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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 torchvision
SAVE_PATH = "outputs/demo/txt2img/"
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 = "sd-2.1"
# version = "SD-XL base"
version_dict = VERSION2SPECS[version]
# if version == "SD-XL base":
# # ratio = st.sidebar.selectbox("Ratio:", list(SD_XL_BASE_RATIOS.keys()), 10)
# ratio = '1.0'
# W, H = SD_XL_BASE_RATIOS[ratio]
# else:
# H = st.sidebar.number_input(
# "H", value=version_dict["H"], min_value=64, max_value=2048
# )
# W = st.sidebar.number_input(
# "W", value=version_dict["W"], min_value=64, max_value=2048
# )
# 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 = 512, 512
C = version_dict["C"]
F = version_dict["f"]
prompt = 'a corgi is sitting on a couch'
negative_prompt = 'ugly, low quality'
init_dict = {
"orig_width": W,
"orig_height": H,
"target_width": W,
"target_height": H,
}
value_dict = init_embedder_options(
get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
init_dict,
prompt=prompt,
negative_prompt=negative_prompt,
)
num_rows, num_cols, sampler = init_sampling(
use_identity_guider=not version_dict["is_guided"]
)
num_samples = num_rows * num_cols
# st.write(f"**Model I:** {version}")
is_legacy=False
return_latents = False
filter=None
out = 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,
)
torchvision.utils.save_image(out, 'debug/myres_2_1.png', nrow=4)
# torchvision.utils.save_image(out, 'debug/myres.png', nrow=4) |