97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
import os
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import time
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import subprocess
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import torch
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from diffusers import AutoencoderKL, DDPMScheduler, EulerDiscreteScheduler, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionXLPipeline
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def load_models(pretrained_model, device, weight_dtype = torch.float16, keep_vae_float32 = False):
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# check if the model is already downloaded:
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if not os.path.exists(pretrained_model['path']):
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download_weights(pretrained_model['url'], pretrained_model['path'])
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print(f"Loading model weights from {pretrained_model['path']} with dtype: {weight_dtype}...")
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try:
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pipe = StableDiffusionXLPipeline.from_single_file(
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pretrained_model['path'], torch_dtype=weight_dtype, use_safetensors=True)
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sd_model_version = "sdxl"
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except:
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pipe = StableDiffusionPipeline.from_single_file(
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pretrained_model['path'], torch_dtype=weight_dtype, use_safetensors=True)
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sd_model_version = "sd15"
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print(f"Loaded {sd_model_version} model!")
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pipe = pipe.to(device, dtype=weight_dtype)
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noise_scheduler = DDPMScheduler.from_config(pipe.scheduler.config)
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vae = pipe.vae
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unet = pipe.unet
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tokenizer_one = pipe.tokenizer
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text_encoder_one = pipe.text_encoder
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vae.requires_grad_(False)
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if keep_vae_float32:
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vae.to(device, dtype=torch.float32)
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else:
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vae.to(device, dtype=weight_dtype)
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if weight_dtype != torch.float32:
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print(f"Warning: VAE will be loaded as {weight_dtype}, this is fine for inference but might not be for training..")
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unet.to(device, dtype=weight_dtype)
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text_encoder_one.requires_grad_(False)
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text_encoder_one.to(device, dtype=weight_dtype)
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tokenizer_two = text_encoder_two = None
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if pretrained_model['version'] == "sdxl":
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tokenizer_two = pipe.tokenizer_2
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text_encoder_two = pipe.text_encoder_2
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text_encoder_two.requires_grad_(False)
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text_encoder_two.to(device, dtype=weight_dtype)
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return (
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pipe,
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tokenizer_one,
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tokenizer_two,
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noise_scheduler,
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text_encoder_one,
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text_encoder_two,
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vae,
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unet,
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), sd_model_version
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def download_weights(url, dest):
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start = time.time()
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print("downloading url: ", url)
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print("downloading to: ", dest, '...')
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# Make sure the destination directory exists
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dest_dir = os.path.dirname(dest)
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if not os.path.exists(dest_dir):
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os.makedirs(dest_dir)
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try:
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subprocess.check_call(["wget", "-q", "-O", dest, url])
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except subprocess.CalledProcessError as e:
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print("Error occurred while downloading:")
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print("Exit status:", e.returncode)
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print("Output:", e.output)
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except Exception as e:
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print("An unexpected error occurred:", e)
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print(f"Downloading {url} took {time.time() - start} seconds")
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def print_trainable_parameters(model, model_name = ''):
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trainable_params = 0
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all_param = 0
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for name, param in model.named_parameters():
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all_param += param.numel()
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if param.requires_grad and "token_embedding" not in name:
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trainable_params += param.numel()
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line_delimiter = "#" * 80
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print(line_delimiter)
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print(
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f"Trainable {model_name} params: {trainable_params/1000000:.1f}M || All params: {all_param/1000000:.1f}M || trainable = {100 * trainable_params / all_param:.2f}%"
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)
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print(line_delimiter) |