'], plot_image_vars['positive_weight_interpretation'], w_max=1.0)
negative, pooled = advanced_encode(clip, plot_image_vars['negative'], plot_image_vars['negative_token_normalization'], plot_image_vars['negative_weight_interpretation'], w_max=1.0)
positive, positive_pooled = advanced_encode(clip, plot_image_vars['positive'], plot_image_vars['positive_token_normalization'], plot_image_vars['positive_weight_interpretation'], w_max=1.0, apply_to_pooled="enable")
positive = [[positive, {"pooled_output": positive_pooled}]]
negative, negative_pooled = advanced_encode(clip, plot_image_vars['negative'], plot_image_vars['negative_token_normalization'], plot_image_vars['negative_weight_interpretation'], w_max=1.0, apply_to_pooled="enable")
negative = [[negative, {"pooled_output": negative_pooled}]]
model = model if model is not None else plot_image_vars["model"]
clip = clip if clip is not None else plot_image_vars["clip"]
vae = vae if vae is not None else plot_image_vars["vae"]
@@ -1195,9 +1201,9 @@ class ttN_TSC_pipeKSampler:
return process_hold_state(self, pipe, image_output, preview_prefix, save_prefix, prompt, extra_pnginfo, my_unique_id)
class ttN_pipeKSamplerAdvanced:
version = '1.0.2'
version = '1.0.3'
empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0)))
upscale_methods = ["None", "nearest-exact", "bilinear", "area"]
upscale_methods = ["None", "nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
crop_methods = ["disabled", "center"]
def __init__(self):
@@ -1916,8 +1922,8 @@ class ttN_imageOUPUT:
"result": (image,)}
class ttN_modelScale:
version = '1.0.0'
upscale_methods = ["nearest-exact", "bilinear", "area"]
version = '1.0.1'
upscale_methods = ["None", "nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
crop_methods = ["disabled", "center"]
@classmethod
@@ -2021,8 +2027,9 @@ class ttN_modelScale:
TTN_VERSIONS = {
"tinyterraNodes": ttN_version,
"pipeLoader": ttN_TSC_pipeLoader.version,
"pipeLoader": ttN_TSC_pipeLoader.version,
"pipeKSampler": ttN_TSC_pipeKSampler.version,
"pipeKSamplerAdvanced": ttN_pipeKSamplerAdvanced.version,
"pipeIN": ttN_pipe_IN.version,
"pipeOUT": ttN_pipe_OUT.version,
"pipeEDIT": ttN_pipe_EDIT.version,
@@ -2043,7 +2050,7 @@ TTN_VERSIONS = {
}
NODE_CLASS_MAPPINGS = {
#ttN/pipe
"ttN pipeLoader": ttN_TSC_pipeLoader,
"ttN pipeLoader": ttN_TSC_pipeLoader,
"ttN pipeKSampler": ttN_TSC_pipeKSampler,
"ttN pipeKSamplerAdvanced": ttN_pipeKSamplerAdvanced,
"ttN xyPlot": ttN_XYPlot,