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torch dynamic profiling

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Vladimir Mandic
2024-05-16 18:40:18 -04:00
parent d63f35e298
commit 554e5c8224
19 changed files with 45 additions and 29 deletions
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modules/processing_original.py
+2 -2
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@@ -97,7 +97,7 @@ def sample_txt2img(p: processing.StableDiffusionProcessingTxt2Img, conditioning,
p.extra_generation_params, p.restore_faces = bak_extra_generation_params, bak_restore_faces
images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], shared.opts.samples_format, info=info, suffix="-before-hires")
if latent_scale_mode is None or p.hr_force: # non-latent upscaling
shared.state.job = 'upscale'
shared.state.job = 'Upscale'
if decoded_samples is None:
decoded_samples = decode_first_stage(p.sd_model, samples.to(dtype=devices.dtype_vae), p.full_quality)
decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -126,7 +126,7 @@ def sample_txt2img(p: processing.StableDiffusionProcessingTxt2Img, conditioning,
if p.hr_sampler_name == "PLMS":
p.hr_sampler_name = 'UniPC'
if p.hr_force or latent_scale_mode is not None:
shared.state.job = 'hires'
shared.state.job = 'HiRes'
if p.denoising_strength > 0:
p.ops.append('hires')
devices.torch_gc() # GC now before running the next img2img to prevent running out of memory