(Studio) Fix controlnet switching. (#2026)

* Fix controlnet switching.

* Fix txt2img + control adapters

apps/stable_diffusion/src/models/model_wrappers.py

@@ -219,7 +219,6 @@ class SharkifyStableDiffusionModel:
             self.model_name = self.model_name + "_" + get_path_stem(use_lora)
         self.use_lora = use_lora
 
-        print(self.model_name)
         self.model_name = self.get_extended_name_for_all_model()
         self.debug = debug
         self.sharktank_dir = sharktank_dir
@@ -241,7 +240,7 @@ class SharkifyStableDiffusionModel:
             args.hf_model_id = self.base_model_id
         self.return_mlir = return_mlir
 
-    def get_extended_name_for_all_model(self):
+    def get_extended_name_for_all_model(self, model_list=None):
         model_name = {}
         sub_model_list = [
             "clip",
@@ -255,6 +254,8 @@ class SharkifyStableDiffusionModel:
             "stencil_adapter",
             "stencil_adapter_512",
         ]
+        if model_list is not None:
+            sub_model_list = model_list
         index = 0
         for model in sub_model_list:
             sub_model = model
@@ -272,7 +273,7 @@ class SharkifyStableDiffusionModel:
                     if stencil is not None:
                         cnet_config = (
                             self.model_namedata
-                            + "_v1-5"
+                            + "_sd15_"
                             + stencil.split("_")[-1]
                         )
                         stencil_names.append(
@@ -283,6 +284,7 @@ class SharkifyStableDiffusionModel:
             else:
                 model_name[model] = get_extended_name(sub_model + model_config)
         index += 1
+
         return model_name
 
     def check_params(self, max_len, width, height):
@@ -765,7 +767,8 @@ class SharkifyStableDiffusionModel:
 
         inputs = tuple(self.inputs["stencil_adapter"])
         model_name = "stencil_adapter_512" if use_large else "stencil_adapter"
-        ext_model_name = self.model_name[model_name]
+        stencil_names = self.get_extended_name_for_all_model([model_name])
+        ext_model_name = stencil_names[model_name]
         if isinstance(ext_model_name, list):
             desired_name = None
             print(ext_model_name)

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_stencil.py

@@ -125,6 +125,31 @@ class StencilPipeline(StableDiffusionPipeline):
         self.controlnet_512_id[index] = None
         self.controlnet_512[index] = None
 
+    def prepare_latents(
+        self,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        dtype,
+    ):
+        latents = torch.randn(
+            (
+                batch_size,
+                4,
+                height // 8,
+                width // 8,
+            ),
+            generator=generator,
+            dtype=torch.float32,
+        ).to(dtype)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+        self.scheduler.is_scale_input_called = True
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
     def prepare_image_latents(
         self,
         image,
@@ -203,10 +228,16 @@ class StencilPipeline(StableDiffusionPipeline):
             self.load_unet_512()
 
         for i, name in enumerate(self.controlnet_names):
+            use_names = []
+            if name is not None:
+                use_names.append(name)
+            else:
+                continue
             if text_embeddings.shape[1] <= self.model_max_length:
                 self.load_controlnet(i, name)
             else:
                 self.load_controlnet_512(i, name)
+            self.controlnet_names = use_names
 
         for i, t in tqdm(enumerate(total_timesteps)):
             step_start_time = time.time()
@@ -461,6 +492,7 @@ class StencilPipeline(StableDiffusionPipeline):
         #     image, use_stencil, height, width, dtype, num_images_per_prompt=1
         # )
         stencil_hints = []
+        self.sd_model.stencils = stencils
         for i, hint in enumerate(preprocessed_hints):
             if hint is not None:
                 hint = controlnet_hint_reshaping(
@@ -475,7 +507,7 @@ class StencilPipeline(StableDiffusionPipeline):
         for i, stencil in enumerate(stencils):
             if stencil == None:
                 continue
-            if len(stencil_hints) >= i:
+            if len(stencil_hints) > i:
                 if stencil_hints[i] is not None:
                     print(f"Using preprocessed controlnet hint for {stencil}")
                     continue
@@ -518,19 +550,30 @@ class StencilPipeline(StableDiffusionPipeline):
 
