import os import gradio as gr import json import logging import torch from PIL import Image import spaces from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download import copy import random import time import re import math import numpy as np # Load LoRAs from JSON loras = [ { "repo": "flymy-ai/qwen-image-realism-lora", "image": "https://huggingface.co/flymy-ai/qwen-image-realism-lora/resolve/main/assets/flymy_realism.png", "trigger_word": "Super Realism portrait of", "trigger_position": "prepend", "title": "Super Realism" }, { "repo": "threecrowco/VolkClipartQwen", "image": "https://huggingface.co/threecrowco/VolkClipartQwen/resolve/main/images/_app_ai-toolkit_output_VolkDrawings_Qwen_v1_samples_1754805220500__000003000_3.jpg", "trigger_word": "volk clipart, black and white, ", "trigger_position": "prepend", "title": "Volk Clipart" }, { "repo": "janekm/analog_film", "image": "https://huggingface.co/spaces/multimodalart/Qwen-Image-LoRA-Explorer/resolve/main/cat.webp", "trigger_word": "fifthel", "trigger_position": "prepend", "weights": "converted_complete.safetensors", "title": "Analog Film" }, { "repo": "itspoidaman/qwenglitch", "image": "https://huggingface.co/itspoidaman/qwenglitch/resolve/main/images/GydaJ5LbEAAWKJU.jpeg", "trigger_word": "qwenglitch", "title": "Glitch" }, { "repo": "alfredplpl/qwen-image-modern-anime-lora", "image": "https://huggingface.co/alfredplpl/qwen-image-modern-anime-lora/resolve/main/sample1.jpg", "trigger_word": "Japanese modern anime style, ", "trigger_position": "prepend", "title": "Modern Anime" }, { "repo": "lichorosario/qwen-image-dottrmstr", "image": "https://huggingface.co/lichorosario/qwen-image-dottrmstr/resolve/main/images/Day_of_the_Tentacle_Remastered_(PC)_08.jpg", "trigger_word": "DOTTRMSTR", "trigger_position": "prepend", "title": "Day of the Tentacle Style" } ] # Initialize the base model dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" base_model = "Qwen/Qwen-Image" # Scheduler configuration from the Qwen-Image-Lightning repository scheduler_config = { "base_image_seq_len": 256, "base_shift": math.log(3), "invert_sigmas": False, "max_image_seq_len": 8192, "max_shift": math.log(3), "num_train_timesteps": 1000, "shift": 1.0, "shift_terminal": None, "stochastic_sampling": False, "time_shift_type": "exponential", "use_beta_sigmas": False, "use_dynamic_shifting": True, "use_exponential_sigmas": False, "use_karras_sigmas": False, } scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config) pipe = DiffusionPipeline.from_pretrained( base_model, scheduler=scheduler, torch_dtype=dtype ).to(device) # Lightning LoRA info (no global state) LIGHTNING_LORA_REPO = "lightx2v/Qwen-Image-Lightning" LIGHTNING_LORA_WEIGHT = "Qwen-Image-Lightning-8steps-V1.0.safetensors" MAX_SEED = np.iinfo(np.int32).max 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 get_image_size(aspect_ratio): """Converts aspect ratio string to width, height tuple.""" if aspect_ratio == "1:1": return 1024, 1024 elif aspect_ratio == "16:9": return 1152, 640 elif aspect_ratio == "9:16": return 640, 1152 elif aspect_ratio == "4:3": return 1024, 768 elif aspect_ratio == "3:4": return 768, 1024 elif aspect_ratio == "3:2": return 1024, 688 elif aspect_ratio == "2:3": return 688, 1024 else: return 1024, 1024 def update_selection(evt: gr.SelectData, aspect_ratio): 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}) ✨" # Update aspect ratio if specified in LoRA config if "aspect" in selected_lora: if selected_lora["aspect"] == "portrait": aspect_ratio = "9:16" elif selected_lora["aspect"] == "landscape": aspect_ratio = "16:9" else: aspect_ratio = "1:1" return ( gr.update(placeholder=new_placeholder), updated_text, evt.index, aspect_ratio, ) def handle_speed_mode(speed_mode): """Update UI based on speed/quality toggle.""" if speed_mode == "Speed (8 steps)": return gr.update(value="Speed mode selected - 8 steps with Lightning LoRA"), 8, 1.0 else: return gr.update(value="Quality mode selected - 45 steps for best quality"), 45, 3.5 @spaces.GPU(duration=70) def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, negative_prompt=""): pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(seed) with calculateDuration("Generating image"): # Generate image image = pipe( prompt=prompt_mash, negative_prompt=negative_prompt, num_inference_steps=steps, true_cfg_scale=cfg_scale, # Use true_cfg_scale for Qwen-Image width=width, height=height, generator=generator, ).images[0] return image @spaces.GPU(duration=70) def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, aspect_ratio, lora_scale, speed_mode, 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"] # Prepare prompt with 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 # Always unload any existing LoRAs first to avoid conflicts with calculateDuration("Unloading existing LoRAs"): pipe.unload_lora_weights() # Load LoRAs based on speed mode if speed_mode == "Speed (8 steps)": with calculateDuration("Loading Lightning LoRA and style LoRA"): # Load Lightning LoRA first pipe.load_lora_weights( LIGHTNING_LORA_REPO, weight_name=LIGHTNING_LORA_WEIGHT, adapter_name="lightning" ) # Load the selected style LoRA weight_name = selected_lora.get("weights", None) pipe.load_lora_weights( lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name="style" ) # Set both adapters active with their weights pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale]) else: # Quality mode - only load the style