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import os |
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import time |
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import torch |
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import gradio as gr |
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from PIL import Image |
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from huggingface_hub import hf_hub_download, list_repo_files |
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from src_inference.pipeline import FluxPipeline |
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from src_inference.lora_helper import set_single_lora |
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BASE_PATH = "black-forest-labs/FLUX.1-dev" |
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LOCAL_LORA_DIR = "./LoRAs" |
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CUSTOM_LORA_DIR = "./Custom_LoRAs" |
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os.makedirs(LOCAL_LORA_DIR, exist_ok=True) |
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os.makedirs(CUSTOM_LORA_DIR, exist_ok=True) |
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print("downloading OmniConsistency base LoRA …") |
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omni_consistency_path = hf_hub_download( |
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repo_id="showlab/OmniConsistency", |
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filename="OmniConsistency.safetensors", |
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local_dir="./Model" |
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) |
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print("loading base pipeline …") |
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pipe = FluxPipeline.from_pretrained( |
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BASE_PATH, torch_dtype=torch.bfloat16 |
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).to("cuda") |
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set_single_lora(pipe.transformer, omni_consistency_path, |
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lora_weights=[1], cond_size=512) |
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def download_all_loras(): |
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lora_names = [ |
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"3D_Chibi", "American_Cartoon", "Chinese_Ink", "Clay_Toy", |
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"Fabric", "Ghibli", "Irasutoya", "Jojo", "LEGO", "Line", |
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"Macaron", "Oil_Painting", "Origami", "Paper_Cutting", |
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"Picasso", "Pixel", "Poly", "Pop_Art", "Rick_Morty", |
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"Snoopy", "Van_Gogh", "Vector" |
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] |
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for name in lora_names: |
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hf_hub_download( |
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repo_id="showlab/OmniConsistency", |
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filename=f"LoRAs/{name}_rank128_bf16.safetensors", |
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local_dir=LOCAL_LORA_DIR, |
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) |
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download_all_loras() |
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def clear_cache(transformer): |
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for _, attn_processor in transformer.attn_processors.items(): |
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attn_processor.bank_kv.clear() |
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def generate_image( |
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lora_name, |
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custom_repo_id, |
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prompt, |
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uploaded_image, |
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width, height, |
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guidance_scale, |
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num_inference_steps, |
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seed |
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): |
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width, height = int(width), int(height) |
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generator = torch.Generator("cpu").manual_seed(seed) |
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if custom_repo_id and custom_repo_id.strip(): |
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repo_id = custom_repo_id.strip() |
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try: |
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files = list_repo_files(repo_id) |
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print("using custom LoRA from:", repo_id) |
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safetensors_files = [f for f in files if f.endswith(".safetensors")] |
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print("found safetensors files:", safetensors_files) |
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if not safetensors_files: |
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raise ValueError("No .safetensors files were found in this repo") |
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fname = safetensors_files[0] |
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lora_path = hf_hub_download( |
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repo_id=repo_id, |
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filename=fname, |
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local_dir=CUSTOM_LORA_DIR, |
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) |
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except Exception as e: |
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raise gr.Error(f"Load custom LoRA failed: {e}") |
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else: |
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lora_path = os.path.join( |
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f"{LOCAL_LORA_DIR}/LoRAs", f"{lora_name}_rank128_bf16.safetensors" |
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) |
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pipe.unload_lora_weights() |
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try: |
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pipe.load_lora_weights( |
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os.path.dirname(lora_path), |
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weight_name=os.path.basename(lora_path) |
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) |
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except Exception as e: |
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raise gr.Error(f"Load LoRA failed: {e}") |
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spatial_image = [uploaded_image.convert("RGB")] |
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subject_images = [] |
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start = time.time() |
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out_img = pipe( |
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prompt, |
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height=(height // 8) * 8, |
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width=(width // 8) * 8, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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max_sequence_length=512, |
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generator=generator, |
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spatial_images=spatial_image, |
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subject_images=subject_images, |
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cond_size=512, |
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).images[0] |
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print(f"inference time: {time.time()-start:.2f}s") |
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clear_cache(pipe.transformer) |
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return uploaded_image, out_img |
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def create_interface(): |
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demo_lora_names = [ |
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"3D_Chibi", "American_Cartoon", "Chinese_Ink", "Clay_Toy", |
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"Fabric", "Ghibli", "Irasutoya", "Jojo", "LEGO", "Line", |
