import gradio as gr import spaces import os import numpy as np os.environ['SPCONV_ALGO'] = 'native' from typing import * import torch import imageio import shutil from PIL import Image, ImageFilter from easydict import EasyDict as edict import utils.constants as constants from haishoku.haishoku import Haishoku from tempfile import NamedTemporaryFile import atexit import random #import accelerate from transformers import AutoTokenizer from trellis.pipelines import TrellisImageTo3DPipeline from trellis.representations import Gaussian, MeshExtractResult from trellis.utils import render_utils, postprocessing_utils from pathlib import Path 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 from utils.color_utils import ( hex_to_rgb, detect_color_format, update_color_opacity, ) from utils.misc import ( get_filename, pause, convert_ratio_to_dimensions, get_seed, get_output_name ) #install_cuda_toolkit,install_torch, _get_output, setup_runtime_env) from utils.image_utils import ( change_color, open_image, build_prerendered_images_by_quality, upscale_image, lerp_imagemath, shrink_and_paste_on_blank, show_lut, apply_lut_to_image_path, multiply_and_blend_images, alpha_composite_with_control, crop_and_resize_image, convert_to_rgba_png, resize_image_with_aspect_ratio, build_prerendered_images_by_quality, get_image_from_dict ) from utils.hex_grid import ( generate_hexagon_grid, generate_hexagon_grid_interface, ) from utils.excluded_colors import ( add_color, delete_color, build_dataframe, on_input, excluded_color_list, on_color_display_select ) # from utils.ai_generator import ( # generate_ai_image, # ) from utils.lora_details import ( upd_prompt_notes, split_prompt_precisely, approximate_token_count, get_trigger_words ) from diffusers import FluxPipeline,FluxImg2ImgPipeline,FluxControlPipeline PIPELINE_CLASSES = { "FluxPipeline": FluxPipeline, "FluxImg2ImgPipeline": FluxImg2ImgPipeline, "FluxControlPipeline": FluxControlPipeline } from utils.version_info import ( versions_html, #initialize_cuda, #release_torch_resources, #get_torch_info ) #from utils.depth_estimation import (get_depth_map_from_state) input_image_palette = [] current_prerendered_image = gr.State("./images/images/Beeuty-1.png") user_dir = constants.TMPDIR # Register the cleanup function atexit.register(cleanup_temp_files) def start_session(req: gr.Request): user_dir = os.path.join(constants.TMPDIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) def end_session(req: gr.Request): user_dir = os.path.join(constants.TMPDIR, str(req.session_hash)) shutil.rmtree(user_dir) def hex_create(hex_size, border_size, input_image_path, start_x, start_y, end_x, end_y, rotation, background_color_hex, background_opacity, border_color_hex, border_opacity, fill_hex, excluded_colors_var, filter_color, x_spacing, y_spacing, add_hex_text_option=None, custom_text_list=None, custom_text_color_list=None): global input_image_palette try: # Load and process the input image input_image = Image.open(input_image_path).convert("RGBA") except Exception as e: print(f"Failed to convert image to RGBA: {e}") # Open the original image without conversion input_image = Image.open(input_image_path) # Ensure the canvas is at least 1344x768 pixels min_width, min_height = 1344, 768 canvas_width = max(min_width, input_image.width) canvas_height = max(min_height, input_image.height) # Create a transparent canvas with the required dimensions new_canvas = Image.new("RGBA", (canvas_width, canvas_height), (0, 0, 0, 0)) # Calculate position to center the input image on the canvas paste_x = (canvas_width - input_image.width) // 2 paste_y = (canvas_height - input_image.height) // 2 # Paste the input image onto the canvas new_canvas.paste(input_image, (paste_x, paste_y)) # Save the 'RGBA' image to a temporary file and update 'input_image_path' with NamedTemporaryFile(delete=False, suffix=".png") as tmp_file: new_canvas.save(tmp_file.name, format="PNG") input_image_path = tmp_file.name constants.temp_files.append(tmp_file.name) # Update 'input_image' with the new image as a file path input_image = Image.open(input_image_path) # Use Haishoku to get the palette from the new image input_palette = Haishoku.loadHaishoku(input_image_path) input_image_palette = input_palette.palette # Update colors with opacity background_color = update_color_opacity( hex_to_rgb(background_color_hex), int(background_opacity * (255 / 100)) ) border_color = update_color_opacity( hex_to_rgb(border_color_hex), int(border_opacity * (255 / 100)) ) # Prepare excluded colors list excluded_color_list = [tuple(lst) for lst in excluded_colors_var] # Generate the hexagon grid images grid_image = generate_hexagon_grid_interface( hex_size, border_size, input_image, start_x, start_y, end_x, end_y, rotation, background_color, border_color, fill_hex, excluded_color_list, filter_color, x_spacing, y_spacing, add_hex_text_option, custom_text_list, custom_text_color_list ) return grid_image def get_model_and_lora(model_textbox): """ Determines the model and LoRA weights based on the model_textbox input. wieghts must be in an array ["Borcherding/FLUX.1-dev-LoRA-FractalLand-v0.1"] """ # If the input is in the list of models, return it with None as LoRA weights if model_textbox in constants.MODELS: return model_textbox, [] # If the input is in the list of LoRA weights, get the corresponding model elif model_textbox in constants.LORA_WEIGHTS: model = constants.LORA_TO_MODEL.get(model_textbox) return model, model_textbox.split() else: # Default to a known model if input is unrecognized default_model = model_textbox return default_model, [] condition_dict = { "depth": 0, "canny": 1, "subject": 4, "coloring": 6, "deblurring": 7, "fill": 9, } # @spaces.GPU(duration=140, progress=gr.Progress(track_tqdm=True)) # def generate_image(pipe, generate_params, progress=gr.Progress(track_tqdm=True)): # return pipe(**generate_params) @spaces.GPU(duration=200, progress=gr.Progress(track_tqdm=True)) def