Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 17:24:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

fix model offload on long prompts and update requirements

Browse Source
This commit is contained in:
Vladimir Mandic
2023-11-23 09:42:24 -05:00
parent 2ab4b512c8
commit 096fd04f53
5 changed files with 7 additions and 44 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/prompt_parser_diffusers.py
+3 -5
View File
@@ -69,7 +69,7 @@ def encode_prompts(pipeline, prompts: list, negative_prompts: list, clip_skip: t
negative_embeds = []
negative_pooleds = []
for i in range(len(prompts)):
prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipeline,prompts[i], negative_prompts[i], clip_skip)
prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipeline, prompts[i], negative_prompts[i], clip_skip)
prompt_embeds.append(prompt_embed)
positive_pooleds.append(positive_pooled)
negative_embeds.append(negative_embed)
@@ -119,8 +119,7 @@ def pad_to_same_length(embeds):
empty_embed = shared.sd_model.encode_prompt("")
except Exception: #SD1.5
empty_embed = shared.sd_model.encode_prompt("",shared.sd_model.device, 1, False)
empty_batched = torch.cat([empty_embed[0]] * embeds[0].shape[0])
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])
for i, embed in enumerate(embeds):
while embed.shape[1] < max_token_count:
@@ -152,7 +151,6 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
negative_prompt_embeds = []
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]
@@ -168,7 +166,7 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
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=pipe.device, should_return_tokens=True)
embed, ntokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[negatives[i]], fragment_weights_batch=[negative_weights[i]], device=pipe.device, should_return_tokens=True)
negative_prompt_embeds.append(embed)
if prompt_embeds[-1].shape[-1] > 768: