diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index fa0612aaad9..615afd217dc 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -13,7 +13,7 @@ # Debugging notes - the original method apply_model is being called for sd1.5 is in modules.sd_hijack_utils and is ldm.models.diffusion.ddpm.LatentDiffusion # For sdxl - OpenAIWrapper will be called, which will call the underlying diffusion_model - +# When controlnet is enabled, the underlying model is not available to use, therefore we skip def find_noise_for_image(p, cond, uncond, cfg_scale, steps): x = p.init_latent @@ -78,11 +78,11 @@ 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", "sigma_adjustment"]) +Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment", "second_order_correction", "noise_sigma_intensity"]) # 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): +def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps, correction_factor, sigma_intensity): x = p.init_latent s_in = x.new_ones([x.shape[0]]) @@ -97,12 +97,8 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): shared.state.sampling_steps = steps for i in trange(1, len(sigmas)): - shared.state.sampling_step += 1 - - x_in = torch.cat([x] * 2) + shared.state.sampling_step += 1 sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2) - - if shared.sd_model.is_sdxl: cond_tensor = cond['crossattn'] uncond_tensor = uncond['crossattn'] @@ -113,46 +109,73 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): image_conditioning = torch.cat([p.image_conditioning] * 2) cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} - c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]] - if i == 1: t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2)) + dt = (sigmas[i] - sigmas[i - 1]) / (2 * sigmas[i]) else: t = dnw.sigma_to_t(sigma_in) + dt = (sigmas[i] - sigmas[i - 1]) / sigmas[i - 1] + + noise = noise_from_model(x, t, dt, sigma_in, cond_in, cfg_scale, dnw, skip) + + if correction_factor > 0: + recalculated_noise = noise_from_model(x + noise, t, dt, sigma_in, cond_in, cfg_scale, dnw, skip) + noise = recalculated_noise * correction_factor + noise * (1 - correction_factor) + + x += noise + + sd_samplers_common.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, dt + + shared.state.nextjob() + return x / (x.std()*(1 - sigma_intensity) + sigmas[-1]*sigma_intensity) + +def noise_from_model(x, t, dt, sigma_in, cond_in, cfg_scale, dnw, skip): + + if cfg_scale == 1: # Case where denoised_uncond should not be calculated - 50% speedup, also good for sdxl in experiments + x_in = x + sigma_in = sigma_in[1:2] + c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]] + cond_in = {"c_concat":[cond_in["c_concat"][0][1:2]], "c_crossattn": [cond_in["c_crossattn"][0][1:2]]} if shared.sd_model.is_sdxl: num_classes_hack = shared.sd_model.model.diffusion_model.num_classes shared.sd_model.model.diffusion_model.num_classes = None + print("\nDIMS") + print(x_in.shape, c_in.shape, t[1:2].shape, cond_in["c_crossattn"][0].shape) try: - eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} ) + eps = shared.sd_model.model(x_in * c_in, t[1:2], {"crossattn": cond_in["c_crossattn"][0]}) finally: shared.sd_model.model.diffusion_model.num_classes = num_classes_hack else: - eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in) + eps = shared.sd_model.apply_model(x_in * c_in, t[1:2], cond=cond_in) - denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2) + return -eps * c_out* dt + else : + x_in = torch.cat([x] * 2) - denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale + c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]] - if i == 1: - d = (x - denoised) / (2 * sigmas[i]) + if shared.sd_model.is_sdxl: + num_classes_hack = shared.sd_model.model.diffusion_model.num_classes + shared.sd_model.model.diffusion_model.num_classes = None + print("\nDIMS") + print(x_in.shape, c_in.shape, t.shape, cond_in["c_crossattn"][0].shape) + try: + eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} ) + finally: + shared.sd_model.model.diffusion_model.num_classes = num_classes_hack else: - d = (x - denoised) / sigmas[i - 1] - - dt = sigmas[i] - sigmas[i - 1] - x = x + d * dt - - sd_samplers_common.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 + eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in) - shared.state.nextjob() + denoised_uncond, denoised_cond = (eps * c_out).chunk(2) - return x / sigmas[-1] + denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale + return -denoised * dt class Script(scripts.Script): def __init__(self): @@ -183,6 +206,8 @@ def ui(self, is_img2img): cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=self.elem_id("cfg")) randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness")) sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment")) + second_order_correction = gr.Slider(label="Correct noise by running model again", minimum=0.0, maximum=1.0, step=0.01, value=0.5, elem_id=self.elem_id("second_order_correction")) + noise_sigma_intensity = gr.Slider(label="Weight scaling std vs sigma based", minimum=-1.0, maximum=2.0, step=0.01, value=0.5, elem_id=self.elem_id("noise_sigma_intensity")) return [ info, @@ -190,10 +215,11 @@ def ui(self, is_img2img): override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, - cfg, randomness, sigma_adjustment, + cfg, randomness, sigma_adjustment, second_order_correction, + noise_sigma_intensity ] - def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment): + def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment, second_order_correction, noise_sigma_intensity): # Override if override_sampler: p.sampler_name = "Euler" @@ -211,7 +237,9 @@ def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subs 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 + and self.cache.sigma_adjustment == sigma_adjustment \ + and self.cache.second_order_correction == second_order_correction \ + and self.cache.noise_sigma_intensity == noise_sigma_intensity same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100 rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p) @@ -231,10 +259,10 @@ def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subs 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]) if sigma_adjustment: - rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st) + rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st, second_order_correction, noise_sigma_intensity) 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) + self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment, second_order_correction, noise_sigma_intensity) combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)