VictorMylle/Kohya-ss-sd-scripts
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Merge 31e339c6a3 into 51435f1718

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laksjdjf
2026-04-04 20:20:55 +00:00
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parent 51435f1718 31e339c6a3
commit 66f183f50f
1 changed files with 36 additions and 66 deletions
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102
gen_img.py
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@@ -542,10 +542,20 @@ class PipelineLike:
uncond_embeddings = torch.cat([uncond_embeddings, tes_uncond_embs[i]], dim=2) # n,77,2048
if do_classifier_free_guidance:
lcm = uncond_embeddings.shape[1] * text_embeddings.shape[1] // math.gcd(uncond_embeddings.shape[1], text_embeddings.shape[1])
if negative_scale is None:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
text_embeddings = torch.cat([
uncond_embeddings.repeat(1, lcm // uncond_embeddings.shape[1], 1),
text_embeddings.repeat(1, lcm // text_embeddings.shape[1], 1),
])
else:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings])
lcm = real_uncond_embeddings.shape[1] * text_embeddings.shape[1] // math.gcd(real_uncond_embeddings.shape[1], text_embeddings.shape[1])
text_embeddings = torch.cat([
uncond_embeddings.repeat(1, lcm // uncond_embeddings.shape[1], 1),
text_embeddings.repeat(1, lcm // text_embeddings.shape[1], 1),
real_uncond_embeddings.repeat(1, lcm // real_uncond_embeddings.shape[1], 1)
])
if self.control_net_lllites or (self.control_nets and self.is_sdxl):
# ControlNetのhintにguide imageを流用する。ControlNetの場合はControlNet側で行う
@@ -1105,22 +1115,17 @@ def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos
"""
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
lcm = chunk_length
for i in range(len(tokens)):
tokens[i] = [bos] + tokens[i] + [eos] + [pad] * (max_length - 2 - len(tokens[i]))
if no_boseos_middle:
weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
else:
w = []
if len(weights[i]) == 0:
w = [1.0] * weights_length
else:
for j in range(max_embeddings_multiples):
w.append(1.0) # weight for starting token in this chunk
w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
w.append(1.0) # weight for ending token in this chunk
w += [1.0] * (weights_length - len(w))
weights[i] = w[:]
target_length = ((len(tokens[i]) + 2) // chunk_length + 1) * chunk_length
lcm = target_length * lcm // math.gcd(target_length, lcm)
tokens[i] = [bos] + tokens[i] + [eos] + [pad] * (target_length - 2 - len(tokens[i]))
weights[i] = [1.0] + weights[i] + [1.0] * (target_length - 1 - len(weights[i]))
for i in range(len(tokens)):
tokens[i] = tokens[i] * (lcm // len(tokens[i]))
weights[i] = weights[i] * (lcm // len(weights[i]))
return tokens, weights
@@ -1138,56 +1143,21 @@ def get_unweighted_text_embeddings(
When the length of tokens is a multiple of the capacity of the text encoder,
it should be split into chunks and sent to the text encoder individually.
"""
max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
if max_embeddings_multiples > 1:
text_embeddings = []
pool = None
for i in range(max_embeddings_multiples):
# extract the i-th chunk
text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()
# cover the head and the tail by the starting and the ending tokens
text_input_chunk[:, 0] = text_input[0, 0]
if pad == eos: # v1
text_input_chunk[:, -1] = text_input[0, -1]
else: # v2
for j in range(len(text_input_chunk)):
if text_input_chunk[j, -1] != eos and text_input_chunk[j, -1] != pad: # 最後に普通の文字がある
text_input_chunk[j, -1] = eos
if text_input_chunk[j, 1] == pad: # BOSだけであとはPAD
text_input_chunk[j, 1] = eos
# in sdxl, value of clip_skip is same for Text Encoder 1 and 2
enc_out = text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True)
text_embedding = enc_out["hidden_states"][-clip_skip]
if not is_sdxl: # SD 1.5 requires final_layer_norm
text_embedding = text_encoder.text_model.final_layer_norm(text_embedding)
if pool is None:
pool = enc_out.get("text_embeds", None) # use 1st chunk, if provided
if pool is not None:
pool = train_util.pool_workaround(text_encoder, enc_out["last_hidden_state"], text_input_chunk, eos)
if no_boseos_middle:
if i == 0:
# discard the ending token
text_embedding = text_embedding[:, :-1]
elif i == max_embeddings_multiples - 1:
# discard the starting token
text_embedding = text_embedding[:, 1:]
else:
# discard both starting and ending tokens
text_embedding = text_embedding[:, 1:-1]
text_embeddings.append(text_embedding)
text_embeddings = torch.concat(text_embeddings, axis=1)
else:
enc_out = text_encoder(text_input, output_hidden_states=True, return_dict=True)
text_embeddings = enc_out["hidden_states"][-clip_skip]
pool = None
text_embeddings = []
for chunk in text_input.chunk(text_input.shape[1] // chunk_length, dim=1):
enc_out = text_encoder(chunk, output_hidden_states=True, return_dict=True)
text_embedding = enc_out["hidden_states"][-clip_skip]
if not is_sdxl: # SD 1.5 requires final_layer_norm
text_embeddings = text_encoder.text_model.final_layer_norm(text_embeddings)
pool = enc_out.get("text_embeds", None) # text encoder 1 doesn't return this
if pool is not None:
pool = train_util.pool_workaround(text_encoder, enc_out["last_hidden_state"], text_input, eos)
text_embedding = text_encoder.text_model.final_layer_norm(text_embedding)
text_embeddings.append(text_embedding)
if pool is None:
pool = enc_out.get("text_embeds", None) # text encoder 1 doesn't return this
if pool is not None:
pool = train_util.pool_workaround(text_encoder, enc_out["last_hidden_state"], text_input, eos)
text_embeddings = torch.cat(text_embeddings, dim=1)
return text_embeddings, pool