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synced 2026-04-16 07:26:59 +08:00
small tweaks
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@ -1731,7 +1731,7 @@ Returns a range of latents from a batch.
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elif len(samples.shape) == 5:
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chosen_latents = samples[:, :, start_index:end_index]
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return ({"samples": chosen_latents,},)
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return ({"samples": chosen_latents.contiguous(),},)
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class InsertLatentToIndex:
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@ -1189,18 +1189,14 @@ class CFGZeroStarAndInit:
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def patch(self, model, use_zero_init, zero_star_steps):
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def cfg_zerostar(args):
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cond = args["cond"]
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uncond = args["uncond"]
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cond_scale = args["cond_scale"]
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#zero init
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timestep = args["timestep"]
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sigmas = args["model_options"]["transformer_options"]["sample_sigmas"]
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matched_step_index = (sigmas == timestep[0] ).nonzero()
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if len(matched_step_index) > 0:
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current_step_index = matched_step_index.item()
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else:
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for i in range(len(sigmas) - 1):
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# walk from beginning of steps until crossing the timestep
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if (sigmas[i] - timestep[0]) * (sigmas[i + 1] - timestep[0]) <= 0:
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current_step_index = i
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break
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@ -1208,7 +1204,11 @@ class CFGZeroStarAndInit:
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current_step_index = 0
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if (current_step_index <= zero_star_steps) and use_zero_init:
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return cond * 0
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return cond * 0
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cond = args["cond"]
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uncond = args["uncond"]
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cond_scale = args["cond_scale"]
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batch_size = cond.shape[0]
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