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author | AUTOMATIC <16777216c@gmail.com> | 2022-09-13 17:12:24 +0000 |
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committer | AUTOMATIC <16777216c@gmail.com> | 2022-09-13 17:12:24 +0000 |
commit | 85b97cc49c4766cb47306e71e552871a0791ea29 (patch) | |
tree | 774cd9f4842c7b6357adc3a9ae17735532e62146 /modules | |
parent | 950064ee96ad0f8bc88836741a7d18ce73065462 (diff) | |
download | stable-diffusion-webui-gfx803-85b97cc49c4766cb47306e71e552871a0791ea29.tar.gz stable-diffusion-webui-gfx803-85b97cc49c4766cb47306e71e552871a0791ea29.tar.bz2 stable-diffusion-webui-gfx803-85b97cc49c4766cb47306e71e552871a0791ea29.zip |
bandaid for broken ddim sampling #389
Diffstat (limited to 'modules')
-rw-r--r-- | modules/sd_samplers.py | 9 |
1 files changed, 7 insertions, 2 deletions
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 95d24299..7ef507f1 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -97,7 +97,7 @@ class VanillaStableDiffusionSampler: def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
- # existing code fails with cetin step counts, like 9
+ # existing code fails with cetain step counts, like 9
try:
self.sampler.make_schedule(ddim_num_steps=p.steps, verbose=False)
except Exception:
@@ -122,7 +122,12 @@ class VanillaStableDiffusionSampler: self.nmask = None
self.init_latent = None
- samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
+ # existing code fails with cetin step counts, like 9
+ try:
+ samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
+ except Exception:
+ samples_ddim, _ = self.sampler.sample(S=p.steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)
+
return samples_ddim
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