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 import spaces #---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' # Replace with your actual token # 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 @torch.inference_mode() 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] #------------------------------------------------------------------------------------------------------------------------------------------------------------# loras = [ #D&D Style { "image": "https://huggingface.co/SouthbayJay/dnd-style-flux/resolve/main/27073771.jpeg", "title": "DnD Style", "repo": "SouthbayJay/dnd-style-flux", "weights": "dnd_style_flux.safetensors", "trigger_word": "dndstyle" }, #DND Weasley { "image": "https://cdn-uploads.huggingface.co/production/uploads/noauth/T9jOsT74oPDh7EwRizkjH.webp", "title": "DnD Weasley", "repo": "weasley24/dnd-Flux-LoRA", "weights": "lora.safetensors", "trigger_word": "TOK" }, #DND_PARTY { "image": "https://cdn-uploads.huggingface.co/production/uploads/noauth/hTDygXMBQ_fbut2mTnqwM.webp", "title": "DnD Weasley 2", "repo": "weasley24/dnd-party-Flux-LoRA", "weights": "lora.safetensors", "trigger_word": "DND_PARTY" }, #DND Sketch Art { "image": "https://cdn-uploads.huggingface.co/production/uploads/noauth/hUedjn4EDxVro01OkIe55.webp", "title": "DnD Sketch Art", "repo": "minaj546/dndsketchart", "weights": "lora.safetensors", "trigger_word": "DnD" }, #DNDDN { "image": "https://huggingface.co/mrTvister/Dnddn/resolve/main/images/photo_6_2025-01-09_21-51-14.jpg", "title": "DnD Portait", "repo": "mrTvister/Dnddn", "weights": "Dandadan.safetensors", "trigger_word": "Anime-style close-up, solid background" }, #99 { "image": "https://huggingface.co/wavymulder/OverlordStyleFLUX/resolve/main/imgs/ComfyUI_00668_.png", "title": "DnD Overlord", "repo": "wavymulder/OverlordStyleFLUX", "weights": "ovld_style_overlord_wavymulder.safetensors", "trigger_word": "ovld style anime" }, #add new ] #--------------------------------------------------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, ) @spaces.GPU(duration=100) 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) with calculateDuration("Generating image"): # Generate image for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( prompt=prompt_mash, 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) final_image = pipe_i2i( prompt=prompt_mash, 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 @spaces.GPU(duration=100) 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): 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'
' 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 != "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'''
Loaded custom LoRA:

{title}

{"Using: "+trigger_word+" as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}
''' 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, "" run_lora.zerogpu = True css = ''' #gen_btn{height: 100%} #gen_column{align-self: stretch} #title{text-align: center} #title h1{font-size: 3em; display:inline-flex; align-items:center; color: #ffd700;} #title img{width: 100px; margin-right: 0.5em} #gallery .grid-wrap{height: 10vh} #lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%; color: #ffd700;} .card_internal{display: flex;height: 100px;margin-top: .5em; border: 2px solid #8b4513; background: #3a3a3a;} .card_internal img{margin-right: 1em; border-radius: 5px;} .styler{--form-gap-width: 0px !important} #progress{height:30px} #progress .generating{display:none} .progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px} .progress-bar {height: 100%;background-color: #d2691e;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out} body {background: url('https://www.transparenttextures.com/patterns/black-linen.png') center; font-family: 'Cinzel', serif; color: #ffd700;} ''' with gr.Blocks(theme="YTheme/Minecraft", css=css, delete_cache=(60, 60)) as app: title = gr.HTML( """

⚔️ Dungeon Master's Canvas ⚔️

Craft the Realm! Generate images of heroic battles, fantastical creatures, and enchanting landscapes. Perfect for Dungeon Masters and players alike.
Visit Our Website | Support Us

""", elem_id="title", ) selected_index = gr.State(None) with gr.Row(): with gr.Column(scale=3): prompt = gr.Textbox( label="📜 Your Epic Quest Description", lines=1, placeholder="Describe your dungeon, creature, or magical item..." ) with gr.Column(scale=1, elem_id="gen_column"): generate_button = gr.Button("Cast the Spell!", variant="primary", elem_id="gen_btn") with gr.Row(): with gr.Column(): selected_info = gr.Markdown("") gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="✨ Magical LoRA Artifacts ✨", allow_preview=False, columns=3, elem_id="gallery", show_share_button=False ) with gr.Group(): custom_lora = gr.Textbox( label="✨ Add a Custom Artifact (LoRA) ✨", placeholder="prithivMLmods/Canopus-LoRA-Flux-Anime" ) gr.Markdown( """[Explore the Tome of LoRA Artifacts 📜](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 Artifact", visible=False) with gr.Column(): progress_bar = gr.Markdown(elem_id="progress", visible=False) result = gr.Image(label="✨ Your D&D Masterpiece ✨") with gr.Row(): with gr.Accordion("🛠️ Advanced Settings ⚙️", open=False): with gr.Row(): input_image = gr.Image(label="📜 Use an Input Image", type="filepath") image_strength = gr.Slider( label="⚡ Denoise Strength", info="Lower values keep the input image’s influence stronger.", minimum=0.1, maximum=1.0, step=0.01, value=0.75 ) with gr.Column(): with gr.Row(): cfg_scale = gr.Slider( label="✨ Creativity Scale (CFG)", minimum=1, maximum=20, step=0.5, value=3.5 ) steps = gr.Slider( label="⏱️ Steps for Generation", minimum=1, maximum=50, step=1, value=28 ) with gr.Row(): width = gr.Slider( label="🖼️ Image Width", minimum=256, maximum=1536, step=64, value=1024 ) height = gr.Slider( label="🖼️ Image Height", minimum=256, maximum=1536, step=64, value=1024 ) with gr.Row(): randomize_seed = gr.Checkbox(True, label="🎲 Randomize Seed") seed = gr.Slider( label="🌟 Seed Value", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True ) lora_scale = gr.Slider( label="🔧 LoRA Intensity", minimum=0, maximum=3, step=0.01, value=0.95 ) 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=[result, seed, progress_bar] ) app.queue() app.launch()