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# Copyright (C) 2025 NVIDIA Corporation. All rights reserved.
#
# This work is licensed under the LICENSE file
# located at the root directory.
import gc
import torch
from visualization_utils import show_images
def _add_object(
pipe,
prompts,
seed_src,
seed_obj,
extended_scale,
source_latents,
structure_transfer_step,
subject_token,
blend_steps,
show_attention=False,
localization_model="attention_points_sam",
is_img_src=False,
img_src_latents=None,
use_offset=False,
display_output=False,
):
gc.collect()
torch.cuda.empty_cache()
out = pipe(
prompt=prompts,
guidance_scale=3.5 if (not is_img_src) else [1,3.5],
height=1024,
width=1024,
max_sequence_length=512,
num_inference_steps=30,
seed=[seed_src, seed_obj],
# Extended Attention
extended_scale=extended_scale,
extended_steps_multi=10,
extended_steps_single=20,
# Structure Transfer
source_latents=source_latents,
structure_transfer_step=structure_transfer_step,
# Latent Blending
subject_token=subject_token,
localization_model=localization_model,
blend_steps=blend_steps,
show_attention=show_attention,
# Real Image Source
is_img_src=is_img_src,
img_src_latents=img_src_latents,
use_offset=use_offset,
# TQDM
tqdm_desc="Running Addit: Generating Edited Image",
)
if display_output:
show_images(out.images)
return out.images
def add_object_generated(
pipe,
prompt_source,
prompt_object,
subject_token,
seed_src,
seed_obj,
show_attention=False,
extended_scale=1.05,
structure_transfer_step=2,
blend_steps=[15],
localization_model="attention_points_sam",
display_output=False
):
gc.collect()
torch.cuda.empty_cache()
# Generate source image and latents for each seed1
print('Generating source image...')
source_image, source_latents = pipe(
prompt=[prompt_source],
guidance_scale=3.5,
height=1024,
width=1024,
max_sequence_length=512,
num_inference_steps=30,
seed=[seed_src],
output_type="both",
tqdm_desc="Generating Source Image",
)
source_image = source_image[0]
# Run the core combination logic
print('Running Addit...')
src_image, edited_image = _add_object(
pipe=pipe,
prompts=[prompt_source, prompt_object],
subject_token=subject_token,
seed_src=seed_src,
seed_obj=seed_obj,
source_latents=source_latents,
structure_transfer_step=structure_transfer_step,
extended_scale=extended_scale,
blend_steps=blend_steps,
show_attention=show_attention,
localization_model=localization_model,
display_output=display_output
)
return src_image, edited_image
def add_object_real(
pipe,
source_image,
prompt_source,
prompt_object,
subject_token,
seed_src,
seed_obj,
localization_model="attention_points_sam",
extended_scale=1.05,
structure_transfer_step=4,
blend_steps=[20],
use_offset=False,
show_attention=False,
use_inversion=False,
display_output=False
):
print('Noising-Denoising Original Image')
gc.collect()
torch.cuda.empty_cache()
# Get initial latents
source_latents = pipe.call_img2img(
prompt=prompt_source,
image=source_image,
num_inference_steps=30,
strength=0.1,
guidance_scale=3.5,
output_type="latent",
generator=torch.Generator(device=pipe.device).manual_seed(0),
tqdm_desc="Encoding Source Image",
).images
# Optional inversion step
img_src_latents = None
if use_inversion:
print('Inverting Image')
gc.collect()
torch.cuda.empty_cache()
latents_list = pipe.call_invert(
prompt=prompt_source,
image=source_latents,
num_inference_steps=30,
guidance_scale=1,
fixed_point_iterations=2,
generator=torch.Generator(device=pipe.device).manual_seed(0),
tqdm_desc="Inverting Source Image",
)
img_src_latents = [x[0] for x in latents_list][::-1]
print('Running Addit')
gc.collect()
torch.cuda.empty_cache()
src_image, edited_image = _add_object(
pipe,
prompts=[prompt_source, prompt_object],
seed_src=seed_src,
seed_obj=seed_obj,
extended_scale=extended_scale,
source_latents=source_latents,
structure_transfer_step=structure_transfer_step,
subject_token=subject_token,
blend_steps=blend_steps,
show_attention=show_attention,
localization_model=localization_model,
is_img_src=True,
img_src_latents=img_src_latents,
use_offset=use_offset,
display_output=display_output,
)
return src_image, edited_image
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