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