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Efficient Loader:
- Added Baked VAE as an option on VAE select.
- Node will no longer reload unnecessarily on changes.

Efficient Sampler:
- Added a 'Preview Image' toggle on the Efficient Ksampler.

Image Overlay:
- Fixed rotation of overlay cropping the overlay image.
- Fixed an issue with the opacity slider not applying transparency correctly.
This commit is contained in:
TSC
2023-04-07 16:06:46 -05:00
committed by GitHub
parent 83903e64f7
commit de81cead21
1 changed files with 63 additions and 28 deletions
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91
efficiency_nodes.py
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@@ -1,4 +1,4 @@
# Efficiency Nodes - A collection of some of my ComfyUI custom nodes
# Efficiency Nodes - A collection of my ComfyUI custom nodes to help streamline workflows and reduce total node count.
# by Luciano Cirino (Discord: TSC#9184) - April 2023
from PIL import Image, ImageFilter, ImageEnhance, ImageOps, ImageDraw, ImageChops, ImageFont
@@ -17,10 +17,8 @@ import folder_paths
import model_management
import importlib
#import matplotlib.pyplot as plt
import random
# Get the absolute path of the parent directory of the current script
my_dir = os.path.dirname(os.path.abspath(__file__))
@@ -48,11 +46,19 @@ def pil2tensor(image: Image.Image) -> torch.Tensor:
# TSC Efficient Loader
# Track what objects have already been loaded into memory (*only for instances of this node)
loaded_objects = {
"ckpt": [], # (ckpt_name, location)
"clip": [], # (ckpt_name, location)
"bvae": [], # (ckpt_name, location)
"vae": [] # (vae_name, location)
}
class TSC_EfficientLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
"vae_name": (folder_paths.get_filename_list("vae"),),
"vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
"clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"positive": ("STRING", {"default": "Positive","multiline": True}),
"negative": ("STRING", {"default": "Negative", "multiline": True}),
@@ -68,24 +74,57 @@ class TSC_EfficientLoader:
CATEGORY = "Efficiency Nodes/Loaders"
def efficientloader(self, ckpt_name, vae_name, clip_skip, positive, negative, empty_latent_width, empty_latent_height, batch_size,
output_vae=True, output_clip=True):
# Load Checkpoint
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
output_vae=False, output_clip=True):
# Baked VAE setup
if vae_name == "Baked VAE":
output_vae = True
# Search for tuple index that contains ckpt_name in "ckpt" array of loaded_lbjects
checkpoint_found = False
for i, entry in enumerate(loaded_objects["ckpt"]):
if entry[0] == ckpt_name:
# Extract the second element of the tuple at 'i' in the "ckpt", "clip", "bvae" arrays
model = loaded_objects["ckpt"][i][1]
clip = loaded_objects["clip"][i][1]
vae = loaded_objects["bvae"][i][1]
checkpoint_found = True
break
# If not found, load ckpt
if checkpoint_found == False:
# Load Checkpoint
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
model = out[0]
clip = out[1]
vae = out[2]
# Update loaded_objects[] array
loaded_objects["ckpt"].append((ckpt_name, out[0]))
loaded_objects["clip"].append((ckpt_name, out[1]))
loaded_objects["bvae"].append((ckpt_name, out[2]))
# Create Empty Latent
latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
# Load VAE
vae_path = folder_paths.get_full_path("vae", vae_name)
vae = comfy.sd.VAE(ckpt_path=vae_path)
# Check for custom VAE
if vae_name != "Baked VAE":
# Check if vae_name exists in "vae" array
if any(entry[0] == vae_name for entry in loaded_objects["vae"]):
# Extract the second tuple entry of the checkpoint
vae = [entry[1] for entry in loaded_objects["vae"] if entry[0] == vae_name][0]
else:
vae_path = folder_paths.get_full_path("vae", vae_name)
