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| author | Greg Fuller <gfuller23@gmail.com> | 2022-10-12 19:44:41 +0000 |
|---|---|---|
| committer | Greg Fuller <gfuller23@gmail.com> | 2022-10-12 19:44:41 +0000 |
| commit | fb3cefb348600497d964c4fd3f99138d7dde0ec5 (patch) | |
| tree | 984c02da13dae6ab0d6c21304a551bbfffea02f2 /scripts/img2imgalt.py | |
| parent | d717eb079cd6b7fa7a4f97c0a10d400bdec753fb (diff) | |
| parent | 698d303b04e293635bfb49c525409f3bcf671dce (diff) | |
| download | stable-diffusion-webui-gfx803-fb3cefb348600497d964c4fd3f99138d7dde0ec5.tar.gz stable-diffusion-webui-gfx803-fb3cefb348600497d964c4fd3f99138d7dde0ec5.tar.bz2 stable-diffusion-webui-gfx803-fb3cefb348600497d964c4fd3f99138d7dde0ec5.zip | |
Merge remote-tracking branch 'upstream/master' into interrogate_include_ranks_in_output
Diffstat (limited to 'scripts/img2imgalt.py')
| -rw-r--r-- | scripts/img2imgalt.py | 4 |
1 files changed, 1 insertions, 3 deletions
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index f9894cb0..313a55d2 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -129,8 +129,6 @@ class Script(scripts.Script): return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
- p.batch_size = 1
- p.batch_count = 1
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
@@ -154,7 +152,7 @@ class Script(scripts.Script): rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
- rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
+ rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
|