93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
from aiohttp import web
|
|
from typing import NamedTuple
|
|
import json
|
|
|
|
import comfy.utils
|
|
from comfy import supported_models
|
|
from comfy import model_detection
|
|
import folder_paths
|
|
import server
|
|
|
|
input_block_name = "model.diffusion_model.input_blocks.0.0.weight"
|
|
|
|
model_names = {
|
|
"SD15": "sd15",
|
|
"SD20": "sd20",
|
|
"SD21UnclipL": "sd21",
|
|
"SD21UnclipH": "sd21",
|
|
"SDXLRefiner": "sdxl",
|
|
"SDXL": "sdxl",
|
|
"SSD1B": "ssd1b",
|
|
"SVD_img2vid": "svd",
|
|
"Stable_Cascade_B": "cascade-b",
|
|
"Stable_Cascade_C": "cascade-c",
|
|
}
|
|
|
|
|
|
class FakeTensor(NamedTuple):
|
|
shape: tuple
|
|
|
|
@staticmethod
|
|
def from_dict(d):
|
|
try:
|
|
return FakeTensor(tuple(d["shape"]))
|
|
except KeyError:
|
|
return d
|
|
|
|
|
|
def inspect_checkpoint(filename):
|
|
try:
|
|
# Read header of safetensors file
|
|
path = folder_paths.get_full_path("checkpoints", filename)
|
|
header = comfy.utils.safetensors_header(path)
|
|
if header:
|
|
cfg = json.loads(header.decode("utf-8"))
|
|
|
|
# Build a fake "state_dict" from the header info to avoid reading the full weights
|
|
for key in cfg:
|
|
if not key == "__metadata__":
|
|
cfg[key] = FakeTensor.from_dict(cfg[key])
|
|
|
|
# Reuse Comfy's model detection
|
|
unet_args = [cfg, "model.diffusion_model.", "F32"]
|
|
try: # latest ComfyUI takes 2 args
|
|
unet_config = model_detection.detect_unet_config(*unet_args[:-1])
|
|
except TypeError as e: # older ComfyUI versions take 3 args
|
|
unet_config = model_detection.detect_unet_config(*unet_args)
|
|
|
|
# Get input count to detect inpaint models
|
|
if input_block := cfg.get(input_block_name, None):
|
|
input_count = input_block.shape[1]
|
|
else:
|
|
input_count = 4
|
|
|
|
# Find a matching base model depending on unet config
|
|
base_model = model_detection.model_config_from_unet_config(unet_config)
|
|
if base_model is None:
|
|
return {"base_model": "unknown"}
|
|
|
|
base_model_class = base_model.__class__
|
|
base_model_name = model_names.get(base_model_class.__name__, "unknown")
|
|
return {
|
|
"base_model": base_model_name,
|
|
"is_inpaint": base_model_name in ["sd15", "sdxl"] and input_count > 4,
|
|
"is_refiner": base_model_class is supported_models.SDXLRefiner,
|
|
}
|
|
return {"base_model": "unknown"}
|
|
except Exception as e:
|
|
return {"base_model": "unknown", "error": f"Failed to detect base model: {e}"}
|
|
|
|
|
|
if _server := getattr(server.PromptServer, "instance", None):
|
|
|
|
@_server.routes.get("/etn/model_info")
|
|
async def model_info(request):
|
|
try:
|
|
info = {
|
|
filename: inspect_checkpoint(filename)
|
|
for filename in folder_paths.get_filename_list("checkpoints")
|
|
}
|
|
return web.json_response(info)
|
|
except Exception as e:
|
|
return web.json_response(dict(error=str(e)), status=500)
|