add seed to output filename, apply pep8 to server
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118
api/serve.py
118
api/serve.py
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@ -32,89 +32,99 @@ output_path = environ.get('ONNX_WEB_OUTPUT_PATH', "../../web_output")
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# schedulers
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scheduler_list = {
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'ddpm': DDPMScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'ddim': DDIMScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'pndm': PNDMScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'lms-discrete': LMSDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'euler-a': EulerAncestralDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'euler': EulerDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'dpm-multi': DPMSolverMultistepScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'ddpm': DDPMScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'ddim': DDIMScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'pndm': PNDMScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'lms-discrete': LMSDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'euler-a': EulerAncestralDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'euler': EulerDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler"),
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'dpm-multi': DPMSolverMultistepScheduler.from_pretrained(model_path, subfolder="scheduler"),
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}
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def get_and_clamp(args, key, default_value, max_value, min_value=1):
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return min(max(int(args.get(key, default_value)), min_value), max_value)
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return min(max(int(args.get(key, default_value)), min_value), max_value)
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def get_from_map(args, key, values, default):
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selected = args.get(key, default)
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if selected in values:
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return values[selected]
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else:
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return values[default]
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selected = args.get(key, default)
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if selected in values:
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return values[selected]
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else:
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return values[default]
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def get_latents_from_seed(seed: int, width: int, height: int) -> np.ndarray:
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# 1 is batch size
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latents_shape = (1, 4, height // 8, width // 8)
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latents_shape = (1, 4, height // 8, width // 8)
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# Gotta use numpy instead of torch, because torch's randn() doesn't support DML
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rng = np.random.default_rng(seed)
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image_latents = rng.standard_normal(latents_shape).astype(np.float32)
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return image_latents
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rng = np.random.default_rng(seed)
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image_latents = rng.standard_normal(latents_shape).astype(np.float32)
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return image_latents
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# 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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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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makedirs(output_path)
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app = Flask(__name__)
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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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user = request.remote_addr
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user = request.remote_addr
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prompt = request.args.get('prompt', default_prompt)
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scheduler = get_from_map(request.args, 'scheduler', scheduler_list, 'euler-a')
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cfg = get_and_clamp(request.args, 'cfg', default_cfg, max_cfg, 0)
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steps = get_and_clamp(request.args, 'steps', default_steps, max_steps)
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height = get_and_clamp(request.args, 'height', default_height, max_height)
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width = get_and_clamp(request.args, 'width', default_width, max_width)
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prompt = request.args.get('prompt', default_prompt)
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scheduler = get_from_map(request.args, 'scheduler',
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scheduler_list, 'euler-a')
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cfg = get_and_clamp(request.args, 'cfg', default_cfg, max_cfg, 0)
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steps = get_and_clamp(request.args, 'steps', default_steps, max_steps)
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height = get_and_clamp(request.args, 'height', default_height, max_height)
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width = get_and_clamp(request.args, 'width', default_width, max_width)
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seed = int(request.args.get('seed', -1))
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if seed == -1:
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seed = np.random.randint(np.iinfo(np.int32).max)
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seed = int(request.args.get('seed', -1))
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if seed == -1:
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seed = np.random.randint(np.iinfo(np.int32).max)
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latents = get_latents_from_seed(seed, width, height)
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latents = get_latents_from_seed(seed, width, height)
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print("txt2img from %s: %s/%s, %sx%s, %s, %s" % (user, cfg, steps, width, height, seed, prompt))
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print("txt2img from %s: %s/%s, %sx%s, %s, %s" %
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(user, cfg, steps, width, height, seed, prompt))
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pipe = OnnxStableDiffusionPipeline.from_pretrained(
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model_path,
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provider="DmlExecutionProvider",
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safety_checker=None,
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scheduler=scheduler
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)
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image = pipe(
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prompt,
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height,
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width,
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num_inference_steps=steps,
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guidance_scale=cfg,
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latents=latents
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).images[0]
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pipe = OnnxStableDiffusionPipeline.from_pretrained(
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model_path,
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provider="DmlExecutionProvider",
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safety_checker=None,
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scheduler=scheduler
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)
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image = pipe(
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prompt,
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height,
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width,
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num_inference_steps=steps,
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guidance_scale=cfg,
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latents=latents
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).images[0]
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output = '%s/txt2img_%s.png' % (output_path, spinalcase(prompt[0:64]))
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print("txt2img output: %s" % (output))
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image.save(output)
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output = '%s/txt2img_%s_%s.png' % (output_path,
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seed, spinalcase(prompt[0:64]))
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print("txt2img output: %s" % (output))
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image.save(output)
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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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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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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
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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
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