ij5 commited on
Commit
bf988c9
1 Parent(s): 43562ed

Update main.py

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Files changed (1) hide show
  1. main.py +10 -18
main.py CHANGED
@@ -1,33 +1,25 @@
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  import flask
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  from flask import Flask, request, json, send_file, Response
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  import torch
 
 
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  from io import BytesIO
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- from min_dalle import MinDalle
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-
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- model = MinDalle(
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- models_root='./pretrained',
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- dtype=torch.float32,
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- device='cuda',
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- is_mega=True,
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- is_reusable=True
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  )
 
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  app = Flask(__name__)
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  @app.post('/sd')
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  def generate():
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  text = request.json['text']
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- image = model.generate_image(
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- text=text,
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- seed=-1,
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- grid_size=2,
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- is_seamless=False,
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- temperature=1,
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- top_k=256,
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- supercondition_factor=32,
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- is_verbose=False
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- )
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  img_io = BytesIO()
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  image.save(img_io)
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  img_io.seek(0)
 
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  import flask
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  from flask import Flask, request, json, send_file, Response
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  import torch
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+ from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
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+ from random import randrange
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  from io import BytesIO
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+ repo = "Bingsu/my-korean-stable-diffusion-v1-5"
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+ euler_ancestral_scheduler = EulerAncestralDiscreteScheduler.from_config(repo, subfolder="scheduler")
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+ pipe = StableDiffusionPipeline.from_pretrained(
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+ repo, scheduler=euler_ancestral_scheduler, torch_dtype=torch.float16,
 
 
 
 
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  )
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+ pipe.to("cuda")
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  app = Flask(__name__)
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  @app.post('/sd')
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  def generate():
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  text = request.json['text']
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+ seed = randrange(1, 9999999999)
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+ generator = torch.Generator('cuda').manual_seed(seed)
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+ image = pipe(text, num_inference_steps=25, generator=generator).images[0]
 
 
 
 
 
 
 
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  img_io = BytesIO()
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  image.save(img_io)
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  img_io.seek(0)