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import os
from data.prefix_instruction import get_image_prompt, get_task_instruction, get_layout_instruction, get_content_instruction
import random
from PIL import Image
from data.degradation_toolkit.degradation_utils import add_degradation
import numpy as np
degradation_list = [
# blur
"blur",
"compression",
"SRx2",
"SRx4",
"pixelate",
"Defocus",
"GaussianBlur",
# sharpen
"oversharpen",
# nosie
"GaussianNoise",
"PoissonNoise",
"SPNoise",
# mosaic
"mosaic",
# contrast
"contrast_strengthen",
"contrast_weaken",
# quantization
"quantization",
"JPEG",
# light
"brighten",
"darken",
"LowLight",
# color
"saturate_strengthen",
"saturate_weaken",
"gray",
"ColorDistortion",
# infilling
"Inpainting",
# rotate
"rotate180",
# other
"Barrel",
"Pincushion",
"Elastic",
# spacial effect
"Rain",
"Frost",
]
def generate_paths_from_id(file_id: str, prompt: str) -> dict:
"""
根据文件ID自动生成所有相关文件的路径
Args:
file_id: str - 文件的唯一标识符 (例如: '5c79f1ea582c3faa093d2e09b906321d')
Returns:
dict: 包含所有生成路径的字典
"""
base_path = 'examples/examples/graph200k'
paths = {
'reference': f'{base_path}/{file_id}/{file_id}_reference.jpg',
'target': f'{base_path}/{file_id}/{file_id}_target.jpg',
'depth': f'{base_path}/{file_id}/{file_id}_depth-anything-v2_Large.jpg',
'canny': f'{base_path}/{file_id}/{file_id}_canny_100_200_512.jpg',
'hed': f'{base_path}/{file_id}/{file_id}_hed_512.jpg',
'normal': f'{base_path}/{file_id}/{file_id}_dsine-normal-map.jpg',
'style_target': f'{base_path}/{file_id}/{file_id}_instantx-style_0.jpg',
'style_source': f'{base_path}/{file_id}/{file_id}_instantx-style_0_style.jpg',
'sam2_mask': f'{base_path}/{file_id}/{file_id}_sam2_mask.jpg',
'prompt': prompt
}
return paths
dense_prediction_data = [
generate_paths_from_id('data-00004-of-00022-7170', prompt="Travel VPN app on a desktop screen. The interface is visible on a laptop in a modern airport lounge, captured from a side angle with natural daylight highlighting the sleek design, while planes can be seen through the large window behind the device."),
generate_paths_from_id('data-00005-of-00022-4396', prompt="A vintage porcelain collector's item. Beneath a blossoming cherry tree in early spring, this treasure is photographed up close, with soft pink petals drifting through the air and vibrant blossoms framing the scene."),
generate_paths_from_id('data-00018-of-00022-4948', prompt="Decorative kitchen salt shaker with intricate design. On a quaint countryside porch in the afternoon's gentle breeze, accompanied by pastel-colored flowers and vintage cutlery, it adds a touch of charm to the rustic scene."),
generate_paths_from_id('data-00013-of-00022-4696', prompt="A lifelike forest creature figurine. Nestled among drifting autumn leaves on a tree-lined walking path, it gazes out as pedestrians bundled in scarves pass by."),
generate_paths_from_id('data-00017-of-00022-8377', prompt="A colorful bike for young adventurers. In a bustling city street during a bright afternoon, it leans against a lamppost, surrounded by hurried pedestrians, with towering buildings providing an urban backdrop."),
]
subject_driven = [
dict(
name='Subject-driven generation',
image_type=["reference", "target"]),
]
subject_driven_text = [[x['name']] for x in subject_driven]
style_transfer_with_subject = [
dict(
name='Style Transfer with Subject',
image_type=["reference", "style_source", "style_target"]),
]
style_transfer_with_subject_text = [[x['name']] for x in style_transfer_with_subject]
condition_subject_fusion = [
dict(
name='Depth+Subject to Image',
image_type=["reference", "depth", "target"]),
dict(
name='Canny+Subject to Image',
image_type=["reference", "canny", "target"]),
dict(
name='Hed+Subject to Image',
