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add img2imgSegmindVegaRT
Browse files
pipelines/img2imgSegmindVegaRT.py
ADDED
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1 |
+
from diffusers import (
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AutoPipelineForImage2Image,
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LCMScheduler,
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AutoencoderTiny,
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)
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from compel import Compel, ReturnedEmbeddingsType
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import torch
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try:
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import intel_extension_for_pytorch as ipex # type: ignore
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except:
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pass
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+
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import psutil
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from config import Args
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from pydantic import BaseModel, Field
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from PIL import Image
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import math
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base_model = "segmind/Segmind-Vega"
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lora_model = "segmind/Segmind-VegaRT"
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taesd_model = "madebyollin/taesdxl"
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default_prompt = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux"
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default_negative_prompt = "blurry, low quality, render, 3D, oversaturated"
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page_content = """
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<h1 class="text-3xl font-bold">Real-Time SDXL Turbo</h1>
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<h3 class="text-xl font-bold">Image-to-Image</h3>
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<p class="text-sm">
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This demo showcases
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<a
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href="https://huggingface.co/stabilityai/sdxl-turbo"
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target="_blank"
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class="text-blue-500 underline hover:no-underline">SDXL Turbo</a>
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Image to Image pipeline using
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<a
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href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/sdxl_turbo"
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target="_blank"
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class="text-blue-500 underline hover:no-underline">Diffusers</a
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> with a MJPEG stream server.
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</p>
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<p class="text-sm text-gray-500">
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Change the prompt to generate different images, accepts <a
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href="https://github.com/damian0815/compel/blob/main/doc/syntax.md"
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target="_blank"
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class="text-blue-500 underline hover:no-underline">Compel</a
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> syntax.
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</p>
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"""
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+
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+
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class Pipeline:
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class Info(BaseModel):
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name: str = "img2img"
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title: str = "Image-to-Image Playground 256"
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description: str = "Generates an image from a text prompt"
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input_mode: str = "image"
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page_content: str = page_content
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+
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class InputParams(BaseModel):
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prompt: str = Field(
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default_prompt,
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title="Prompt",
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field="textarea",
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id="prompt",
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)
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negative_prompt: str = Field(
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default_negative_prompt,
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title="Negative Prompt",
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field="textarea",
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id="negative_prompt",
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hide=True,
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)
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seed: int = Field(
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2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
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)
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steps: int = Field(
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4, min=1, max=15, title="Steps", field="range", hide=True, id="steps"
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)
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width: int = Field(
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1024, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
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)
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height: int = Field(
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1024, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
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)
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guidance_scale: float = Field(
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0.2,
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min=0,
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max=20,
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step=0.001,
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title="Guidance Scale",
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field="range",
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hide=True,
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id="guidance_scale",
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)
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strength: float = Field(
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0.5,
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min=0.25,
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max=1.0,
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step=0.001,
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title="Strength",
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field="range",
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hide=True,
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id="strength",
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)
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def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
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if args.safety_checker:
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self.pipe = AutoPipelineForImage2Image.from_pretrained(
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base_model,
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variant="fp16",
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)
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else:
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self.pipe = AutoPipelineForImage2Image.from_pretrained(
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base_model,
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safety_checker=None,
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variant="fp16",
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)
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if args.use_taesd:
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self.pipe.vae = AutoencoderTiny.from_pretrained(
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taesd_model, torch_dtype=torch_dtype, use_safetensors=True
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).to(device)
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self.pipe.load_lora_weights(lora_model)
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self.pipe.fuse_lora()
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self.pipe.scheduler = LCMScheduler.from_pretrained(
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base_model, subfolder="scheduler"
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)
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self.pipe.set_progress_bar_config(disable=True)
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self.pipe.to(device=device, dtype=torch_dtype)
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if device.type != "mps":
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self.pipe.unet.to(memory_format=torch.channels_last)
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# check if computer has less than 64GB of RAM using sys or os
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if psutil.virtual_memory().total < 64 * 1024**3:
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self.pipe.enable_attention_slicing()
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if args.torch_compile:
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print("Running torch compile")
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self.pipe.unet = torch.compile(
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self.pipe.unet,
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)
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self.pipe.vae = torch.compile(
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self.pipe.vae,
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)
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self.pipe(
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prompt="warmup",
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image=[Image.new("RGB", (768, 768))],
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)
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if args.compel:
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self.pipe.compel_proc = Compel(
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tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2],
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text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2],
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returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
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requires_pooled=[False, True],
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)
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+
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def predict(self, params: "Pipeline.InputParams") -> Image.Image:
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generator = torch.manual_seed(params.seed)
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prompt = params.prompt
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+
negative_prompt = params.negative_prompt
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prompt_embeds = None
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+
pooled_prompt_embeds = None
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+
negative_prompt_embeds = None
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+
negative_pooled_prompt_embeds = None
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+
if hasattr(self.pipe, "compel_proc"):
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+
prompt_embeds = self.pipe.compel_proc(
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+
[params.prompt, params.negative_prompt]
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+
)
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+
prompt = None
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+
negative_prompt = None
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173 |
+
prompt_embeds = prompt_embeds[0:1]
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+
pooled_prompt_embeds = pooled_prompt_embeds[0:1]
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+
negative_prompt_embeds = prompt_embeds[1:2]
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+
negative_pooled_prompt_embeds = pooled_prompt_embeds[1:2]
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+
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+
steps = params.steps
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+
strength = params.strength
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180 |
+
if int(steps * strength) < 1:
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steps = math.ceil(1 / max(0.10, strength))
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+
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+
results = self.pipe(
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image=params.image,
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prompt=prompt,
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negative_prompt=negative_prompt,
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prompt_embeds=prompt_embeds,
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+
pooled_prompt_embeds=pooled_prompt_embeds,
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+
negative_prompt_embeds=negative_prompt_embeds,
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
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generator=generator,
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strength=strength,
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+
num_inference_steps=steps,
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guidance_scale=params.guidance_scale,
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+
width=params.width,
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+
height=params.height,
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output_type="pil",
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)
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+
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+
nsfw_content_detected = (
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+
results.nsfw_content_detected[0]
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+
if "nsfw_content_detected" in results
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else False
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)
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if nsfw_content_detected:
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return None
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result_image = results.images[0]
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+
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return result_image
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