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Clean up commented out code
Browse files
app.py
CHANGED
@@ -1,31 +1,13 @@
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import gradio as gr
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from PIL import Image
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import re
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import os
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import requests
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import numpy as np
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from fair import text_to_image
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from share_btn import community_icon_html, loading_icon_html, share_js
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model_id = "runwayml/stable-diffusion-v1-5"
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device = "cuda"
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#word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
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#word_list = word_list_dataset["train"]['text']
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#is_gpu_busy = False
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def infer(prompt):
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#global is_gpu_busy
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samples = 4
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steps = 50
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scale = 7.5
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# for filter in word_list:
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# if re.search(rf"\b{filter}\b", prompt):
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# raise gr.Error("Unsafe content found. Please try again with different prompts.")
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#
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image_file = text_to_image(prompt)
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image = np.array(Image.open(image_file).convert('RGB'))
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images = [image]
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@@ -155,44 +137,6 @@ css = """
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block = gr.Blocks(css=css)
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examples = [
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[
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'The spirit of a tamagotchi wandering in the city of Paris',
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# 4,
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# 45,
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# 7.5,
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# 1024,
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],
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[
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'A delicious ceviche cheesecake slice',
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# 4,
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# 45,
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# 7,
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# 1024,
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],
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[
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'A pao de queijo foodcart in front of a japanese castle',
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# 4,
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# 45,
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# 7,
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# 1024,
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],
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[
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'alone in the amusement park by Edward Hopper',
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# 4,
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# 45,
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# 7,
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# 1024,
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],
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[
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"A large cabin on top of a sunny mountain in the style of Dreamworks, artstation",
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# 4,
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# 45,
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# 7,
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# 1024,
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],
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]
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with block:
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gr.HTML(
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@@ -235,55 +179,15 @@ with block:
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rounded=(False, True, True, False),
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full_width=False,
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)
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# gallery = gr.Image(type="filepath").style(grid=[2], height="auto")
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gallery = gr.Gallery(
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label="Generated images", show_label=False, elem_id="gallery"
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).style(grid=[1], height="auto")
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# with gr.Group(elem_id="container-advanced-btns"):
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# advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
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# with gr.Group(elem_id="share-btn-container"):
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# community_icon = gr.HTML(community_icon_html)
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# loading_icon = gr.HTML(loading_icon_html)
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# share_button = gr.Button("Share to community", elem_id="share-btn")
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#
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# with gr.Row(elem_id="advanced-options"):
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# gr.Markdown("Advanced settings are temporarily unavailable")
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# samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
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# steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
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# scale = gr.Slider(
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# label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
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# )
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# seed = gr.Slider(
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# label="Seed",
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# minimum=0,
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# maximum=2147483647,
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# step=1,
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# randomize=True,
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# )
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#ex = gr.Examples(examples=examples, fn=infer, inputs=text, outputs=[gallery], cache_examples=True, postprocess=False)
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#ex.dataset.headers = [""]
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text.submit(infer, inputs=text, outputs=[gallery])
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btn.click(infer, inputs=text, outputs=[gallery])
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# None,
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# [],
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# text,
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# _js="""
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# () => {
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# const options = document.querySelector("body > gradio-app").querySelector("#advanced-options");
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# options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none";
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# }""",
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# )
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# share_button.click(
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# None,
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# [],
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# [],
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# _js=share_js,
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# )
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gr.HTML(
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"""
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<div class="footer">
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import gradio as gr
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from PIL import Image
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import numpy as np
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from fair import text_to_image
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model_id = "runwayml/stable-diffusion-v1-5"
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device = "cuda"
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def infer(prompt):
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image_file = text_to_image(prompt)
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image = np.array(Image.open(image_file).convert('RGB'))
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images = [image]
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block = gr.Blocks(css=css)
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with block:
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gr.HTML(
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rounded=(False, True, True, False),
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full_width=False,
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)
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gallery = gr.Gallery(
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label="Generated images", show_label=False, elem_id="gallery"
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).style(grid=[1], height="auto")
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text.submit(infer, inputs=text, outputs=[gallery])
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btn.click(infer, inputs=text, outputs=[gallery])
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gr.HTML(
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"""
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<div class="footer">
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fair.py
CHANGED
@@ -11,7 +11,6 @@ import tempfile
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SERVER_ADRESS="https://faircompute.com:8000/api/v1"
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#SERVER_ADRESS="http://localhost:8000/api/v1"
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DOCKER_IMAGE="faircompute/stable-diffusion:pytorch-1.13.1-cu116"
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#DOCKER_IMAGE="sha256:e06453fe869556ea3e63572a935aed4261337b261fdf7bda370472b0587409a9"
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class FairApiClient:
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def __init__(self, server_address: str):
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SERVER_ADRESS="https://faircompute.com:8000/api/v1"
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#SERVER_ADRESS="http://localhost:8000/api/v1"
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DOCKER_IMAGE="faircompute/stable-diffusion:pytorch-1.13.1-cu116"
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class FairApiClient:
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def __init__(self, server_address: str):
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