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multidiffusion-upscaler-for-automatic1111
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Merge pull request #396 from Jaylen-Lee/main 

fix noise inversion
pull/402/head
Kahsolt 2024-07-07 00:31:56 +08:00 committed by GitHub
parent 574a096313 b29322b8ca
commit 8c3ead6913
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2 changed files with 7 additions and 6 deletions
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3
scripts/tileglobal.py
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@ -126,7 +126,7 @@ class Script(scripts.Script):
def process(self, p: Processing,
enabled: bool, method: str,
keep_input_size: bool,
keep_input_size: bool,
window_size:int, overlap: int, tile_batch_size: int,
scale_factor: float,
noise_inverse: bool, noise_inverse_steps: int, noise_inverse_retouch: float, noise_inverse_renoise_strength: float, noise_inverse_renoise_kernel: int,
@ -146,7 +146,6 @@ class Script(scripts.Script):
# store canvas size settings
if hasattr(p, "init_images"):
p.init_images_original_md = [img.copy() for img in p.init_images]
p.width_original_md = p.width
p.height_original_md = p.height
p.current_scale_num = 1

10
tile_methods/demofusion.py
View File

@ -285,7 +285,7 @@ class DemoFusion(AbstractDiffusion):
current_num += len(bboxes)
if current_num > (self.global_num_tiles//2) and (current_num-self.global_tile_bs) < (self.global_num_tiles//2):
res = len(bboxes) - (current_num - self.global_num_tiles//2)
x_in_i = torch.cat([x_in[:,:,bbox[1]+jitter_range:end:self.p.current_scale_num,bbox[0]+jitter_range:end:self.p.current_scale_num] if idx<res else x_in_g[:,:,bbox[1]+jitter_range:end:self.p.current_scale_num,bbox[0]+jitter_range:end:self.p.current_scale_num] for bbox in bboxes],dim=0)
x_in_i = torch.cat([x_in[:,:,bbox[1]+jitter_range:end:self.p.current_scale_num,bbox[0]+jitter_range:end:self.p.current_scale_num] if idx<res else x_in_g[:,:,bbox[1]+jitter_range:end:self.p.current_scale_num,bbox[0]+jitter_range:end:self.p.current_scale_num] for idx,bbox in enumerate(bboxes)],dim=0)
elif current_num > (self.global_num_tiles//2):
x_in_i = torch.cat([x_in_g[:,:,bbox[1]+jitter_range:end:self.p.current_scale_num,bbox[0]+jitter_range:end:self.p.current_scale_num] for bbox in bboxes],dim=0)
else:
@ -345,7 +345,9 @@ class DemoFusion(AbstractDiffusion):
def get_noise(self, x_in:Tensor, sigma_in:Tensor, cond_in:Dict[str, Tensor], step:int) -> Tensor:
# NOTE: The following code is analytically wrong but aesthetically beautiful
cond_in_original = cond_in.copy()
self.repeat_3 = True
return self.sample_one_step_local(x_in, sigma_in, cond_in_original)
self.cosine_factor = 0.5 * (1 + torch.cos(torch.pi *torch.tensor(((self.p.current_step + 1) / (self.t_enc+1)))))
jitter_range = self.jitter_range
_,_,H,W = x_in.shape
x_in_ = F.pad(x_in,(jitter_range, jitter_range, jitter_range, jitter_range),'constant',value=0)
return self.sample_one_step(x_in_, sigma_in, cond_in_original)[:,:,jitter_range:jitter_range+H,jitter_range:jitter_range+W]