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feat(api): take outpainting dimensions from query params

This commit is contained in:
Sean Sube 2023-01-14 16:59:38 -06:00
parent 899d41a38d
commit d9bbb9bb5a
1 changed files with 16 additions and 11 deletions
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27
api/onnx_web/serve.py
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@ -264,25 +264,25 @@ def run_img2img_pipeline(model, provider, scheduler, prompt, negative_prompt, cf
print('saved img2img output: %s' % (output))
def run_inpaint_pipeline(model, provider, scheduler, prompt, negative_prompt, cfg, steps, seed, output, height, width, source_image, mask_image):
def run_inpaint_pipeline(model, provider, scheduler, prompt, negative_prompt, cfg, steps, seed, output, height, width, source_image, mask_image, left, right, top, bottom):
pipe = load_pipeline(OnnxStableDiffusionInpaintPipeline,
model, provider, scheduler)
latents = get_latents_from_seed(seed, width, height)
rng = np.random.RandomState(seed)
extra = 256
full_source, full_mask, full_noise, full_dims = expand_image(source_image, mask_image, (extra, extra, extra, extra))
full_width, full_height = full_dims
if left > 0 or right > 0 or top > 0 or bottom > 0:
print('expanding image for outpainting')
source_image, mask_image, _full_noise, _full_dims = expand_image(source_image, mask_image, (left, right, top, bottom))
image = pipe(
prompt,
generator=rng,
guidance_scale=cfg,
height=height,
image=full_source,
image=source_image,
latents=latents,
mask_image=full_mask,
mask_image=mask_image,
negative_prompt=negative_prompt,
num_inference_steps=steps,
width=width,
@ -374,6 +374,7 @@ def list_schedulers():
def img2img():
input_file = request.files.get('source')
input_image = Image.open(BytesIO(input_file.read())).convert('RGB')
input_image.thumbnail((width, height))
strength = get_and_clamp_float(request.args, 'strength', 0.5, 1.0)
@ -384,7 +385,6 @@ def img2img():
(prompt, cfg, negative_prompt, steps, strength, height, width))
print("img2img output: %s" % (output_full))
input_image.thumbnail((width, height))
executor.submit_stored(output_file, run_img2img_pipeline, model, provider,
scheduler, prompt, negative_prompt, cfg, steps, seed, output_full, strength, input_image)
@ -438,21 +438,26 @@ def txt2img():
def inpaint():
source_file = request.files.get('source')
source_image = Image.open(BytesIO(source_file.read())).convert('RGB')
source_image.thumbnail((width, height))
mask_file = request.files.get('mask')
mask_image = Image.open(BytesIO(mask_file.read())).convert('RGB')
mask_image.thumbnail((width, height))
(model, provider, scheduler, prompt, negative_prompt, cfg, steps, height,
width, seed) = pipeline_from_request()
left = get_and_clamp_int(request.args, 'left', 0, source_file.width)
right = get_and_clamp_int(request.args, 'right', 0, source_file.width)
top = get_and_clamp_int(request.args, 'top', 0, source_file.height)
bottom = get_and_clamp_int(request.args, 'bottom', 0, source_file.height)
(output_file, output_full) = make_output_path(
'inpaint', seed, (prompt, cfg, steps, height, width, seed))
'inpaint', seed, (prompt, cfg, steps, height, width, seed, left, right, top, bottom))
print("inpaint output: %s" % output_full)
source_image.thumbnail((width, height))
mask_image.thumbnail((width, height))
executor.submit_stored(output_file, run_inpaint_pipeline, model, provider, scheduler, prompt, negative_prompt,
cfg, steps, seed, output_full, height, width, source_image, mask_image)
cfg, steps, seed, output_full, height, width, source_image, mask_image, left, right, top, bottom)
return jsonify({
'output': output_file,