diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 19 |
1 files changed, 8 insertions, 11 deletions
diff --git a/modules/processing.py b/modules/processing.py index aaecb104..23b0c08f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -65,6 +65,7 @@ class Processed: def __init__(self, p: StableDiffusionProcessing, images_list, seed, info):
self.images = images_list
self.prompt = p.prompt
+ self.negative_prompt = p.negative_prompt
self.seed = seed
self.info = info
self.width = p.width
@@ -76,6 +77,7 @@ class Processed: def js(self):
obj = {
"prompt": self.prompt if type(self.prompt) != list else self.prompt[0],
+ "negative_prompt": self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0],
"seed": int(self.seed if type(self.seed) != list else self.seed[0]),
"width": self.width,
"height": self.height,
@@ -104,15 +106,14 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see subnoise = None
if subseeds is not None:
subseed = 0 if i >= len(subseeds) else subseeds[i]
- torch.manual_seed(subseed)
- subnoise = torch.randn(noise_shape, device=shared.device)
+
+ subnoise = devices.randn(subseed, noise_shape)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this, so I do not dare change it for now because
# it will break everyone's seeds.
- torch.manual_seed(seed)
- noise = torch.randn(noise_shape, device=shared.device)
+ noise = devices.randn(seed, noise_shape)
if subnoise is not None:
#noise = subnoise * subseed_strength + noise * (1 - subseed_strength)
@@ -120,12 +121,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see if noise_shape != shape:
#noise = torch.nn.functional.interpolate(noise.unsqueeze(1), size=shape[1:], mode="bilinear").squeeze()
- # noise_shape = (64, 80)
- # shape = (64, 72)
-
- torch.manual_seed(seed)
- x = torch.randn(shape, device=shared.device)
- dx = (shape[2] - noise_shape[2]) // 2 # -4
+ x = devices.randn(seed, shape)
+ dx = (shape[2] - noise_shape[2]) // 2
dy = (shape[1] - noise_shape[1]) // 2
w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
@@ -463,7 +460,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.image_mask is not None:
init_mask = latent_mask
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
- latmask = np.moveaxis(np.array(latmask, dtype=np.float64), 2, 0) / 255
+ latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
latmask = latmask[0]
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))
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