nodes/Kosinkadink-ComfyUI-AnimateDiff-Evolved
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Use new calc_cond_batch function while maintaining backwards compatibility

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Jedrzej Kosinski
2024-04-03 04:09:24 -05:00
parent 1e8fba9384
commit 430c49ef04
1 changed files with 10 additions and 7 deletions
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animatediff/sampling.py
+10 -7
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@@ -409,7 +409,7 @@ def evolved_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
model_options["transformer_options"]["ad_params"] = ADGS.create_exposed_params()
if not ADGS.is_using_sliding_context():
cond_pred, uncond_pred = calc_cond_uncond_batch_wrapper(model, cond, uncond_, x, timestep, model_options)
cond_pred, uncond_pred = calc_cond_uncond_batch_wrapper(model, [cond, uncond_], x, timestep, model_options)
else:
cond_pred, uncond_pred = sliding_calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
@@ -428,6 +428,7 @@ def evolved_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
return cfg_result
# TODO: properly carry over changes from calc_cond_batch
# sliding_calc_cond_uncond_batch inspired by ashen's initial hack for 16-frame sliding context:
# https://github.com/comfyanonymous/ComfyUI/compare/master...ashen-sensored:ComfyUI:master
def sliding_calc_cond_uncond_batch(model, cond, uncond, x_in: Tensor, timestep, model_options):
@@ -523,7 +524,7 @@ def sliding_calc_cond_uncond_batch(model, cond, uncond, x_in: Tensor, timestep,
sub_cond = get_resized_cond(cond, full_idxs, len(ctx_idxs)) if cond is not None else None
sub_uncond = get_resized_cond(uncond, full_idxs, len(ctx_idxs)) if uncond is not None else None
sub_cond_out, sub_uncond_out = calc_cond_uncond_batch_wrapper(model, sub_cond, sub_uncond, sub_x, sub_timestep, model_options)
sub_cond_out, sub_uncond_out = calc_cond_uncond_batch_wrapper(model, [sub_cond, sub_uncond], sub_x, sub_timestep, model_options)
if ADGS.params.context_options.fuse_method == ContextFuseMethod.RELATIVE:
full_length = ADGS.params.full_length
@@ -560,10 +561,10 @@ def sliding_calc_cond_uncond_batch(model, cond, uncond, x_in: Tensor, timestep,
return cond_final, uncond_final
def calc_cond_uncond_batch_wrapper(model, cond, uncond, x_in, timestep, model_options):
def calc_cond_uncond_batch_wrapper(model, conds: list[dict], x_in: Tensor, timestep, model_options):
# check if conds or unconds contain lora_hook
contains_lora_hooks = False
for cond_uncond in [cond, uncond]:
for cond_uncond in conds:
if cond_uncond is None:
continue
for t in cond_uncond:
@@ -573,9 +574,11 @@ def calc_cond_uncond_batch_wrapper(model, cond, uncond, x_in, timestep, model_op
if contains_lora_hooks:
break
if contains_lora_hooks:
return calc_cond_uncond_batch_lora_hook(model, cond, uncond, x_in, timestep, model_options)
return comfy.samplers.calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options)
return calc_cond_uncond_batch_lora_hook(model, conds[0], conds[1], x_in, timestep, model_options)
# keep for backwards compatibility, for now
if not hasattr(comfy.samplers, "calc_cond_batch"):
return comfy.samplers.calc_cond_uncond_batch(model, conds[0], conds[1], x_in, timestep, model_options)
return comfy.samplers.calc_cond_batch(model, conds, x_in, timestep, model_options)
def calc_cond_uncond_batch_lora_hook(model, cond, uncond, x_in, timestep, model_options):
out_cond = torch.zeros_like(x_in)