import os import uuid import gradio as gr import spaces from clip_slider_pipeline import CLIPSliderFlux from diffusers import FluxPipeline, AutoencoderTiny import torch import numpy as np import cv2 from PIL import Image from diffusers.utils import load_image from diffusers.utils import export_to_video import random # load pipelines base_model = "black-forest-labs/FLUX.1-schnell" taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to("cuda") pipe = FluxPipeline.from_pretrained(base_model, vae=taef1, torch_dtype=torch.bfloat16) pipe.transformer.to(memory_format=torch.channels_last) # pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True) # pipe.enable_model_cpu_offload() clip_slider = CLIPSliderFlux(pipe, device=torch.device("cuda")) MAX_SEED = 2**32-1 def save_images_with_unique_filenames(image_list, save_directory): if not os.path.exists(save_directory): os.makedirs(save_directory) paths = [] for image in image_list: unique_filename = f"{uuid.uuid4()}.png" file_path = os.path.join(save_directory, unique_filename) image.save(file_path) paths.append(file_path) return paths def convert_to_centered_scale(num): if num % 2 == 0: # even start = -(num // 2 - 1) end = num // 2 else: # odd start = -(num // 2) end = num // 2 return tuple(range(start, end + 1)) @spaces.GPU(duration=200) def generate(prompt, concept_1, concept_2, scale, randomize_seed=True, seed=42, recalc_directions=True, iterations=200, steps=3, interm_steps=33, guidance_scale=3.5, x_concept_1="", x_concept_2="", avg_diff_x=None, total_images=[], progress=gr.Progress() ): slider_x = [concept_2, concept_1] # check if avg diff for directions need to be re-calculated if randomize_seed: seed = random.randint(0, MAX_SEED) if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]) or recalc_directions: progress(0, desc="Calculating directions...") avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations) x_concept_1, x_concept_2 = slider_x[0], slider_x[1] images = [] high_scale = scale low_scale = -1 * scale for i in progress.tqdm(range(interm_steps), desc="Generating images"): cur_scale = low_scale + (high_scale - low_scale) * i / (interm_steps - 1) image = clip_slider.generate(prompt, width=768, height=768, guidance_scale=guidance_scale, scale=cur_scale, seed=seed, num_inference_steps=steps, avg_diff=avg_diff) images.append(image) canvas = Image.new('RGB', (256*interm_steps, 256)) for i, im in enumerate(images): canvas.paste(im.resize((256,256)), (256 * i, 0)) comma_concepts_x = f"{slider_x[1]}, {slider_x[0]}" scale_total = convert_to_centered_scale(interm_steps) scale_min = scale_total[0] scale_max = scale_total[-1] scale_middle = scale_total.index(0) post_generation_slider_update = gr.update(label=comma_concepts_x, value=0, minimum=scale_min, maximum=scale_max, interactive=True) avg_diff_x = avg_diff.cpu() return x_concept_1,x_concept_2, avg_diff_x, export_to_video(images, f"{uuid.uuid4()}.mp4", fps=5), canvas, images, images[scale_middle], post_generation_slider_update, seed def update_pre_generated_images(slider_value, total_images): number_images = len(total_images) if(number_images > 0): scale_tuple = convert_to_centered_scale(number_images) return total_images[scale_tuple.index(slider_value)][0] else: return None def reset_recalc_directions(): return True intro = """

Latent Navigation

Exploring CLIP text space with FLUX.1 schnell 🪐

based on & inspired by CLIP directions by Ethan Smith | code | Duplicate Space

""" css=''' #strip, #video{max-height: 256px; min-height: 80px} #video .empty{min-height: 80px} #strip img{object-fit: cover} .gradio-container{max-width: 960px !important} ''' examples = [["a dog in the park", "winter", "summer", 1.5], ["a house", "USA suburb", "Europe", 2.5], ["a tomato", "rotten", "super fresh", 2.5]] with gr.Blocks(css=css) as demo: gr.HTML(intro) x_concept_1 = gr.State("") x_concept_2 = gr.State("") total_images = gr.Gallery(visible=False) avg_diff_x = gr.State() recalc_directions = gr.State(False) with gr.Row(): with gr.Column(): with gr.Group(): prompt = gr.Textbox(label="Prompt", info="Describe what to be steered by the directions", placeholder="A dog in the park") with gr.Row(): concept_1 = gr.Textbox(label="1st direction to steer", placeholder="winter") concept_2 = gr.Textbox(label="2nd direction to steer", placeholder="summer") x = gr.Slider(minimum=0, value=1.75, step=0.1, maximum=4.0, label="Strength", info="maximum strength on each direction (unstable beyond 2.5)") submit = gr.Button("Generate directions") with gr.Column(): with gr.Group(elem_id="group"): post_generation_image = gr.Image(label="Generated Images", type="filepath", elem_id="interactive") post_generation_slider = gr.Slider(minimum=-10, maximum=10, value=0, step=1, label="From 1st to 2nd direction") with gr.Row(): with gr.Column(scale=4): image_seq = gr.Image(label="Strip", elem_id="strip", height=80) with gr.Column(scale=2, min_width=100): output_image = gr.Video(label="Looping video", elem_id="video", loop=True, autoplay=True) with gr.Accordion(label="Advanced options", open=False): interm_steps = gr.Slider(label = "Num of intermediate images", minimum=3, value=7, maximum=65, step=2) with gr.Row(): iterations = gr.Slider(label = "Num iterations for clip directions", minimum=0, value=200, maximum=400, step=1) steps = gr.Slider(label = "Num inference steps", minimum=1, value=3, maximum=4, step=1) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=0.1, maximum=10.0, step=0.1, value=3.5, ) with gr.Column(): randomize_seed = gr.Checkbox(True, label="Randomize seed") seed = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, label="Seed", interactive=True, randomize=True) examples_gradio = gr.Examples( examples=examples, inputs=[prompt, concept_1, concept_2, x], fn=generate, outputs=[x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq, total_images, post_generation_image, post_generation_slider, seed], cache_examples="lazy" ) submit.click( fn=generate, inputs=[prompt, concept_1, concept_2, x, randomize_seed, seed, recalc_directions, iterations, steps, interm_steps, guidance_scale, x_concept_1, x_concept_2, avg_diff_x, total_images], outputs=[x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq, total_images, post_generation_image, post_generation_slider, seed] ) iterations.change( fn=reset_recalc_directions, outputs=[recalc_directions] ) seed.change( fn=reset_recalc_directions, outputs=[recalc_directions] ) post_generation_slider.change( fn=update_pre_generated_images, inputs=[post_generation_slider, total_images], outputs=[post_generation_image], queue=False, show_progress="hidden", concurrency_limit=None ) if __name__ == "__main__": demo.launch()