Delete app (19).py

app (19).py

@@ -1,237 +0,0 @@
-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
-from transformers import pipeline
-
-# 번역 모델 로드
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
-
-# 한글 메뉴 이름 dictionary
-korean_labels = {
-    "Prompt": "프롬프트",
-    "1st direction to steer": "첫 번째 방향",
-    "2nd direction to steer": "두 번째 방향",
-    "Strength": "강도",
-    "Generate directions": "방향 생성",
-    "Generated Images": "생성된 이미지",
-    "From 1st to 2nd direction": "첫 번째에서 두 번째 방향으로",
-    "Strip": "이미지 스트립",
-    "Looping video": "루프 비디오",
-    "Advanced options": "고급 옵션",
-    "Num of intermediate images": "중간 이미지 수",
-    "Num iterations for clip directions": "클립 방향 반복 횟수",
-    "Num inference steps": "추론 단계 수",
-    "Guidance scale": "가이던스 스케일",
-    "Randomize seed": "시드 무작위화",
-    "Seed": "시드"
-}
-
-# 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))
-
-def translate_if_korean(text):
-    if any('\u3131' <= char <= '\u3163' or '\uac00' <= char <= '\ud7a3' for char in text):
-        return translator(text)[0]['translation_text']
-    return text
-
-@spaces.GPU(duration=85)
-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()
-    ):
-    # 프롬프트와 컨셉 번역
-    prompt = translate_if_korean(prompt)
-    concept_1 = translate_if_korean(concept_1)
-    concept_2 = translate_if_korean(concept_2)
-    
-    print(f"Prompt: {prompt}, ← {concept_2}, {concept_1} ➡️ . scale {scale}, interm steps {interm_steps}")
-    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()
-
-    video_path = f"{uuid.uuid4()}.mp4"
-    print(video_path)
-    return x_concept_1,x_concept_2, avg_diff_x, export_to_video(images, video_path, 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
-
-examples = [["a dog in the park", "winter", "summer", 1.5], ["a house", "USA suburb", "Europe", 2.5], ["a tomato", "rotten", "super fresh", 2.5]]
-
-css = """
-footer {
-    visibility: hidden;
-}
-"""
-
-with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
-    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=korean_labels["Prompt"], info="설명할 내용을 입력하세요", placeholder="공원에 있는 강아지")
-                with gr.Row():
-                    concept_1 = gr.Textbox(label=korean_labels["1st direction to steer"], info="시작 상태", placeholder="겨울")
-                    concept_2 = gr.Textbox(label=korean_labels["2nd direction to steer"], info="종료 상태", placeholder="여름")
-            x = gr.Slider(minimum=0, value=1.75, step=0.1, maximum=4.0, label=korean_labels["Strength"], info="각 방향의 최대 강도 (2.5 이상은 불안정)")
-            submit = gr.Button(korean_labels["Generate directions"])
-        with gr.Column():
-            with gr.Group(elem_id="group"):
-                post_generation_image = gr.Image(label=korean_labels["Generated Images"], type="filepath", elem_id="interactive")
-                post_generation_slider = gr.Slider(minimum=-10, maximum=10, value=0, step=1, label=korean_labels["From 1st to 2nd direction"])
-    with gr.Row():
-        with gr.Column(scale=4):
-            image_seq = gr.Image(label=korean_labels["Strip"], elem_id="strip", height=80)
-        with gr.Column(scale=2, min_width=100):
-            output_image = gr.Video(label=korean_labels["Looping video"], elem_id="video", loop=True, autoplay=True)
-    with gr.Accordion(label=korean_labels["Advanced options"], open=False):
-        interm_steps = gr.Slider(label=korean_labels["Num of intermediate images"], minimum=3, value=7, maximum=65, step=2)
-        with gr.Row():
-            iterations = gr.Slider(label=korean_labels["Num iterations for clip directions"], minimum=0, value=200, maximum=400, step=1)
-            steps = gr.Slider(label=korean_labels["Num inference steps"], minimum=1, value=3, maximum=4, step=1)
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label=korean_labels["Guidance scale"],
-                minimum=0.1,
-                maximum=10.0,
-                step=0.1,
-                value=3.5,
-            )
-            with gr.Column():
-                randomize_seed = gr.Checkbox(True, label=korean_labels["Randomize seed"])
-                seed = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, label=korean_labels["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()