Add 1D LUTS, UI update

LUT/gist_heat.cube

@@ -0,0 +1,258 @@
+TITLE "Colormap gist_heat"
+LUT_1D_SIZE 256
+0.0000000 0.0000000 0.0000000
+0.0058824 0.0000000 0.0000000
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+1.0000000 1.0000000 1.0000000

app.py

@@ -27,8 +27,6 @@ import logging
 #logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)
 import gc
 
-IS_SHARED_SPACE = constants.IS_SHARED_SPACE
-
 # Import functions from modules
 from utils.file_utils import cleanup_temp_files
 
@@ -753,20 +751,42 @@ def replace_input_with_sketch_image(sketch_image):
     sketch, is_dict = get_image_from_dict(sketch_image)
     return sketch
 ####################################### DEPTH ESTIMATION #######################################
+
+@spaces.GPU(progress=gr.Progress(track_tqdm=True))
+def load_trellis_model():
+    global TRELLIS_PIPELINE
+    loaded = False
+    if TRELLIS_PIPELINE == None:
+        try:
+            TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+            TRELLIS_PIPELINE.cuda()
+            # Preload with a dummy image to finalize initialization
+            try:
+                TRELLIS_PIPELINE.preprocess_image(Image.fromarray(np.zeros((512, 512, 4), dtype=np.uint8)))    # Preload rembg
+            except:
+                pass
+            print("TRELLIS_PIPELINE loaded\n")
+            gr.Info("TRELLIS_PIPELINE loaded")
+            loaded = True
+        except Exception as e:
+            print(f"Error preloading TRELLIS_PIPELINE: {e}")
+            gr.Error(f"Failed to load TRELLIS_PIPELINE: {e}")
+            TRELLIS_PIPELINE = None
+    
+
 def load_3d_models(is_open: bool = True) -> bool:
     if is_open:
         gr.Info("Loading 3D models...")
-        global image_processor, depth_model, TRELLIS_PIPELINE
+        global image_processor, depth_model
         image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
         depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", ignore_mismatched_sizes=True)
-        TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-        try:
-            # Preload with a dummy image to finalize initialization
-            TRELLIS_PIPELINE.preprocess_image(Image.fromarray(np.zeros((256, 256, 3), dtype=np.uint8)))
-        except Exception as e:
-            print(f"Error preloading TRELLIS_PIPELINE: {e}")
+        print("DPT models loaded\n")
+        # is_open = load_trellis_model()
+        # if is_open:
         print("3D models loaded")
         gr.Info("3D models loaded.")
+        #else:
+        #     gr.Error("Failed to load TRELLIS_PIPELINE.")
     return gr.update(interactive = is_open)
 
 def unload_3d_models(is_open: bool = False) -> bool:
@@ -775,12 +795,12 @@ def unload_3d_models(is_open: bool = False) -> bool:
         global image_processor, depth_model, TRELLIS_PIPELINE
         if TRELLIS_PIPELINE:
             TRELLIS_PIPELINE.to("cpu")
-            del TRELLIS_PIPELINE
+            TRELLIS_PIPELINE = None
         if depth_model:
             del image_processor
             del depth_model
-        #torch.cuda.empty_cache()
-        #torch.cuda.ipc_collect()
+        # torch.cuda.empty_cache()
+        # torch.cuda.ipc_collect()
         gc.collect()
         print("3D models unloaded and CUDA memory freed")
         gr.Info("3D models unloaded.")
@@ -928,7 +948,7 @@ def generate_3d_asset_part2(depth_img, image_path, output_name, seed, steps, mod
     depth_img = Image.open(depth_img).convert("RGBA")
     # Preprocess and run the Trellis pipeline with fixed sampler settings
     try:
-        TRELLIS_PIPELINE.to(device)
+        #TRELLIS_PIPELINE.cuda()
         processed_image = TRELLIS_PIPELINE.preprocess_image(resized_image, max_resolution=model_resolution)
         outputs = TRELLIS_PIPELINE.run(
             processed_image,
@@ -1154,19 +1174,12 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
                         color_picker = gr.ColorPicker(label="Pick a color to exclude",value="#505050")
                     with gr.Column():
                         filter_color = gr.Checkbox(label="Filter Excluded Colors from Sampling", value=False,)
+                        fill_hex = gr.Checkbox(label="Fill Hex with color from Image", value=True)
                 exclude_color_button = gr.Button("Exclude Color", elem_id="exlude_color_button", elem_classes="solid")
                 color_display = gr.DataFrame(label="List of Excluded RGBA Colors", headers=["R", "G", "B", "A"], elem_id="excluded_colors", type="array", value=build_dataframe(excluded_color_list), interactive=True, elem_classes="solid centered")
                 selected_row = gr.Number(0, label="Selected Row", visible=False)
-                delete_button = gr.Button("Delete Row", elem_id="delete_exclusion_button", elem_classes="solid")
-                fill_hex = gr.Checkbox(label="Fill Hex with color from Image", value=True)
+                delete_button = gr.Button("Delete Row", elem_id="delete_exclusion_button", elem_classes="solid")                
             with gr.Accordion("Image Filters", open = False):
-                with gr.Row():
-                    with gr.Column():
-                        composite_color = gr.ColorPicker(label="Color", value="#ede9ac44")
-                    with gr.Column():
-                        composite_opacity = gr.Slider(label="Opacity %", minimum=0, maximum=100, value=50, interactive=True)
-                with gr.Row():
-                    composite_button = gr.Button("Composite", elem_classes="solid")
                 with gr.Row():
                     with gr.Column():
                         lut_filename = gr.Textbox(
@@ -1179,7 +1192,10 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
                             file_count="single",
                             file_types=[".cube"],
                             type="filepath",
-                            label="LUT cube File")
+                            label="LUT cube File",
+                            height=120)
+                with gr.Row():
+                    apply_lut_button = gr.Button("Apply Filter (LUT)", elem_classes="solid", elem_id="apply_lut_button")
                 with gr.Row():
                     lut_example_image = gr.Image(type="pil", label="Filter (LUT) Example Image", value=constants.default_lut_example_img)
                 with gr.Row():
@@ -1199,18 +1215,20 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
                             examples_per_page = 15,
                         )
                     
