nan
08309f719d
- Added a new cutout tool for selecting regions in images, allowing users to choose mesh shapes based on selected areas. - Introduced depth generation capabilities for rasterized images, including normal data extraction. - Created jobs for generating depth planes and region data, enhancing the AI data generation workflow. - Developed a selection refinement process for cutout regions, improving accuracy in generated outputs. - Established a new region module for handling rasterized image regions and their associated AI generation tasks. - Enhanced node graph builders for asset generation, facilitating smoother integration of new features.
130 lines
4.0 KiB
Python
130 lines
4.0 KiB
Python
from pathlib import Path
|
|
|
|
import bpy
|
|
|
|
from ..runtime import JobOperatorBase
|
|
from ..properties import BEN2_MODEL_KEY, REALESRGAN_GENERAL_WDN_MODEL_KEY
|
|
from .common.ai_job import (
|
|
require_background_input_path,
|
|
da3_parameters,
|
|
require_input_path,
|
|
moge2_parameters,
|
|
require_environment,
|
|
require_model,
|
|
)
|
|
|
|
|
|
def _open_job_directory(directory):
|
|
directory = Path(directory)
|
|
if not directory.is_dir():
|
|
raise RuntimeError(f"Job directory does not exist: {directory}")
|
|
result = bpy.ops.wm.path_open(filepath=str(directory))
|
|
if "FINISHED" not in result:
|
|
raise RuntimeError(f"Unable to open job directory: {directory}")
|
|
|
|
|
|
class DebugDa3(JobOperatorBase, bpy.types.Operator):
|
|
bl_idname = "anyimage.debug_da3"
|
|
bl_label = "Debug DA3"
|
|
job_type = "debug-da3"
|
|
starting_message = "Starting DA3 API inference"
|
|
|
|
def request(self, context):
|
|
require_environment()
|
|
settings = context.scene.anyimage_settings
|
|
model = settings.model
|
|
require_model(model)
|
|
return {
|
|
**da3_parameters(
|
|
settings,
|
|
require_input_path(settings.input_path),
|
|
model,
|
|
settings.process_res,
|
|
1,
|
|
),
|
|
"device": settings.device.lower(),
|
|
"inference_mode": "joint",
|
|
"remove_background_model": "",
|
|
}
|
|
|
|
def response(self, _context, result):
|
|
_open_job_directory(result.directory)
|
|
return f"Saved DA3 API prediction to {result.directory}"
|
|
|
|
|
|
class DebugMoge2(JobOperatorBase, bpy.types.Operator):
|
|
bl_idname = "anyimage.debug_moge2"
|
|
bl_label = "Debug MoGe-2"
|
|
job_type = "debug-moge2"
|
|
starting_message = "Starting MoGe-2 inference"
|
|
|
|
def request(self, context):
|
|
require_environment()
|
|
settings = context.scene.anyimage_settings
|
|
require_model(settings.moge_model)
|
|
return {
|
|
**moge2_parameters(
|
|
settings,
|
|
require_input_path(settings.input_path),
|
|
settings.moge_model,
|
|
settings.moge_resolution_level,
|
|
1,
|
|
),
|
|
"device": settings.device.lower(),
|
|
}
|
|
|
|
def response(self, _context, result):
|
|
_open_job_directory(result.directory)
|
|
return f"Exported MoGe-2 data to {result.directory}"
|
|
|
|
|
|
class DebugBen2(JobOperatorBase, bpy.types.Operator):
|
|
bl_idname = "anyimage.debug_ben2"
|
|
bl_label = "Debug BEN2"
|
|
job_type = "debug-ben2"
|
|
starting_message = "Starting BEN2 background removal"
|
|
|
|
def request(self, context):
|
|
require_environment()
|
|
settings = context.scene.anyimage_settings
|
|
require_model(BEN2_MODEL_KEY)
|
|
return {
|
|
"input": str(require_background_input_path(settings.background_input_path)),
|
|
"model": BEN2_MODEL_KEY,
|
|
"device": settings.device.lower(),
|
|
"video_frames": settings.video_frames,
|
|
"start_frame": 1,
|
|
}
|
|
|
|
def response(self, _context, result):
|
|
_open_job_directory(result.directory)
|
|
return f"Removed background to {result.directory}"
|
|
|
|
|
|
class DebugUpscale(JobOperatorBase, bpy.types.Operator):
|
|
bl_idname = "anyimage.debug_upscale"
|
|
bl_label = "Debug Upscale"
|
|
job_type = "debug-upscale"
|
|
starting_message = "Starting Upscale inference"
|
|
|
|
model: bpy.props.StringProperty(
|
|
default=REALESRGAN_GENERAL_WDN_MODEL_KEY,
|
|
options={"HIDDEN", "SKIP_SAVE"},
|
|
)
|
|
|
|
def request(self, context):
|
|
require_environment()
|
|
settings = context.scene.anyimage_settings
|
|
require_model(self.model)
|
|
return {
|
|
"input": str(require_input_path(settings.input_path)),
|
|
"model": self.model,
|
|
"device": settings.device.lower(),
|
|
"video_frames": settings.video_frames,
|
|
"start_frame": 1,
|
|
}
|
|
|
|
def response(self, _context, result):
|
|
_open_job_directory(result.directory)
|
|
return f"Saved upscale API prediction to {result.directory}"
|