lllyasviel/stable-diffusion-webui-forge
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Gradio 4 + WebUI 1.10

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layerdiffusion
2024-07-26 08:51:34 -07:00
parent e95333c556
commit e26abf87ec
201 changed files with 7562 additions and 4834 deletions
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8
modules/models/diffusion/ddpm_edit.py
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@@ -341,7 +341,7 @@ class DDPM(pl.LightningModule):
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
@@ -901,7 +901,7 @@ class LatentDiffusion(DDPM):
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
# hybrid case, cond is expected to be a dict
pass
else:
if not isinstance(cond, list):
@@ -937,7 +937,7 @@ class LatentDiffusion(DDPM):
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
assert 'original_image_size' in self.split_input_params, 'BoundingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
@@ -947,7 +947,7 @@ class LatentDiffusion(DDPM):
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# get top left positions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)

4
modules/models/diffusion/uni_pc/uni_pc.py
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@@ -323,7 +323,7 @@ def model_wrapper(
def model_fn(x, t_continuous, condition, unconditional_condition):
"""
The noise predicition model function that is used for DPM-Solver.
The noise prediction model function that is used for DPM-Solver.
"""
if t_continuous.reshape((-1,)).shape[0] == 1:
t_continuous = t_continuous.expand((x.shape[0]))
@@ -445,7 +445,7 @@ class UniPC:
s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
x0 = torch.clamp(x0, -s, s) / s
return x0.to(x)
return x0
def model_fn(self, x, t):
"""