generate_image_lowmem( text, neg_prompt=None, model_name="black-forest-labs/FLUX.1-dev", lora_weights=None, conditioned_image=None, image_width=1368, image_height=848, guidance_scale=3.5, num_inference_steps=30, seed=0, true_cfg_scale=1.0, pipeline_name="FluxPipeline", strength=0.75, additional_parameters=None, progress=gr.Progress(track_tqdm=True) ): #from torch import cuda, bfloat16, float32, Generator, no_grad, backends # Retrieve the pipeline class from the mapping pipeline_class = PIPELINE_CLASSES.get(pipeline_name) if not pipeline_class: raise ValueError(f"Unsupported pipeline type '{pipeline_name}'. " f"Available options: {list(PIPELINE_CLASSES.keys())}") #initialize_cuda() device = "cuda" if torch.cuda.is_available() else "cpu" from src.condition import Condition print(f"device:{device}\nmodel_name:{model_name}\nlora_weights:{lora_weights}\n") #print(f"\n {get_torch_info()}\n") # Disable gradient calculations with torch.no_grad(): # Initialize the pipeline inside the context manager pipe = pipeline_class.from_pretrained( model_name, torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32 ).to(device) # Optionally, don't use CPU offload if not necessary # alternative version that may be more efficient # pipe.enable_sequential_cpu_offload() if pipeline_name == "FluxPipeline": pipe.enable_model_cpu_offload() pipe.vae.enable_slicing() #pipe.vae.enable_tiling() else: pipe.enable_model_cpu_offload() # Access the tokenizer from the pipeline tokenizer = pipe.tokenizer # Check if add_prefix_space is set and convert to slow tokenizer if necessary if getattr(tokenizer, 'add_prefix_space', False): tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, device_map = 'cpu') # Update the pipeline's tokenizer pipe.tokenizer = tokenizer pipe.to(device) flash_attention_enabled = torch.backends.cuda.flash_sdp_enabled() if flash_attention_enabled == False: #Enable xFormers memory-efficient attention (optional) #pipe.enable_xformers_memory_efficient_attention() print("\nEnabled xFormers memory-efficient attention.\n") else: pipe.attn_implementation="flash_attention_2" print("\nEnabled flash_attention_2.\n") condition_type = "subject" # Load LoRA weights # note: does not yet handle multiple LoRA weights with different names, needs .set_adapters(["depth", "hyper-sd"], adapter_weights=[0.85, 0.125]) if lora_weights: for lora_weight in lora_weights: lora_configs = constants.LORA_DETAILS.get(lora_weight, []) lora_weight_set = False if lora_configs: for config in lora_configs: # Load LoRA weights with optional weight_name and adapter_name if 'weight_name' in config: weight_name = config.get("weight_name") adapter_name = config.get("adapter_name") lora_collection = config.get("lora_collection") if weight_name and adapter_name and lora_collection and lora_weight_set == False: pipe.load_lora_weights( lora_collection, weight_name=weight_name, adapter_name=adapter_name, token=constants.HF_API_TOKEN ) lora_weight_set = True print(f"\npipe.load_lora_weights({lora_weight}, weight_name={weight_name}, adapter_name={adapter_name}, lora_collection={lora_collection}\n") elif weight_name and adapter_name==None and lora_collection and lora_weight_set == False: pipe.load_lora_weights( lora_collection, weight_name=weight_name, token=constants.HF_API_TOKEN ) lora_weight_set = True print(f"\npipe.load_lora_weights({lora_weight}, weight_name={weight_name}, adapter_name={adapter_name}, lora_collection={lora_collection}\n") elif weight_name and adapter_name and lora_weight_set == False: pipe.load_lora_weights( lora_weight, weight_name=weight_name, adapter_name=adapter_name, token=constants.HF_API_TOKEN ) lora_weight_set = True print(f"\npipe.load_lora_weights({lora_weight}, weight_name={weight_name}, adapter_name={adapter_name}\n") elif weight_name and adapter_name==None and lora_weight_set == False: pipe.load_lora_weights( lora_weight, weight_name=weight_name, token=constants.HF_API_TOKEN ) lora_weight_set = True print(f"\npipe.load_lora_weights({lora_weight}, weight_name={weight_name}, adapter_name={adapter_name}\n") elif lora_weight_set == False: pipe.load_lora_weights( lora_weight, token=constants.HF_API_TOKEN ) lora_weight_set = True print(f"\npipe.load_lora_weights({lora_weight}, weight_name={weight_name}, adapter_name={adapter_name}\n") # Apply 'pipe' configurations if present if 'pipe' in config: pipe_config = config['pipe'] for method_name, params in pipe_config.items(): method = getattr(pipe, method_name, None) if method: print(f"Applying pipe method: {method_name} with params: {params}") method(**params) else: print(f"Method {method_name} not found in pipe.") if 'condition_type' in config: condition_type = config['condition_type'] if condition_type == "coloring": #pipe.enable_coloring() print("\nEnabled coloring.\n") elif condition_type == "deblurring": #pipe.enable_deblurring() print("\nEnabled deblurring.\n") elif condition_type == "fill": #pipe.enable_fill() print("\nEnabled fill.\n") elif condition_type == "depth": #pipe.enable_depth() print("\nEnabled depth.\n") elif condition_type == "canny": #pipe.enable_canny() print("\nEnabled canny.\n") elif condition_type == "subject": #pipe.enable_subject() print("\nEnabled subject.\n") else: print(f"Condition type {condition_type} not implemented.") else: pipe.load_lora_weights(lora_weight, use_auth_token=constants.HF_API_TOKEN) # Set the random seed for reproducibility generator = torch.Generator(device=device).manual_seed(seed) conditions = [] if conditioned_image is not None: conditioned_image = crop_and_resize_image(conditioned_image, image_width, image_height) condition = Condition(condition_type, conditioned_image) conditions.append(condition) print(f"\nAdded conditioned image.\n {conditioned_image.size}") # Prepare the parameters for image generation additional_parameters ={ "strength": strength, "image": conditioned_image, } else: print("\nNo conditioned image provided.") if neg_prompt!