Update app.py

app.py

@@ -1,78 +1,36 @@
 import gradio as gr
 import torch
 import numpy as np
-import modin.pandas as pd
 from PIL import Image
 from diffusers import DiffusionPipeline
-import os
-from sys import executable as pyexecutable
-import subprocess
-import pathlib
-import gc
-def Gitclone(URI: str, ClonePath: str = "") -> int:
-    if ClonePath == "":
-        while True:
-            i = subprocess.run(["git", "clone", URI])
-            if i.returncode == 0:
-                del i
-                gc.collect()
-                return 0
-            else:
-                del i
-    else:
-        while True:
-            i = subprocess.run(["git", "clone", URI, ClonePath])
-            if i.returncode == 0:
-                del i
-                gc.collect()
-                return 0
-            else:
-                del i
-def DownLoad(URI: str, DownloadPath: str, DownLoadFileName: str) -> int:
-    while True:
-        i = subprocess.run(["aria2c", "-c", "-x", "16", "-s", "16", "-k", "1M", "-m", "0", "--enable-mmap=false", "--console-log-level=error", "-d", DownloadPath, "-o", DownLoadFileName, URI])
-        if i.returncode == 0:
-            del i
-            gc.collect()
-            return 0
-        else:
-            del i
-user_home = pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-os.chdir(user_home / "stable-diffusion-webui")
-# download additional network model
-print("Downloading additional network model")
-DownLoad(r"https://civitai.com/api/download/models/39885",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"Better_light.safetensors")
-# load main models
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-def load_model(model_url):
-    return DiffusionPipeline.from_pretrained(model_url, use_safetensors=True).to(device)
-model_url = "aipicasso/cool-japan-diffusion-2-1-0" # Замените на свою полную ссылку на модель
-pipe = load_model(model_url)
-refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True).to(device)
-upscaler = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True).to(device)
-def genie(prompt, negative_prompt, height, width, scale, steps, seed, upscaling):
-    generator = torch.Generator(device=device).manual_seed(seed)
-    int_image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=steps, height=height, width=width, guidance_scale=scale, num_images_per_prompt=1, generator=generator, output_type="latent").images
-    if upscaling == 'Yes':
-        image = refiner(prompt=prompt, image=int_image).images[0]
-        upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
-        torch.cuda.empty_cache()
-        return (image, upscaled)
-    else:
-        image = refiner(prompt=prompt, negative_prompt=negative_prompt, image=int_image).images[0]   
-        torch.cuda.empty_cache()
-    return (image, image)
-   
-gr.Interface(fn=genie, inputs=[gr.Textbox(label='Что вы хотите, чтобы ИИ генерировал'), 
-    gr.Textbox(label='Что вы не хотите, чтобы ИИ генерировал'), 
-    gr.Slider(512, 1024, 768, step=128, label='Высота картинки'),
-    gr.Slider(512, 1024, 768, step=128, label='Ширина картинки'),
-    gr.Slider(1, 15, 10, step=.25, label='Шкала расхождения'), 
-    gr.Slider(25, maximum=100, value=50, step=25, label='Количество итераций'), 
-    gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True, label='Зерно'),
-    gr.Radio(['Да', 'Нет'], label='Ремастеринг?')], 
-    outputs=['image', 'image'],
-    title="Стабильная Диффузия - Japan-SD-2-1-0", 
-    description="", 
-    article="").launch(debug=True, max_threads=80)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+def resize(height, width, img):
+    img = Image.open(img)
+    img = img.resize((height, width))
+    return img
+def infer(source_img, prompt, negative_prompt, height, width, guide, steps, seed, strength):
+    generator = torch.Generator(device).manual_seed(seed)     
+    source_image = resize(height, width, source_img)
+    source_image.save('source.png')
+    model_url = "https://civitai.com/api/download/models/39885"
+    pipe = DiffusionPipeline.from_pretrained(model_url, torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+    pipe = pipe.to(device)
+    image = pipe(prompt, negative_prompt=negative_prompt, image=source_image, strength=strength, guidance_scale=guide, num_inference_steps=steps).images[0]
+    return image
+gr.Interface(
+    fn=infer,
+    inputs=[
+        gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"),
+        gr.Textbox(label='Что вы хотит��, чтобы ИИ генерировал'),
+        gr.Textbox(label='Что вы не хотите, чтобы ИИ генерировал'),
+        gr.Slider(512, 1024, 768, step=1, label='Ширина картинки'),
+        gr.Slider(512, 1024, 768, step=1, label='Высота картинки'),                          
+        gr.Slider(2, 15, value=7, label='Шкала расхождения'),
+        gr.Slider(1, 25, value=10, step=1, label='Количество итераций'),
+        gr.Slider(label="Зерно", minimum=0, maximum=987654321987654321, step=1, randomize=True), 
+        gr.Slider(label='Сила', minimum=0, maximum=1, step=.05, value=.5),
+    ], 
+    outputs='image',
+    title="Стабильная Диффузия - Dreamlike-Photoreal-2.0",
+    article=""
+).launch(debug=True, max_threads=80)