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Running
on
Zero
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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 .gradio_tasks import dense_prediction_data
style_transfer = [
dict(
name='Style Transfer',
image_type=["target", "style_source", "style_target"]),
]
style_transfer_text = [[x['name']] for x in style_transfer]
style_condition_fusion = [
dict(
name='Canny+Style to Image',
image_type=["canny", "style_source", "style_target"]),
dict(
name='Depth+Style to Image',
image_type=["depth", "style_source", "style_target"]),
dict(
name='Hed+Style to Image',
image_type=["hed", "style_source", "style_target"]),
dict(
name='Normal+Style to Image',
image_type=["normal", "style_source", "style_target"]),
dict(
name='Pose+Style to Image',
image_type=["openpose", "style_source", "style_target"]),
dict(
name='SAM2+Style to Image',
image_type=["sam2_mask", "style_source", "style_target"]),
dict(
name='Mask+Style to Image',
image_type=["mask", "style_source", "style_target"]),
]
style_condition_fusion_text = [[x['name']] for x in style_condition_fusion]
def process_style_transfer_tasks(x):
for task in style_transfer:
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 = None
steps = None
outputs = [mask, grid_h, grid_w, layout_prompt, task_prompt, content_prompt, upsampling_noise, steps] + rets
break
return outputs
def process_style_condition_fusion_tasks(x):
for task in style_condition_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])]
x = dense_prediction_data[0]
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 = None
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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