=None: true_cfg_scale=1.1 additional_parameters ={ "negative_prompt": neg_prompt, "true_cfg_scale": true_cfg_scale, } # handle long prompts by splitting them if approximate_token_count(text) > 76: prompt, prompt2 = split_prompt_precisely(text) prompt_parameters = { "prompt" : prompt, "prompt_2": prompt2 } else: prompt_parameters = { "prompt" :text } additional_parameters.update(prompt_parameters) # Combine all parameters generate_params = { "height": image_height, "width": image_width, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps, "generator": generator, } if additional_parameters: generate_params.update(additional_parameters) generate_params = {k: v for k, v in generate_params.items() if v is not None} print(f"generate_params: {generate_params}") # Generate the image result = pipe(**generate_params) #generate_image(pipe,generate_params) image = result.images[0] # Clean up del result del conditions del generator # Delete the pipeline and clear cache del pipe torch.cuda.empty_cache() torch.cuda.ipc_collect() print(torch.cuda.memory_summary(device=None, abbreviated=False)) return image def generate_ai_image_local ( map_option, prompt_textbox_value, neg_prompt_textbox_value, model="black-forest-labs/FLUX.1-dev", lora_weights=None, conditioned_image=None, height=512, width=912, num_inference_steps=30, guidance_scale=3.5, seed=777, pipeline_name="FluxPipeline", strength=0.75, progress=gr.Progress(track_tqdm=True) ): print(f"Generating image with lowmem") try: if map_option != "Prompt": prompt = constants.PROMPTS[map_option] negative_prompt = constants.NEGATIVE_PROMPTS.get(map_option, "") else: prompt = prompt_textbox_value negative_prompt = neg_prompt_textbox_value or "" #full_prompt = f"{prompt} {negative_prompt}" additional_parameters = {} if lora_weights: for lora_weight in lora_weights: lora_configs = constants.LORA_DETAILS.get(lora_weight, []) for config in lora_configs: if 'parameters' in config: additional_parameters.update(config['parameters']) elif 'trigger_words' in config: trigger_words = get_trigger_words(lora_weight) prompt = f"{trigger_words} {prompt}" for key, value in additional_parameters.items(): if key in ['height', 'width', 'num_inference_steps', 'max_sequence_length']: additional_parameters[key] = int(value) elif key in ['guidance_scale','true_cfg_scale']: additional_parameters[key] = float(value) height = additional_parameters.pop('height', height) width = additional_parameters.pop('width', width) num_inference_steps = additional_parameters.pop('num_inference_steps', num_inference_steps) guidance_scale = additional_parameters.pop('guidance_scale', guidance_scale) print("Generating image with the following parameters:\n") print(f"Model: {model}") print(f"LoRA Weights: {lora_weights}") print(f"Prompt: {prompt}") print(f"Neg Prompt: {negative_prompt}") print(f"Height: {height}") print(f"Width: {width}") print(f"Number of Inference Steps: {num_inference_steps}") print(f"Guidance Scale: {guidance_scale}") print(f"Seed: {seed}") print(f"Additional Parameters: {additional_parameters}") print(f"Conditioned Image: {conditioned_image}") print(f"Conditioned Image Strength: {strength}") print(f"pipeline: {pipeline_name}") image = generate_image_lowmem( text=prompt, model_name=model, neg_prompt=negative_prompt, lora_weights=lora_weights, conditioned_image=conditioned_image, image_width=width, image_height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, seed=seed, pipeline_name=pipeline_name, strength=strength, additional_parameters=additional_parameters ) with NamedTemporaryFile(delete=False, suffix=".png") as tmp: image.save(tmp.name, format="PNG") constants.temp_files.append(tmp.name) print(f"Image saved to {tmp.name}") return tmp.name except Exception as e: print(f"Error generating AI image: {e}") #gc.collect() return None def generate_input_image_click(image_input, map_option, prompt_textbox_value, negative_prompt_textbox_value, model_textbox_value, randomize_seed=True, seed=None, use_conditioned_image=False, strength=0.5, image_format="16:9", scale_factor=(8/3), progress=gr.Progress(track_tqdm=True)): seed = get_seed(randomize_seed, seed) # Get the model and LoRA weights model, lora_weights = get_model_and_lora(model_textbox_value) global current_prerendered_image conditioned_image=None formatted_map_option = map_option.lower().replace(' ', '_') if use_conditioned_image: print(f"Conditioned path: {current_prerendered_image.value}.. converting to RGB\n") # ensure the conditioned image is an image and not a string, cannot use RGBA if isinstance(current_prerendered_image.value, str): conditioned_image = open_image(current_prerendered_image.value).convert("RGB") print(f"Conditioned Image: {conditioned_image.size}.. converted to RGB\n") # use image_input as the conditioned_image if it is not None elif image_input is not None: conditioned_image = open_image(image_input).convert("RGB") print(f"Conditioned Image set to modify Input Image!\nClear to generate new image.") gr.Info("Conditioned Image set to modify Input Image! Clear to generate new image",duration=5) # Convert image_format from a string split by ":" into two numbers divided width_ratio, height_ratio = map(int, image_format.split(":")) aspect_ratio = width_ratio / height_ratio width, height = convert_ratio_to_dimensions(aspect_ratio, 576) pipeline = "FluxPipeline" if conditioned_image is not None: pipeline = "FluxImg2ImgPipeline" # Generate the AI image and get the image path image_path = generate_ai_image_local( map_option, prompt_textbox_value, negative_prompt_textbox_value, model, lora_weights, conditioned_image, strength=strength, height=height, width=width, seed=seed, pipeline_name=pipeline, ) # Open the generated image try: image = Image.open(image_path).convert("RGBA") except Exception as e: print(f"Failed to open generated image: {e}") return image_path, seed # Return the original image path if opening fails # Upscale the image upscaled_image = upscale_image(image, scale_factor) # Save the upscaled image to a temporary file with NamedTemporaryFile(delete=False, suffix=".png", prefix=f"{formatted_map_option}_") as tmp_upscaled: upscaled_image.save(tmp_upscaled.name, format="PNG") constants.temp_files.append(tmp_upscaled.name) print(f"Upscaled image saved to {tmp_upscaled.name}") gc.collect() # Return the path of the upscaled image return tmp_upscaled.name, seed def update_prompt_visibility(map_option): is_visible = (map_option == "Prompt") return ( gr.update(visible=is_visible), gr.update(visible=is_visible), gr.update(visible=is_visible) ) def update_prompt_notes(model_textbox_value): return upd_prompt_notes(model_textbox_value) def on_prerendered_gallery_selection(event_data: gr.SelectData): global current_prerendered_image selected_index = event_data.index selected_image = constants.pre_rendered_maps_paths[selected_index] print(f"Template Image Selected: {selected_image} ({event_data.index})\n") gr.Info(f"Template Image Selected: {selected_image} ({event_data.index})",duration=5) current_prerendered_image.value = selected_image return current_prerendered_image def combine_images_with_lerp(input_image, output_image, alpha): in_image = open_image(input_image) out_image = open_image(output_image) print(f"Combining images with alpha: {alpha}") return lerp_imagemath(in_image, out_image, alpha) def add_border(image, mask_width, mask_height, blank_color): bordered_image_output = Image.open(image).convert("RGBA") margin_color = detect_color_format(blank_color) print(f"Adding border to image with width: {mask_width}, height: {mask_height}, color: {margin_color}") return shrink_and_paste_on_blank(bordered_image_output, mask_width, mask_height, margin_color) ####################################### DEPTH ESTIMATION ####################################### def preprocess_image(image: Image.Image) -> Image.Image: """ Preprocess the input image. Args: image (Image.Image): The input image. Returns: Image.Image: The preprocessed image. """ processed_image = TRELLIS_PIPELINE.preprocess_image(image) return processed_image def pack_state(gs: Gaussian, mesh: MeshExtractResult, name: str) -> dict: return { 'gaussian': { **gs.init_params, '_xyz': gs._xyz.cpu().numpy(), '_features_dc': gs._features_dc.cpu().numpy(), '_scaling': gs._scaling.cpu().numpy(), '_rotation': gs._rotation.cpu().numpy(), '_opacity': gs._opacity.cpu().numpy(), }, 'mesh': { 'vertices': mesh.vertices.cpu().numpy(), 'faces': mesh.faces.cpu().numpy(), }, 'name': name } def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]: gs = Gaussian( aabb=state['gaussian']['aabb'], sh_degree=state['gaussian']['sh_degree'], mininum_kernel_size=state['gaussian']['mininum_kernel_size'], scaling_bias=state['gaussian']['scaling_bias'], opacity_bias=state['gaussian']['opacity_bias'], scaling_activation=state['gaussian']['scaling_activation'], ) gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda') gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda') gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda') gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda') gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda') mesh = edict( vertices=torch.tensor(state['mesh']['vertices'], device='cuda'), faces=torch.tensor(state['mesh']['faces'], device='cuda'), ) name = state['name'] return gs, mesh, name @spaces.GPU(duration=150,progress=gr.Progress(track_tqdm=True)) def generate_3d_asset(depth_image_source, randomize_seed, seed, input_image, output_image, overlay_image, bordered_image_output, req: gr.Request, progress=gr.Progress(track_tqdm=True)): # Choose the image based on source if depth_image_source == "Input Image": image_path = input_image elif depth_image_source == "Output Image": image_path = output_image elif depth_image_source == "Image with Margins": image_path = bordered_image_output else: # "Overlay Image" image_path = overlay_image output_name = get_output_name(input_image, output_image, overlay_image, bordered_image_output) # Ensure the file exists if not Path(image_path).exists(): raise ValueError("Image file not found.") # Determine the final seed using default MAX_SEED from constants final_seed = np.random.randint(0, constants.MAX_SEED) if randomize_seed else seed # Open image using standardized defaults image_raw = Image.open(image_path).convert("RGB") # Preprocess and run the Trellis pipeline with fixed sampler settings # Returns: # dict: The information of the generated 3D model. # str: The path to the video of the 3D model. processed_image = TRELLIS_PIPELINE.preprocess_image(image_raw, max_resolution=1536) outputs = TRELLIS_PIPELINE.run( processed_image, seed=final_seed, formats=["gaussian", "mesh"], preprocess_image=False, sparse_structure_sampler_params={ "steps": 12, "cfg_strength": 7.5, }, slat_sampler_params={ "steps": 12, "cfg_strength": 3.0, }, ) # Validate the mesh mesh = outputs['mesh'][0] # Depending on the mesh format (it might be a dict or an object) meshisdict = isinstance(mesh, dict) if meshisdict: vertices = mesh['vertices'] faces = mesh['faces'] else: vertices = mesh.vertices faces = mesh.faces # Check mesh properties print(f"Mesh vertices: {vertices.shape}, faces: {faces.shape}") if faces.max() >= vertices.shape[0]: raise ValueError(f"Invalid mesh: face index {faces.max()} exceeds vertex count {vertices.shape[0]}") # Ensure data is on GPU and has correct type if not vertices.is_cuda or not faces.is_cuda: raise ValueError("Mesh data must be on GPU") if vertices.dtype != torch.float32 or faces.dtype != torch.int32: if meshisdict: mesh['faces'] = faces.to(torch.int32) mesh['vertices'] = vertices.to(torch.float32) else: mesh.faces = faces.to(torch.int32) mesh.vertices = vertices.to(torch.float32) #raise ValueError("Mesh vertices must be float32, faces must be int32") # Save the video to a temporary file user_dir = os.path.join(constants.TMPDIR, str(req.session_hash)) os.makedirs(user_dir, exist_ok=True) video = render_utils.render_video(outputs['gaussian'][0], resolution=512, num_frames=64, r=1, fov=45)['color'] video_geo = render_utils.render_video(outputs['mesh'][0], resolution=512, num_frames=64, r=1, fov=45)['normal'] video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))] video_path = os.path.join(user_dir, f'{output_name}.mp4') imageio.mimsave(video_path, video, fps=8) snapshot_results = render_utils.render_snapshot_depth(outputs['mesh'][0], resolution=1536, r=1, fov=80) depth_snapshot = Image.fromarray(snapshot_results['normal'][0]).convert("L") state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], output_name) #depth_snapshot = get_depth_map_from_state(state, image_raw.size[0], image_raw.size[1]) torch.cuda.empty_cache() return [state, video_path, depth_snapshot] @spaces.GPU(duration=90,progress=gr.Progress(track_tqdm=True)) def extract_glb( state: dict, mesh_simplify: float, texture_size: int, req: gr.Request,progress=gr.Progress(track_tqdm=True) ) -> Tuple[str, str]: """ Extract a GLB file from the 3D model. Args: state (dict): The state of the generated 3D model. mesh_simplify (float): The mesh simplification factor. texture_size (int): The texture resolution. Returns: str: The path to the extracted GLB file. """ user_dir = os.path.join(constants.TMPDIR, str(req.session_hash)) gs, mesh, name = unpack_state(state) glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False) glb_path = os.path.join(user_dir, f'{name}.glb') glb.export(glb_path) torch.cuda.empty_cache() return glb_path, glb_path @spaces.GPU(progress=gr.Progress(track_tqdm=True)) def extract_gaussian(state: dict, req: gr.Request, progress=gr.Progress(track_tqdm=True)) -> Tuple[str, str]: """ Extract a Gaussian file from the 3D model. Args: state (dict): The state of the generated 3D model. Returns: str: The path to the extracted Gaussian file. """ user_dir = os.path.join(constants.TMPDIR, str(req.session_hash)) gs, _, name = unpack_state(state) gaussian_path = os.path.join(user_dir, f'{name}.ply') gs.save_ply(gaussian_path) torch.cuda.empty_cache() return gaussian_path, gaussian_path @spaces.GPU() def getVersions(): return versions_html() #generate_input_image_click.zerogpu = True #generate_depth_button_click.zerogpu = True #def main(debug=False): title = "HexaGrid Creator" #description = "Customizable Hexagon Grid Image Generator" examples = [["assets//examples//hex_map_p1.png", 32, 1, 0, 0, 0, 0, 0, "#ede9ac44","#12165380", True]] gr.set_static_paths(paths=["images/","images/images","images/prerendered","LUT/","fonts/","assets/"]) # Gradio Blocks Interface with gr.Blocks(css_paths="style_20250128.css", title=title, theme='Surn/beeuty',delete_cache=(21600,86400)) as hexaGrid: with gr.Row(): gr.Markdown(""" # HexaGrid Creator ## Transform Your Images into Mesmerizing Hexagon Grid Masterpieces! ⬢""", elem_classes="intro") with gr.Row(): with gr.Accordion("Welcome to HexaGrid Creator, the ultimate tool for transforming your images into stunning hexagon grid artworks. Whether you're a tabletop game enthusiast, a digital artist, or someone who loves unique patterns, HexaGrid Creator has something for you.", open=False, elem_classes="intro"): gr.Markdown (""" ## Drop an image into the Input Image and get started! ## What is HexaGrid Creator? HexaGrid Creator is a web-based application that allows you to apply a hexagon grid overlay to any image. You can customize the size, color, and opacity of the hexagons, as well as the background and border colors. The result is a visually striking image that looks like it was made from hexagonal tiles! ### What Can You Do? - **Generate Hexagon Grids:** Create beautiful hexagon grid overlays on any image with fully customizable parameters. - **AI-Powered Image Generation:** Use advanced AI models to generate images based on your prompts and apply hexagon grids to them. - **Color Exclusion:** Select and exclude specific colors from your hexagon grid for a cleaner and more refined look. - **Interactive Customization:** Adjust hexagon size, border size, rotation, background color, and more in real-time. - **Depth and 3D Model Generation:** Generate depth maps and 3D models from your images for enhanced visualization. - **Image Filter [Look-Up Table (LUT)] Application:** Apply filters (LUTs) to your images for color grading and enhancement. - **Pre-rendered Maps:** Access a library of pre-rendered hexagon maps for quick and easy customization. - **Add Margins:** Add customizable margins around your images for a polished finish. ### Why You'll Love It - **Fun and Easy to Use:** With an intuitive interface and real-time previews, creating hexagon grids has never been this fun! - **Endless Creativity:** Unleash your creativity with endless customization options and see your images transform in unique ways. - **Hexagon-Inspired Theme:** Enjoy a delightful yellow and purple theme inspired by hexagons! ⬢ - **Advanced AI Models:** Leverage advanced AI models and LoRA weights for high-quality image generation and customization. ### Get Started 1. **Upload or Generate an Image:** Start by uploading your own image or generate one using our AI-powered tool. 2. **Customize Your Grid:** Play around with the settings to create the perfect hexagon grid overlay. 3. **Download and Share:** Once you're happy with your creation, download it and share it with the world! ### Advanced Features - **Generative AI Integration:** Utilize models like `black-forest-labs/FLUX.1-dev` and various LoRA weights for generating unique images. - **Pre-rendered Maps:** Access a library of pre-rendered hexagon maps for quick and easy customization. - **Image Filter [Look-Up Table (LUT)] Application:** Apply filters (LUTs) to your images for color grading and enhancement. - **Depth and 3D Model Generation:** Create depth maps and 3D models from your images for enhanced visualization. - **Add Margins:** Customize margins around your images for a polished finish. Join the hive and start creating with HexaGrid Creator today! """, elem_classes="intro") with gr.Row(): with gr.Column(scale=2): input_image = gr.Image( label="Input Image", type="filepath", interactive=True, elem_classes="centered solid imgcontainer", key="imgInput", image_mode=None, format="PNG" ) # New code to convert input image to RGBA PNG