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Fix scheduling, lint

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This commit is contained in:
AI-Casanova
2023-12-18 23:42:31 -06:00
parent 0482d5a448
commit ff94b197c1
1 changed files with 32 additions and 30 deletions
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modules/prompt_parser_diffusers.py
+32 -30
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@@ -6,10 +6,8 @@ from compel import ReturnedEmbeddingsType
from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
from modules import shared, prompt_parser, devices
debug = shared.log.info if os.environ.get('SD_PROMPT_DEBUG', None) is not None else lambda *args, **kwargs: None
CLIP_SKIP_MAPPING = {
None: ReturnedEmbeddingsType.LAST_HIDDEN_STATES_NORMALIZED,
1: ReturnedEmbeddingsType.LAST_HIDDEN_STATES_NORMALIZED,
@@ -59,6 +57,7 @@ class DiffusersTextualInversionManager(BaseTextualInversionManager):
debug(f'Prompt: expand={prompt}')
return self.pipe.tokenizer.encode(prompt, add_special_tokens=False)
def get_prompt_schedule(p, prompt, steps):
t0 = time.time()
temp = []
@@ -69,14 +68,16 @@ def get_prompt_schedule(p, prompt, steps):
for s in range(steps):
if len(temp) < s + 1 <= chunk[0]:
temp.append(chunk[1])
debug(f'Prompt: schedule={temp} time={time.time()-t0}')
debug(f'Prompt: schedule={temp} time={time.time() - t0}')
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):
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:
t0 = time.time()
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
@@ -87,23 +88,24 @@ def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int,
p.negative_pooleds = []
cache = {}
for i in range(len(positive_schedule)):
cached = cache.get(positive_schedule[i]+negative_schedule[i], None)
for i in range(max(len(positive_schedule), len(negative_schedule))):
cached = cache.get(positive_schedule[i % len(positive_schedule)] + negative_schedule[i % len(negative_schedule)], None)
if cached is not None:
prompt_embed, positive_pooled, negative_embed, negative_pooled = cached
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)
prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe,
positive_schedule[i % len(positive_schedule)],
negative_schedule[i % len(negative_schedule)],
clip_skip)
if prompt_embed is not None:
p.prompt_embeds.append(torch.cat([prompt_embed]*len(prompts), dim=0))
p.prompt_embeds.append(torch.cat([prompt_embed] * len(prompts), dim=0))
if negative_embed is not None:
p.negative_embeds.append(torch.cat([negative_embed]*len(negative_prompts), dim=0))
p.negative_embeds.append(torch.cat([negative_embed] * len(negative_prompts), dim=0))
if positive_pooled is not None and shared.sd_model_type == "sdxl":
p.positive_pooleds.append(torch.cat([positive_pooled]*len(prompts), dim=0))
p.positive_pooleds.append(torch.cat([positive_pooled] * len(prompts), dim=0))
if negative_pooled is not None and shared.sd_model_type == "sdxl":
p.negative_pooleds.append(torch.cat([negative_pooled]*len(negative_prompts), dim=0))
p.negative_pooleds.append(torch.cat([negative_pooled] * len(negative_prompts), dim=0))
debug(f"Prompt Parser: Elapsed Time {time.time() - t0}")
return
@@ -138,9 +140,9 @@ def prepare_embedding_providers(pipe, clip_skip):
def pad_to_same_length(pipe, embeds):
device = pipe.device if str(pipe.device) != 'meta' else devices.device
try: #SDXL
try: # SDXL
empty_embed = pipe.encode_prompt("")
except Exception: #SD1.5
except Exception: # SD1.5
empty_embed = pipe.encode_prompt("", device, 1, False)
empty_batched = torch.cat([empty_embed[0].to(embeds[0].device)] * embeds[0].shape[0])
max_token_count = max([embed.shape[1] for embed in embeds])
@@ -174,7 +176,7 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
prompt_embeds = []
negative_prompt_embeds = []
pooled_prompt_embeds = None
negative_pooled_prompt_embeds = None
negative_pooled_prompt_embeds = None
for i in range(len(embedding_providers)):
# add BREAK keyword that splits the prompt into multiple fragments
text = positives[i]
@@ -188,8 +190,8 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
if len(text[:pos]) > 0:
embed, ptokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[text[:pos]], fragment_weights_batch=[weights[:pos]], device=device, should_return_tokens=True)
provider_embed.append(embed)
text = text[pos+1:]
weights = weights[pos+1:]
text = text[pos + 1:]
weights = weights[pos + 1:]
prompt_embeds.append(torch.cat(provider_embed, dim=1))
# negative prompt has no keywords
embed, ntokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[negatives[i]], fragment_weights_batch=[negative_weights[i]], device=device, should_return_tokens=True)
@@ -198,17 +200,17 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
if prompt_embeds[-1].shape[-1] > 768:
if shared.opts.diffusers_pooled == "weighted":
pooled_prompt_embeds = prompt_embeds[-1][
torch.arange(prompt_embeds[-1].shape[0], device=device),
(ptokens.to(dtype=torch.int, device=device) == 49407)
.int()
.argmax(dim=-1),
]
torch.arange(prompt_embeds[-1].shape[0], device=device),
(ptokens.to(dtype=torch.int, device=device) == 49407)
.int()
.argmax(dim=-1),
]
negative_pooled_prompt_embeds = negative_prompt_embeds[-1][
torch.arange(negative_prompt_embeds[-1].shape[0], device=device),
(ntokens.to(dtype=torch.int, device=device) == 49407)
.int()
.argmax(dim=-1),
]
torch.arange(negative_prompt_embeds[-1].shape[0], device=device),
(ntokens.to(dtype=torch.int, device=device) == 49407)
.int()
.argmax(dim=-1),
]
else:
pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[prompt_2], device=device) if prompt_embeds[-1].shape[-1] > 768 else None
negative_pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[neg_prompt_2], device=device) if negative_prompt_embeds[-1].shape[-1] > 768 else None