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Browse files- __pycache__/app.cpython-38.pyc +0 -0
- app.py +23 -11
__pycache__/app.cpython-38.pyc
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Binary files a/__pycache__/app.cpython-38.pyc and b/__pycache__/app.cpython-38.pyc differ
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app.py
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@@ -4,11 +4,14 @@ import gradio as gr
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import torch
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from diffusers import DiffusionPipeline
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-
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if inCloud:
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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-
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#pipe = pipe.to("cuda")
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# Recommended if your computer has < 64 GB of RAM
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@@ -22,19 +25,30 @@ Una noche, el ladrón desenterró el oro y se lo llevó. Cuando el rico descubri
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—Aquí tiene su tesoro. Sabe que nunca habría gastado sus lingotes. ¿Qué más le da, entonces, que sean piedras? Así por lo menos dejará de sufrir.'''
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]
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def translate(text):
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new_text = text
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return new_text
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def
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text_array =
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return text_array
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def generation(
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images = []
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images.append(pipe(text_array[0]).images[0])
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images.append(pipe(text_array[
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images.append(pipe(text_array[
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return images
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def generation_test(img):
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@@ -46,8 +60,8 @@ def generation_test(img):
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demo = gr.Blocks()
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title = '#
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description = '
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with demo:
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gr.Markdown(title)
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@@ -66,10 +80,8 @@ with demo:
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examples=textos,
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inputs=text_input,
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#outputs=im_2,
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-
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)
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with gr.Row():
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imgs_output = []
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import torch
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from diffusers import DiffusionPipeline
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import platform
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inCloud = True
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if inCloud:
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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if platform.system() == "Darwin": #Apple OS
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pipe = pipe.to("mps")
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#pipe = pipe.to("cuda")
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# Recommended if your computer has < 64 GB of RAM
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—Aquí tiene su tesoro. Sabe que nunca habría gastado sus lingotes. ¿Qué más le da, entonces, que sean piedras? Así por lo menos dejará de sufrir.'''
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]
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+
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def translate(text):
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new_text = text
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return new_text
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def generate_texts_array(text, max_length = 70):
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text_array = []
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start = 0
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while start < len(text):
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end = start + max_length
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part = text[start:end]
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text_array.append(part)
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start = end
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return text_array
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def generation(text):
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images = []
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text_array = generate_texts_array(text)
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images.append(pipe(text_array[0]).images[0])
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images.append(pipe(text_array[1]).images[0])
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images.append(pipe(text_array[2]).images[0])
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return images
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def generation_test(img):
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demo = gr.Blocks()
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title = '# Generando historias graficas '
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description = 'Escribe un texto y se generara una secuencia de imagenes basadas en el texto proporcionado'
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with demo:
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gr.Markdown(title)
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examples=textos,
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inputs=text_input,
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#outputs=im_2,
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)
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with gr.Row():
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imgs_output = []
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