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Stable Cascade prompt parser support

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Disty0
2024-05-26 16:01:27 +03:00
parent c9414d49b4
commit be264d14d6
2 changed files with 20 additions and 8 deletions
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modules/processing_args.py
+6 -2
View File
@@ -103,7 +103,7 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
shared.log.debug(f'Sampler: steps={len(timesteps)} timesteps={timesteps}')
except Exception as e:
shared.log.error(f'Sampler timesteps: {e}')
if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__ and 'Onnx' not in model.__class__.__name__:
if shared.opts.prompt_attention != 'Fixed attention' and ('StableDiffusion' in model.__class__.__name__ or 'StableCascade' in model.__class__.__name__) and 'Onnx' not in model.__class__.__name__:
try:
prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, steps=steps, clip_skip=clip_skip)
parser = shared.opts.prompt_attention
@@ -119,13 +119,17 @@ def set_pipeline_args(p, model, prompts: list, negative_prompts: list, prompts_2
if 'prompt' in possible:
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and len(p.prompt_embeds) > 0 and p.prompt_embeds[0] is not None:
args['prompt_embeds'] = p.prompt_embeds[0]
if 'XL' in model.__class__.__name__ and len(getattr(p, 'positive_pooleds', [])) > 0:
if 'StableCascade' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
args['prompt_embeds_pooled'] = p.positive_pooleds[0].unsqueeze(0)
elif 'XL' in model.__class__.__name__ and len(getattr(p, 'positive_pooleds', [])) > 0:
args['pooled_prompt_embeds'] = p.positive_pooleds[0]
else:
args['prompt'] = prompts
if 'negative_prompt' in possible:
if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and len(p.negative_embeds) > 0 and p.negative_embeds[0] is not None:
args['negative_prompt_embeds'] = p.negative_embeds[0]
if 'StableCascade' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
args['negative_prompt_embeds_pooled'] = p.negative_pooleds[0].unsqueeze(0)
if 'XL' in model.__class__.__name__ and len(getattr(p, 'negative_pooleds', [])) > 0:
args['negative_pooled_prompt_embeds'] = p.negative_pooleds[0]
else:
modules/prompt_parser_diffusers.py
+14 -6
View File
@@ -130,7 +130,7 @@ def get_tokens(msg, prompt):
def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, clip_skip: typing.Optional[int] = None):
if 'StableDiffusion' not in pipe.__class__.__name__ and 'DemoFusion' not in pipe.__class__.__name__:
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 cache.get('model_type', None) == shared.sd_model_type:
@@ -202,11 +202,16 @@ def get_prompts_with_weights(prompt: str):
def prepare_embedding_providers(pipe, clip_skip) -> list[EmbeddingsProvider]:
device = pipe.device if str(pipe.device) != 'meta' else devices.device
embeddings_providers = []
if 'XL' in pipe.__class__.__name__:
if 'StableCascade' in pipe.__class__.__name__:
embedding_type = -(clip_skip)
elif 'XL' in pipe.__class__.__name__:
embedding_type = -(clip_skip + 1)
else:
embedding_type = clip_skip
if getattr(pipe, "tokenizer", None) is not None and getattr(pipe, "text_encoder", None) is not None:
if getattr(pipe, "prior_pipe", None) is not None and getattr(pipe.prior_pipe, "tokenizer", None) is not None and getattr(pipe.prior_pipe, "text_encoder", None) is not None:
provider = EmbeddingsProvider(tokenizer=pipe.prior_pipe.tokenizer, text_encoder=pipe.prior_pipe.text_encoder, truncate=False, returned_embeddings_type=embedding_type, device=device)
embeddings_providers.append(provider)
elif getattr(pipe, "tokenizer", None) is not None and getattr(pipe, "text_encoder", None) is not None:
provider = EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate=False, returned_embeddings_type=embedding_type, device=device)
embeddings_providers.append(provider)
if getattr(pipe, "tokenizer_2", None) is not None and getattr(pipe, "text_encoder_2", None) is not None:
@@ -216,11 +221,14 @@ def prepare_embedding_providers(pipe, clip_skip) -> list[EmbeddingsProvider]:
def pad_to_same_length(pipe, embeds):
if not hasattr(pipe, 'encode_prompt'):
if not hasattr(pipe, 'encode_prompt') and not (hasattr(pipe, "prior_pipe") and hasattr(pipe.prior_pipe, "encode_prompt")):
return embeds
device = pipe.device if str(pipe.device) != 'meta' else devices.device
try: # SDXL
empty_embed = pipe.encode_prompt("")
try:
if getattr(pipe, "prior_pipe", None) and getattr(pipe.prior_pipe, "text_encoder", None) is not None: # Cascade
empty_embed = pipe.prior_pipe.encode_prompt(device, 1, 1, False, "")
else: # SDXL
empty_embed = pipe.encode_prompt("")
except TypeError: # SD1.5
empty_embed = pipe.encode_prompt("", device, 1, False)
max_token_count = max([embed.shape[1] for embed in embeds])