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Update app.py
#5
by
yoinked
- opened
app.py
CHANGED
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@@ -2,7 +2,7 @@ import spaces
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import os
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import gc
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import gradio as gr
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import gradio_client as grcl
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import numpy as np
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import torch
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import json
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@@ -13,7 +13,7 @@ from PIL import Image, PngImagePlugin
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from datetime import datetime
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from diffusers.models import AutoencoderKL
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from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
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GRAD_CLIENT = grcl.Client("https://yoinked-da-nsfw-checker.hf.space/")
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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@@ -83,7 +83,11 @@ def generate(
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upscale_by: float = 1.5,
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add_quality_tags: bool = True,
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progress=gr.Progress(track_tqdm=True),
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):
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generator = utils.seed_everything(seed)
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width, height = utils.aspect_ratio_handler(
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@@ -195,16 +199,16 @@ def generate(
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def genwrap(*args, **kwargs):
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ipth, mtd = generate(*args, **kwargs)
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r = GRAD_CLIENT.predict(ipth, "chen-evangelion", 0.4, False, False, api_name="/classify")
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ratings = val[0]
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rating = rating['confidences']
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highestval, classtype = -1, "aa"
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for o in rating:
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if classtype not in ["general", "sensitive"]: #
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return ipth, mtd
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if torch.cuda.is_available():
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pipe = load_pipeline(MODEL)
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@@ -336,6 +340,8 @@ with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo:
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step=1,
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value=28,
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)
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with gr.Column(scale=3):
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with gr.Blocks():
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run_button = gr.Button("Generate", variant="primary")
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@@ -399,6 +405,7 @@ with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo:
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upscaler_strength,
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upscale_by,
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add_quality_tags,
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],
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outputs=[result, gr_metadata],
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api_name="run",
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import os
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import gc
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import gradio as gr
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# import gradio_client as grcl
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import numpy as np
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import torch
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import json
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from datetime import datetime
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from diffusers.models import AutoencoderKL
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from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
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#GRAD_CLIENT = grcl.Client("https://yoinked-da-nsfw-checker.hf.space/")
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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upscale_by: float = 1.5,
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add_quality_tags: bool = True,
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progress=gr.Progress(track_tqdm=True),
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nsfw_neg=True
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):
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if nsfw_neg:
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prompt += "general, "
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negative_prompt += ", explicit, questionable, nude, naked, pussy, penis, uncensored" # mikudayo
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generator = utils.seed_everything(seed)
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width, height = utils.aspect_ratio_handler(
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def genwrap(*args, **kwargs):
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ipth, mtd = generate(*args, **kwargs)
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#r = GRAD_CLIENT.predict(ipth, "chen-evangelion", 0.4, False, False, api_name="/classify")
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#ratings = val[0]
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#rating = rating['confidences']
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#highestval, classtype = -1, "aa"
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#for o in rating:
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# if o['confidence'] > highestval:
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# highestval = o['confidence']
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# classtype = o['label']
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#if classtype not in ["general", "sensitive"]: # i hate code
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# return "https://upload.wikimedia.org/wikipedia/commons/b/bf/Bucephala-albeola-010.jpg", mtd
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return ipth, mtd
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if torch.cuda.is_available():
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pipe = load_pipeline(MODEL)
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step=1,
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value=28,
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)
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with gr.Accordion(label="lewdie.... lewd lewdie...", open=False):
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nsfwtoggle = gr.Checkbox(label="Anti-NSFW [dont disable this if korean]", value=True)
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with gr.Column(scale=3):
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with gr.Blocks():
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run_button = gr.Button("Generate", variant="primary")
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upscaler_strength,
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upscale_by,
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add_quality_tags,
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nsfwtoggle
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],
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outputs=[result, gr_metadata],
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api_name="run",
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