fix potential numerical problems
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22fc28958f
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4cf0c778da
@ -1 +1 @@
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version = '2.1.727'
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version = '2.1.728'
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@ -3,6 +3,7 @@ import os
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import torch
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import modules.patch
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import modules.path
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import fcbh.sample
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import fcbh.model_management
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import fcbh.latent_formats
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import modules.inpaint_worker
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@ -278,6 +279,14 @@ def vae_parse(latent):
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return {'samples': result}
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@torch.no_grad()
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@torch.inference_mode()
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def noise_parse(latent: torch.Tensor, seed: int, noise_inds=None):
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noise = fcbh.sample.prepare_noise(latent, seed=seed, noise_inds=noise_inds)
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s, m = torch.std_mean(latent, dim=1, keepdim=True)
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return m + s * noise
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@torch.no_grad()
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@torch.inference_mode()
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def calculate_sigmas_all(sampler, model, scheduler, steps):
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@ -468,8 +477,10 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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denoise=denoise)[switch:] * k_sigmas
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len_sigmas = len(sigmas) - 1
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residual_noise = modules.patch.eps_record
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assert isinstance(residual_noise, torch.Tensor)
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residual_noise = noise_parse(
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modules.patch.eps_record,
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seed=image_seed+1,
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noise_inds=sampled_latent["batch_index"] if "batch_index" in sampled_latent else None)
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if modules.inpaint_worker.current_task is not None:
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modules.inpaint_worker.current_task.swap()
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@ -480,7 +491,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=final_clip),
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latent=sampled_latent,
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steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True,
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seed=image_seed,
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seed=image_seed+1,
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denoise=denoise,
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callback_function=callback,
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cfg=cfg_scale,
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@ -1,3 +1,7 @@
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# 2.1.728
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* Fixed some potential numerical problems since 2.1.723
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# 2.1.723
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* Improve Fooocus Anime a bit by using better SD1.5 refining formulation.
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