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infer.py
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infer.py
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"""
Modified from:
Break-A-Scene: https://github.com/google/break-a-scene
"""
import argparse
from diffusers import DiffusionPipeline, DDIMScheduler
import torch
import random
import numpy as np
from PIL import Image
from ptp_utils import load_learned_embed
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import os
def set_seed(seed: int):
"""
Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch``.
Args:
seed (:obj:`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def image_grid(imgs, rows, cols):
assert len(imgs) == rows*cols
w, h = imgs[0].size
grid = Image.new('RGB', size=(cols*w, rows*h))
grid_w, grid_h = grid.size
for i, img in enumerate(imgs):
grid.paste(img, box=(i%cols*w, i//cols*h))
return grid
@torch.no_grad()
def infer_with_embed(embed_path, pretrained, prompt, num_samples=2, num_rows=1, return_all_images=False, disable_progress_bar=False):
text_encoder, tokenizer = load_learned_embed(embed_path, pretrained)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
pipe = StableDiffusionPipeline.from_pretrained(
pretrained,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
).to("cuda")
if disable_progress_bar:
pipe.set_progress_bar_config(leave=False)
pipe.set_progress_bar_config(disable=True)
all_images = []
for _ in range(num_rows):
images = pipe([prompt] * num_samples, num_inference_steps=50, guidance_scale=7.5).images
all_images.extend(images)
grid = image_grid(all_images, num_rows, num_samples)
if return_all_images:
return grid, all_images
else:
return grid
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a inference script.")
parser.add_argument(
"--num_samples",
type=int,
default=4,
help="Number of samples to generate.",
)
parser.add_argument(
"--num_rows",
type=int,
default=2,
help="Number of rows to generate.",
)
parser.add_argument(
"--seed",
type=int,
default=0,
help="Number of rows to generate.",
)
parser.add_argument(
"--save_full_model",
action='store_true',
help="Whether to store the entire model (or only save the embeddings).",
)
parser.add_argument(
"--embed_path",
type=str,
default=None,
help="Path to the learned embedding.",
)
parser.add_argument(
"--model_dir",
type=str,
default=None,
help="Path to the full model checkpoint.",
)
parser.add_argument(
"--pretrained",
type=str,
default="stabilityai/stable-diffusion-2-1-base",
help="[stabilityai/stable-diffusion-2, stabilityai/stable-diffusion-2-base, CompVis/stable-diffusion-v1-4, runwayml/stable-diffusion-v1-5]",
)
parser.add_argument(
"--prompt",
type=str,
required=True,
)
parser.add_argument(
"--save_path",
type=str,
required=True,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
grid, all_images = infer_with_embed(args.embed_path, args.pretrained, args.prompt, num_samples=args.num_samples, num_rows=args.num_rows, return_all_images=True)
image_path = os.path.join(args.save_path, args.prompt.replace(' ', '-'))
if not os.path.exists(image_path):
from pathlib import Path
Path(image_path).mkdir(parents=True, exist_ok=True)
for idx, img in enumerate(all_images):
img.save(os.path.join(image_path, '{}.png'.format(idx)))
grid.save(os.path.join(args.save_path, args.prompt.replace(' ', '-') + '.png'))