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import argparse
import hashlib
import json
import os.path
import torch
from diffusers import DPMSolverMultistepScheduler
from diffusers.models import T2IAdapter
from PIL import Image
from mixofshow.pipelines.pipeline_regionally_t2iadapter import RegionallyT2IAdapterPipeline
def sample_image(pipe,
input_prompt,
input_neg_prompt=None,
generator=None,
num_inference_steps=50,
guidance_scale=7.5,
sketch_adaptor_weight=1.0,
region_sketch_adaptor_weight='',
keypose_adaptor_weight=1.0,
region_keypose_adaptor_weight='',
**extra_kargs
):
keypose_condition = extra_kargs.pop('keypose_condition')
if keypose_condition is not None:
keypose_adapter_input = [keypose_condition] * len(input_prompt)
else:
keypose_adapter_input = None
sketch_condition = extra_kargs.pop('sketch_condition')
if sketch_condition is not None:
sketch_adapter_input = [sketch_condition] * len(input_prompt)
else:
sketch_adapter_input = None
images = pipe(
prompt=input_prompt,
negative_prompt=input_neg_prompt,
keypose_adapter_input=keypose_adapter_input,
keypose_adaptor_weight=keypose_adaptor_weight,
region_keypose_adaptor_weight=region_keypose_adaptor_weight,
sketch_adapter_input=sketch_adapter_input,
sketch_adaptor_weight=sketch_adaptor_weight,
region_sketch_adaptor_weight=region_sketch_adaptor_weight,
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
**extra_kargs).images
return images
def build_model(pretrained_model, device):
pipe = RegionallyT2IAdapterPipeline.from_pretrained(pretrained_model, torch_dtype=torch.float16).to(device)
assert os.path.exists(os.path.join(pretrained_model, 'new_concept_cfg.json'))
with open(os.path.join(pretrained_model, 'new_concept_cfg.json'), 'r') as json_file:
new_concept_cfg = json.load(json_file)
pipe.set_new_concept_cfg(new_concept_cfg)
pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained(pretrained_model, subfolder='scheduler')
pipe.keypose_adapter = T2IAdapter.from_pretrained('TencentARC/t2iadapter_openpose_sd14v1', torch_dtype=torch.float16).to(device)
pipe.sketch_adapter = T2IAdapter.from_pretrained('TencentARC/t2iadapter_sketch_sd14v1', torch_dtype=torch.float16).to(device)
return pipe
def prepare_text(prompt, region_prompts, height, width):
'''
Args:
prompt_entity: [subject1]-*-[attribute1]-*-[Location1]|[subject2]-*-[attribute2]-*-[Location2]|[global text]
Returns:
full_prompt: subject1, attribute1 and subject2, attribute2, global text
context_prompt: subject1 and subject2, global text
entity_collection: [(subject1, attribute1), Location1]
'''
region_collection = []
regions = region_prompts.split('|')
for region in regions:
if region == '':
break
prompt_region, neg_prompt_region, pos = region.split('-*-')
prompt_region = prompt_region.replace('[', '').replace(']', '')
neg_prompt_region = neg_prompt_region.replace('[', '').replace(']', '')
pos = eval(pos)
if len(pos) == 0:
pos = [0, 0, 1, 1]
else:
pos[0], pos[2] = pos[0] / height, pos[2] / height
pos[1], pos[3] = pos[1] / width, pos[3] / width
region_collection.append((prompt_region, neg_prompt_region, pos))
return (prompt, region_collection)
def parse_args():
parser = argparse.ArgumentParser('', add_help=False)
parser.add_argument('--pretrained_model', default='experiments/composed_edlora/anythingv4/hina+kario+tezuka+mitsuha+son_anythingv4/combined_model_base', type=str)
parser.add_argument('--sketch_condition', default=None, type=str)
parser.add_argument('--sketch_adaptor_weight', default=1.0, type=float)
parser.add_argument('--region_sketch_adaptor_weight', default='', type=str)