-                with gr.Row():
-                    apply_lut_button = gr.Button("Apply Filter (LUT)", elem_classes="solid", elem_id="apply_lut_button")
-                    
                 lut_file.change(get_filename, inputs=[lut_file], outputs=[lut_filename])
-                lut_filename.change(show_lut, inputs=[lut_filename, lut_example_image], outputs=[lut_example_image])
+                lut_filename.change(show_lut, inputs=[lut_filename, lut_example_image], outputs=[lut_example_image], scroll_to_output=True)
                 apply_lut_button.click(
                     lambda lut_filename, input_image: gr.Warning("Please upload an Input Image to get started.") if input_image is None else apply_lut_to_image_path(lut_filename, input_image)[1], 
                     inputs=[lut_filename, input_image], 
                     outputs=[input_image], 
                     scroll_to_output=True
                 )
-            
+            with gr.Accordion("Color Composite", open = False):
+                with gr.Row():
+                    composite_color = gr.ColorPicker(label="Color", value="#ede9ac44")
+                    composite_opacity = gr.Slider(label="Opacity %", minimum=0, maximum=100, value=50, interactive=True)
+                with gr.Row():
+                    composite_button = gr.Button("Composite", elem_classes="solid")
     with gr.Row():
         with gr.Accordion("Generate AI Image (click here for options)", open = False):
             with gr.Row():
@@ -1229,7 +1247,7 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
                         lines=2,
                         visible=False
                     )
-                    with gr.Accordion("Choose Style Model*", open=True):
+                    with gr.Accordion("Choose Image Style*", open=True):
                         lora_gallery = gr.Gallery(
                             [(open_image(image_path), title) for image_path, title in lora_models],
                             label="Styles",
@@ -1455,6 +1473,10 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
         scroll_to_output=True
     )
     generate_input_image.click(
+        fn=unload_3d_models,
+        trigger_mode="always_last",
+        outputs=[generate_3d_asset_button]
+    ).then(
         fn=generate_input_image_click,
         inputs=[input_image,map_options, prompt_textbox, negative_prompt_textbox, model_textbox, randomize_seed, seed_slider, gr.State(False), sketch_image, image_guidance_stength, image_size_ratio],
         outputs=[input_image, seed_slider], scroll_to_output=True
@@ -1512,6 +1534,10 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
 
