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vsp_script.py
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vsp_script.py
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import torch
from pipelines.inverted_ve_pipeline import STYLE_DESCRIPTION_DICT, create_image_grid
import os
from pipelines.pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
import argparse
from utils import parse_config, memory_efficient, init_latent, load_config
parser = argparse.ArgumentParser()
parser.add_argument('--style', type=str, default='fire')
args = parser.parse_args()
if __name__ == "__main__":
# load pre_saved_json
config_path = os.path.join("./config", "{}.json".format(args.style))
config = parse_config(config_path)
result_dir = 'results'
if not os.path.exists(result_dir):
os.makedirs(result_dir)
ref_dir = "./assets/ref" # generated images
if not os.path.exists(ref_dir):
os.makedirs(ref_dir)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if device == 'cpu':
torch_dtype = torch.float32
else:
torch_dtype = torch.float16
# load config
activate_layer_indices_list, activate_step_indices_list,\
ref_seeds, inf_seeds,\
attn_map_save_steps, precomputed_path, guidance_scale, use_inf_negative_prompt,\
style_name_list, ref_object_list, inf_object_list, ref_with_style_description, inf_with_style_description,\
use_shared_attention, adain_queries, adain_keys, adain_values, use_advanced_sampling\
= load_config(config) # load config
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch_dtype)
print('SDXL')
memory_efficient(pipe, device)
for ref_object in ref_object_list:
for inf_object in inf_object_list:
for style_name in style_name_list:
style_description_pos, style_description_neg = STYLE_DESCRIPTION_DICT[style_name][0], STYLE_DESCRIPTION_DICT[style_name][1]
if ref_with_style_description:
ref_prompt = style_description_pos.replace("{object}",ref_object)
else:
ref_prompt = ref_object
if inf_with_style_description:
inf_prompt = style_description_pos.replace("{object}",inf_object)
else:
inf_prompt = inf_object
for activate_layer_indices in activate_layer_indices_list:
for activate_step_indices in activate_step_indices_list:
str_activate_layer, str_activate_step = pipe.activate_layer(activate_layer_indices=activate_layer_indices,
attn_map_save_steps=attn_map_save_steps,
activate_step_indices=activate_step_indices,
use_shared_attention=use_shared_attention,
adain_queries=adain_queries,
adain_keys=adain_keys,
adain_values=adain_values,
)
for ref_seed in ref_seeds:
# ref_latent = pipe.get_init_latent(precomputed_path,ref_seed)
ref_latent = init_latent(pipe, device_name=device, dtype=torch_dtype, seed=ref_seed)
latents = [ref_latent]
for inf_seed in inf_seeds:
# latents.append(pipe.get_init_latent(precomputed_path, inf_seed))
inf_latent = init_latent(pipe, device_name=device, dtype=torch_dtype, seed=inf_seed)
latents.append(inf_latent)
latents = torch.cat(latents, dim=0)
latents.to(device)
images = pipe(
prompt=ref_prompt,
negative_prompt = style_description_neg,
guidance_scale = guidance_scale,
latents=latents,
num_images_per_prompt = len(inf_seeds)+1,
target_prompt = inf_prompt,
use_inf_negative_prompt = use_inf_negative_prompt,
use_advanced_sampling=use_advanced_sampling
)[0]
ref_image = images[0]
ref_image.save(os.path.join(ref_dir, f"ref_{style_name}_{ref_object}.png"))
#make grid
n_row = 1
n_col = len(inf_seeds)+1
grid = create_image_grid(images, n_row, n_col)
new_inf_seeds = [inf_seed for inf_seed in inf_seeds]
save_name = f"style_{style_name}_ref_{ref_seed}_{ref_object}_inf_{new_inf_seeds}_{inf_object}activate_layer_{str_activate_layer}_step_{str_activate_step}.png"
save_path = os.path.join(result_dir, save_name)
grid.save(save_path)
print("saved to ", save_path)