vae = comfy.sd.VAE(ckpt_path=vae_path)
# Update loaded_objects[] array
loaded_objects["vae"].append((vae_name, vae))
# Extract CLIP
clip = out[1] #second entry
# CLIP skip
clip = clip.clone()
clip.clip_layer(clip_skip)
return (out[0], [[clip.encode(positive), {}]], [[clip.encode(negative), {}]], {"samples":latent}, vae, out[1], )
return (model, [[clip.encode(positive), {}]], [[clip.encode(negative), {}]], {"samples":latent}, vae, clip, )
# TSC KSampler (Efficient)
@@ -115,6 +154,7 @@ class TSC_KSampler:
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"preview_image": (["Disabled", "Enabled"],),
},
"optional": { "optional_vae": ("VAE",), },
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
@@ -126,7 +166,8 @@ class TSC_KSampler:
OUTPUT_NODE = True
CATEGORY = "Efficiency Nodes/Sampling"
def sample(self, sampler_state, my_unique_id, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, prompt=None, extra_pnginfo=None, optional_vae=(None,)):
def sample(self, sampler_state, my_unique_id, model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, preview_image, denoise=1.0, prompt=None, extra_pnginfo=None, optional_vae=(None,)):
global last_helds
last_results = last_helds["results"][my_unique_id]
@@ -147,7 +188,7 @@ class TSC_KSampler:
latent = samples[0]["samples"]
last_helds["latent"][my_unique_id] = latent
if vae == (None,):
if vae == (None,) or preview_image == "Disabled":
last_results = None
print('\033[38;2;62;116;77mTSC_Nodes:\033[0m TSC_KSampler({}): no vae input detected, preview disabled'.format(my_unique_id))
return {"ui": {"images": list()}, "result": (model, positive, negative, {"samples": latent}, vae, empty_image, )}
@@ -216,7 +257,6 @@ class TSC_KSampler:
# TSC Image Overlay
class TSC_ImageOverlay:
#upscale_methods = ["nearest-exact", "bilinear", "area"]
@classmethod
def INPUT_TYPES(cls):
@@ -253,12 +293,10 @@ class TSC_ImageOverlay:
#Extract overlay size and store in Tuple "overlay_size" (WxH)
overlay_size = overlay.size()
overlay_size = (overlay_size[2], overlay_size[1])
#print(overlay_size)
if size_option == "Fit":
overlay_size = (base.size[0],base.size[1])
elif size_option == "Resize by rescale_factor":
overlay_size = tuple(int(dimension * rescale_factor) for dimension in overlay_size)
#overlay_size = (int(overlay.size()[0] * rescale_factor), int(overlay.size()[1] * rescale_factor))
elif size_option == "Resize to width & heigth":
overlay_size = (size[0], size[1])
@@ -269,6 +307,7 @@ class TSC_ImageOverlay:
overlay = tensor2pil(overlay)
# Add Alpha channel to overlay
overlay = overlay.convert('RGBA')
overlay.putalpha(Image.new("L", overlay.size, 255))
# If mask connected, check if the overlay image has an alpha channel
@@ -276,21 +315,16 @@ class TSC_ImageOverlay:
# Convert mask to pil and resize
mask = tensor2pil(mask)
mask = mask.resize(overlay.size)
#Test
#samples = mask.movedim(-1, 1)
#mask = comfy.utils.common_upscale(samples, overlay_size[0], overlay_size[1], resize_method, False)
#mask = mask.movedim(1, -1)
#mask = tensor2pil(mask)
# Apply mask as overlay's alpha
overlay.putalpha(ImageOps.invert(mask))
# Rotate the overlay image
overlay = overlay.rotate(rotation, expand=False)
overlay = overlay.rotate(rotation, expand=True)
# Apply opacity on overlay image
overlay = overlay.point(lambda x: int(x * (1 - opacity / 100)))
r, g, b, a = overlay.split()
a = a.point(lambda x: max(0, int(x * (1 - opacity / 100))))
overlay.putalpha(a)
# Paste the overlay image onto the base image
if mask is None:
@@ -328,6 +362,7 @@ class TSC_EvaluateInts:
print(f"{python_expression} = \033[92m INT: " + str(int_result) + " , FLOAT: " + str(float_result) + "\033[0m")
return (int_result, float_result,)
# TSC Evaluate Strings
class TSC_EvaluateStrs:
@classmethod