diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 11 |
1 files changed, 9 insertions, 2 deletions
diff --git a/modules/processing.py b/modules/processing.py index 94d2dd62..ec8651ae 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -259,6 +259,13 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see return x
+def decode_first_stage(model, x):
+ with devices.autocast(disable=x.dtype == devices.dtype_vae):
+ x = model.decode_first_stage(x)
+
+ return x
+
+
def get_fixed_seed(seed):
if seed is None or seed == '' or seed == -1:
return int(random.randrange(4294967294))
@@ -400,7 +407,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: samples_ddim = samples_ddim.to(devices.dtype)
- x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
+ x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
del samples_ddim
@@ -533,7 +540,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.scale_latent:
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
else:
- decoded_samples = self.sd_model.decode_first_stage(samples)
+ decoded_samples = decode_first_stage(self.sd_model, samples)
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
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