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# modules/model.py
import os
import torch
from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline
from transformers import AutoencoderKL
def get_checkpoints(folder):
checkpoints = []
for file in os.listdir(folder):
if file.endswith(('.safetensors', '.ckpt', '.pt', '.pth')):
checkpoints.append(file)
return checkpoints
def load_model(checkpoint, vae, checkpoint_folder, vae_folder):
# Memilih pipeline yang sesuai
if "sdxl" in checkpoint.lower():
pipeline_class = StableDiffusionXLPipeline
else:
pipeline_class = StableDiffusionPipeline
# Load checkpoint
if checkpoint in get_checkpoints(checkpoint_folder):
checkpoint_path = os.path.join(checkpoint_folder, checkpoint)
try:
model = pipeline_class.from_single_file(checkpoint_path, torch_dtype=torch.float16)
except Exception as e:
model = pipeline_class.from_pretrained(checkpoint_path, torch_dtype=torch.float16)
else:
if checkpoint.startswith("http"):
try:
model = pipeline_class.from_single_file(checkpoint, torch_dtype=torch.float16)
except Exception as e:
model = pipeline_class.from_pretrained(checkpoint, torch_dtype=torch.float16)
else:
model = pipeline_class.from_pretrained(checkpoint, torch_dtype=torch.float16)
# Load VAE
if vae != "none":
if vae in get_checkpoints(vae_folder):
vae_path = os.path.join(vae_folder, vae)
vae_model = AutoencoderKL.from_pretrained(vae_path, torch_dtype=torch.float16)
else:
vae_model = AutoencoderKL.from_pretrained(vae, torch_dtype=torch.float16)
model.vae = vae_model
return model
def get_model_and_vae_options():
checkpoint_folder = "../models/checkpoint/"
vae_folder = "../models/vae/"
model_file = "../models/models.py"
# Membaca model dan VAE dari models/model.py
exec(open(model_file).read())
# Mendapatkan daftar checkpoint dan VAE dari folder
checkpoints = get_checkpoints(checkpoint_folder)
vae_files = get_checkpoints(vae_folder)
# Menggabungkan daftar checkpoint, model Diffusers, dan VAE
all_models = checkpoints + diffusers
all_vaes = ["none"] + vae_files + vae
# Mengubah format dropdown
formatted_models = [os.path.basename(model) if not model.startswith("http") else model for model in all_models]
formatted_vaes = [os.path.basename(vae) if not vae.startswith("http") else vae for vae in all_vaes]
return formatted_models, formatted_vaes
# Wrapper untuk fungsi generate_image di text2img
def generate_image(text, checkpoint, vae):
checkpoint_folder = "../models/checkpoint/"
vae_folder = "../models/vae/"
model = load_model(checkpoint, vae, checkpoint_folder, vae_folder)
image = model([text])[0]
return image
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