fix progress for highres, increase img2img steps
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4ab1b6cc88
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@ -32,8 +32,9 @@ def run_txt2img_pipeline(
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) -> None:
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# TODO: add to params
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highres_scale = 4
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highres_steps = 15
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highres_strength = 0.5
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highres_steps = 25
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highres_strength = 0.2
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highres_steps_post = int((params.steps - highres_steps) / highres_strength)
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latents = get_latents_from_seed(params.seed, size, batch=params.batch)
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@ -87,8 +88,9 @@ def run_txt2img_pipeline(
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for image, output in zip(result.images, outputs):
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if highres_scale > 1:
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highres_progress = ChainProgress.from_progress(progress)
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# load img2img pipeline once
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highpipe = load_pipeline(
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highres_pipe = load_pipeline(
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server,
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OnnxStableDiffusionImg2ImgPipeline,
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params.model,
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@ -104,32 +106,32 @@ def run_txt2img_pipeline(
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if params.lpw:
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logger.debug("using LPW pipeline for img2img")
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rng = torch.manual_seed(params.seed)
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result = highpipe.img2img(
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result = highres_pipe.img2img(
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tile,
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params.prompt,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=params.steps - highres_steps,
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num_inference_steps=highres_steps_post,
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strength=highres_strength,
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eta=params.eta,
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callback=progress,
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callback=highres_progress,
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)
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return result.images[0]
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else:
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rng = np.random.RandomState(params.seed)
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result = highpipe(
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result = highres_pipe(
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params.prompt,
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tile,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=params.steps - highres_steps,
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num_inference_steps=highres_steps_post,
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strength=highres_strength,
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eta=params.eta,
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callback=progress,
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callback=highres_progress,
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)
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return result.images[0]
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