     #use conditioned_image as the input_image for generate_input_image_click
     generate_input_image_from_gallery.click(
+        fn=unload_3d_models,
+        trigger_mode="always_last",
+        outputs=[generate_3d_asset_button]
+    ).then(
         fn=generate_input_image_click,
         inputs=[input_image, map_options, prompt_textbox, negative_prompt_textbox, model_textbox,randomize_seed, seed_slider, gr.State(True), sketch_image , image_guidance_stength, image_size_ratio],
         outputs=[input_image, seed_slider], scroll_to_output=True
@@ -1569,6 +1595,9 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
     #     outputs=[depth_map_output, model_output, model_file], scroll_to_output=True
     # )
     accordian_3d.expand(
+        fn=load_trellis_model,
+        trigger_mode="always_last"
+    ).then(
         fn=load_3d_models,
         trigger_mode="always_last",
         outputs=[generate_3d_asset_button],
@@ -1646,12 +1675,13 @@ if __name__ == "__main__":
 
     # image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
     # depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", ignore_mismatched_sizes=True)
-    # TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-    # TRELLIS_PIPELINE.to(device)
-    # try:
-    #    TRELLIS_PIPELINE.preprocess_image(Image.fromarray(np.zeros((64, 64, 3), dtype=np.uint8)))    # Preload rembg
-    # except:
-    #     pass
+    # if constants.IS_SHARED_SPACE:
+    #     TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+    #     TRELLIS_PIPELINE.to(device)
+    #     try:
+    #        TRELLIS_PIPELINE.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
+    #     except:
+    #         pass
     hexaGrid.queue(default_concurrency_limit=1,max_size=12,api_open=False)
     hexaGrid.launch(allowed_paths=["assets","/","./assets","images","./images", "./images/prerendered", 'e:/TMP'], favicon_path="./assets/favicon.ico", max_file_size="10mb")
 

trellis/pipelines/base.py

@@ -11,12 +11,15 @@ class Pipeline:
     def __init__(
         self,
         models: dict[str, nn.Module] = None,
+        device: torch.device = torch.device("cpu")
     ):
         if models is None:
-            return
-        self.models = models
-        for model in self.models.values():
-            model.eval()
+            self.models = {}
+        else:
+            self.models = models
+            for model in self.models.values():
+                model.eval()
+        self.to(device)
 
     @staticmethod
     def from_pretrained(path: str) -> "Pipeline":
@@ -41,7 +44,7 @@ class Pipeline:
             for k, v in args['models'].items()
         }
 
-        new_pipeline = Pipeline(_models)
+        new_pipeline = Pipeline(_models)  # defaults to cpu
         new_pipeline._pretrained_args = args
         return new_pipeline
 

utils/image_utils.py

@@ -528,24 +528,6 @@ def resize_and_crop_image(image: Image, new_width: int = 512, new_height: int =
 
 ##################################################### LUTs ############################################################
 
-class Color1DLUT(ImageFilter.Filter):
-    """Custom filter to apply a 1D LUT to an RGB image."""
-    def __init__(self, table, size):
-        self.table = table
-        self.size = size
-        if size != 256:
-            raise ValueError("Only 1D LUTs with size 256 are supported")
-        # Create a 768-entry LUT (256 for R, G, B) scaled to 0-255
-        lut_r = [int(table[i][0] * 255) for i in range(256)]
-        lut_g = [int(table[i][1] * 255) for i in range(256)]
-        lut_b = [int(table[i][2] * 255) for i in range(256)]
-        self.lut = lut_r + lut_g + lut_b
-
-    def filter(self, image):
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
-        return image.point(self.lut)
-
 def is_3dlut_row(row: List[str]) -> bool:
     """
     Check if one line in the file has exactly 3 numeric values.
@@ -562,25 +544,8 @@ def is_3dlut_row(row: List[str]) -> bool:
     except ValueError:
         return False
 
-def read_lut(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> Union[ImageFilter.Color3DLUT, Color1DLUT]:
-    """
-    Read a LUT from a .cube file and return a filter object.
-
-    Detects whether the file contains a 1D or 3D LUT based on keywords
-    "LUT_1D_SIZE" or "LUT_3D_SIZE". Initially assumes a 3D LUT if no size
-    keyword is specified.
+def get_lut_type(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> str:
 
-    Args:
-        path_lut: Path to the LUT file (string or os.PathLike).
-        num_channels: Number of color channels in the LUT (default is 3).
-
-    Returns:
-        ImageFilter.Color3DLUT for 3D LUTs or Color1DLUT for 1D LUTs.
-
-    Raises:
-        FileNotFoundError: If the file does not exist.
-        ValueError: If the LUT data is invalid or size mismatches.
-    """
     with open(path_lut) as f:
         lines = f.read().splitlines()
 
