Update app.py
Browse files
app.py
CHANGED
@@ -1,5 +1,4 @@
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import random
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import gradio as gr
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import numpy as np
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import torch
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@@ -7,6 +6,7 @@ import spaces
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from diffusers import FluxPipeline
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from PIL import Image
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from diffusers.utils import export_to_gif
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HEIGHT = 256
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WIDTH = 1024
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@@ -18,29 +18,32 @@ pipe = FluxPipeline.from_pretrained(
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torch_dtype=torch.bfloat16
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).to(device)
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def split_image(input_image, num_splits=4):
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# Create a list to store the output images
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output_images = []
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# Split the image into four 256x256 sections
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for i in range(num_splits):
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left = i * 256
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right = (i + 1) * 256
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box = (left, 0, right, 256)
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output_images.append(input_image.crop(box))
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return output_images
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@spaces.GPU(duration=190)
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def predict(prompt, seed=42, randomize_seed=False, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
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prompt_template = f"""
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A
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The gif is of {prompt}.
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"""
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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image = pipe(
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prompt=prompt_template,
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guidance_scale=guidance_scale,
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@@ -50,60 +53,52 @@ def predict(prompt, seed=42, randomize_seed=False, guidance_scale=5.0, num_infer
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height=HEIGHT,
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width=WIDTH
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).images[0]
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return export_to_gif(split_image(image, 4), "flux.gif", fps=4), image, seed
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demo = gr.Interface(fn=predict, inputs="text", outputs="image")
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css = """
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footer {
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visibility: hidden;
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}
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"""
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examples = [
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"
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"
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"
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]
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with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
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with gr.Column(elem_id="col-container"):
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with gr.Row():
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prompt = gr.Text(label="
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submit = gr.Button("
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output = gr.Image(label="GIF", show_label=False)
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output_stills = gr.Image(label="
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seed = gr.Slider(
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label="
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="
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minimum=1,
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maximum=15,
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step=0.1,
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value=3.5,
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)
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num_inference_steps = gr.Slider(
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label="
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minimum=1,
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maximum=50,
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step=1,
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value=28,
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)
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gr.Examples(
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examples=examples,
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fn=predict,
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@@ -111,11 +106,12 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
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outputs=[output, output_stills, seed],
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cache_examples="lazy"
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)
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gr.on(
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triggers=[submit.click, prompt.submit],
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fn=predict,
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inputs=[prompt, seed, randomize_seed, guidance_scale, num_inference_steps],
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outputs
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)
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demo.launch()
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import random
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import gradio as gr
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import numpy as np
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import torch
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from diffusers import FluxPipeline
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from PIL import Image
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from diffusers.utils import export_to_gif
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from transformers import pipeline
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HEIGHT = 256
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WIDTH = 1024
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torch_dtype=torch.bfloat16
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).to(device)
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
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def split_image(input_image, num_splits=4):
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output_images = []
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for i in range(num_splits):
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left = i * 256
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right = (i + 1) * 256
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box = (left, 0, right, 256)
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output_images.append(input_image.crop(box))
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return output_images
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def translate_to_english(text):
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return translator(text)[0]['translation_text']
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@spaces.GPU(duration=190)
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def predict(prompt, seed=42, randomize_seed=False, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
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if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
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prompt = translate_to_english(prompt)
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prompt_template = f"""
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A side by side 4 frame image showing consecutive stills from a looped gif moving from left to right. The gif is of {prompt}.
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"""
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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image = pipe(
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prompt=prompt_template,
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guidance_scale=guidance_scale,
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height=HEIGHT,
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width=WIDTH
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).images[0]
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return export_to_gif(split_image(image, 4), "flux.gif", fps=4), image, seed
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css = """
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footer { visibility: hidden;}
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"""
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examples = [
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"๊ณ ์์ด๊ฐ ๊ณต์ค์์ ๋ฐ์ ํ๋๋ ๋ชจ์ต",
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"ํฌ๋๊ฐ ์๋ฉ์ด๋ฅผ ์ข์ฐ๋ก ํ๋๋ ๋ชจ์ต",
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"๊ฝ์ด ํผ์ด๋๋ ๊ณผ์ "
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]
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with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
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with gr.Column(elem_id="col-container"):
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with gr.Row():
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prompt = gr.Text(label="ํ๋กฌํํธ", show_label=False, max_lines=1, placeholder="ํ๋กฌํํธ๋ฅผ ์
๋ ฅํ์ธ์")
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submit = gr.Button("์ ์ถ", scale=0)
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output = gr.Image(label="GIF", show_label=False)
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output_stills = gr.Image(label="์คํธ ์ด๋ฏธ์ง", show_label=False, elem_id="stills")
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with gr.Accordion("๊ณ ๊ธ ์ค์ ", open=False):
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seed = gr.Slider(
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label="์๋",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="์๋ ๋ฌด์์ํ", value=True)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="๊ฐ์ด๋์ค ์ค์ผ์ผ",
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minimum=1,
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maximum=15,
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step=0.1,
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value=3.5,
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)
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num_inference_steps = gr.Slider(
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label="์ถ๋ก ๋จ๊ณ ์",
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minimum=1,
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maximum=50,
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step=1,
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value=28,
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)
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gr.Examples(
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examples=examples,
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fn=predict,
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outputs=[output, output_stills, seed],
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cache_examples="lazy"
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)
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gr.on(
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triggers=[submit.click, prompt.submit],
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fn=predict,
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inputs=[prompt, seed, randomize_seed, guidance_scale, num_inference_steps],
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outputs=[output, output_stills, seed]
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)
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demo.launch()
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