Add option to img2imgalt.py to use sigma adjustment instead of original method for #736

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)