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Merge pull request #1398 from ogmaresca/save-embeddings-to-metadata

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Add embeddings to metadata
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cmdr2 2023-07-13 12:57:56 +05:30 committed by GitHub
commit 3980625be6
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1 changed files with 26 additions and 3 deletions
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29
ui/easydiffusion/utils/save_utils.py
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@ -1,6 +1,8 @@
import os
import re
import time
import regex
from datetime import datetime
from functools import reduce
@ -30,11 +32,12 @@ TASK_TEXT_MAPPING = {
"lora_alpha": "LoRA Strength",
"use_hypernetwork_model": "Hypernetwork model",
"hypernetwork_strength": "Hypernetwork Strength",
"use_embedding_models": "Embedding models",
"tiling": "Seamless Tiling",
"use_face_correction": "Use Face Correction",
"use_upscale": "Use Upscaling",
"upscale_amount": "Upscale By",
"latent_upscaler_steps": "Latent Upscaler Steps"
"latent_upscaler_steps": "Latent Upscaler Steps",
}
time_placeholders = {
@ -202,6 +205,9 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
req_metadata = req.dict()
task_data_metadata = task_data.dict()
app_config = app.getConfig()
using_diffusers = app_config.get("test_diffusers", False)
# Save the metadata in the order defined in TASK_TEXT_MAPPING
metadata = {}
for key in TASK_TEXT_MAPPING.keys():
@ -209,6 +215,24 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
metadata[key] = req_metadata[key]
elif key in task_data_metadata:
metadata[key] = task_data_metadata[key]
elif key is "use_embedding_models" and using_diffusers:
embeddings_extensions = {".pt", ".bin", ".safetensors"}
def scan_directory(directory_path: str):
used_embeddings = []
for entry in os.scandir(directory_path):
if entry.is_file():
entry_extension = os.path.splitext(entry.name)[1]
if entry_extension not in embeddings_extensions:
continue
embedding_name_regex = regex.compile(r"(^|[\s,])" + regex.escape(os.path.splitext(entry.name)[0]) + r"([+-]*$|[\s,]|[+-]+[\s,])")
if embedding_name_regex.search(req.prompt) or embedding_name_regex.search(req.negative_prompt):
used_embeddings.append(entry.path)
elif entry.is_dir():
used_embeddings.extend(scan_directory(entry.path))
return used_embeddings
used_embeddings = scan_directory(os.path.join(app.MODELS_DIR, "embeddings"))
metadata["use_embedding_models"] = ", ".join(used_embeddings) if len(used_embeddings) > 0 else None
# Clean up the metadata
if req.init_image is None and "prompt_strength" in metadata:
@ -222,8 +246,7 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
if task_data.use_upscale != "latent_upscaler" and "latent_upscaler_steps" in metadata:
del metadata["latent_upscaler_steps"]
app_config = app.getConfig()
if not app_config.get("test_diffusers", False):
if not using_diffusers:
for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip", "tiling", "latent_upscaler_steps"] if x in metadata):
del metadata[key]