LoRA with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"): weight_name = selected_lora.get("weights", None) pipe.load_lora_weights( lora_path, weight_name=weight_name, low_cpu_mem_usage=True ) # Set random seed for reproducibility with calculateDuration("Randomizing seed"): if randomize_seed: seed = random.randint(0, MAX_SEED) # Get image dimensions from aspect ratio width, height = get_image_size(aspect_ratio) # Generate the image final_image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale) return final_image, seed def get_huggingface_safetensors(link): split_link = link.split("/") if len(split_link) != 2: raise Exception("Invalid Hugging Face repository link format.") print(f"Repository attempted: {split_link}") # Load model card model_card = ModelCard.load(link) base_model = model_card.data.get("base_model") print(f"Base model: {base_model}") # Validate model type (for Qwen-Image) acceptable_models = {"Qwen/Qwen-Image"} models_to_check = base_model if isinstance(base_model, list) else [base_model] if not any(model in acceptable_models for model in models_to_check): raise Exception("Not a Qwen-Image LoRA!") # Extract image and trigger word 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 # Initialize Hugging Face file system fs = HfFileSystem() try: list_of_files = fs.ls(link, detail=False) # Find safetensors file safetensors_name = None for file in list_of_files: filename = file.split("/")[-1] if filename.endswith(".safetensors"): safetensors_name = filename break if not safetensors_name: raise Exception("No valid *.safetensors file found in the repository.") except Exception as e: print(e) raise Exception("You didn't include 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): print(f"Checking a custom model on: {link}") if link.endswith('.safetensors'): if 'huggingface.co' in link: parts = link.split('/') try: hf_index = parts.index('huggingface.co') username = parts[hf_index + 1] repo_name = parts[hf_index + 2] repo = f"{username}/{repo_name}" safetensors_name = parts[-1] try: model_card = ModelCard.load(repo) trigger_word = model_card.data.get("instance_prompt", "") image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None) image_url = f"https://huggingface.co/{repo}/resolve/main/{image_path}" if image_path else None except: trigger_word = "" image_url = None return repo_name, repo, safetensors_name, trigger_word, image_url except: raise Exception("Invalid safetensors URL format") 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 existing_item_index is None: new_item = { "image": image, "title": title, "repo": repo, "weights": path, "trigger_word": trigger_word } print(new_item) loras.append(new_item) existing_item_index = len(loras) - 1 # Get the actual index after adding 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-Qwen-Image LoRA, this was the issue: {e}") return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-Qwen-Image LoRA"), gr.update(visible=True), 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} #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%} .card_internal{display: flex;height: 100px;margin-top: .5em} .card_internal img{margin-right: 1em} .styler{--form-gap-width: 0px !important} #speed_status{padding: .5em; border-radius: 5px; margin: 1em 0} ''' with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 60)) as app: title = gr.HTML( """Qwen-Image

LoRA Explorer

""", elem_id="title", ) selected_index = gr.State(None) with gr.Row(): with gr.Column(scale=3): prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA") 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(): selected_info = gr.Markdown("") gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=3, elem_id="gallery", show_share_button=False ) with gr.Group(): custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="username/qwen-image-custom-lora") gr.Markdown("[Check Qwen-Image LoRAs](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image)", elem_id="lora_list") custom_lora_info = gr.HTML(visible=False) custom_lora_button = gr.Button("Remove custom LoRA", visible=False) with gr.Column(): result = gr.Image(label="Generated Image") with gr.Row(): speed_mode = gr.Radio( label="Generation Mode", choices=["Speed (8 steps)", "Quality (45 steps)"], value="Quality (45 steps)", info="Speed mode uses Lightning LoRA for faster generation" ) speed_status = gr.Markdown("Quality mode active", elem_id="speed_status") with gr.Row(): with gr.Accordion("Advanced Settings", open=False): with gr.Column(): with gr.Row(): aspect_ratio = gr.Radio( label="Aspect Ratio", choices=["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3"], value="1:1" ) with gr.Row(): cfg_scale = gr.Slider( label="Guidance Scale (True CFG)", minimum=1.0, maximum=5.0, step=0.1, value=3.5, info="Lower for speed mode, higher for quality" ) steps = gr.Slider( label="Steps", minimum=4, maximum=50, step=1, value=45, info="Automatically set by speed mode" ) 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=2, step=0.01, value=1.0) # Event handlers gallery.select( update_selection, inputs=[aspect_ratio], outputs=[prompt, selected_info, selected_index, aspect_ratio] ) speed_mode.change( handle_speed_mode, inputs=[speed_mode], outputs=[speed_status, steps, cfg_scale] ) 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, cfg_scale, steps, selected_index, randomize_seed, seed, aspect_ratio, lora_scale, speed_mode], outputs=[result, seed] ) app.queue() app.launch()