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from pytorch_lightning import seed_everything
from scripts.demo.streamlit_helpers import *
from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
import argparse
import tqdm
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model_version', type=str, default='2.1',
choices=['2.1', '2.1-768', 'xl'])
parser.add_argument("--num_samples", type=int, default=4)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--prompt", type=str, default="a corgi is sitting on a couch")
parser.add_argument("--prompt_listpath", type=str, default="", help="path to a txt file with a list of prompts")
parser.add_argument("--negative_prompt", type=str, default="ugly, low quality")
parser.add_argument('--save_path', type=str, default='outputs/demo/txt2img/')
args = parser.parse_args()
seed_everything(args.seed)
save_path = args.save_path
version_map = {
'2.1': 'sd-2.1',
'2.1-768': 'sd-2.1-768',
'xl': 'SD-XL base',
}
SD_XL_BASE_RATIOS = {
"0.5": (704, 1408),
"0.52": (704, 1344),
"0.57": (768, 1344),
"0.6": (768, 1280),
"0.68": (832, 1216),
"0.72": (832, 1152),
"0.78": (896, 1152),
"0.82": (896, 1088),
"0.88": (960, 1088),
"0.94": (960, 1024),
"1.0": (1024, 1024),
"1.07": (1024, 960),
"1.13": (1088, 960),
"1.21": (1088, 896),
"1.29": (1152, 896),
"1.38": (1152, 832),
"1.46": (1216, 832),
"1.67": (1280, 768),
"1.75": (1344, 768),
"1.91": (1344, 704),
"2.0": (1408, 704),
"2.09": (1472, 704),
"2.4": (1536, 640),
"2.5": (1600, 640),
"2.89": (1664, 576),
"3.0": (1728, 576),
}
VERSION2SPECS = {
"SD-XL base": {
"H": 1024,
"W": 1024,
"C": 4,
"f": 8,
"is_legacy": False,
"config": "configs/inference/sd_xl_base.yaml",
"ckpt": "checkpoints/sd_xl_base_0.9.safetensors",
"is_guided": True,
},
"sd-2.1": {
"H": 512,
"W": 512,
"C": 4,
"f": 8,
"is_legacy": True,
"config": "configs/inference/sd_2_1.yaml",
"ckpt": "checkpoints/v2-1_512-ema-pruned.safetensors",
"is_guided": True,
},
"sd-2.1-768": {
"H": 768,
"W": 768,
"C": 4,
"f": 8,
"is_legacy": True,
"config": "configs/inference/sd_2_1_768.yaml",
"ckpt": "checkpoints/v2-1_768-ema-pruned.safetensors",
},
"SDXL-Refiner": {
"H": 1024,
"W": 1024,
"C": 4,
"f": 8,
"is_legacy": True,
"config": "configs/inference/sd_xl_refiner.yaml",
"ckpt": "checkpoints/sd_xl_refiner_0.9.safetensors",
"is_guided": True,
},
}
version = args.model_version
version = version_map[version]
version_dict = VERSION2SPECS[version]
# initialize model
state = init_st(version_dict)
if state["msg"]:
st.info(state["msg"])
model = state["model"]
if version == "SD-XL base":
ratio = '1.0'
W, H = SD_XL_BASE_RATIOS[ratio]
else:
W, H = version_dict['W'], version_dict['H']
C = version_dict["C"]
F = version_dict["f"]
if args.prompt_listpath:
with open(args.prompt_listpath, 'r') as f:
prompts = f.readlines()
prompts = [p.strip() for p in prompts]
else:
prompts = [args.prompt]
negative_prompt = args.negative_prompt
init_dict = {
"orig_width": W,
"orig_height": H,
"target_width": W,
"target_height": H,
}
for prompt in tqdm.tqdm(prompts):
print('Current Prompt: >>>>> {} <<<<<'.format(prompt))
value_dict = init_embedder_options(
get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
init_dict,
prompt=prompt,
negative_prompt=negative_prompt,
)
_, _, sampler = init_sampling(
use_identity_guider=not version_dict["is_guided"]
)
num_samples = args.num_samples
is_legacy=False
return_latents = False
filter=None
with torch.no_grad():
samples = do_sample(
state["model"],
sampler,
value_dict,
num_samples,
H,
W,
C,
F,
force_uc_zero_embeddings=["txt"] if not is_legacy else [],
return_latents=return_latents,
filter=filter,
)
if samples is not None:
perform_save_locally(save_path, samples)
print("Saved samples to {}. Enjoy.".format(save_path))
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