VictorMylle/Kohya-ss-sd-scripts
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fix to work regional LoRA

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Kohya S
2023-09-03 17:50:27 +09:00
parent 867e7d3238
commit 207fc8b256
2 changed files with 18 additions and 7 deletions
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15
networks/lora.py
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@@ -241,9 +241,13 @@ class LoRAInfModule(LoRAModule):
else: else:
area = x.size()[1] area = x.size()[1]
mask = self.network.mask_dic[area] mask = self.network.mask_dic.get(area, None)
if mask is None: if mask is None:
raise ValueError(f"mask is None for resolution {area}") # raise ValueError(f"mask is None for resolution {area}")
# emb_layers in SDXL doesn't have mask
# print(f"mask is None for resolution {area}, {x.size()}")
mask_size = (1, x.size()[1]) if len(x.size()) == 2 else (1, *x.size()[1:-1], 1)
return torch.ones(mask_size, dtype=x.dtype, device=x.device) / self.network.num_sub_prompts
if len(x.size()) != 4: if len(x.size()) != 4:
mask = torch.reshape(mask, (1, -1, 1)) mask = torch.reshape(mask, (1, -1, 1))
return mask return mask
@@ -348,9 +352,10 @@ class LoRAInfModule(LoRAModule):
out[-self.network.batch_size :] = x[-self.network.batch_size :] # real_uncond out[-self.network.batch_size :] = x[-self.network.batch_size :] # real_uncond
# print("to_out_forward", self.lora_name, self.network.sub_prompt_index, self.network.num_sub_prompts) # print("to_out_forward", self.lora_name, self.network.sub_prompt_index, self.network.num_sub_prompts)
# for i in range(len(masks)): # if num_sub_prompts > num of LoRAs, fill with zero
# if masks[i] is None: for i in range(len(masks)):
# masks[i] = torch.zeros_like(masks[-1]) if masks[i] is None:
masks[i] = torch.zeros_like(masks[0])
mask = torch.cat(masks) mask = torch.cat(masks)
mask_sum = torch.sum(mask, dim=0) + 1e-4 mask_sum = torch.sum(mask, dim=0) + 1e-4

10
sdxl_gen_img.py
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@@ -451,10 +451,11 @@ class PipelineLike:
tes_text_embs = [] tes_text_embs = []
tes_uncond_embs = [] tes_uncond_embs = []
tes_real_uncond_embs = [] tes_real_uncond_embs = []
# use last pool
for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders): for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
token_replacer = self.get_token_replacer(tokenizer) token_replacer = self.get_token_replacer(tokenizer)
# use last text_pool, because it is from text encoder 2
text_embeddings, text_pool, uncond_embeddings, uncond_pool, _ = get_weighted_text_embeddings( text_embeddings, text_pool, uncond_embeddings, uncond_pool, _ = get_weighted_text_embeddings(
tokenizer, tokenizer,
text_encoder, text_encoder,
@@ -529,6 +530,11 @@ class PipelineLike:
c_vector = torch.cat([emb1, emb2, emb3], dim=1).to(self.device, dtype=text_embeddings.dtype).repeat(batch_size, 1) c_vector = torch.cat([emb1, emb2, emb3], dim=1).to(self.device, dtype=text_embeddings.dtype).repeat(batch_size, 1)
uc_vector = torch.cat([uc_emb1, emb2, emb3], dim=1).to(self.device, dtype=text_embeddings.dtype).repeat(batch_size, 1) uc_vector = torch.cat([uc_emb1, emb2, emb3], dim=1).to(self.device, dtype=text_embeddings.dtype).repeat(batch_size, 1)
if reginonal_network:
# use last pool for conditioning
num_sub_prompts = len(text_pool) // batch_size
text_pool = text_pool[num_sub_prompts - 1 :: num_sub_prompts] # last subprompt
c_vector = torch.cat([text_pool, c_vector], dim=1) c_vector = torch.cat([text_pool, c_vector], dim=1)
uc_vector = torch.cat([uncond_pool, uc_vector], dim=1) uc_vector = torch.cat([uncond_pool, uc_vector], dim=1)
@@ -762,7 +768,7 @@ class PipelineLike:
image = image.cpu().permute(0, 2, 3, 1).float().numpy() image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if torch.cuda.is_available(): if torch.cuda.is_available():
torch.cuda.empty_cache() torch.cuda.empty_cache()
if output_type == "pil": if output_type == "pil":
# image = self.numpy_to_pil(image) # image = self.numpy_to_pil(image)