def on_input_image_change(image_path): if image_path is None: gr.Warning("Please upload an Input Image to get started.") return None img, img_path = convert_to_rgba_png(image_path) return img_path input_image.change( fn=on_input_image_change, inputs=[input_image], outputs=[input_image], scroll_to_output=True, ) with gr.Column(): with gr.Accordion("Hex Coloring and Exclusion", open = False): with gr.Row(): with gr.Column(): 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,) 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) 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( value="", label="Look Up Table (LUT) File Name", elem_id="lutFileName") with gr.Column(): lut_file = gr.File( value=None, file_count="single", file_types=[".cube"], type="filepath", label="LUT cube File") with gr.Row(): lut_example_image = gr.Image(type="pil", label="Filter (LUT) Example Image", value=constants.default_lut_example_img) with gr.Row(): with gr.Column(): gr.Markdown(""" ### Included Filters (LUTs) There are several included Filters: Try them on the example image before applying to your Input Image. """, elem_id="lut_markdown") with gr.Column(): gr.Examples(elem_id="lut_examples", examples=[[f] for f in constants.lut_files], inputs=[lut_filename], outputs=[lut_filename], label="Select a Filter (LUT) file. Populate the LUT File Name field" ) 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]) 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)[0], inputs=[lut_filename, input_image], outputs=[input_image], scroll_to_output=True ) with gr.Row(): with gr.Accordion("Generate AI Image (click here for options)", open = False): with gr.Row(): with gr.Column(): model_options = gr.Dropdown( label="Choose an AI Model*", choices=constants.MODELS + constants.LORA_WEIGHTS + ["Manual Entry"], value="Cossale/Frames2-Flex.1", elem_classes="solid" ) model_textbox = gr.Textbox( label="LORA/Model", value="Cossale/Frames2-Flex.1", elem_classes="solid", elem_id="inference_model", visible=False ) # Update map_options to a Dropdown with choices from constants.PROMPTS keys with gr.Row(): with gr.Column(): map_options = gr.Dropdown( label="Map Options*", choices=list(constants.PROMPTS.keys()), value="Alien Landscape", elem_classes="solid", scale=0 ) # Add Dropdown for sizing of Images, height and width based on selection. Options are 16x9, 16x10, 4x5, 1x1 # The values of height and width are based on common resolutions for each aspect ratio # Default to 16x9, 912x512 image_size_ratio = gr.Dropdown(label="Image Aspect Ratio", choices=["16:9", "16:10", "4:5", "4:3", "2:1","3:2","1:1", "9:16", "10:16", "5:4", "3:4","1:2", "2:3"], value="16:9", elem_classes="solid", type="value", scale=0, interactive=True) with gr.Column(): seed_slider = gr.Slider( label="Seed", minimum=0, maximum=constants.MAX_SEED, step=1, value=0, scale=0, randomize=True, elem_id="rnd_seed" ) randomize_seed = gr.Checkbox(label="Randomize seed", value=False, scale=0, interactive=True) prompt_textbox = gr.Textbox( label="Prompt", visible=False, elem_classes="solid", value="top-down, (rectangular tabletop_map) alien planet map, Battletech_boardgame scifi world with forests, lakes, oceans, continents and snow at the top and bottom, (middle is dark, no_reflections, no_shadows), from directly above. From 100,000 feet looking straight down", lines=4 ) negative_prompt_textbox = gr.Textbox( label="Negative Prompt", visible=False, elem_classes="solid", value="Earth, low quality, bad anatomy, blurry, cropped, worst quality, shadows, people, humans, reflections, shadows, realistic map of the Earth, isometric, text" ) prompt_notes_label = gr.Label( "You should use FRM$ as trigger words. @1.5 minutes", elem_classes="solid centered small", show_label=False, visible=False ) # Keep the change event to maintain functionality map_options.change( fn=update_prompt_visibility, inputs=[map_options], outputs=[prompt_textbox, negative_prompt_textbox, prompt_notes_label] ) with gr.Row(): generate_input_image = gr.Button( "Generate from Input Image & Options ", elem_id="generate_input_image", elem_classes="solid" ) with gr.Column(scale=2): with gr.Accordion("Template Images", open = False): with gr.Row(): with gr.Column(scale=2): # Gallery from PRE_RENDERED_IMAGES GOES HERE prerendered_image_gallery = gr.Gallery(label="Image Gallery", show_label=True, value=build_prerendered_images_by_quality(3,'thumbnail'), elem_id="gallery", elem_classes="solid", type="filepath", columns=[3], rows=[3], preview=False ,object_fit="contain", height="auto", format="png",allow_preview=False) with gr.Column(): image_guidance_stength = gr.Slider(label="Image Guidance Strength (prompt percentage)", minimum=0, maximum=1.0, value=0.85, step=0.01, interactive=True) replace_input_image_button = gr.Button( "Replace Input Image", elem_id="prerendered_replace_input_image_button", elem_classes="solid" ) generate_input_image_from_gallery = gr.Button( "Generate AI Image from Template Image & Options", elem_id="generate_input_image_from_gallery", elem_classes="solid" ) with gr.Accordion("Advanced Hexagon Settings", open = False): with gr.Row(): start_x = gr.Number(label="Start X", value=20, minimum=-512, maximum= 512, precision=0) start_y = gr.Number(label="Start Y", value=20, minimum=-512, maximum= 512, precision=0) end_x = gr.Number(label="End X", value=-20, minimum=-512, maximum= 512, precision=0) end_y = gr.Number(label="End Y", value=-20, minimum=-512, maximum= 512, precision=0) with gr.Row(): x_spacing = gr.Number(label="Adjust Horizontal spacing", value=-8, minimum=-200, maximum=200, precision=1) y_spacing = gr.Number(label="Adjust Vertical spacing", value=3, minimum=-200, maximum=200, precision=1) with gr.Row(): rotation = gr.Slider(-90, 180, 0.0, 0.1, label="Hexagon Rotation (degree)") add_hex_text = gr.Dropdown(label="Add Text to Hexagons", choices=[None, "Row-Column Coordinates", "Sequential Numbers", "Playing Cards Sequential", "Playing Cards Alternate Red and Black", "Custom List"], value=None) with