Prompt editing only applies to images in first batch of desired batch size when batch count > 1 #535
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@ -115,6 +115,7 @@ class VanillaStableDiffusionSampler:
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self.mask = p.mask
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self.mask = p.mask
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self.nmask = p.nmask
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self.nmask = p.nmask
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self.init_latent = p.init_latent
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self.init_latent = p.init_latent
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self.step = 0
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samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
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samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
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@ -127,6 +128,7 @@ class VanillaStableDiffusionSampler:
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self.mask = None
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self.mask = None
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self.nmask = None
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self.nmask = None
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self.init_latent = None
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self.init_latent = None
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self.step = 0
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# existing code fails with cetin step counts, like 9
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# existing code fails with cetin step counts, like 9
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try:
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try:
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@ -206,6 +208,7 @@ class KDiffusionSampler:
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self.model_wrap_cfg.mask = p.mask
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self.model_wrap_cfg.mask = p.mask
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self.model_wrap_cfg.nmask = p.nmask
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self.model_wrap_cfg.nmask = p.nmask
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self.model_wrap_cfg.init_latent = p.init_latent
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self.model_wrap_cfg.init_latent = p.init_latent
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self.model_wrap.step = 0
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if hasattr(k_diffusion.sampling, 'trange'):
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
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@ -216,6 +219,8 @@ class KDiffusionSampler:
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sigmas = self.model_wrap.get_sigmas(p.steps)
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sigmas = self.model_wrap.get_sigmas(p.steps)
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x = x * sigmas[0]
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x = x * sigmas[0]
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self.model_wrap_cfg.step = 0
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if hasattr(k_diffusion.sampling, 'trange'):
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
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