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from fastapi import FastAPI |
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from fastapi.staticfiles import StaticFiles |
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from fastapi.responses import FileResponse |
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import subprocess |
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import os |
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import json |
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import uuid |
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import cgi |
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import cgitb; cgitb.enable() |
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import logging |
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import torch |
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from diffusers import ( |
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StableDiffusionPipeline, |
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DPMSolverMultistepScheduler, |
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EulerDiscreteScheduler, |
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) |
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app = FastAPI() |
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@app.get("/generate") |
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def generate_image(prompt, model): |
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torch.cuda.empty_cache() |
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modelArray = model.split(",") |
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modelName = modelArray[0] |
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modelVersion = modelArray[1] |
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pipeline = StableDiffusionPipeline.from_pretrained( |
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str(modelName), torch_dtype=torch.float16 |
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) |
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pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config) |
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pipeline = pipeline.to("cuda") |
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image = pipeline(prompt, num_inference_steps=50, height=512, width=512).images[0] |
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filename = str(uuid.uuid4()) + ".jpg" |
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image.save(filename) |
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assertion = { |
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"assertions": [ |
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{ |
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"label": "com.truepic.custom.ai", |
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"data": { |
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"model_name": modelName, |
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"model_version": modelVersion, |
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"prompt": prompt, |
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}, |
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} |
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] |
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} |
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json_object = json.dumps(assertion) |
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subprocess.check_output( |
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[ |
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"./truepic", |
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"sign", |
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filename, |
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"--assertions-inline", |
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json_object, |
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"--output", |
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(os.getcwd() + "/static/" + filename), |
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] |
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) |
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return {"response": filename} |
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@app.post("/verify") |
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def verify_image(): |
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form = cgi.FieldStorage() |
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logging.debug('form') |
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logging.debug(form) |
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fileitem = form['fileUpload'] |
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logging.debug('fileitem') |
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logging.debug(fileitem) |
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if fileitem.filename: |
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fn = os.path.basename(fileitem.filename) |
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open(fn, 'wb').write(fileitem.file.read()) |
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return {"response": fileitem.filename} |
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app.mount("/", StaticFiles(directory="static", html=True), name="static") |
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@app.get("/") |
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def index() -> FileResponse: |
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return FileResponse(path="/app/static/index.html", media_type="text/html") |
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