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separate model type detection for base and refiner

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This commit is contained in:
Vladimir Mandic
2023-08-29 17:36:07 -04:00
parent fd57787557
commit 7dc4460fdc
4 changed files with 57 additions and 28 deletions
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modules/prompt_parser_diffusers.py
+25 -19
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@@ -81,7 +81,11 @@ def compel_encode_prompt(
shared.log.warning(f"Prompt parser: Compel not supported: {type(pipeline).__name__}")
return (None, None, None, None)
if shared.sd_model_type == "sdxl":
if not is_refiner and shared.sd_model_type == "sdxl":
embedding_type = ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED
if clip_skip is not None and clip_skip > 1:
shared.log.warning(f"Prompt parser SDXL unsupported: clip_skip={clip_skip}")
elif is_refiner and shared.sd_refiner_type == "sdxl":
embedding_type = ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED
if clip_skip is not None and clip_skip > 1:
shared.log.warning(f"Prompt parser SDXL unsupported: clip_skip={clip_skip}")
@@ -105,29 +109,31 @@ def compel_encode_prompt(
device=shared.device
)
if shared.sd_model_type == "sdxl":
compel_te2 = Compel(
tokenizer=pipeline.tokenizer_2,
text_encoder=pipeline.text_encoder_2,
returned_embeddings_type=embedding_type,
requires_pooled=True,
device=shared.device
)
if not is_refiner:
positive_te1 = compel_te1(prompt)
positive_te2, positive_pooled = compel_te2(prompt_2)
positive = torch.cat((positive_te1, positive_te2), dim=-1)
negative_te1 = compel_te1(negative_prompt)
negative_te2, negative_pooled = compel_te2(negative_prompt_2)
negative = torch.cat((negative_te1, negative_te2), dim=-1)
else:
positive, positive_pooled = compel_te2(prompt)
negative, negative_pooled = compel_te2(negative_prompt)
if not is_refiner and shared.sd_model_type == "sdxl":
compel_te2 = Compel(tokenizer=pipeline.tokenizer_2, text_encoder=pipeline.text_encoder_2, returned_embeddings_type=embedding_type, requires_pooled=True, device=shared.device)
positive_te1 = compel_te1(prompt)
positive_te2, positive_pooled = compel_te2(prompt_2)
positive = torch.cat((positive_te1, positive_te2), dim=-1)
negative_te1 = compel_te1(negative_prompt)
negative_te2, negative_pooled = compel_te2(negative_prompt_2)
negative = torch.cat((negative_te1, negative_te2), dim=-1)
parsed = compel_te1.parse_prompt_string(prompt)
debug(f"Prompt parser Compel: {parsed}")
[prompt_embed, negative_embed] = compel_te2.pad_conditioning_tensors_to_same_length([positive, negative])
return prompt_embed, positive_pooled, negative_embed, negative_pooled
if is_refiner and shared.sd_refiner_type == "sdxl":
compel_te2 = Compel(tokenizer=pipeline.tokenizer_2, text_encoder=pipeline.text_encoder_2, returned_embeddings_type=embedding_type, requires_pooled=True, device=shared.device)
positive, positive_pooled = compel_te2(prompt)
negative, negative_pooled = compel_te2(negative_prompt)
parsed = compel_te1.parse_prompt_string(prompt)
debug(f"Prompt parser Compel: {parsed}")
[prompt_embed, negative_embed] = compel_te2.pad_conditioning_tensors_to_same_length([positive, negative])
return prompt_embed, positive_pooled, negative_embed, negative_pooled
# neither base+sdxl nor refiner+sdxl
positive, negative = compel_te1(prompt), compel_te1(negative_prompt)
[prompt_embed, negative_embed] = compel_te1.pad_conditioning_tensors_to_same_length([positive, negative])
return prompt_embed, None, negative_embed, None