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main.py

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+import flask
+from flask import Flask, request, json, send_file, Response
+import torch
+from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
+from random import randrange
+
+repo = "Bingsu/my-korean-stable-diffusion-v1-5"
+euler_ancestral_scheduler = EulerAncestralDiscreteScheduler.from_config(repo, subfolder="scheduler")
+pipe = StableDiffusionPipeline.from_pretrained(
+    repo, scheduler=euler_ancestral_scheduler, torch_dtype=torch.float16,
+)
+pipe.to("cuda")
+
+app = Flask(__name__)
+
+@app.post('/sd')
+def generate():
+    text = request.json['text']
+    seed = randrange(1, 9999999999)
+    generator = torch.Generator('cuda').manual_seed(seed)
+    image = pipe(text, num_inference_steps=25, generator=generator).images[0]
+    return Response(image, mimetype='image/png', direct_passthrough=True)
+
+
+if __name__ == '__main__':
+    app.run('0.0.0.0', 8282, debug=True)

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu116
+torch
+transformers
+accelerate
+diffusers