diff options
Diffstat (limited to 'scripts/img2imgalt.py')
| -rw-r--r-- | scripts/img2imgalt.py | 35 |
1 files changed, 23 insertions, 12 deletions
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 7813bbcc..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):
@@ -74,34 +74,45 @@ 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, original_negative_prompt, cfg, st, randomness]
- return [original_prompt, cfg, st]
-
- def run(self, p, original_prompt, cfg, st):
+ 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:
- 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)
-
+ 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, 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])])
+
+ 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
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