gr.Row(): custom_text_list = gr.TextArea(label="Custom Text List", value=constants.cards_alternating, visible=False,) custom_text_color_list = gr.TextArea(label="Custom Text Color List", value=constants.card_colors_alternating, visible=False) with gr.Row(): hex_text_info = gr.Markdown(""" ### Text Color uses the Border Color and Border Opacity, unless you use a custom list. ### The Custom Text List and Custom Text Color List are comma separated lists. ### The custom color list is a comma separated list of hex colors. #### Example: "A,2,3,4,5,6,7,8,9,10,J,Q,K", "red,#0000FF,#00FF00,red,#FFFF00,#00FFFF,#FF8000,#FF00FF,#FF0080,#FF8000,#FF0080,lightblue" """, elem_id="hex_text_info", visible=False) add_hex_text.change( fn=lambda x: ( gr.update(visible=(x == "Custom List")), gr.update(visible=(x == "Custom List")), gr.update(visible=(x != None)) ), inputs=add_hex_text, outputs=[custom_text_list, custom_text_color_list, hex_text_info] ) with gr.Row(): hex_size = gr.Number(label="Hexagon Size", value=90, minimum=1, maximum=768) border_size = gr.Slider(-5,25,value=2,step=1,label="Border Size") with gr.Row(): background_color = gr.ColorPicker(label="Background Color", value="#000000", interactive=True) background_opacity = gr.Slider(0,100,0,1,label="Background Opacity %") border_color = gr.ColorPicker(label="Border Color", value="#7b7b7b", interactive=True) border_opacity = gr.Slider(0,100,50,1,label="Border Opacity %") with gr.Row(): hex_button = gr.Button("Generate Hex Grid!", elem_classes="solid", elem_id="btn-generate") with gr.Row(): output_image = gr.Image(label="Hexagon Grid Image", image_mode = "RGBA", elem_classes="centered solid imgcontainer", format="PNG", type="filepath", key="ImgOutput",interactive=True) overlay_image = gr.Image(label="Hexagon Overlay Image", image_mode = "RGBA", elem_classes="centered solid imgcontainer", format="PNG", type="filepath", key="ImgOverlay",interactive=True) with gr.Row(): output_overlay_composite = gr.Slider(0,100,50,0.5, label="Interpolate Intensity") output_blend_multiply_composite = gr.Slider(0,100,50,0.5, label="Overlay Intensity") output_alpha_composite = gr.Slider(0,100,50,0.5, label="Alpha Composite Intensity") with gr.Accordion("Add Margins (bleed)", open=False): with gr.Row(): border_image_source = gr.Radio(label="Add Margins around which Image", choices=["Input Image", "Overlay Image"], value="Overlay Image") with gr.Row(): mask_width = gr.Number(label="Margins Width", value=10, minimum=0, maximum=100, precision=0) mask_height = gr.Number(label="Margins Height", value=10, minimum=0, maximum=100, precision=0) with gr.Row(): margin_color = gr.ColorPicker(label="Margin Color", value="#333333FF", interactive=True) margin_opacity = gr.Slider(0,100,95,0.5,label="Margin Opacity %") with gr.Row(): add_border_button = gr.Button("Add Margins", elem_classes="solid", variant="secondary") with gr.Row(): bordered_image_output = gr.Image(label="Image with Margins", image_mode="RGBA", elem_classes="centered solid imgcontainer", format="PNG", type="filepath", key="ImgBordered",interactive=False, show_download_button=True, show_fullscreen_button=True, show_share_button=True) with gr.Accordion("Height Maps and 3D", open=False): with gr.Row(): with gr.Column(): # Use standard seed settings only seed_3d = gr.Slider(0, constants.MAX_SEED, label="Seed (3D Generation)", value=0, step=1, randomize=True) randomize_seed_3d = gr.Checkbox(label="Randomize Seed (3D Generation)", value=True) with gr.Column(): depth_image_source = gr.Radio( label="Depth Image Source", choices=["Input Image", "Output Image", "Overlay Image", "Image with Margins"], value="Input Image" ) with gr.Row(): generate_3d_asset_button = gr.Button("Generate 3D Asset", elem_classes="solid", variant="secondary") with gr.Row(): # For display: video output and 3D model preview (GLTF) video_output = gr.Video(label="3D Asset Video", autoplay=True, loop=True, height=400) with gr.Row(): depth_output = gr.Image(label="Depth Map", image_mode="L", elem_classes="centered solid imgcontainer", format="PNG", type="filepath", key="DepthOutput",interactive=False, show_download_button=True, show_fullscreen_button=True, show_share_button=True) with gr.Accordion("GLB Extraction Settings", open=False): with gr.Row(): mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01) texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512) with gr.Row(): extract_glb_btn = gr.Button("Extract GLB", interactive=False) extract_gaussian_btn = gr.Button("Extract Gaussian", interactive=False) with gr.Row(): model_output = gr.Model3D(label="Extracted 3D Model", clear_color=[1.0, 1.0, 1.0, 1.0], elem_classes="centered solid imgcontainer", interactive=True) model_file = gr.File(label="3D GLTF", elem_classes="solid small centered") is_multiimage = gr.State(False) output_buf = gr.State() with gr.Row(): gr.Examples(examples=[ ["assets//examples//hex_map_p1.png", False, True, -32,-31,80,80,-1.8,0,35,0,1,"#FFD0D0", 15], ["assets//examples//hex_map_p1_overlayed.png", False, False, -32,-31,80,80,-1.8,0,35,0,1,"#FFD0D0", 75], ["assets//examples//hex_flower_logo.png", False, True, -95,-95,100,100,-24,-2,190,30,2,"#FF8951", 50], ["assets//examples//hexed_fract_1.png", False, True, 0,0,0,0,0,0,10,0,0,"#000000", 5], ["assets//examples//tmpzt3mblvk.png", False, True, -20,10,0,0,-6,-2,35,30,1,"#ffffff", 0], ], inputs=[input_image, filter_color, fill_hex, start_x, start_y, end_x, end_y, x_spacing, y_spacing, hex_size, rotation, border_size, border_color, border_opacity], elem_id="examples") # with gr.Row(): # login_button = gr.LoginButton(size="sm", elem_classes="solid centered", elem_id="hf_login_btn") with gr.Row(): gr.HTML(value=getVersions(), visible=True, elem_id="versions") # Handlers hexaGrid.load(start_session) hexaGrid.unload(end_session) color_display.select(on_color_display_select,inputs=[color_display], outputs=[selected_row]) color_display.input(on_input,inputs=[color_display], outputs=[color_display, gr.State(excluded_color_list)]) delete_button.click(fn=delete_color, inputs=[selected_row, color_display], outputs=[color_display]) exclude_color_button.click(fn=add_color, inputs=[color_picker, gr.State(excluded_color_list)], outputs=[color_display, gr.State(excluded_color_list)]) hex_button.click( fn=lambda