app.py

import gradio as gr
import torch, torchvision
from torchvision import transforms
import torch.nn.functional as F
import numpy as np
import PIL
from PIL import Image, ImageColor
from diffusers import DiffusionPipeline
from diffusers import DDIMScheduler, DDPMScheduler, DEISMultistepScheduler, LCMScheduler, PNDMScheduler, UniPCMultistepScheduler
import base64
from urllib.parse import quote_plus
from io import BytesIO

device = ("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu")

class MSPipeline(DiffusionPipeline):
  def __init__(self, unet, scheduler):
    super().__init__()
    self.register_modules(unet=unet, scheduler=scheduler)

  @torch.no_grad()
  def __call__(self, batch_size = 1, num_inference_steps = 1000):
    x = torch.randn(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size).to(device)

    self.scheduler.set_timesteps(num_inference_steps)

    for t in self.progress_bar(self.scheduler.timesteps):
      #x = self.scheduler.scale_model_input(x, t)
      model_output = self.unet(x, t).sample
      x = self.scheduler.step(model_output, t, x).prev_sample

    x = x.cpu().permute(0, 2, 3, 1).clip(0, 1).numpy() #* 255
    x = self.numpy_to_pil(x)

    return x


pipe = MSPipeline.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", use_safetensors=True).to(device)


def clear_pix(x):
  datas = []
  for pixel in list(x.getdata()):
    r,g,b,a = pixel
    if a == 0 or a < 150:
      datas.append((0,0,0,0))
    else:
      datas.append((r,g,b,255))
  x.putdata(datas)
  return x


def show_3D(image, print_link = False):
  if isinstance(image, PIL.Image.Image):
    buffer = BytesIO()
    image.save(buffer, format="PNG")
    skin_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
  else:
    with open(image, "rb") as f:
      skin_base64 = base64.b64encode(f.read()).decode("utf-8")

  data = f"data:image/png;base64,{skin_base64}"
  quoted = quote_plus(data)
  url = f"https://wine-ineff.github.io/SkinViewFrame/skin.html?color=c2b4ff&data={quoted}"

  if print_link == True:
    print(url)
  return url


def generate(schedulers, inference_steps, images_num):
  if schedulers == "DDIMScheduler":
    pipe.scheduler = DDIMScheduler.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", subfolder="scheduler")
  elif schedulers == "DDPMScheduler":
    pipe.scheduler = DDPMScheduler.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", subfolder="scheduler")
  elif schedulers == "DEISMultistepScheduler":
    pipe.scheduler = DEISMultistepScheduler.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", subfolder="scheduler")
  elif schedulers == "LCMScheduler":
    pipe.scheduler = LCMScheduler.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", subfolder="scheduler")
  elif schedulers == "PNDMScheduler":
    pipe.scheduler = PNDMScheduler.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", subfolder="scheduler")
  elif schedulers == "UniPCMultistepScheduler":
    pipe.scheduler = UniPCMultistepScheduler.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", subfolder="scheduler")

  images = pipe(batch_size = images_num, num_inference_steps = inference_steps)
  return images, update_iframe(images)


def update_iframe(images):
  iframe_html = "<div style='display: grid; gap: 10px'>" #grid-template-columns: repeat(2, 1fr); 
  for img in images:
    iframe_url = show_3D(clear_pix(img), print_link=True)
    iframe_html += f"<iframe style='min-width: 100%;' src='{iframe_url}'></iframe>"
  iframe_html += "</div>"
  return iframe_html


interface = gr.Blocks()
with interface:
  gr.HTML(f"""
    <h1 style="min-width: 100%; text-align: center;">Mineskin Diffusion</h1>
    <p>This space run with {f"<b>{device.upper()}</b>." if device != "cpu" else f"<b>{device.upper()}</b>. This space will be work very slow. Pleasy use a <a href='https://colab.research.google.com/#fileId=https://huggingface.co/WiNE-iNEFF/Mineskin-Diffusion-v1.0/blob/main/MineskinDiffusion.ipynb' style='text-decoration: none; color: orange;'>Google Colab notebook</a> for get fully experience of model or dublicate this space with your devices"}</p>
  """)
  with gr.Tabs():
    with gr.TabItem("v1.0"):
      with gr.Column():
        scheduler_type = gr.Dropdown(
            ["DDIMScheduler", "DDPMScheduler", "DEISMultistepScheduler", "LCMScheduler", "PNDMScheduler", "UniPCMultistepScheduler"],
            label = "Type of scheduler"
        )

        with gr.Row():
          inference_steps = gr.Number(
              label = "Amount of denoising steps",
              value = 30, minimum = 5, maximum = 1000
          )
          images_num = gr.Number(
              label = "Amount of skins",
              value = 4, minimum = 1, maximum = 4
          )

        gallery = gr.Gallery(columns=4, object_fit="scale-down", min_width=76)
        iframe_output = gr.HTML()
        gen_btn = gr.Button("Generate")
        gen_btn.click(fn=generate, outputs=[gallery, iframe_output], inputs=[scheduler_type, inference_steps, images_num])

interface.launch()