image_type=["reference", "hed", "target"]),
dict(
name='Normal+Subject to Image',
image_type=["reference", "normal", "target"]),
dict(
name='SAM2+Subject to Image',
image_type=["reference", "sam2_mask", "target"]),
]
condition_subject_fusion_text = [[x['name']] for x in condition_subject_fusion]
image_restoration_with_subject = [
dict(name=degradation, image_type=["reference", degradation, "target"])
for degradation in degradation_list
]
image_restoration_with_subject_text = [[x['name']] for x in image_restoration_with_subject]
condition_subject_style_fusion = [
dict(
name='Depth+Subject+Style to Image',
image_type=["reference", "depth", "style_source", "style_target"]),
dict(
name='Canny+Subject+Style to Image',
image_type=["reference", "canny", "style_source", "style_target"]),
dict(
name='Hed+Subject+Style to Image',
image_type=["reference", "hed", "style_source", "style_target"]),
dict(
name='Normal+Subject+Style to Image',
image_type=["reference", "normal", "style_source", "style_target"]),
dict(
name='SAM2+Subject+Style to Image',
image_type=["reference", "sam2_mask", "style_source", "style_target"]),
]
condition_subject_style_fusion_text = [[x['name']] for x in condition_subject_style_fusion]
def process_subject_driven_tasks(x):
for task in subject_driven:
if task['name'] == x[0]:
image_type = task['image_type']
image_prompt_list = [get_image_prompt(x)[0] for x in image_type]
image_prompt_list = [f"[IMAGE{idx+1}] {image_prompt}" for idx, image_prompt in enumerate(image_prompt_list)]
condition_prompt = ", ".join(image_prompt_list[:-1])
target_prompt = image_prompt_list[-1]
task_prompt = get_task_instruction(condition_prompt, target_prompt)
# sample examples
valid_data = [x for x in dense_prediction_data if all([x.get(t, None) is not None and os.path.exists(x[t]) for t in image_type])]
n_samples = random.randint(2, min(len(valid_data), 3))
images = random.sample(valid_data, k=n_samples)
rets = []
for image in images:
for t in image_type:
rets.append(Image.open(image[t]))
content_prompt = get_content_instruction() + images[-1]['prompt']
grid_h = n_samples
grid_w = len(image_type)
mask = task.get('mask', [0 for _ in range(grid_w - 1)] + [1])
layout_prompt = get_layout_instruction(grid_w, grid_h)
upsampling_noise = 0.6
steps = None
outputs = [mask, grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps] + rets
break
return outputs
def process_condition_subject_fusion_tasks(x):
for task in condition_subject_fusion:
if task['name'] == x[0]:
image_type = task['image_type']
image_prompt_list = [get_image_prompt(x)[0] for x in image_type]
image_prompt_list = [f"[IMAGE{idx+1}] {image_prompt}" for idx, image_prompt in enumerate(image_prompt_list)]
condition_prompt = ", ".join(image_prompt_list[:-1])
target_prompt = image_prompt_list[-1]
task_prompt = get_task_instruction(condition_prompt, target_prompt)
# sample examples
valid_data = [x for x in dense_prediction_data if all([x.get(t, None) is not None and os.path.exists(x[t]) for t in image_type])]
n_samples = random.randint(2, min(len(valid_data), 3))
images = random.sample(valid_data, k=n_samples)
rets = []
for image in images:
for t in image_type:
rets.append(Image.open(image[t]))
content_prompt = get_content_instruction() + images[-1]['prompt']
grid_h = n_samples
grid_w = len(image_type)
mask = task.get('mask', [0 for _ in range(grid_w - 1)] + [1])
layout_prompt = get_layout_instruction(grid_w, grid_h)
upsampling_noise = 0.6
steps = None
outputs = [mask, grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps] + rets
break
return outputs
def process_style_transfer_with_subject_tasks(x):
for task in style_transfer_with_subject:
if task['name'] == x[0]:
image_type = task['image_type']
image_prompt_list = [get_image_prompt(x)[0] for x in image_type]
