nodes/FizzleDorf-ComfyUI_FizzNodes
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start and end frame controls actually work now

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FizzleDorf
2024-08-27 21:31:21 -04:00
parent 0e30c12400
commit da49dd3e86
2 changed files with 19 additions and 14 deletions
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BatchFuncs.py
+11 -6
View File
@@ -313,16 +313,16 @@ def interpolate_prompt_series(animation_prompts, max_frames, start_frame, pre_te
# if it is an in-between frame and the prompts differ, composable diffusion will be performed.
return (cur_prompt_series, nxt_prompt_series, weight_series)
def BatchPoolAnimConditioning(cur_prompt_series, nxt_prompt_series, weight_series, clip):
def BatchPoolAnimConditioning(cur_prompt_series, nxt_prompt_series, weight_series, clip, settings:ScheduleSettings):
pooled_out = []
cond_out = []
max_size = 0
if max_size == 0:
for i in range(len(cur_prompt_series)):
for i in range(0, settings.end_frame):
tokens = clip.tokenize(str(cur_prompt_series[i]))
cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
max_size = max(max_size, cond_to.shape[1])
for i in range(len(cur_prompt_series)):
for i in range(settings.start_frame, settings.end_frame):
tokens = clip.tokenize(str(cur_prompt_series[i]))
cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
@@ -380,11 +380,16 @@ def BatchGLIGENConditioning(cur_prompt_series, nxt_prompt_series, weight_series,
return cond_out, pooled_out
def BatchPoolAnimConditioningSDXL(cur_prompt_series, nxt_prompt_series, weight_series):
def BatchPoolAnimConditioningSDXL(cur_prompt_series, nxt_prompt_series, weight_series, settings:ScheduleSettings):
pooled_out = []
cond_out = []
for i in range(len(cur_prompt_series)):
max_size = 0
if max_size == 0:
for i in range(0, settings.end_frame):
tokens = clip.tokenize(str(cur_prompt_series[i]))
cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True)
max_size = max(max_size, cond_to.shape[1])
for i in range(settings.start_frame,settings.end_frame):
interpolated_conditioning = addWeighted(cur_prompt_series[i],
nxt_prompt_series[i],
weight_series[i])
ScheduleTypes.py
+8 -8
View File
@@ -46,8 +46,8 @@ def batch_prompt_schedule(settings:ScheduleSettings,clip):
neg_cur_prompt, neg_nxt_prompt, weight = interpolate_prompt_seriesA(neg, settings)
# Apply composable diffusion across the batch
p = BatchPoolAnimConditioning(pos_cur_prompt, pos_nxt_prompt, weight, clip, )
n = BatchPoolAnimConditioning(neg_cur_prompt, neg_nxt_prompt, weight, clip, )
p = BatchPoolAnimConditioning(pos_cur_prompt, pos_nxt_prompt, weight, clip, settings)
n = BatchPoolAnimConditioning(neg_cur_prompt, neg_nxt_prompt, weight, clip, settings)
# return positive and negative conditioning as well as the current and next prompts for each
return (p, n,)
@@ -63,13 +63,13 @@ def batch_prompt_schedule_latentInput(settings:ScheduleSettings,clip, latents):
pos_cur_prompt, pos_nxt_prompt, weight = interpolate_prompt_seriesA(pos, settings)
# Apply composable diffusion across the batch
p = BatchPoolAnimConditioning(pos_cur_prompt, pos_nxt_prompt, weight, clip)
p = BatchPoolAnimConditioning(pos_cur_prompt, pos_nxt_prompt, weight, clip, settings)
# Interpolate the negative prompt weights over frames
neg_cur_prompt, neg_nxt_prompt, weight = interpolate_prompt_seriesA(neg, settings)
# Apply composable diffusion across the batch
n = BatchPoolAnimConditioning(neg_cur_prompt, neg_nxt_prompt, weight, clip)
n = BatchPoolAnimConditioning(neg_cur_prompt, neg_nxt_prompt, weight, clip, settings)
return (p, n, latents,)
@@ -144,8 +144,8 @@ def batch_prompt_schedule_SDXL(settings:ScheduleSettings,clip):
pc, pn, pw = BatchInterpolatePromptsSDXL(posG, posL, clip, settings,)
nc, nn, nw = BatchInterpolatePromptsSDXL(negG, negL, clip, settings,)
p = BatchPoolAnimConditioningSDXL(pc, pn, pw)
n = BatchPoolAnimConditioningSDXL(nc, nn, nw)
p = BatchPoolAnimConditioningSDXL(pc, pn, pw, settings)
n = BatchPoolAnimConditioningSDXL(nc, nn, nw, settings)
return (p, n,)
@@ -163,8 +163,8 @@ def batch_prompt_schedule_SDXL_latentInput(settings:ScheduleSettings,clip, laten
pc, pn, pw = BatchInterpolatePromptsSDXL(posG, posL, clip, settings)
nc, nn, nw = BatchInterpolatePromptsSDXL(negG, negL, clip, settings)
p = BatchPoolAnimConditioningSDXL(pc, pn, pw)
n = BatchPoolAnimConditioningSDXL(nc, nn, nw)
p = BatchPoolAnimConditioningSDXL(pc, pn, pw, settings)
n = BatchPoolAnimConditioningSDXL(nc, nn, nw, settings)
return (p, n, latents,)