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Clip_IO
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Update for WebUI 1.6.0

pull/13/head
File_xor 2023-09-23 19:40:52 +09:00
parent b6b7589702
commit d04907f269
1 changed files with 31 additions and 11 deletions
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42
scripts/Clip_IO.py
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@ -44,7 +44,7 @@ class Clip_IO(scripts.Script):
mode_positive = gradio.Dropdown(["Disabled", "Simple", "Directive"], value = "Disabled", max_choices = 1, label = "Positive prompt mode")
mode_negative = gradio.Dropdown(["Disabled", "Simple", "Directive"], value = "Disabled", max_choices = 1, label = "Negative prompt mode")
pass
with gradio.Accordion("Pre/Post-process"):
with gradio.Accordion("Pre/Post-process", open = False):
pre_batch_process = gradio.TextArea(max_lines=1024, label="Pre-batch-process")
post_batch_process = gradio.TextArea(max_lines=1024, label="Post-batch-process-process")
pass
@ -473,7 +473,7 @@ class Clip_IO(scripts.Script):
pass
pass
def my_get_learned_conditioning(model, prompts, steps, p: processing.StableDiffusionProcessing = None, is_negative = True):
def my_get_learned_conditioning(model, prompts: prompt_parser.SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False, p: processing.StableDiffusionProcessing = None, is_negative = True):
"""converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
and the sampling step at which this condition is to be replaced by the next one.
@ -497,7 +497,7 @@ class Clip_IO(scripts.Script):
prompt_schedules = [[[steps, prompt]] for prompt in prompts]
pass
else:
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompts, steps)
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling)
pass
res = []
@ -509,7 +509,7 @@ class Clip_IO(scripts.Script):
res.append(cached)
continue
texts: list[str] = [x[1] for x in prompt_schedule]
texts = prompt_parser.SdConditioning([x[1] for x in prompt_schedule], copy_from=prompts)
if Clip_IO.enabled and (Clip_IO.mode_positive == "Simple" and not is_negative or Clip_IO.mode_negative == "Simple" and is_negative):
conds = []
for text in texts:
@ -528,7 +528,15 @@ class Clip_IO(scripts.Script):
cond_schedule = []
for i, (end_at_step, text) in enumerate(prompt_schedule):
cond_schedule.append(prompt_parser.ScheduledPromptConditioning(end_at_step, conds[i].to(devices.device)))
if isinstance(conds, dict):
cond = {k: v[i] for k, v in conds.items()}
pass
else:
cond = conds[i]
pass
cond_schedule.append(prompt_parser.ScheduledPromptConditioning(end_at_step, cond))
pass
cache[prompt] = cond_schedule
res.append(cond_schedule)
@ -536,7 +544,7 @@ class Clip_IO(scripts.Script):
return res
pass
def my_get_multicond_learned_conditioning(model, prompts, steps, p: processing.StableDiffusionProcessing = None) -> prompt_parser.MulticondLearnedConditioning:
def my_get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False, p: processing.StableDiffusionProcessing = None) -> prompt_parser.MulticondLearnedConditioning:
"""same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
For each prompt, the list is obtained by splitting the prompt using the AND separator.
@ -545,11 +553,12 @@ class Clip_IO(scripts.Script):
res_indexes, prompt_flat_list, prompt_indexes = prompt_parser.get_multicond_prompt_list(prompts)
learned_conditioning = prompt_parser.get_learned_conditioning(model, prompt_flat_list, steps, p, is_negative = False)
learned_conditioning = prompt_parser.get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling, p, is_negative = False)
res = []
for indexes in res_indexes:
res.append([prompt_parser.ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
pass
return prompt_parser.MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
pass
@ -631,7 +640,7 @@ class Clip_IO(scripts.Script):
pass
def get_my_get_conds_with_caching(p: processing.StableDiffusionProcessing):
def my_get_conds_with_caching(function, required_prompts, steps, caches, extra_network_data):
def my_get_conds_with_caching(function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
"""
Returns the result of calling function(shared.sd_model, required_prompts, steps)
using a cache to store the result if the same arguments have been used before.
@ -643,18 +652,29 @@ class Clip_IO(scripts.Script):
caches is a list with items described above.
"""
if shared.opts.use_old_scheduling:
old_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, False)
new_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, True)
if old_schedules != new_schedules:
p.extra_generation_params["Old prompt editing timelines"] = True
pass
pass
cached_params = p.cached_params(required_prompts, steps, extra_network_data, hires_steps, shared.opts.use_old_scheduling)
for cache in caches:
if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
if cache[0] is not None and cached_params == cache[0] and not Clip_IO.enabled:
return cache[1]
cache = caches[0]
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps, p)
cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data)
cache[0] = cached_params
return cache[1]
pass
return my_get_conds_with_caching
def get_inner_function(outer, new_inner):