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"Macaron", "Oil_Painting", "Origami", "Paper_Cutting", |
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"Picasso", "Pixel", "Poly", "Pop_Art", "Rick_Morty", |
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"Snoopy", "Van_Gogh", "Vector" |
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] |
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def update_trigger_word(lora_name, prompt): |
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for name in demo_lora_names: |
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trigger = " ".join(name.split("_")) + " style," |
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prompt = prompt.replace(trigger, "") |
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new_trigger = " ".join(lora_name.split("_"))+ " style," |
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return new_trigger + prompt |
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examples = [ |
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["3D_Chibi", "", "3D Chibi style, Two smiling colleagues enthusiastically high-five in front of a whiteboard filled with technical notes about multimodal learning, reflecting a moment of success and collaboration at OpenAI.", |
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Image.open("./test_imgs/00.png"), 680, 1024, 3.5, 24, 42], |
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["Clay_Toy", "", "Clay Toy style, Three team members from OpenAI are gathered around a laptop in a cozy, festive setting, with holiday decorations in the background; one waves cheerfully while the others engage in light conversation, reflecting a relaxed and collaborative atmosphere.", |
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Image.open("./test_imgs/01.png"), 560, 1024, 3.5, 24, 42], |
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["American_Cartoon", "", "American Cartoon style, In a dramatic and comedic moment from a classic Chinese film, an intense elder with a white beard and red hat grips a younger man, declaring something with fervor, while the subtitle at the bottom reads 'I want them all' — capturing both tension and humor.", |
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Image.open("./test_imgs/02.png"), 568, 1024, 3.5, 24, 42], |
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["Origami", "", "Origami style, A thrilled fan wearing a Portugal football kit poses energetically with a smiling Cristiano Ronaldo, who gives a thumbs-up, as they stand side by side in a casual, cheerful moment—capturing the excitement of meeting a football legend.", |
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Image.open("./test_imgs/03.png"), 768, 672, 3.5, 24, 42], |
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["Vector", "", "Vector style, A man glances admiringly at a passing woman, while his girlfriend looks at him in disbelief, perfectly capturing the theme of shifting attention and misplaced priorities in a humorous, relatable way.", |
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Image.open("./test_imgs/04.png"), 512, 1024, 3.5, 24, 42] |
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] |
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header = """ |
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<div style="text-align: center; display: flex; justify-content: left; gap: 5px;"> |
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<a href="https://arxiv.org/abs/2505.18445"><img src="https://img.shields.io/badge/ariXv-2505.18445-A42C25.svg" alt="arXiv"></a> |
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<a href="https://huggingface.co/showlab/OmniConsistency"><img src="https://img.shields.io/badge/🤗_HuggingFace-Model-ffbd45.svg" alt="HuggingFace"></a> |
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<a href="https://github.com/showlab/OmniConsistency"><img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub"></a> |
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</div> |
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""" |
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with gr.Blocks() as demo: |
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gr.Markdown("# OmniConsistency LoRA Image Generation") |
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gr.Markdown("Select a LoRA, enter a prompt, and upload an image to generate a new image with OmniConsistency.") |
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gr.HTML(header) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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image_input = gr.Image(type="pil", label="Upload Image") |
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prompt_box = gr.Textbox(label="Prompt", |
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value="3D Chibi style,", |
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info="Remember to include the necessary trigger words if you're using a custom LoRA." |
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) |
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lora_dropdown = gr.Dropdown( |
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demo_lora_names, label="Select built-in LoRA") |
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custom_repo_box = gr.Textbox( |
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label="Enter Custom LoRA", |
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placeholder="LoRA Hugging Face path (e.g., 'username/repo_name')", |
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info="If you want to use a custom LoRA, enter its Hugging Face repo ID here and built-in LoRA will be Overridden. Leave empty to use built-in LoRAs. [Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)" |
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) |
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gen_btn = gr.Button("Generate") |
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with gr.Column(scale=1): |
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output_image = gr.ImageSlider(label="Generated Image") |
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with gr.Accordion("Advanced Options", open=False): |
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height_box = gr.Textbox(value="1024", label="Height") |
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width_box = gr.Textbox(value="1024", label="Width") |
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guidance_slider = gr.Slider( |
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0.1, 20, value=3.5, step=0.1, label="Guidance Scale") |
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steps_slider = gr.Slider( |
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1, 50, value=25, step=1, label="Inference Steps") |
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seed_slider = gr.Slider( |
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1, 2_147_483_647, value=42, step=1, label="Seed") |
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lora_dropdown.select(fn=update_trigger_word, inputs=[lora_dropdown,prompt_box], |
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outputs=prompt_box) |
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gr.Examples( |
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examples=examples, |
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inputs=[lora_dropdown, custom_repo_box, prompt_box, image_input, |
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height_box, width_box, guidance_slider, steps_slider, seed_slider], |
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outputs=output_image, |
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fn=generate_image, |
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cache_examples=False, |
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label="Examples" |
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) |
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gen_btn.click( |
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fn=generate_image, |
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inputs=[lora_dropdown, custom_repo_box, prompt_box, image_input, |
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width_box, height_box, guidance_slider, steps_slider, seed_slider], |
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outputs=output_image |
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) |
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return demo |
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if __name__ == "__main__": |
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demo = create_interface() |
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demo.launch(ssr_mode=False) |
