diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 12 |
1 files changed, 8 insertions, 4 deletions
diff --git a/modules/processing.py b/modules/processing.py index 849f6b19..9c3673de 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -94,7 +94,7 @@ def txt2img_image_conditioning(sd_model, x, width, height): return image_conditioning
-class StableDiffusionProcessing():
+class StableDiffusionProcessing:
"""
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
"""
@@ -102,7 +102,6 @@ class StableDiffusionProcessing(): if sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
- self.sd_model = sd_model
self.outpath_samples: str = outpath_samples
self.outpath_grids: str = outpath_grids
self.prompt: str = prompt
@@ -156,6 +155,10 @@ class StableDiffusionProcessing(): self.all_subseeds = None
self.iteration = 0
+ @property
+ def sd_model(self):
+ return shared.sd_model
+
def txt2img_image_conditioning(self, x, width=None, height=None):
self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
@@ -236,7 +239,6 @@ class StableDiffusionProcessing(): raise NotImplementedError()
def close(self):
- self.sd_model = None
self.sampler = None
@@ -471,7 +473,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if k == 'sd_model_checkpoint':
sd_models.reload_model_weights() # make onchange call for changing SD model
- p.sd_model = shared.sd_model
if k == 'sd_vae':
sd_vae.reload_vae_weights() # make onchange call for changing VAE
@@ -608,6 +609,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
+ for x in x_samples_ddim:
+ devices.test_for_nans(x, "vae")
+
x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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