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| import os | |
| import json | |
| import copy | |
| import time | |
| import random | |
| import logging | |
| import numpy as np | |
| from typing import Any, Dict, List, Optional, Union | |
| import torch | |
| from PIL import Image | |
| import gradio as gr | |
| from diffusers import ( | |
| DiffusionPipeline, | |
| AutoencoderTiny, | |
| AutoencoderKL, | |
| AutoPipelineForImage2Image, | |
| FluxPipeline, | |
| FlowMatchEulerDiscreteScheduler) | |
| from huggingface_hub import ( | |
| hf_hub_download, | |
| HfFileSystem, | |
| ModelCard, | |
| snapshot_download) | |
| from diffusers.utils import load_image | |
| from modules.version_info import ( | |
| versions_html, | |
| #initialize_cuda, | |
| #release_torch_resources, | |
| #get_torch_info | |
| ) | |
| from modules.image_utils import ( | |
| change_color, | |
| open_image, | |
| build_prerendered_images, | |
| 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 | |
| ) | |
| from modules.constants import ( | |
| LORA_DETAILS, LORAS as loras, MODELS, | |
| default_lut_example_img, | |
| lut_files, | |
| MAX_SEED, | |
| lut_folder,cards, | |
| cards_alternating, | |
| card_colors, | |
| card_colors_alternating, | |
| pre_rendered_maps_paths | |
| ) | |
| from modules.excluded_colors import ( | |
| add_color, | |
| delete_color, | |
| build_dataframe, | |
| on_input, | |
| excluded_color_list, | |
| on_color_display_select | |
| ) | |
| from modules.misc import ( | |
| get_filename, | |
| convert_ratio_to_dimensions, | |
| update_dimensions_on_ratio | |
| ) | |
| from modules.lora_details import ( | |
| approximate_token_count, | |
| split_prompt_precisely, | |
| ) | |
| import spaces | |
| input_image_palette = [] | |
| current_prerendered_image = gr.State("./images/images/Beeuty-1.png") | |
| #---if workspace = local or colab--- | |
| # Authenticate with Hugging Face | |
| # from huggingface_hub import login | |
| # Log in to Hugging Face using the provided token | |
| # hf_token = 'hf-token-authentication' | |
| # login(hf_token) | |
| def calculate_shift( | |
| image_seq_len, | |
| base_seq_len: int = 256, | |
| max_seq_len: int = 4096, | |
| base_shift: float = 0.5, | |
| max_shift: float = 1.16, | |
| ): | |
| m = (max_shift - base_shift) / (max_seq_len - base_seq_len) | |
| b = base_shift - m * base_seq_len | |
| mu = image_seq_len * m + b | |
| return mu | |
| def retrieve_timesteps( | |
| scheduler, | |
| num_inference_steps: Optional[int] = None, | |
| device: Optional[Union[str, torch.device]] = None, | |
| timesteps: Optional[List[int]] = None, | |
| sigmas: Optional[List[float]] = None, | |
| **kwargs, | |
| ): | |
| if timesteps is not None and sigmas is not None: | |
| raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") | |
| if timesteps is not None: | |
| scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) | |
| timesteps = scheduler.timesteps | |
| num_inference_steps = len(timesteps) | |
| elif sigmas is not None: | |
| scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) | |
| timesteps = scheduler.timesteps | |
| num_inference_steps = len(timesteps) | |
| else: | |
| scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) | |
| timesteps = scheduler.timesteps | |
| return timesteps, num_inference_steps | |
| # FLUX pipeline | |
| def flux_pipe_call_that_returns_an_iterable_of_images( | |
| self, | |
| prompt: Union[str, List[str]] = None, | |
| prompt_2: Optional[Union[str, List[str]]] = None, | |
| height: Optional[int] = None, | |
| width: Optional[int] = None, | |
| num_inference_steps: int = 28, | |
| timesteps: List[int] = None, | |
| guidance_scale: float = 3.5, | |
| num_images_per_prompt: Optional[int] = 1, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| latents: Optional[torch.FloatTensor] = None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| pooled_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| joint_attention_kwargs: Optional[Dict[str, Any]] = None, | |
| max_sequence_length: int = 512, | |
| good_vae: Optional[Any] = None, | |
| ): | |
| height = height or self.default_sample_size * self.vae_scale_factor | |
| width = width or self.default_sample_size * self.vae_scale_factor | |
| self.check_inputs( | |
| prompt, | |
| prompt_2, | |
| height, | |
| width, | |
| prompt_embeds=prompt_embeds, | |
| pooled_prompt_embeds=pooled_prompt_embeds, | |
| max_sequence_length=max_sequence_length, | |
| ) | |
| self._guidance_scale = guidance_scale | |
| self._joint_attention_kwargs = joint_attention_kwargs | |
| self._interrupt = False | |
| batch_size = 1 if isinstance(prompt, str) else len(prompt) | |
| device = self._execution_device | |
| lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None | |
| prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt( | |
| prompt=prompt, | |
| prompt_2=prompt_2, | |
| prompt_embeds=prompt_embeds, | |
| pooled_prompt_embeds=pooled_prompt_embeds, | |
| device=device, | |
| num_images_per_prompt=num_images_per_prompt, | |
| max_sequence_length=max_sequence_length, | |
| lora_scale=lora_scale, | |
| ) | |
| num_channels_latents = self.transformer.config.in_channels // 4 | |
| latents, latent_image_ids = self.prepare_latents( | |
| batch_size * num_images_per_prompt, | |
| num_channels_latents, | |
| height, | |
| width, | |
| prompt_embeds.dtype, | |
| device, | |
| generator, | |
| latents, | |
| ) | |
| sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) | |
| image_seq_len = latents.shape[1] | |
| mu = calculate_shift( | |
| image_seq_len, | |
| self.scheduler.config.base_image_seq_len, | |
| self.scheduler.config.max_image_seq_len, | |
| self.scheduler.config.base_shift, | |
| self.scheduler.config.max_shift, | |
| ) | |
| timesteps, num_inference_steps = retrieve_timesteps( | |
| self.scheduler, | |
| num_inference_steps, | |
| device, | |
| timesteps, | |
| sigmas, | |
| mu=mu, | |
| ) | |
| self._num_timesteps = len(timesteps) | |
| guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None | |
| for i, t in enumerate(timesteps): | |
| if self.interrupt: | |
| continue | |
| timestep = t.expand(latents.shape[0]).to(latents.dtype) | |
| noise_pred = self.transformer( | |
| hidden_states=latents, | |
| timestep=timestep / 1000, | |
| guidance=guidance, | |
| pooled_projections=pooled_prompt_embeds, | |
| encoder_hidden_states=prompt_embeds, | |
| txt_ids=text_ids, | |
| img_ids=latent_image_ids, | |
| joint_attention_kwargs=self.joint_attention_kwargs, | |
| return_dict=False, | |
| )[0] | |
| latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor) | |
| latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor | |
| image = self.vae.decode(latents_for_image, return_dict=False)[0] | |
| yield self.image_processor.postprocess(image, output_type=output_type)[0] | |
| latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] | |
| torch.cuda.empty_cache() | |
| latents = self._unpack_latents(latents, height, width, self.vae_scale_factor) | |
| latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor | |
| image = good_vae.decode(latents, return_dict=False)[0] | |
| self.maybe_free_model_hooks() | |
| torch.cuda.empty_cache() | |
| yield self.image_processor.postprocess(image, output_type=output_type)[0] | |
| #--------------------------------------------------Model Initialization-----------------------------------------------------------------------------------------# | |
| dtype = torch.bfloat16 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| base_model = "black-forest-labs/FLUX.1-dev" | |
| #TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.# | |
| taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) | |
| good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device) | |
| pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device) | |
| pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model, | |
| vae=good_vae, | |
| transformer=pipe.transformer, | |
| text_encoder=pipe.text_encoder, | |
| tokenizer=pipe.tokenizer, | |
| text_encoder_2=pipe.text_encoder_2, | |
| tokenizer_2=pipe.tokenizer_2, | |
| torch_dtype=dtype | |
| ) | |
| MAX_SEED = 2**32-1 | |
| pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) | |
| class calculateDuration: | |
| def __init__(self, activity_name=""): | |
| self.activity_name = activity_name | |
| def __enter__(self): | |
| self.start_time = time.time() | |
| return self | |
| def __exit__(self, exc_type, exc_value, traceback): | |
| self.end_time = time.time() | |
| self.elapsed_time = self.end_time - self.start_time | |
| if self.activity_name: | |
