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import torch |
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from torchvision import transforms |
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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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from pipelines.pix2pix.pix2pix_turbo import Pix2Pix_Turbo |
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from pipelines.utils.canny_gpu import ScharrOperator |
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default_prompt = "close-up photo of the joker" |
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page_content = """ |
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<h1 class="text-3xl font-bold">Real-Time pix2pix_turbo</h1> |
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<h3 class="text-xl font-bold">pix2pix turbo</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://github.com/GaParmar/img2img-turbo" |
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target="_blank" |
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class="text-blue-500 underline hover:no-underline">One-Step Image Translation with Text-to-Image Models |
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</a> |
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</p> |
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<p class="text-sm text-gray-500"> |
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Web app <a href="https://github.com/radames/Real-Time-Latent-Consistency-Model" target="_blank" class="text-blue-500 underline hover:no-underline"> |
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Real-Time Latent Consistency Models |
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</a> |
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</p> |
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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 SDXL" |
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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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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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width: int = Field( |
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512, 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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512, min=2, max=15, title="Height", disabled=True, hide=True, id="height" |
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) |
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strength: float = Field( |
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1.0, |
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min=0.01, |
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max=10.0, |
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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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deterministic: bool = Field( |
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True, |
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hide=True, |
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title="Deterministic", |
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field="checkbox", |
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id="deterministic", |
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) |
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canny_low_threshold: float = Field( |
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0.0, |
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min=0, |
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max=1.0, |
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step=0.001, |
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title="Canny Low Threshold", |
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field="range", |
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hide=True, |
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id="canny_low_threshold", |
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) |
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canny_high_threshold: float = Field( |
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1.0, |
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min=0, |
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max=1.0, |
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step=0.001, |
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title="Canny High Threshold", |
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field="range", |
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hide=True, |
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id="canny_high_threshold", |
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) |
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debug_canny: bool = Field( |
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False, |
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title="Debug Canny", |
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field="checkbox", |
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hide=True, |
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id="debug_canny", |
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) |
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def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype): |
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self.model = Pix2Pix_Turbo("edge_to_image") |
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self.canny_torch = ScharrOperator(device=device) |
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self.device = device |
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self.last_time = 0.0 |
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def predict(self, params: "Pipeline.InputParams") -> Image.Image: |
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canny_pil, canny_tensor = self.canny_torch( |
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params.image, |
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params.canny_low_threshold, |
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params.canny_high_threshold, |
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output_type="pil,tensor", |
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) |
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canny_tensor = torch.cat((canny_tensor, canny_tensor, canny_tensor), dim=1) |
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output_image = self.model( |
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canny_tensor, |
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params.prompt, |
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params.deterministic, |
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params.strength, |
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) |
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output_pil = transforms.ToPILImage()(output_image[0].cpu() * 0.5 + 0.5) |
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result_image = output_pil |
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if params.debug_canny: |
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w0, h0 = (200, 200) |
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control_image = canny_pil.resize((w0, h0)) |
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w1, h1 = result_image.size |
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result_image.paste(control_image, (w1 - w0, h1 - h0)) |
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return result_image |
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