nodes/sn0w12-ComfyUI-Sn0w-Scripts
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Added normal simple sampler, updated lora finding again

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sn0w12
2024-02-19 16:22:18 +01:00
parent a4b9d4f42f
commit d4a2bd26c1
5 changed files with 91 additions and 9 deletions
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__init__.py
+3
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@@ -6,6 +6,7 @@ from .src.lora_tester import LoraTestNode
from .src.character_select import CharacterSelectNode
from .src.prompt_combine import CombineStringNode
from .src.simple_sampler_xl import SimpleSamplerXlNode
from .src.simple_sampler import SimpleSamplerNode
from .src.lora_stacker import LoraStackerNode
from .src.load_lora_xl import LoraLoraXLNode
from .src.load_lora_15 import LoraLora15Node
@@ -24,6 +25,7 @@ NODE_CLASS_MAPPINGS = {
"Character Selector": CharacterSelectNode,
"Prompt Combine": CombineStringNode,
"Simple Sampler XL": SimpleSamplerXlNode,
"Simple Sampler": SimpleSamplerNode,
"Lora Stacker": LoraStackerNode,
"Load Lora XL": LoraLoraXLNode,
"Load Lora 1.5": LoraLora15Node,
@@ -43,6 +45,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"Character Selector": "Character Selector",
"Prompt Combine": "Prompt Combine",
"Simple Sampler XL": "Simple Sampler XL",
"Simple Sampler": "Simple Sampler",
"Lora Stacker": "Lora Stacker",
"Load Lora XL": "Load Lora XL",
"Load Lora 1.5": "Load Lora 1.5",
characters.json
+6 -1
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@@ -187,7 +187,7 @@
{
"name": "Hanekawa Tsubasa",
"associated_string": "hanekawa tsubasa, monogatari \\(series\\), bakemonogatari",
"prompt": "braid, braided ponytail, brown hair, large breasts, long hair, red eyes, single braid"
"prompt": "braid, braided ponytail, brown hair, large breasts, long hair, purple eyes, single braid"
},
{
"name": "Iori Utahime",
@@ -198,5 +198,10 @@
"name": "Tsunade",
"associated_string": "tsunade \\(naruto\\), naruto \\(series\\), naruto",
"prompt": "blonde hair, forehead mark, large breasts, long hair, parted bangs, brown eyes, mature female, hair intakes"
},
{
"name": "Asuna (blue archive)",
"associated_string": "asuna \\(blue archive\\), asuna \\(bunny\\) (blue archive), blue archive",
"prompt": "blue eyes, blue ribbon, hair ribbon, large breasts, light brown hair, very long hair, ribbon"
}
]
prompts.json
+5 -1
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@@ -1,6 +1,6 @@
[
{
"name": "Positive_XL",
"name": "Positive",
"prompt": "(masterpiece, exceptional, best aesthetic, best quality, masterpiece, extremely detailed:1.1), "
},
{
@@ -10,5 +10,9 @@
{
"name": "Negative_safe_XL",
"prompt": "(lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts:1.15), (rating:explicit, rating:questionable, rating:sensitive, signature, watermark, username, gumroad username, patreon username, artist name:1.1), multiple views, text, error, blurry"
},
{
"name": "Negative_1.5",
"prompt": "embedding:aid210, embedding:easynegative, embedding:verybadimagenegative_v1.3, speech bubble, disfigured, extra limbs, low quality, blurred, multiple views, futanari, loli, chibi, gumroad username, patreon username, (extra fingers, watermark, deformed hands, polydactyl, asymmetrical breasts, monochrome, greyscale:1.1)"
}
]
src/load_lora_character.py
+10 -7
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@@ -78,10 +78,8 @@ class LoadLoraCharacterNode:
# Extract just the filenames for comparison
lora_filenames = [path.split('\\')[-1] for path in lora_paths]