@@ -602,28 +567,10 @@ def read_lut(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> Union[
             lut_type = "1D"
         elif is_3dlut_row(parts):
             table.append(tuple(float(val) for val in parts))
+    return lut_type
+
 
-    # Process based on LUT type
-    if lut_type == "3D":
-        if size is None:
-            # Calculate size assuming 3D LUT
-            len_table = len(table)
-            if len_table == 0:
-                raise ValueError("No valid LUT data found")
-            size = round(len_table ** (1 / 3))
-            if size ** 3 != len_table:
-                raise ValueError(f"Number of table entries {len_table} is not a perfect cube")
-        elif len(table) != size ** 3:
-            raise ValueError(f"Expected {size**3} entries for 3D LUT, got {len(table)}")
-        return ImageFilter.Color3DLUT(size, table, channels=num_channels)
-    else:  # lut_type == "1D"
-        if size is None:
-            raise ValueError("LUT_1D_SIZE not specified for 1D LUT")
-        if len(table) != size:
-            raise ValueError(f"Expected {size} entries for 1D LUT, got {len(table)}")
-        return Color1DLUT(table, size)
-
-def read_3Dlut(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> ImageFilter.Color3DLUT:
+def read_3d_lut(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> ImageFilter.Color3DLUT:
     """
     Read LUT from a raw file.
 
@@ -647,7 +594,31 @@ def read_3Dlut(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> Imag
     lut_table = [row2val(row) for row in lut_raw if is_3dlut_row(row.split(" "))]
     return ImageFilter.Color3DLUT(size, lut_table, num_channels)
 
-def apply_lut(img: Image, lut_path: str = "", lut: ImageFilter.Color3DLUT = None) -> Image:
+def apply_1d_lut(image, lut_file):
+    # Read the 1D LUT
+    with open(lut_file) as f:
+        lines = f.read().splitlines()
+    table = []
+    for line in lines:
+        if not line.startswith(("#", "LUT", "TITLE", "DOMAIN")) and line.strip():
+            values = [float(v) for v in line.split()]
+            table.append(tuple(values))
+    
+    # Convert image to grayscale
+    if image.mode != 'L':
+        image = image.convert('L')
+    img_array = np.array(image) / 255.0  # Normalize to [0, 1]
+    
+    # Map grayscale values to colors
+    lut_size = len(table)
+    indices = (img_array * (lut_size - 1)).astype(int)
+    colors = np.array(table)[indices]
+    
+    # Create RGB image
+    rgb_image = Image.fromarray((colors * 255).astype(np.uint8), mode='RGB')
+    return rgb_image
+
+def apply_3d_lut(img: Image, lut_path: str = "", lut: ImageFilter.Color3DLUT = None) -> Image:
     """
     Apply a LUT to an image and return a PIL Image with the LUT applied.
 
@@ -667,11 +638,30 @@ def apply_lut(img: Image, lut_path: str = "", lut: ImageFilter.Color3DLUT = None
     if lut is None:
         if lut_path == "":
             raise ValueError("Either lut_path or lut argument must be provided.")
-        lut = read_lut(lut_path)
+        lut = read_3d_lut(lut_path)
     return img.filter(lut)
 
+def apply_lut(image, lut_filename: str) -> Image:
+    """
+    Apply a LUT to an image and return the result.
+    Args:
+        image (str or PIL.Image.Image): The image to apply the LUT to.
+        lut_filename (str): The path to the LUT file.
+    Returns:
+        PIL.Image.Image: The image with the LUT applied.
+    """
+    if isinstance(image, str):
+        image = open_image(image)
+    if lut_filename is not None:
+        if (get_lut_type(lut_filename) == "3D"):
+            lut = read_3d_lut(lut_filename)
+            image =  apply_3d_lut(image, lut=lut)
+        else:
+            image = apply_1d_lut(image, lut_filename)
+    return image
+
 def show_lut(lut_filename: str, lut_example_image: Image = default_lut_example_img) -> Image:
-    if lut_filename is not None:        
+    if lut_filename is not None:
         try:
             lut_example_image = apply_lut(lut_example_image, lut_filename)
         except Exception as e:
@@ -704,10 +694,6 @@ def apply_1d_lut(image, lut_file):
     rgb_image = Image.fromarray((colors * 255).astype(np.uint8), mode='RGB')
     return rgb_image
 
-
-
-
-
 def apply_lut_to_image_path(lut_filename: str, image_path: str) -> tuple[Image, str]:
     """
     Apply a LUT to an image and return the result.