         # guidance scale as a float32 tensor.
         guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
-
-        # Prepare input image latent
-        init_latents, final_timesteps = self.prepare_image_latents(
-            image=image,
-            batch_size=batch_size,
-            height=height,
-            width=width,
-            generator=generator,
-            num_inference_steps=num_inference_steps,
-            strength=strength,
-            dtype=dtype,
-            resample_type=resample_type,
-        )
+        if image is not None:
+            # Prepare input image latent
+            init_latents, final_timesteps = self.prepare_image_latents(
+                image=image,
+                batch_size=batch_size,
+                height=height,
+                width=width,
+                generator=generator,
+                num_inference_steps=num_inference_steps,
+                strength=strength,
+                dtype=dtype,
+                resample_type=resample_type,
+            )
+        else:
+            # Prepare initial latent.
+            init_latents = self.prepare_latents(
+                batch_size=batch_size,
+                height=height,
+                width=width,
+                generator=generator,
+                num_inference_steps=num_inference_steps,
+                dtype=dtype,
+            )
+            final_timesteps = self.scheduler.timesteps
 
         # Get Image latents
         latents = self.produce_stencil_latents(

apps/stable_diffusion/web/ui/img2img_ui.py

@@ -105,7 +105,7 @@ def img2img_inf(
 
     for i, stencil in enumerate(stencils):
         if images[i] is None and stencil is not None:
-            return
+            continue
         if images[i] is not None:
             if isinstance(images[i], dict):
                 images[i] = images[i]["composite"]
@@ -120,6 +120,8 @@ def img2img_inf(
     else:
         # TODO: enable t2i + controlnets
         image = None
+    if image:
+        image, _, _ = resize_stencil(image, width, height)
 
     # set ckpt_loc and hf_model_id.
     args.ckpt_loc = ""
@@ -152,7 +154,6 @@ def img2img_inf(
             stencil_count += 1
     if stencil_count > 0:
         args.hf_model_id = "runwayml/stable-diffusion-v1-5"
-        image, _, _ = resize_stencil(image, width, height)
     elif "Shark" in args.scheduler:
         print(
             f"Shark schedulers are not supported. Switching to EulerDiscrete "
@@ -162,6 +163,7 @@ def img2img_inf(
     cpu_scheduling = not args.scheduler.startswith("Shark")
     args.precision = precision
     dtype = torch.float32 if precision == "fp32" else torch.half
+    print(stencils)
     new_config_obj = Config(
         "img2img",
         args.hf_model_id,
@@ -180,7 +182,12 @@ def img2img_inf(
     if (
         not global_obj.get_sd_obj()
         or global_obj.get_cfg_obj() != new_config_obj
+        or any(
+            global_obj.get_cfg_obj().stencils[idx] != stencil
+            for idx, stencil in enumerate(stencils)
+        )
     ):
+        print("clearing config because you changed something important")
         global_obj.clear_cache()
         global_obj.set_cfg_obj(new_config_obj)
         args.batch_count = batch_count
@@ -632,7 +639,7 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
                             [cnet_1_image],
                         )
 
-                        cnet_1_model.input(
+                        cnet_1_model.change(
                             fn=(
                                 lambda m, w, h, s, i, p: update_cn_input(
                                     m, w, h, s, i, p, 0
@@ -739,7 +746,7 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
                             label="Preprocessed Hint",
                             interactive=True,
                         )
-                        cnet_2_model.select(
+                        cnet_2_model.change(
                             fn=(
                                 lambda m, w, h, s, i, p: update_cn_input(
                                     m, w, h, s, i, p, 0