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fix caching for img2imgalt

This commit is contained in:
AUTOMATIC 2022-09-14 19:41:55 +03:00
parent 91c56c51c7
commit d51847c184
1 changed files with 16 additions and 10 deletions
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26
scripts/img2imgalt.py
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@ -1,3 +1,5 @@
from collections import namedtuple
import numpy as np import numpy as np
from tqdm import trange from tqdm import trange
@ -56,9 +58,14 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
return x / x.std() return x / x.std()
cache = [None, None, None, None, None]
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt"])
class Script(scripts.Script): class Script(scripts.Script):
def __init__(self):
self.cache = None
def title(self): def title(self):
return "img2img alternative test" return "img2img alternative test"
@ -67,7 +74,7 @@ class Script(scripts.Script):
def ui(self, is_img2img): def ui(self, is_img2img):
original_prompt = gr.Textbox(label="Original prompt", lines=1) original_prompt = gr.Textbox(label="Original prompt", lines=1)
cfg = gr.Slider(label="Decode CFG scale", minimum=0.1, maximum=3.0, step=0.1, value=1.0) cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50) st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
return [original_prompt, cfg, st] return [original_prompt, cfg, st]
@ -77,19 +84,18 @@ class Script(scripts.Script):
p.batch_count = 1 p.batch_count = 1
def sample_extra(x, conditioning, unconditional_conditioning): def sample_extra(x, conditioning, unconditional_conditioning):
lat = tuple([int(x*10) for x in p.init_latent.cpu().numpy().flatten().tolist()]) lat = (p.init_latent.cpu().numpy() * 10).astype(int)
if cache[0] is not None and cache[1] == cfg and cache[2] == st and len(cache[3]) == len(lat) and sum(np.array(cache[3])-np.array(lat)) < 100 and cache[4] == original_prompt: same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt
noise = cache[0] same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
if same_everything:
noise = self.cache.noise
else: else:
shared.state.job_count += 1 shared.state.job_count += 1
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt]) cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
noise = find_noise_for_image(p, cond, unconditional_conditioning, cfg, st) noise = find_noise_for_image(p, cond, unconditional_conditioning, cfg, st)
cache[0] = noise self.cache = Cached(noise, cfg, st, lat, original_prompt)
cache[1] = cfg
cache[2] = st
cache[3] = lat
cache[4] = original_prompt
sampler = samplers[p.sampler_index].constructor(p.sd_model) sampler = samplers[p.sampler_index].constructor(p.sd_model)