Maxed-Out-99
fc3aba2e38
Add timed GIF load, stitch, and save nodes; modular Krea 2 editors and prompt encoders; a downscale node; and compatible Set/Get frontend nodes. Also improve video-save progress and chooser positioning, update schema and release checks, and bump the package version to 3.3.1.
34 lines
1.8 KiB
Python
34 lines
1.8 KiB
Python
"""Release checks using synthetic images only; no model weights or personal media."""
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import ast
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from pathlib import Path
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import torch
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import torch.nn.functional as F
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def functions(path,names):
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tree=ast.parse(Path(path).read_text(encoding='utf-8'))
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return compile(ast.Module(body=[n for n in tree.body if isinstance(n,ast.FunctionDef) and n.name in names],type_ignores=[]),path,'exec')
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ns={'F':F,'torch':torch}
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exec(functions('nodes/krea2_edit_core.py',{'fit_encode_image','fit_boost_mask'}),ns)
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class VAE:
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def encode(self,image):
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self.image=image
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return image.movedim(-1,1)
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for h,w,th,tw in [(100,100,96,88),(120,240,96,96),(240,120,96,96),(96,96,96,96)]:
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image=torch.zeros(1,h,w,3);image[:,h//4:3*h//4,w//4:3*w//4]=1
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mask=image[...,0].clone();vae=VAE()
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ns['fit_encode_image'](image,vae,th//8,tw//8,{},(0,),'fit')
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cropped=ns['fit_boost_mask'](mask,image,th,tw)
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aligned=F.interpolate(cropped[:,None],size=vae.image.shape[1:3],mode='bicubic',antialias=True).clamp(0,1)[:,0]
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torch.testing.assert_close(aligned,vae.image[...,0])
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assert ns['fit_boost_mask'](None,image,th,tw) is None
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print('PASS: boost-mask crop matches encoded image for square, near-square, portrait and landscape references')
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# The original published model class remains unchanged.
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import subprocess
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old=ast.parse(subprocess.check_output(['git','show','HEAD:nodes/krea.py']).decode());new=ast.parse(Path('nodes/krea.py').read_text())
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get=lambda tree,name:next(n for n in tree.body if isinstance(n,ast.ClassDef) and n.name==name)
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assert ast.dump(get(old,'Krea2EditModelPatchMXD'))==ast.dump(get(new,'Krea2EditModelPatchMXD'))
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for tree in [old,new]:
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c=get(tree,'Krea2EditGroundedEncodeMXD');tree.schema=ast.dump(next(n for n in c.body if isinstance(n,ast.FunctionDef) and n.name=='INPUT_TYPES'))
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assert old.schema==new.schema
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print('PASS: published legacy model and legacy prompt input schema preserved')
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