Files
SHARK-Studio/apps/shark_studio/web/utils/metadata/png_metadata.py
Ean Garvey 68e9281778 (Studio2) Refactors SD pipeline to rely on turbine-models pipeline, fixes to LLM, gitignore (#2129)
* Shark Studio SDXL support, HIP driver support, simpler device info, small fixes

* Fixups to llm API/UI and ignore user config files.

* Small fixes for unifying pipelines.

* Update requirements.txt for iree-turbine (#2130)

* Fix Llama2 on CPU (#2133)

* Filesystem cleanup and custom model fixes (#2127)

* Fix some formatting issues

* Remove IREE pin (fixes exe issue) (#2126)

* Update find links for IREE packages (#2136)

* Shark Studio SDXL support, HIP driver support, simpler device info, small fixes

* Abstract out SD pipelines from Studio Webui (WIP)

* Switch from pin to minimum torch version and fix index url

* Fix device parsing.

* Fix linux setup

* Fix custom weights.

---------

Co-authored-by: saienduri <77521230+saienduri@users.noreply.github.com>
Co-authored-by: gpetters-amd <159576198+gpetters-amd@users.noreply.github.com>
Co-authored-by: gpetters94 <gpetters@protonmail.com>
2024-05-28 13:18:31 -04:00

217 lines
6.2 KiB
Python

import re
from pathlib import Path
from apps.shark_studio.web.utils.file_utils import (
get_checkpoint_pathfile,
)
from apps.shark_studio.api.sd import EMPTY_SD_MAP as sd_model_map
from apps.shark_studio.modules.schedulers import (
scheduler_model_map,
)
re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
re_param = re.compile(re_param_code)
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
def parse_generation_parameters(x: str):
res = {}
prompt = ""
negative_prompt = ""
done_with_prompt = False
*lines, lastline = x.strip().split("\n")
if len(re_param.findall(lastline)) < 3:
lines.append(lastline)
lastline = ""
for i, line in enumerate(lines):
line = line.strip()
if line.startswith("Negative prompt:"):
done_with_prompt = True
line = line[16:].strip()
if done_with_prompt:
negative_prompt += ("" if negative_prompt == "" else "\n") + line
else:
prompt += ("" if prompt == "" else "\n") + line
res["Prompt"] = prompt
res["Negative prompt"] = negative_prompt
for k, v in re_param.findall(lastline):
v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
m = re_imagesize.match(v)
if m is not None:
res[k + "-1"] = m.group(1)
res[k + "-2"] = m.group(2)
else:
res[k] = v
# Missing CLIP skip means it was set to 1 (the default)
if "Clip skip" not in res:
res["Clip skip"] = "1"
hypernet = res.get("Hypernet", None)
if hypernet is not None:
res[
"Prompt"
] += f"""<hypernet:{hypernet}:{res.get("Hypernet strength", "1.0")}>"""
if "Hires resize-1" not in res:
res["Hires resize-1"] = 0
res["Hires resize-2"] = 0
return res
def try_find_model_base_from_png_metadata(file: str, folder: str = "models") -> str:
custom = ""
# Remove extension from file info
if file.endswith(".safetensors") or file.endswith(".ckpt"):
file = Path(file).stem
# Check for the file name match with one of the local ckpt or safetensors files
if Path(get_checkpoint_pathfile(file + ".ckpt", folder)).is_file():
custom = file + ".ckpt"
if Path(get_checkpoint_pathfile(file + ".safetensors", folder)).is_file():
custom = file + ".safetensors"
return custom
def find_model_from_png_metadata(
key: str, metadata: dict[str, str | int]
) -> tuple[str, str]:
png_hf_id = ""
png_custom = ""
if key in metadata:
model_file = metadata[key]
png_custom = try_find_model_base_from_png_metadata(model_file)
# Check for a model match with one of the default model list (ex: "Linaqruf/anything-v3.0")
if model_file in sd_model_map:
png_custom = model_file
# If nothing had matched, check vendor/hf_model_id
if not png_custom and model_file.count("/"):
png_hf_id = model_file
# No matching model was found
if not png_custom and not png_hf_id:
print(
"Import PNG info: Unable to find a matching model for %s" % model_file
)
return png_custom, png_hf_id
def find_vae_from_png_metadata(key: str, metadata: dict[str, str | int]) -> str:
vae_custom = ""
if key in metadata:
vae_file = metadata[key]
vae_custom = try_find_model_base_from_png_metadata(vae_file, "vae")
# VAE input is optional, should not print or throw an error if missing
return vae_custom
def find_lora_from_png_metadata(
key: str, metadata: dict[str, str | int]
) -> tuple[str, str]:
lora_hf_id = ""
lora_custom = ""
if key in metadata:
lora_file = metadata[key]
lora_custom = try_find_model_base_from_png_metadata(lora_file, "lora")
# If nothing had matched, check vendor/hf_model_id
if not lora_custom and lora_file.count("/"):
lora_hf_id = lora_file
# LoRA input is optional, should not print or throw an error if missing
return lora_custom, lora_hf_id
def import_png_metadata(
pil_data,
prompt,
negative_prompt,
steps,
sampler,
cfg_scale,
seed,
width,
height,
custom_model,
custom_lora,
hf_lora_id,
custom_vae,
):
try:
png_info = pil_data.info["parameters"]
metadata = parse_generation_parameters(png_info)
(png_custom_model, png_hf_model_id) = find_model_from_png_metadata(
"Model", metadata
)
(lora_custom_model, lora_hf_model_id) = find_lora_from_png_metadata(
"LoRA", metadata
)
vae_custom_model = find_vae_from_png_metadata("VAE", metadata)
negative_prompt = metadata["Negative prompt"]
steps = int(metadata["Steps"])
cfg_scale = float(metadata["CFG scale"])
seed = int(metadata["Seed"])
width = float(metadata["Size-1"])
height = float(metadata["Size-2"])
if "Model" in metadata and png_custom_model:
custom_model = png_custom_model
elif "Model" in metadata and png_hf_model_id:
custom_model = png_hf_model_id
if "LoRA" in metadata and lora_custom_model:
custom_lora = lora_custom_model
hf_lora_id = ""
if "LoRA" in metadata and lora_hf_model_id:
custom_lora = "None"
hf_lora_id = lora_hf_model_id
if "VAE" in metadata and vae_custom_model:
custom_vae = vae_custom_model
if "Prompt" in metadata:
prompt = metadata["Prompt"]
if "Sampler" in metadata:
if metadata["Sampler"] in scheduler_model_map:
sampler = metadata["Sampler"]
else:
print(
"Import PNG info: Unable to find a scheduler for %s"
% metadata["Sampler"]
)
except Exception as ex:
if pil_data and pil_data.info.get("parameters"):
print("import_png_metadata failed with %s" % ex)
pass
return (
None,
prompt,
negative_prompt,
steps,
sampler,
cfg_scale,
seed,
width,
height,
custom_model,
custom_lora,
hf_lora_id,
custom_vae,
)