Upload app (19).py

app (19).py

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+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()