Merge remote-tracking branch 'origin/master'
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commit
be0f82df12
1 changed files with 18 additions and 8 deletions
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@ -76,10 +76,10 @@ class Script(scripts.Script):
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original_prompt = gr.Textbox(label="Original prompt", lines=1)
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original_prompt = gr.Textbox(label="Original prompt", lines=1)
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cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
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cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
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st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
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st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
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randomness = gr.Slider(label="randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
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return [original_prompt, cfg, st, randomness]
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return [original_prompt, cfg, st]
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def run(self, p, original_prompt, cfg, st, randomness):
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def run(self, p, original_prompt, cfg, st):
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p.batch_size = 1
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p.batch_size = 1
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p.batch_count = 1
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p.batch_count = 1
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@ -90,18 +90,28 @@ class Script(scripts.Script):
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same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
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same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
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if same_everything:
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if same_everything:
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noise = self.cache.noise
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rec_noise = self.cache.noise
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else:
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else:
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shared.state.job_count += 1
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shared.state.job_count += 1
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cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
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cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
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uncond = p.sd_model.get_learned_conditioning(p.batch_size * [""])
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uncond = p.sd_model.get_learned_conditioning(p.batch_size * [""])
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noise = find_noise_for_image(p, cond, uncond, cfg, st)
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rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
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self.cache = Cached(noise, cfg, st, lat, original_prompt)
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self.cache = Cached(rec_noise, cfg, st, lat, original_prompt)
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rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
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combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
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sampler = samplers[p.sampler_index].constructor(p.sd_model)
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sampler = samplers[p.sampler_index].constructor(p.sd_model)
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samples_ddim = sampler.sample(p, noise, conditioning, unconditional_conditioning)
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sigmas = sampler.model_wrap.get_sigmas(p.steps)
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return samples_ddim
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noise_dt = combined_noise - ( p.init_latent / sigmas[0] )
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p.seed = p.seed + 1
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return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning)
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p.sample = sample_extra
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p.sample = sample_extra
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