| print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds") | |
| else: | |
| print(f"Elapsed time: {self.elapsed_time:.6f} seconds") | |
| def update_selection(evt: gr.SelectData, width, height): | |
| selected_lora = loras[evt.index] | |
| new_placeholder = f"Type a prompt for {selected_lora['title']}" | |
| lora_repo = selected_lora["repo"] | |
| updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✅" | |
| if "aspect" in selected_lora: | |
| if selected_lora["aspect"] == "portrait": | |
| width = 768 | |
| height = 1024 | |
| elif selected_lora["aspect"] == "landscape": | |
| width = 1024 | |
| height = 768 | |
| else: | |
| width = 1024 | |
| height = 1024 | |
| return ( | |
| gr.update(placeholder=new_placeholder), | |
| updated_text, | |
| evt.index, | |
| width, | |
| height, | |
| ) | |
| def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress): | |
| pipe.to("cuda") | |
| generator = torch.Generator(device="cuda").manual_seed(seed) | |
| if approximate_token_count(prompt_mash) > 76: | |
| prompt, prompt2 = split_prompt_precisely(prompt_mash) | |
| with calculateDuration("Generating image"): | |
| # Generate image | |
| for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( | |
| prompt=prompt, | |
| prompt2=prompt2, | |
| num_inference_steps=steps, | |
| guidance_scale=cfg_scale, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| joint_attention_kwargs={"scale": lora_scale}, | |
| output_type="pil", | |
| good_vae=good_vae, | |
| ): | |
| yield img | |
| def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed): | |
| generator = torch.Generator(device="cuda").manual_seed(seed) | |
| pipe_i2i.to("cuda") | |
| image_input = load_image(image_input_path) | |
| if approximate_token_count(prompt_mash) > 76: | |
| prompt, prompt2 = split_prompt_precisely(prompt_mash) | |
| final_image = pipe_i2i( | |
| prompt=prompt, | |
| prompt2=prompt2, | |
| image=image_input, | |
| strength=image_strength, | |
| num_inference_steps=steps, | |
| guidance_scale=cfg_scale, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| joint_attention_kwargs={"scale": lora_scale}, | |
| output_type="pil", | |
| ).images[0] | |
| return final_image | |
| def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): | |
| if selected_index is None: | |
| raise gr.Error("You must select a LoRA before proceeding.🧨") | |
| selected_lora = loras[selected_index] | |
| lora_path = selected_lora["repo"] | |
| trigger_word = selected_lora["trigger_word"] | |
| if(trigger_word): | |
| if "trigger_position" in selected_lora: | |
| if selected_lora["trigger_position"] == "prepend": | |
| prompt_mash = f"{trigger_word} {prompt}" | |
| else: | |
| prompt_mash = f"{prompt} {trigger_word}" | |
| else: | |
| prompt_mash = f"{trigger_word} {prompt}" | |
| else: | |
| prompt_mash = prompt | |
| with calculateDuration("Unloading LoRA"): | |
| pipe.unload_lora_weights() | |
| pipe_i2i.unload_lora_weights() | |
| #LoRA weights flow | |
| with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"): | |
| pipe_to_use = pipe_i2i if image_input is not None else pipe | |
| weight_name = selected_lora.get("weights", None) | |
| pipe_to_use.load_lora_weights( | |
| lora_path, | |
| weight_name=weight_name, | |
| low_cpu_mem_usage=True | |
| ) | |
| with calculateDuration("Randomizing seed"): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| if(image_input is not None): | |
| print(f"\nGenerating image to image with seed: {seed}\n") | |
| final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed) | |
| yield final_image, seed, gr.update(visible=False) | |
| else: | |
| image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress) | |
| final_image = None | |
| step_counter = 0 | |
| for image in image_generator: | |
| step_counter+=1 | |
| final_image = image | |
| progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>' | |
| yield image, seed, gr.update(value=progress_bar, visible=True) | |
| yield final_image, seed, gr.update(value=progress_bar, visible=False) | |
| def get_huggingface_safetensors(link): | |
| split_link = link.split("/") | |
| if(len(split_link) == 2): | |
| model_card = ModelCard.load(link) | |
| base_model = model_card.data.get("base_model") | |
| print(base_model) | |
| #Allows Both | |
| if base_model not in MODELS: | |
| #if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")): | |
