Update img2imgalt.py

WIP

scripts/img2imgalt.py

@@ -49,7 +49,12 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         t = dnw.sigma_to_t(sigma_in)
 
         if shared.sd_model.is_sdxl:
-            eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )
+            num_classes_hack = shared.sd_model.model.diffusion_model.num_classes
+            shared.sd_model.model.diffusion_model.num_classes = None
+            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:
             eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
 
@@ -78,13 +83,6 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
 
 # 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):
-    if shared.sd_model.is_sdxl:
-        cond_tensor = cond['crossattn']
-        uncond_tensor = uncond['crossattn']
-        cond_in = torch.cat([uncond_tensor, cond_tensor])
-    else:
-        cond_in = torch.cat([uncond, cond])
-
     x = p.init_latent
 
     s_in = x.new_ones([x.shape[0]])
@@ -124,7 +122,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
 
 
         if shared.sd_model.is_sdxl:
-            eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )
+            num_classes_hack = shared.sd_model.model.diffusion_model.num_classes
+            shared.sd_model.model.diffusion_model.num_classes = None
+            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:
             eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
 
@@ -211,9 +214,19 @@ def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subs
                                 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
 
+            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)
+
             if same_everything:
                 rec_noise = self.cache.noise
             else:
+                # This prevents a crash, because I don't know how to access the underlying .diffusion_model yet when controlnet is enabled. WIP
+                # modules.sd_unet -> we're good
+                # scripts.hook -> we're cooked
+                if "scripts.hook" in str(shared.sd_model.model.diffusion_model.forward.__module__):
+                    print("turn off any controlnets, do 1 pass and then turn controlnet back on to cache noise")
+                    p.steps = 1
+                    return sd_samplers.create_sampler(p.sampler_name, p.sd_model).sample_img2img(p, p.init_latent, rand_noise, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
+
                 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])
@@ -223,8 +236,6 @@ def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subs
                     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:], 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)
-
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
 
             sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)