aboutsummaryrefslogtreecommitdiff
path: root/scripts/img2imgalt.py
diff options
context:
space:
mode:
authorAUTOMATIC <16777216c@gmail.com>2023-05-21 17:37:09 +0300
committerAUTOMATIC <16777216c@gmail.com>2023-05-21 17:37:09 +0300
commit1f3182924ba8e70d0e0fc3ed270782f324376ba3 (patch)
tree27a9e5167e5b981dfe56f5084ea8e1e8743f3fc0 /scripts/img2imgalt.py
parent89f9faa63388756314e8a1d96cf86bf5e0663045 (diff)
parentfdaf0147b6d2a5f599464bb7c65817ef5832eff1 (diff)
Merge branch 'dev' into release_candidate
Diffstat (limited to 'scripts/img2imgalt.py')
-rw-r--r--scripts/img2imgalt.py14
1 files changed, 7 insertions, 7 deletions
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index bb00fb3f..1e833fa8 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -149,9 +149,9 @@ class Script(scripts.Script):
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment"))
return [
- info,
+ info,
override_sampler,
- override_prompt, original_prompt, original_negative_prompt,
+ override_prompt, original_prompt, original_negative_prompt,
override_steps, st,
override_strength,
cfg, randomness, sigma_adjustment,
@@ -191,17 +191,17 @@ class Script(scripts.Script):
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:], 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)
-
+
sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
sigmas = sampler.model_wrap.get_sigmas(p.steps)
-
+
noise_dt = combined_noise - (p.init_latent / sigmas[0])
-
+
p.seed = p.seed + 1
-
+
return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
p.sample = sample_extra