parser.add_argument('--keypose_condition', default=None, type=str)
parser.add_argument('--keypose_adaptor_weight', default=1.0, type=float)
parser.add_argument('--region_keypose_adaptor_weight', default='', type=str)
parser.add_argument('--save_dir', default=None, type=str)
parser.add_argument('--prompt', default='photo of a toy', type=str)
parser.add_argument('--negative_prompt', default='', type=str)
parser.add_argument('--prompt_rewrite', default='', type=str)
parser.add_argument('--seed', default=16141, type=int)
parser.add_argument('--suffix', default='', type=str)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
pipe = build_model(args.pretrained_model, device)
if args.sketch_condition is not None and os.path.exists(args.sketch_condition):
sketch_condition = Image.open(args.sketch_condition).convert('L')
width_sketch, height_sketch = sketch_condition.size
print('use sketch condition')
else:
sketch_condition, width_sketch, height_sketch = None, 0, 0
print('skip sketch condition')
if args.keypose_condition is not None and os.path.exists(args.keypose_condition):
keypose_condition = Image.open(args.keypose_condition).convert('RGB')
width_pose, height_pose = keypose_condition.size
print('use pose condition')
else:
keypose_condition, width_pose, height_pose = None, 0, 0
print('skip pose condition')
if width_sketch != 0 and width_pose != 0:
assert width_sketch == width_pose and height_sketch == height_pose, 'conditions should be same size'
width, height = max(width_pose, width_sketch), max(height_pose, height_sketch)
kwargs = {
'sketch_condition': sketch_condition,
'keypose_condition': keypose_condition,
'height': height,
'width': width,
}
prompts = [args.prompt]
prompts_rewrite = [args.prompt_rewrite]
input_prompt = [prepare_text(p, p_w, height, width) for p, p_w in zip(prompts, prompts_rewrite)]
save_prompt = input_prompt[0][0]
image = sample_image(
pipe,
input_prompt=input_prompt,
input_neg_prompt=[args.negative_prompt] * len(input_prompt),
generator=torch.Generator(device).manual_seed(args.seed),
sketch_adaptor_weight=args.sketch_adaptor_weight,
region_sketch_adaptor_weight=args.region_sketch_adaptor_weight,
keypose_adaptor_weight=args.keypose_adaptor_weight,
region_keypose_adaptor_weight=args.region_keypose_adaptor_weight,
**kwargs)
print(f'save to: {args.save_dir}')
configs = [
f'pretrained_model: {args.pretrained_model}\n',
f'context_prompt: {args.prompt}\n', f'neg_context_prompt: {args.negative_prompt}\n',
f'sketch_condition: {args.sketch_condition}\n', f'sketch_adaptor_weight: {args.sketch_adaptor_weight}\n',
f'region_sketch_adaptor_weight: {args.region_sketch_adaptor_weight}\n',
f'keypose_condition: {args.keypose_condition}\n', f'keypose_adaptor_weight: {args.keypose_adaptor_weight}\n',
f'region_keypose_adaptor_weight: {args.region_keypose_adaptor_weight}\n', f'random seed: {args.seed}\n',
f'prompt_rewrite: {args.prompt_rewrite}\n'
]
hash_code = hashlib.sha256(''.join(configs).encode('utf-8')).hexdigest()[:8]
save_prompt = save_prompt.replace(' ', '_')
# save_name = f'{save_prompt}---{args.suffix}---{hash_code}.png'
# save_dir = os.path.join(args.save_dir, f'seed_{args.seed}')
save_name = f'{save_prompt}---{args.suffix}(seed{args.seed})---{hash_code}.png'
save_dir = args.save_dir
save_path = os.path.join(save_dir, save_name)
save_config_path = os.path.join(save_dir, save_name.replace('.png', '.txt'))
os.makedirs(save_dir, exist_ok=True)
image[0].save(os.path.join(save_dir, save_name))
with open(save_config_path, 'w') as fw:
fw.writelines(configs)
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