image_prompt_list = [f"[IMAGE{idx+1}] {image_prompt}" for idx, image_prompt in enumerate(image_prompt_list)]
condition_prompt = ", ".join(image_prompt_list[:-1])
target_prompt = image_prompt_list[-1]
task_prompt = get_task_instruction(condition_prompt, target_prompt)
# sample examples
valid_data = [x for x in dense_prediction_data if all([x.get(t, None) is not None and os.path.exists(x[t]) for t in image_type])]
n_samples = random.randint(2, min(len(valid_data), 3))
images = random.sample(valid_data, k=n_samples)
rets = []
for image in images:
for t in image_type:
if t == "style_source":
target = Image.open(image["style_target"])
source = Image.open(image[t])
source = source.resize(target.size)
rets.append(source)
else:
rets.append(Image.open(image[t]))
content_prompt = ""
grid_h = n_samples
grid_w = len(image_type)
mask = task.get('mask', [0 for _ in range(grid_w - 1)] + [1])
layout_prompt = get_layout_instruction(grid_w, grid_h)
upsampling_noise = 0.6
steps = None
outputs = [mask, grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps] + rets
break
return outputs
def process_condition_subject_style_fusion_tasks(x):
for task in condition_subject_style_fusion:
if task['name'] == x[0]:
image_type = task['image_type']
image_prompt_list = [get_image_prompt(x)[0] for x in image_type]
image_prompt_list = [f"[IMAGE{idx+1}] {image_prompt}" for idx, image_prompt in enumerate(image_prompt_list)]
condition_prompt = ", ".join(image_prompt_list[:-1])
target_prompt = image_prompt_list[-1]
task_prompt = get_task_instruction(condition_prompt, target_prompt)
# sample examples
valid_data = [x for x in dense_prediction_data if all([x.get(t, None) is not None and os.path.exists(x[t]) for t in image_type])]
n_samples = random.randint(2, min(len(valid_data), 3))
images = random.sample(valid_data, k=n_samples)
rets = []
for image in images:
for t in image_type:
if t == "style_source":
target = Image.open(image["style_target"])
source = Image.open(image[t])
source = source.resize(target.size)
rets.append(source)
else:
rets.append(Image.open(image[t]))
content_prompt = ""
grid_h = n_samples
grid_w = len(image_type)
mask = task.get('mask', [0 for _ in range(grid_w - 1)] + [1])
layout_prompt = get_layout_instruction(grid_w, grid_h)
upsampling_noise = 0.6
steps = None
outputs = [mask, grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps] + rets
break
return outputs
def process_image_restoration_with_subject_tasks(x):
for task in image_restoration_with_subject:
if task['name'] == x[0]:
image_type = task['image_type']
image_prompt_list = [get_image_prompt(x)[0] for x in image_type]
image_prompt_list = [f"[IMAGE{idx+1}] {image_prompt}" for idx, image_prompt in enumerate(image_prompt_list)]
condition_prompt = ", ".join(image_prompt_list[:-1])
target_prompt = image_prompt_list[-1]
task_prompt = get_task_instruction(condition_prompt, target_prompt)
# sample examples
valid_data = dense_prediction_data
n_samples = random.randint(2, min(len(valid_data), 3))
images = random.sample(valid_data, k=n_samples)
rets = []
for image in images:
for t in image_type:
if t == "target":
rets.append(Image.open(image["target"]))
elif t == "reference":
rets.append(Image.open(image["reference"]))
else:
deg_image, _ = add_degradation(np.array(Image.open(image["target"])), deg_type=t)
rets.append(deg_image)
content_prompt = get_content_instruction() + images[-1]['prompt']
grid_h = n_samples
grid_w = len(image_type)
mask = task.get('mask', [0 for _ in range(grid_w - 1)] + [1])
layout_prompt = get_layout_instruction(grid_w, grid_h)
upsampling_noise = 0.6
steps = None
outputs = [mask, grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps] + rets
break
return outputs
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