From b64994b973ad8f4268bf785f25f92b66c8dced40 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 16 Sep 2022 19:24:48 +0300 Subject: [PATCH] added original negative prompt to img2img alt --- scripts/img2imgalt.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index dbda3255..7f1f53a7 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -59,7 +59,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps): return x / x.std() -Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt"]) +Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt"]) class Script(scripts.Script): @@ -74,19 +74,20 @@ class Script(scripts.Script): def ui(self, is_img2img): original_prompt = gr.Textbox(label="Original prompt", lines=1) + original_negative_prompt = gr.Textbox(label="Original negative 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, original_negative_prompt, cfg, st, randomness] - def run(self, p, original_prompt, cfg, st, randomness): + def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness): p.batch_size = 1 p.batch_count = 1 def sample_extra(x, conditioning, unconditional_conditioning): lat = (p.init_latent.cpu().numpy() * 10).astype(int) - 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 + 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 and self.cache.original_negative_prompt == original_negative_prompt same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100 if same_everything: @@ -94,9 +95,9 @@ class Script(scripts.Script): 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 * [""]) + uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt]) rec_noise = find_noise_for_image(p, cond, uncond, cfg, st) - self.cache = Cached(rec_noise, cfg, st, lat, original_prompt) + self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_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])])