fix control final resize

modules/control/run.py

@@ -112,7 +112,7 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
     # hires/refine defined outside of main init
     p.enable_hr = enable_hr
     p.hr_sampler_name = processing.get_sampler_name(hr_sampler_index)
-    p.hr_denoising_strength = hr_denoising_strength # TODO
+    p.hr_denoising_strength = hr_denoising_strength
     p.hr_upscaler = hr_upscaler
     p.hr_force = hr_force
     p.hr_second_pass_steps = hr_second_pass_steps
@@ -472,7 +472,7 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
                             p.init_images = [processed_image]
                             shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
                         else:
-                            p.init_hr(p.scale_by, p.resize_name)
+                            p.init_hr(p.scale_by, p.resize_name, force=True)
                             shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
                     elif has_models: # actual control
                         p.is_control = True

modules/processing_class.py

@@ -492,13 +492,13 @@ class StableDiffusionProcessingControl(StableDiffusionProcessingImg2Img):
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): # abstract
         pass
 
-    def init_hr(self, scale = None, upscaler = None):
+    def init_hr(self, scale = None, upscaler = None, force = False):
         scale = scale or self.scale_by
         upscaler = upscaler or self.resize_name
         if upscaler == 'None' or scale == 1.0:
             return
         self.is_hr_pass = True
-        self.hr_force = True
+        self.hr_force = force
         self.hr_upscaler = upscaler
         self.hr_upscale_to_x, self.hr_upscale_to_y = 8 * int(self.width * scale / 8), 8 * int(self.height * scale / 8)
         # hypertile_set(self, hr=True)

modules/processing_diffusers.py

@@ -187,7 +187,11 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
             generator = p.generator
         else:
             generator_device = devices.cpu if shared.opts.diffusers_generator_device == "CPU" else shared.device
-            generator = [torch.Generator(generator_device).manual_seed(s) for s in p.seeds]
+            try:
+                generator = [torch.Generator(generator_device).manual_seed(s) for s in p.seeds]
+            except Exception as e:
+                shared.log.error(f'Torch generator: seeds={p.seeds} device={generator_device} {e}')
+                generator = None
         prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
         parser = 'Fixed attention'
         clip_skip = kwargs.pop("clip_skip", 1)
@@ -457,7 +461,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
     # optional second pass
     if p.enable_hr:
         p.is_hr_pass = True
-        p.init_hr(p.hr_scale, p.hr_upscaler)
+        p.init_hr(p.hr_scale, p.hr_upscaler, force=p.hr_force)
         prev_job = shared.state.job
 
         # hires runs on original pipeline
@@ -472,6 +476,8 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
                 save_intermediate(latents=output.images, suffix="-before-hires")
             shared.state.job = 'upscale'
             output.images = resize_hires(p, latents=output.images)
+            if hasattr(p, 'task_args') and p.task_args.get('image', None) is not None: # replace input with output so it can be used by hires/refine
+                p.task_args['image'] = output.images
             sd_hijack_hypertile.hypertile_set(p, hr=True)
 
         latent_upscale = shared.latent_upscale_modes.get(p.hr_upscaler, None)

modules/shared.py

@@ -347,7 +347,7 @@ options_templates.update(options_section(('sd', "Execution & Models"), {
     "comma_padding_backtrack": OptionInfo(20, "Prompt padding", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1, "visible": backend == Backend.ORIGINAL }),
     "sd_checkpoint_cache": OptionInfo(0, "Cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": backend == Backend.ORIGINAL }),
     "sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
-    "sd_disable_ckpt": OptionInfo(False, "Disallow models in ckpt format"),
+    "sd_disable_ckpt": OptionInfo(False, "Disallow models in ckpt format", gr.Checkbox, {"visible": False}),
 }))
 
 options_templates.update(options_section(('cuda', "Compute Settings"), {