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refactor backend detection

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This commit is contained in:
Vladimir Mandic
2024-06-06 20:41:10 -04:00
parent c8d6f03209
commit 5574833f0d
55 changed files with 171 additions and 170 deletions
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modules/prompt_parser_diffusers.py
+15 -23
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@@ -104,7 +104,7 @@ def get_prompt_schedule(prompt, steps):
def get_tokens(msg, prompt):
global token_dict, token_type # pylint: disable=global-statement
if shared.backend != shared.Backend.DIFFUSERS:
if not shared.native:
return
if shared.sd_loaded and hasattr(shared.sd_model, 'tokenizer') and shared.sd_model.tokenizer is not None:
if token_dict is None or token_type != shared.sd_model_type:
@@ -133,7 +133,7 @@ def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, c
if 'StableDiffusion' not in pipe.__class__.__name__ and 'DemoFusion' not in pipe.__class__.__name__ and 'StableCascade' not in pipe.__class__.__name__:
shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
return
elif prompts == cache.get('prompts', None) and negative_prompts == cache.get('negative_prompts', None) and clip_skip == cache.get('clip_skip', None) and cache.get('model_type', None) == shared.sd_model_type:
elif prompts == cache.get('prompts', None) and negative_prompts == cache.get('negative_prompts', None) and clip_skip == cache.get('clip_skip', None) and cache.get('model_type', None) == shared.sd_model_type and steps == cache.get('steps', None):
p.prompt_embeds = cache.get('prompt_embeds', None)
p.positive_pooleds = cache.get('positive_pooleds', None)
p.negative_embeds = cache.get('negative_embeds', None)
@@ -154,36 +154,28 @@ def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, c
for i in range(max(len(positive_schedule), len(negative_schedule))):
positive_prompt = positive_schedule[i % len(positive_schedule)]
negative_prompt = negative_schedule[i % len(negative_schedule)]
if cache.get('model_type', None) != shared.sd_model_type:
cache[positive_prompt + negative_prompt] = None
results = None
elif clip_skip == cache.get('clip_skip', None):
results = cache.get(positive_prompt + negative_prompt, None)
else:
results = None
if results is None:
results = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
cache[positive_prompt + negative_prompt] = results
prompt_embed, positive_pooled, negative_embed, negative_pooled = results
prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
if prompt_embed is not None:
p.prompt_embeds.append(torch.cat([prompt_embed] * len(prompts), dim=0))
cache['prompt_embeds'] = p.prompt_embeds
if negative_embed is not None:
p.negative_embeds.append(torch.cat([negative_embed] * len(negative_prompts), dim=0))
cache['negative_embeds'] = p.negative_embeds
if positive_pooled is not None:
p.positive_pooleds.append(torch.cat([positive_pooled] * len(prompts), dim=0))
cache['positive_pooleds'] = p.positive_pooleds
if negative_pooled is not None:
p.negative_pooleds.append(torch.cat([negative_pooled] * len(negative_prompts), dim=0))
cache['negative_pooleds'] = p.negative_pooleds
cache['prompts'] = prompts
cache['negative_prompts'] = negative_prompts
cache['clip_skip'] = clip_skip
cache['model_type'] = shared.sd_model_type
cache.update({
'prompt_embeds': p.prompt_embeds,
'negative_embeds': p.negative_embeds,
'positive_pooleds': p.positive_pooleds,
'negative_pooleds': p.negative_pooleds,
'scheduled_prompt': p.scheduled_prompt,
'prompts': prompts,
'negative_prompts': negative_prompts,
'clip_skip': clip_skip,
'steps': steps,
'model_type': shared.sd_model_type
})
if debug_enabled:
get_tokens('positive', prompts[0])
get_tokens('negative', negative_prompts[0])