michael/stable-diffusion-webui
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Merge remote-tracking branch 'origin/master'

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AUTOMATIC 2022-09-16 12:43:37 +03:00
commit be0f82df12
1 changed files with 18 additions and 8 deletions
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26
scripts/img2imgalt.py
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@ -76,10 +76,10 @@ class Script(scripts.Script):
original_prompt = gr.Textbox(label="Original prompt", lines=1)
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)
randomness = gr.Slider(label="randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
return [original_prompt, cfg, st, randomness]
return [original_prompt, cfg, st]
def run(self, p, original_prompt, cfg, st):
def run(self, p, original_prompt, cfg, st, randomness):
p.batch_size = 1
p.batch_count = 1
@ -90,18 +90,28 @@ class Script(scripts.Script):
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
rec_noise = self.cache.noise
else:
shared.state.job_count += 1
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [""])
noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(noise, cfg, st, lat, original_prompt)
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt)
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
sampler = samplers[p.sampler_index].constructor(p.sd_model)
samples_ddim = sampler.sample(p, noise, conditioning, unconditional_conditioning)
return samples_ddim
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)
p.sample = sample_extra