| raise Exception("Flux LoRA Not Found!") | |
| # Only allow "black-forest-labs/FLUX.1-dev" | |
| #if base_model != "black-forest-labs/FLUX.1-dev": | |
| #raise Exception("Only FLUX.1-dev is supported, other LoRA models are not allowed!") | |
| image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None) | |
| trigger_word = model_card.data.get("instance_prompt", "") | |
| image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None | |
| fs = HfFileSystem() | |
| try: | |
| list_of_files = fs.ls(link, detail=False) | |
| for file in list_of_files: | |
| if(file.endswith(".safetensors")): | |
| safetensors_name = file.split("/")[-1] | |
| if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))): | |
| image_elements = file.split("/") | |
| image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}" | |
| except Exception as e: | |
| print(e) | |
| gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA") | |
| raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA") | |
| return split_link[1], link, safetensors_name, trigger_word, image_url | |
| def check_custom_model(link): | |
| if(link.startswith("https://")): | |
| if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")): | |
| link_split = link.split("huggingface.co/") | |
| return get_huggingface_safetensors(link_split[1]) | |
| else: | |
| return get_huggingface_safetensors(link) | |
| def add_custom_lora(custom_lora): | |
| global loras | |
| if(custom_lora): | |
| try: | |
| title, repo, path, trigger_word, image = check_custom_model(custom_lora) | |
| print(f"Loaded custom LoRA: {repo}") | |
| card = f''' | |
| <div class="custom_lora_card"> | |
| <span>Loaded custom LoRA:</span> | |
| <div class="card_internal"> | |
| <img src="{image}" /> | |
| <div> | |
| <h3>{title}</h3> | |
| <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small> | |
| </div> | |
| </div> | |
| </div> | |
| ''' | |
| existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None) | |
| if(not existing_item_index): | |
| new_item = { | |
| "image": image, | |
| "title": title, | |
| "repo": repo, | |
| "weights": path, | |
| "trigger_word": trigger_word | |
| } | |
| print(new_item) | |
| existing_item_index = len(loras) | |
| loras.append(new_item) | |
| return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word | |
| except Exception as e: | |
| gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA") | |
| return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=False), gr.update(), "", None, "" | |
| else: | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "" | |
| def remove_custom_lora(): | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "" | |
| def on_prerendered_gallery_selection(event_data: gr.SelectData): | |
| global current_prerendered_image | |
| selected_index = event_data.index | |
| selected_image = pre_rendered_maps_paths[selected_index] | |
| print(f"Gallery Image Selected: {selected_image}\n") | |
| current_prerendered_image.value = selected_image | |
| return current_prerendered_image | |
| run_lora.zerogpu = True | |
| title = "Hex Game Maker" | |
| with gr.Blocks(css_paths="style_20250128.css", title=title, theme='Surn/beeuty', delete_cache=(7200, 7200)) as app: | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| # Hex Game Maker | |
| ## Transform Your Images into Mesmerizing Hexagon Grid Masterpieces! ⬢""", elem_classes="intro") | |
| with gr.Row(): | |
| with gr.Accordion("Welcome to Hex Game Maker, 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 Hex Game Maker? | |
| Hex Game Maker 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") | |
| selected_index = gr.State(None) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| progress_bar = gr.Markdown(elem_id="progress",visible=False) | |
| input_image = gr.Image( | |
| label="Input Image", | |
| type="filepath", | |
| interactive=True, | |
| elem_classes="centered solid imgcontainer", | |
| key="imgInput", | |
| image_mode=None, | |
| format="PNG", | |
| show_download_button=True, | |
| ) | |
| 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.input( | |
| fn=on_input_image_change, | |
| inputs=[input_image], | |
| outputs=[input_image], scroll_to_output=True, | |
| ) | |
| with gr.Column(scale=0): | |