# Assume character_name is a string containing the character's name.
character_name_parts = character_name.lower().split()
# Assume lora_filenames is a list of filenames to search through.
character_name_lower = character_name.lower()
character_name_parts = character_name_lower.split()
closest_match = None
lowest_distance = float('inf')
@@ -89,10 +87,15 @@ class LoadLoraCharacterNode:
# Convert filename to lowercase for case-insensitive comparison.
filename_lower = filename.lower()
# Check if any part of the character name is in the filename.
if any(part in filename_lower for part in character_name_parts):
# Calculate the levenshtein distance for a closer match.
total_distance = sum(self.levenshtein_distance(part, filename_lower) for part in character_name_parts)
# Calculate the Levenshtein distance for the full character name as one of the metrics.
full_name_distance = self.levenshtein_distance(character_name_lower, filename_lower)
# Calculate the sum of distances for each part of the character name.
parts_distance = sum(self.levenshtein_distance(part, filename_lower) for part in character_name_parts)
# Use the minimum of full name distance and parts distance as the total distance.
total_distance = min(full_name_distance, parts_distance)
print_sn0w("Distance: " + str(total_distance) + " Lora: " + filename)
if total_distance < lowest_distance:
src/simple_sampler.py
+67
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@@ -0,0 +1,67 @@
from nodes import KSamplerAdvanced, VAEDecode, VAEEncode, EmptyLatentImage, ImageScaleBy, CLIPTextEncode
from comfy_extras.nodes_upscale_model import ImageUpscaleWithModel
import comfy.samplers
class SimpleSamplerNode:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"clip": ("CLIP", ),
"vae": ("VAE", ),
"add_noise": (["enable", "disable"], ),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
"width": ("INT", {"default": 0, "min": 0}),
"height": ("INT", {"default": 0, "min": 0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("STRING", {"default": ""}),
"negative": ("STRING", {"default": ""}),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"return_with_leftover_noise": (["disable", "enable"], ),
"hires": ("BOOLEAN", {"default": False},),
"upscale_model": ("UPSCALE_MODEL",),
"hires_steps": ("INT", {"default": 5, "min": 1, "max": 20}),
}
}
RETURN_TYPES = ("IMAGE", "CONDITIONING", "CONDITIONING")
RETURN_NAMES = ("IMAGE", "POSITIVE", "NEGATIVE")
FUNCTION = "sample"
CATEGORY = "sampling"
def sample(self, model, clip, vae, add_noise, noise_seed, steps, cfg, width, height, sampler_name, scheduler, positive, negative, start_at_step, end_at_step, return_with_leftover_noise, hires, upscale_model, hires_steps):
k_sampler_node = KSamplerAdvanced()
vae_decode = VAEDecode()
vae_encode = VAEEncode()
text_encode = CLIPTextEncode()
upscaler = ImageUpscaleWithModel()
scale_image = ImageScaleBy()
latent_image = EmptyLatentImage().generate(width, height)[0]
positive_prompt = text_encode.encode(clip, positive)[0]
negative_prompt = text_encode.encode(clip, negative)[0]
# Sampling
samples = k_sampler_node.sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive_prompt, negative_prompt, latent_image, start_at_step, end_at_step, return_with_leftover_noise)[0]
if (hires):
# Decode the samples
image = vae_decode.decode(vae, samples)[0]
total_hires_steps = 25 - hires_steps
upscaled_image = upscaler.upscale(upscale_model, image)[0]
upscaled_image = scale_image.upscale(upscaled_image, "nearest-exact", 0.5)[0]
upscaled_latent = vae_encode.encode(vae, upscaled_image)[0]
upscaled_samples = k_sampler_node.sample(model, add_noise, noise_seed, 25, cfg, sampler_name, scheduler, positive_prompt, negative_prompt, upscaled_latent, total_hires_steps, 1000, return_with_leftover_noise)[0]
image = vae_decode.decode(vae, upscaled_samples)
else:
image = vae_decode.decode(vae, samples)
return (image[0], positive_prompt, negative_prompt)