hex_size, border_size, input_image, start_x, start_y, end_x, end_y, rotation, background_color, background_opacity, border_color, border_opacity, fill_hex, color_display, filter_color, x_spacing, y_spacing, add_hex_text, custom_text_list, custom_text_color_list: gr.Warning("Please upload an Input Image to get started.") if input_image is None else hex_create(hex_size, border_size, input_image, start_x, start_y, end_x, end_y, rotation, background_color, background_opacity, border_color, border_opacity, fill_hex, color_display, filter_color, x_spacing, y_spacing, add_hex_text, custom_text_list, custom_text_color_list), inputs=[hex_size, border_size, input_image, start_x, start_y, end_x, end_y, rotation, background_color, background_opacity, border_color, border_opacity, fill_hex, color_display, filter_color, x_spacing, y_spacing, add_hex_text, custom_text_list, custom_text_color_list], outputs=[output_image, overlay_image], scroll_to_output=True ) generate_input_image.click( fn=generate_input_image_click, inputs=[input_image,map_options, prompt_textbox, negative_prompt_textbox, model_textbox, randomize_seed, seed_slider, gr.State(False), gr.State(0.5), image_size_ratio], outputs=[input_image, seed_slider], scroll_to_output=True ) model_textbox.change( fn=update_prompt_notes, inputs=model_textbox, outputs=prompt_notes_label,preprocess=False ) model_options.change( fn=lambda x: (gr.update(visible=(x == "Manual Entry")), gr.update(value=x) if x != "Manual Entry" else gr.update()), inputs=model_options, outputs=[model_textbox, model_textbox] ) model_options.change( fn=update_prompt_notes, inputs=model_options, outputs=prompt_notes_label ) composite_button.click( fn=lambda input_image, composite_color, composite_opacity: gr.Warning("Please upload an Input Image to get started.") if input_image is None else change_color(input_image, composite_color, composite_opacity), inputs=[input_image, composite_color, composite_opacity], outputs=[input_image] ) #use conditioned_image as the input_image for generate_input_image_click generate_input_image_from_gallery.click( fn=generate_input_image_click, inputs=[input_image, map_options, prompt_textbox, negative_prompt_textbox, model_textbox,randomize_seed, seed_slider, gr.State(True), image_guidance_stength, image_size_ratio], outputs=[input_image, seed_slider], scroll_to_output=True ) # Update the state variable with the prerendered image filepath when an image is selected prerendered_image_gallery.select( fn=on_prerendered_gallery_selection, inputs=None, outputs=[gr.State(current_prerendered_image)], # Update the state with the selected image show_api=False ) # replace input image with selected gallery image replace_input_image_button.click( lambda: current_prerendered_image.value, inputs=None, outputs=[input_image], scroll_to_output=True ) output_overlay_composite.change( fn=combine_images_with_lerp, inputs=[input_image, output_image, output_overlay_composite], outputs=[overlay_image], scroll_to_output=True ) output_blend_multiply_composite.change( fn=multiply_and_blend_images, inputs=[input_image, output_image, output_blend_multiply_composite], outputs=[overlay_image], scroll_to_output=True ) output_alpha_composite.change( fn=alpha_composite_with_control, inputs=[input_image, output_image, output_alpha_composite], outputs=[overlay_image], scroll_to_output=True ) add_border_button.click( fn=lambda image_source, mask_w, mask_h, color, opacity, input_img, overlay_img: add_border(input_img if image_source == "Input Image" else overlay_img, mask_w, mask_h, update_color_opacity(detect_color_format(color), opacity * 2.55)), inputs=[border_image_source, mask_width, mask_height, margin_color, margin_opacity, input_image, overlay_image], outputs=[bordered_image_output], scroll_to_output=True ) # 3D Generation # generate_depth_button.click( # fn=generate_depth_button_click, # inputs=[depth_image_source, resized_width_slider, z_scale_slider, input_image, output_image, overlay_image, bordered_image_output], # outputs=[depth_map_output, model_output, model_file], scroll_to_output=True # ) # Chain the buttons generate_3d_asset_button.click( fn=generate_3d_asset, inputs=[depth_image_source, randomize_seed_3d, seed_3d, input_image, output_image, overlay_image, bordered_image_output], outputs=[output_buf, video_output, depth_output], scroll_to_output=True ).then( lambda: (gr.Button(interactive=True), gr.Button(interactive=True)), outputs=[extract_glb_btn, extract_gaussian_btn] ) # Extraction callbacks remain unchanged from previous behavior extract_glb_btn.click( fn=extract_glb, inputs=[output_buf, mesh_simplify, texture_size], outputs=[model_output, model_file] ).then( lambda: gr.Button(interactive=True), outputs=[model_file] ) extract_gaussian_btn.click( fn=extract_gaussian, inputs=[output_buf], outputs=[model_output, model_file] ).then( lambda: gr.Button(interactive=True), outputs=[model_file] ) if __name__ == "__main__": constants.load_env_vars(constants.dotenv_path) logging.basicConfig( format="[%(levelname)s] %(asctime)s %(message)s", level=logging.INFO ) logging.info("Environment Variables: %s" % os.environ) # if _get_output(["nvcc", "--version"]) is None: # logging.info("Installing CUDA toolkit...") # install_cuda_toolkit() # else: # logging.info("Detected CUDA: %s" % _get_output(["nvcc", "--version"])) # logging.info("Installing CUDA extensions...") # setup_runtime_env() #main(os.getenv("DEBUG") == "1") #main() #-------------- ------------------------------------------------MODEL INITIALIZATION------------------------------------------------------------# # Load models once during module import TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large") TRELLIS_PIPELINE.cuda() 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")