Image processing changes

Image processing changes

modules/processing.py

@@ -528,7 +528,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
 
     if type(p) == StableDiffusionProcessingTxt2Img:
-        if p.enable_hr and p.hr_prompt != '':
+        if p.enable_hr and p.hr_prompt == '':
+            p.all_hr_prompts, p.all_hr_negative_prompts = p.all_prompts, p.all_negative_prompts
+        elif p.enable_hr and p.hr_prompt != '':
             if type(p.prompt) == list:
                 p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
             else:
@@ -555,14 +557,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-    _, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
-    if type(p) == StableDiffusionProcessingTxt2Img:
-        if p.enable_hr and p.hr_prompt != '':
-            _, hr_extra_network_data = extra_networks.parse_prompts(p.all_hr_prompts[0:1])
-            if p.all_hr_prompts != p.all_prompts:
-                extra_network_data.update(hr_extra_network_data)
-
-
     if p.scripts is not None:
         p.scripts.process(p)
 
@@ -600,13 +594,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
                 sd_vae_approx.model()
 
-            if not p.disable_extra_networks:
-                extra_networks.activate(p, extra_network_data)
-
-        with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
-            processed = Processed(p, [], p.seed, "")
-            file.write(processed.infotext(p, 0))
-
         if state.job_count == -1:
             state.job_count = p.n_iter
 
@@ -623,9 +610,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
 
             if type(p) == StableDiffusionProcessingTxt2Img:
-                if p.enable_hr and p.hr_prompt != '':
+                if p.enable_hr:
-                    hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                    if p.hr_prompt == '':
-                    hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                        hr_prompts, hr_negative_prompts = prompts, negative_prompts
+                    else:
+                        hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                        hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
 
             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
@@ -633,19 +623,40 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(prompts) == 0:
                 break
 
-            prompts, _ = extra_networks.parse_prompts(prompts)
+            prompts, extra_network_data = extra_networks.parse_prompts(prompts)
+            if type(p) == StableDiffusionProcessingTxt2Img:
+                if p.enable_hr and hr_prompts != prompts:
+                    _, hr_extra_network_data = extra_networks.parse_prompts(hr_prompts)
+                    extra_network_data.update(hr_extra_network_data)
+
+
+
+            if not p.disable_extra_networks:
+                with devices.autocast():
+                    extra_networks.activate(p, extra_network_data)
 
             if p.scripts is not None:
                 p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
 
+            # params.txt should be saved after scripts.process_batch, since the
+            # infotext could be modified by that callback
+            # Example: a wildcard processed by process_batch sets an extra model
+            # strength, which is saved as "Model Strength: 1.0" in the infotext
+            if n == 0:
+                with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+                    processed = Processed(p, [], p.seed, "")
+                    file.write(processed.infotext(p, 0))
+
             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+
             if type(p) == StableDiffusionProcessingTxt2Img:
-                if p.enable_hr and p.hr_prompt != '':
+                if p.enable_hr:
-                    hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps,
+                    if prompts != hr_prompts:
-                                                cached_uc)
+                        hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps, cached_uc)
-                    hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps,
+                        hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps, cached_c)
-                                               cached_c)
+                    else:
+                        hr_uc, hr_c = uc, c
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -658,20 +669,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 if type(p) == StableDiffusionProcessingTxt2Img:
                     if p.enable_hr:
-                        if p.hr_prompt != '':
+                        samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
-                            samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
-                        else:
-                            samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=c,
-                                                    hr_unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
-                                                    subseed_strength=p.subseed_strength, prompts=prompts)
-                    else:
-                        samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
-                                                subseed_strength=p.subseed_strength, prompts=prompts)
-
                 else:
-                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
+                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
-                                            subseeds=subseeds,
-                                            subseed_strength=p.subseed_strength, prompts=prompts)
 
             x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
             for x in x_samples_ddim: