Mirrors
/
stable-diffusion-webui
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

[img2imgalt] Fix seed & Allow batch.

This commit is contained in:
aoirusann 2022-10-12 13:02:28 +08:00 committed by AUTOMATIC1111
parent 57e03cdd24
commit f421f2af2d
1 changed files with 1 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
scripts/img2imgalt.py
View File

@ -129,8 +129,6 @@ class Script(scripts.Script):
return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment] 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): 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): 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) 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) 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) combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)