From 258a2d4f064c2c3c0d63c7cf8966d2260fea3f33 Mon Sep 17 00:00:00 2001 From: Martin Cairns Date: Mon, 26 Sep 2022 21:13:23 +0100 Subject: [PATCH] Add option to img2imgalt.py to use sigma adjustment instead of original method for #736 --- scripts/img2imgalt.py | 68 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 62 insertions(+), 6 deletions(-) diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 7b4ba2447..0ef137f7d 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -59,7 +59,55 @@ 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", "original_negative_prompt"]) +Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"]) + + +# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736 +def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): + x = p.init_latent + + s_in = x.new_ones([x.shape[0]]) + dnw = K.external.CompVisDenoiser(shared.sd_model) + sigmas = dnw.get_sigmas(steps).flip(0) + + shared.state.sampling_steps = steps + + for i in trange(1, len(sigmas)): + shared.state.sampling_step += 1 + + x_in = torch.cat([x] * 2) + sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2) + cond_in = torch.cat([uncond, cond]) + + c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)] + + if i == 1: + t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2)) + else: + t = dnw.sigma_to_t(sigma_in) + + eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in) + denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2) + + denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale + + if i == 1: + d = (x - denoised) / (2 * sigmas[i]) + else: + d = (x - denoised) / sigmas[i - 1] + + dt = sigmas[i] - sigmas[i - 1] + x = x + d * dt + + sd_samplers.store_latent(x) + + # This shouldn't be necessary, but solved some VRAM issues + del x_in, sigma_in, cond_in, c_out, c_in, t, + del eps, denoised_uncond, denoised_cond, denoised, d, dt + + shared.state.nextjob() + + return x / sigmas[-1] class Script(scripts.Script): @@ -78,9 +126,10 @@ class Script(scripts.Script): 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] + sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False) + return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment] - def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness): + def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment): p.batch_size = 1 p.batch_count = 1 @@ -88,7 +137,10 @@ class Script(scripts.Script): def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): 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 and self.cache.original_negative_prompt == original_negative_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 \ + and self.cache.sigma_adjustment == sigma_adjustment same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100 if same_everything: @@ -97,8 +149,11 @@ class Script(scripts.Script): 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 * [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) + if sigma_adjustment: + rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st) + else: + rec_noise = find_noise_for_image(p, cond, uncond, cfg, st) + self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment) rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])]) @@ -121,6 +176,7 @@ class Script(scripts.Script): p.extra_generation_params["Decode CFG scale"] = cfg p.extra_generation_params["Decode steps"] = st p.extra_generation_params["Randomness"] = randomness + p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment processed = processing.process_images(p)