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Merge branch 'LucianoCirino:main' into feat/xy_capsule

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Dr.Lt.Data
2023-09-01 09:51:04 +09:00
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parent 6389391aa5 59b500e80f
commit 51932c7fc8
1 changed files with 27 additions and 23 deletions
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efficiency_nodes.py
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@@ -311,8 +311,8 @@ class TSC_LoRA_Stacker:
loras = [(lora_name, lora_weight, lora_weight) for lora_name, lora_weight in zip(loras, weights) if
lora_name != "None"]
else:
model_strs = [kwargs.get(f"model_str_{i}", model_strength) for i in range(1, lora_count + 1)]
clip_strs = [kwargs.get(f"clip_str_{i}", clip_strength) for i in range(1, lora_count + 1)]
model_strs = [kwargs.get(f"model_str_{i}") for i in range(1, lora_count + 1)]
clip_strs = [kwargs.get(f"clip_str_{i}") for i in range(1, lora_count + 1)]
loras = [(lora_name, model_str, clip_str) for lora_name, model_str, clip_str in
zip(loras, model_strs, clip_strs) if lora_name != "None"]
@@ -525,7 +525,7 @@ class TSC_KSampler:
#---------------------------------------------------------------------------------------------------------------
def vae_decode_latent(vae, samples, vae_decode):
return VAEDecodeTiled.decode_tiled(vae,samples)[0] if "tiled" in vae_decode else VAEDecode().decode(vae,samples)[0]
return VAEDecodeTiled().decode(vae,samples,512)[0] if "tiled" in vae_decode else VAEDecode().decode(vae,samples)[0]
# ---------------------------------------------------------------------------------------------------------------
def sample_latent_image(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
@@ -3972,13 +3972,13 @@ NODE_CLASS_MAPPINGS = {
########################################################################################################################
# HirRes Fix Script with model latent upscaler (https://github.com/city96/SD-Latent-Upscaler)
comfy_latent_upscaler = None
sd_latent_upscaler_path = os.path.join(custom_nodes_dir, "SD-Latent-Upscaler")
if os.path.exists(sd_latent_upscaler_path):
city96_latent_upscaler = None
city96_latent_upscaler_path = os.path.join(custom_nodes_dir, "SD-Latent-Upscaler")
if os.path.exists(city96_latent_upscaler_path):
printout = "Adding City96's 'SD-Latent-Upscaler' selections to the 'HighRes-Fix' node..."
print(f"{message('Efficiency Nodes:')} {printout}", end="")
try:
comfy_latent_upscaler = import_module("SD-Latent-Upscaler.comfy_latent_upscaler")
city96_latent_upscaler = import_module("SD-Latent-Upscaler.comfy_latent_upscaler")
print(f"\r{message('Efficiency Nodes:')} {printout}{success('Success!')}")
except ImportError:
print(f"\r{message('Efficiency Nodes:')} {printout}{error('Failed!')}")
@@ -3987,14 +3987,13 @@ if os.path.exists(sd_latent_upscaler_path):
class TSC_HighRes_Fix:
default_upscale_methods = LatentUpscaleBy.INPUT_TYPES()["required"]["upscale_method"][0]
city96_upscale_methods = list()
latent_versions = []
if comfy_latent_upscaler:
latent_versions = comfy_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["latent_ver"][0]
latent_versions_updated = ["SD-Latent-Upscaler." + ver for ver in latent_versions]
allowed_scalings_raw = comfy_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["scale_factor"][0]
allowed_scalings = [float(scale) for scale in allowed_scalings_raw]
upscale_methods = default_upscale_methods + latent_versions_updated
if city96_latent_upscaler:
city96_upscale_methods = ["SD-Latent-Upscaler." + ver for ver in city96_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["latent_ver"][0]]
city96_scalings_raw = city96_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["scale_factor"][0]
city96_scalings_float = [float(scale) for scale in city96_scalings_raw]
upscale_methods = default_upscale_methods + city96_upscale_methods
@classmethod
def INPUT_TYPES(cls):
@@ -4021,36 +4020,41 @@ class TSC_HighRes_Fix:
if iterations > 0:
# For latent methods from SD-Latent-Upscaler
if latent_upscale_method in self.latent_versions_updated:
if latent_upscale_method in self.city96_upscale_methods:
# Remove extra characters added
latent_upscale_method = latent_upscale_method.replace("SD-Latent-Upscaler.", "")
# Set function to comfy_latent_upscaler.LatentUpscaler.upscale()
upscale_function = comfy_latent_upscaler.LatentUpscaler
# Set function to city96_latent_upscaler.LatentUpscaler
upscale_function = city96_latent_upscaler.LatentUpscaler
# Find the nearest valid scaling in allowed_scalings
nearest_scaling = min(self.allowed_scalings, key=lambda x: abs(x - upscale_by))
# Find the nearest valid scaling in city96_scalings_float
nearest_scaling = min(self.city96_scalings_float, key=lambda x: abs(x - upscale_by))
# Retrieve the index of the nearest scaling
nearest_scaling_index = self.allowed_scalings.index(nearest_scaling)
nearest_scaling_index = self.city96_scalings_float.index(nearest_scaling)
# Use the index to get the raw string representation
nearest_scaling_raw = self.allowed_scalings_raw[nearest_scaling_index]
nearest_scaling_raw = self.city96_scalings_raw[nearest_scaling_index]
upscale_by = float_to_string(upscale_by)
# Check if the input upscale_by value was different from the nearest valid value
if upscale_by != nearest_scaling_raw:
print(f"{warning('HighRes-Fix Warning:')} "
f"When using 'SD-Latent-Upscaler.{latent_upscale_method}', 'upscale_by' must be one of {self.allowed_scalings_raw}.\n"
f"When using 'SD-Latent-Upscaler.{latent_upscale_method}', 'upscale_by' must be one of {self.city96_scalings_raw}.\n"
f"Rounding to the nearest valid value ({nearest_scaling_raw}).\033[0m")
upscale_by = nearest_scaling_raw
# For default upscale methods
elif latent_upscale_method in self.default_upscale_methods:
# Set function to LatentUpscaleBy.upscale()
upscale_function = LatentUpscaleBy
else: # Default
upscale_function = LatentUpscaleBy
latent_upscale_method = self.default_upscale_methods[0]
print(f"{warning('HiResFix Script Warning:')} Chosen latent upscale method not found! "
f"defaulting to '{latent_upscale_method}'.\n")
# Construct the script output
script = script or {}
script["hiresfix"] = (latent_upscale_method, upscale_by, hires_steps, denoise, iterations, upscale_function)