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app.py

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+import fn
+import gradio as gr
+
+with gr.Blocks() as demo:
+    prompt = gr.Textbox(label='prompt')
+    negative_prompt = gr.Textbox(label='negative_prompt')
+    model = gr.Textbox(label='model')
+    guidance_scale = gr.Textbox(value=5.0, label='guidance_scale')
+    steps = gr.Textbox(value=20, label='steps')
+    seed = gr.Textbox(value=-1, label='seed')
+    run = gr.Button()
+    dst_image = gr.Image(label="Result", interactive=False)
+
+    run.click(
+        fn=fn.run,
+        inputs=[prompt, negative_prompt, model, guidance_scale, steps, seed],
+        outputs=[dst_image],
+    )
+
+if __name__ == '__main__':
+    demo.launch()

fn.py

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+import os
+import io
+import base64
+import torch
+from torch.cuda import amp
+import numpy as np
+from PIL import Image
+from diffusers import AutoPipelineForText2Image, AutoencoderKL, DPMSolverMultistepScheduler
+
+pipe = None
+
+def load_model(_model = None, _vae = None, loras = []):
+    global pipe
+
+    _model = _model or 'cagliostrolab/animagine-xl-3.0'
+
+    if _vae:
+        # "stabilityai/sdxl-vae"
+        vae = AutoencoderKL.from_pretrained(_vae, torch_dtype=torch.float16)
+        pipe = AutoPipelineForText2Image.from_pretrained(
+            _model,
+            torch_dtype=torch.float16,
+            vae=vae,
+            )
+    else:
+        pipe = AutoPipelineForText2Image.from_pretrained(
+            _model,
+            torch_dtype=torch.float16,
+            )
+
+    # DPM++ 2M Karras
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+        pipe.scheduler.config,
+        algorithm_type="sde-dpmsolver++",
+        use_karras_sigmas=True
+        )
+
+    for lora in loras:
+        pipe.load_lora_weights(".", weight_name=lora + ".safetensors")
+
+    pipe.to("cuda")
+    pipe.enable_vae_slicing()
+
+def pil_to_webp(img):
+    buffer = io.BytesIO()
+    img.save(buffer, 'webp')
+
+    return buffer.getvalue()
+
+def bin_to_base64(bin):
+    return base64.b64encode(bin).decode('ascii')
+
+def run(prompt = None, negative_prompt = None, model = None, guidance_scale = None, steps = None, seed = None):
+    global pipe
+    
+    if not pipe:
+        load_model(model)
+
+    _prompt = "masterpiece, best quality, 1girl, portrait"
+    _negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name"
+
+    prompt = prompt or _prompt
+    negative_prompt = negative_prompt or _negative_prompt
+    guidance_scale = float(guidance_scale) if guidance_scale else 5.0
+    steps = int(steps) if steps else 20
+    seed = int(seed) if seed else -1
+
+    generator = None
+    if seed != -1:
+        generator = torch.manual_seed(seed)
+
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=steps,
+        clip_skip=2,
+        generator=generator,
+        ).images[0]
+
+    return image

install.bat

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+@echo off
+
+rem -------------------------------------------
+rem NOT guaranteed to work on Windows
+
+set REPOS=https://huggingface.co/spaces/aka7774/stablediffusion
+set APPDIR=stablediffusion
+set VENV=venv
+
+rem -------------------------------------------
+
+set INSTALL_DIR=%~dp0
+cd /d %INSTALL_DIR%
+
+:git_clone
+set DL_URL=%REPOS%
+set DL_DST=%APPDIR%
+git clone %DL_URL% %APPDIR%
+if exist %DL_DST% goto install_python
+
+set DL_URL=https://github.com/git-for-windows/git/releases/download/v2.41.0.windows.3/PortableGit-2.41.0.3-64-bit.7z.exe
+set DL_DST=PortableGit-2.41.0.3-64-bit.7z.exe
+curl -L -o %DL_DST% %DL_URL%
+if not exist %DL_DST% bitsadmin /transfer dl %DL_URL% %DL_DST%
+%DL_DST% -y
+del %DL_DST%
+
+set GIT=%INSTALL_DIR%PortableGit\bin\git
+%GIT% clone %REPOS%
+
+:install_python
+set DL_URL=https://github.com/indygreg/python-build-standalone/releases/download/20240107/cpython-3.10.13+20240107-i686-pc-windows-msvc-shared-install_only.tar.gz
+set DL_DST="%INSTALL_DIR%python.tar.gz"
+curl -L -o %DL_DST% %DL_URL%
+if not exist %DL_DST% bitsadmin /transfer dl %DL_URL% %DL_DST%
+tar -xzf %DL_DST%
+
+set PYTHON=%INSTALL_DIR%python\python.exe
+set PATH=%PATH%;%INSTALL_DIR%python310\Scripts
+
+:install_venv
+cd %APPDIR%
+%PYTHON% -m venv %VENV%
+set PYTHON=%VENV%\Scripts\python.exe
+
+:install_pip
+set DL_URL=https://bootstrap.pypa.io/get-pip.py
+set DL_DST=%INSTALL_DIR%get-pip.py
+curl -o %DL_DST% %DL_URL%
+if not exist %DL_DST% bitsadmin /transfer dl %DL_URL% %DL_DST%
+%PYTHON% %DL_DST%
+
+%PYTHON% -m pip install gradio
+%PYTHON% -m pip install -r requirements.txt
+
+pause

main.py

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+import os
+import sys
+import time
+import signal
+import psutil
+import io
+
+from fastapi import FastAPI, Request, status, Form, UploadFile
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import Response
+
+import fn
+import gradio as gr
+from app import demo
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=['*'],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+gr.mount_gradio_app(app, demo, path="/gradio")
+
+@app.post("/run")
+async def api_run(prompt = None, negative_prompt = None, model = None, guidance_scale = None, steps = None, seed = None):
+    try:
+        dst_image = fn.run(prompt, negative_prompt, model, guidance_scale, steps, seed)
+        bin = fn.pil_to_webp(dst_image)
+
+        return Response(content=bin, media_type="image/webp")
+    except Exception as e:
+        return {"error": str(e)}
+

requirements.txt

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+fastapi
+uvicorn
+diffusers
+torch
+numpy
+opencv-python
+transformers
+accelerate
+python-multipart

venv.sh

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+#!/usr/bin/bash
+
+python3 -m venv venv
+curl -kL https://bootstrap.pypa.io/get-pip.py | venv/bin/python
+
+venv/bin/python -m pip install gradio
+venv/bin/python -m pip install -r requirements.txt