62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
from diffusers import OnnxStableDiffusionPipeline
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from flask import Flask, make_response, request, send_file
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from io import BytesIO
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from os import environ, path, makedirs
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# defaults
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empty_prompt = "a photo of an astronaut eating a hamburger"
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max_height = 512
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max_width = 512
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max_steps = 50
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max_cfg = 8
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# paths
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model_path = environ.get('ONNX_WEB_MODEL_PATH', "../../stable_diffusion_onnx")
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output_path = environ.get('ONNX_WEB_OUTPUT_PATH', "../../web_output")
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# queue
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image_queue = set()
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def setup():
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if not path.exists(model_path):
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raise RuntimeError('model path must exist')
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if not path.exists(output_path):
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makedirs(output_path)
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# setup
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setup()
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app = Flask(__name__)
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pipe = OnnxStableDiffusionPipeline.from_pretrained(model_path, provider="DmlExecutionProvider", safety_checker=None)
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# routes
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@app.route('/')
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def hello():
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return 'Hello, %s' % (__name__)
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@app.route('/txt2img')
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def txt2img():
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if len(image_queue) > 0:
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return 'Queue full: %s' % (image_queue)
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user = request.remote_addr
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prompt = request.args.get('prompt', empty_prompt)
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height = request.args.get('height', max_height)
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width = request.args.get('width', max_width)
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steps = int(request.args.get('steps', max_steps))
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cfg = int(request.args.get('cfg', max_cfg))
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print("txt2img from %s: %s/%s, %sx%s, %s" % (user, cfg, steps, width, height, prompt))
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image_queue.add(user)
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image = pipe(prompt, height, width, num_inference_steps=steps, guidance_scale=cfg).images[0]
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# image.save("astronaut_rides_horse.png")
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img_io = BytesIO()
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image.save(img_io, 'PNG', quality=100)
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img_io.seek(0)
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image_queue.remove(user)
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res = make_response(send_file(img_io, mimetype='image/png'))
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res.headers.add('Access-Control-Allow-Origin', '*')
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return res |