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Fix batch & optimize

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AI-Casanova
2023-12-17 19:37:05 -06:00
parent 9e7757b7fe
commit 0b3e1f475d
3 changed files with 24 additions and 15 deletions
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modules/processing.py
+1
View File
@@ -231,6 +231,7 @@ class StableDiffusionProcessing:
self.hdr_maximize = hdr_maximize
self.hdr_max_center = hdr_max_center
self.hdr_max_boundry = hdr_max_boundry
self.scheduled_prompt: bool = False
self.prompt_embeds = []
self.positive_pooleds = []
self.negative_embeds = []
modules/processing_diffusers.py
+9 -7
View File
@@ -89,13 +89,15 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if kwargs.get('latents', None) is None:
return kwargs
kwargs = correction_callback(p, timestep, kwargs)
try:
kwargs["prompt_embeds"] = p.prompt_embeds[step + 1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
kwargs["negative_prompt_embeds"] = p.negative_embeds[step + 1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
except:
pass
if p.scheduled_prompt:
try:
i = (step + 1) % len(p.prompt_embeds)
kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
kwargs["negative_prompt_embeds"] = p.negative_embeds[i][0:1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
except Exception as e:
shared.log.debug(f"Callback: {e}")
shared.state.current_latent = kwargs['latents']
if shared.cmd_opts.profile and shared.profiler is not None:
shared.profiler.step()
modules/prompt_parser_diffusers.py
+14 -8
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@@ -58,33 +58,39 @@ class DiffusersTextualInversionManager(BaseTextualInversionManager):
debug(f'Prompt: expand={prompt}')
return self.pipe.tokenizer.encode(prompt, add_special_tokens=False)
def get_prompt_schedule(prompt, steps):
def get_prompt_schedule(p, prompt, steps):
temp = []
schedule = prompt_parser.get_learned_conditioning_prompt_schedules([prompt], steps)[0]
for chunk in schedule:
for s in range(steps):
if len(temp) < s + 1 <= chunk[0]:
temp.append(chunk[1])
return temp
return temp, len(schedule) > 1
def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, step: int = 1, clip_skip: typing.Optional[int] = None):
if 'StableDiffusion' not in pipe.__class__.__name__ and 'DemoFusion':
shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
return None, None, None, None
else:
positive_schedule = get_prompt_schedule(prompts[0], steps)
negative_schedule = get_prompt_schedule(negative_prompts[0], steps)
positive_schedule, scheduled = get_prompt_schedule(p, prompts[0], steps)
negative_schedule, neg_scheduled = get_prompt_schedule(p, negative_prompts[0], steps)
p.scheduled_prompt = scheduled or neg_scheduled
p.prompt_embeds = []
p.positive_pooleds = []
p.negative_embeds = []
p.negative_pooleds = []
cache = {}
for i in range(len(positive_schedule)):
prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe,
positive_schedule[i],
negative_schedule[i],
clip_skip)
cached = cache.get(positive_schedule[i]+negative_schedule[i], None)
if cached is not None:
prompt_embed, positive_pooled, negative_embed, negative_pooled = cached
else:
prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe,
positive_schedule[i],
negative_schedule[i],
clip_skip)
if prompt_embed is not None:
p.prompt_embeds.append(torch.cat([prompt_embed]*len(prompts), dim=0))
if negative_embed is not None: