mrq/stable-diffusion-webui
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added original negative prompt to img2img alt

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AUTOMATIC 2022-09-16 19:24:48 +03:00
parent e49b1c5d73
commit b64994b973
1 changed files with 7 additions and 6 deletions
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13
scripts/img2imgalt.py
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@ -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])])