| 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=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 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("Generative AI", open = False): | |
| with gr.Column(scale=3): | |
| prompt = gr.Textbox(label="Prompt", lines=1, placeholder=":/ choose the LoRA and type the prompt ", 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") | |
| with gr.Column(scale=1, elem_id="gen_column"): | |
| generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn") | |
| with gr.Row(): | |
| with gr.Column(scale=0): | |
| selected_info = gr.Markdown("") | |
| gallery = gr.Gallery( | |
| [(item["image"], item["title"]) for item in loras], | |
| label="LoRA Styles", | |
| allow_preview=False, | |
| columns=3, | |
| elem_id="gallery", | |
| show_share_button=False | |
| ) | |
| with gr.Group(): | |
| custom_lora = gr.Textbox(label="Enter Custom LoRA", placeholder="prithivMLmods/Canopus-LoRA-Flux-Anime") | |
| gr.Markdown("[Check the list of FLUX LoRA's](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list") | |
| custom_lora_info = gr.HTML(visible=False) | |
| custom_lora_button = gr.Button("Remove custom LoRA", visible=False) | |
| with gr.Column(scale=2): | |
| # conditioning_image = gr.Image(label="Conditioning Image", | |
| # type="filepath", | |
| # interactive=True, | |
| # elem_classes="centered solid imgcontainer", | |
| # key="imgConditioning", | |
| # image_mode=None, | |
| # format="PNG", | |
| # show_download_button=True | |
| # ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| # Gallery from PRE_RENDERED_IMAGES GOES HERE | |
| prerendered_image_gallery = gr.Gallery(label="Image Gallery", show_label=True, value=build_prerendered_images(pre_rendered_maps_paths), 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(scale=1): | |
| #image_guidance_stength = gr.Slider(label="Image Guidance Strength", minimum=0, maximum=1.0, value=0.25, 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 Gallery", | |
| elem_id="generate_input_image_from_gallery", | |
| elem_classes="solid" | |
| ) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| with gr.Row(): | |
| image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75) | |
| with gr.Column(): | |
| with gr.Row(): | |
| cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5) | |
| steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28) | |
| with gr.Row(): | |
| 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" | |
| ) | |
| # 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, 1024x576 | |
| image_size_ratio = gr.Dropdown(label="Image Size", 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) | |
| width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=576) | |
| height = gr.Slider(label="Height", minimum=256, maximum=2560, step=16, value=1024, interactive=False) | |
| image_size_ratio.change( | |
| fn=update_dimensions_on_ratio, | |
| inputs=[image_size_ratio, width], | |
| outputs=[width, height] | |
| ) | |
| width.change( | |
| fn=lambda *args: update_dimensions_on_ratio(*args)[1], | |
| inputs=[image_size_ratio, width], | |
| outputs=[height] | |
| ) | |
| with gr.Row(): | |
| randomize_seed = gr.Checkbox(True, label="Randomize seed") | |
| seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) | |
| lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95) | |
| with gr.Row(): | |
| gr.HTML(value=versions_html(), visible=True, elem_id="versions") | |
| # Event Handlers | |
| 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 | |
| ) | |
| gallery.select( | |
| update_selection, | |
| inputs=[width, height], | |
| outputs=[prompt, selected_info, selected_index, width, height] | |
| ) | |
| custom_lora.input( | |
| add_custom_lora, | |
| inputs=[custom_lora], | |
| outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt] | |
| ) | |
| custom_lora_button.click( | |
| remove_custom_lora, | |
| outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora] | |
| ) | |
| gr.on( | |
| triggers=[generate_button.click, prompt.submit], | |
| fn=run_lora, | |
| inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale], | |
| outputs=[input_image, seed, progress_bar] | |
| ) | |
| app.queue() | |
| app.launch(allowed_paths=["assets","/","./assets","images","./images", "./images/prerendered"], favicon_path="./assets/favicon.ico", max_file_size="10mb") |