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remove no_grad and simplified prompt paddings

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leffff
2025-10-16 06:50:57 +00:00
parent 588c12ab98
commit 327ab84d19
1 changed files with 11 additions and 9 deletions
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src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
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@@ -17,6 +17,7 @@ from typing import Callable, Dict, List, Optional, Union
import regex as re
import torch
from torch.nn import functional as F
from transformers import Qwen2VLProcessor, Qwen2_5_VLForConditionalGeneration, CLIPTextModel, CLIPTokenizer
from ...callbacks import MultiPipelineCallbacks, PipelineCallback
@@ -303,17 +304,19 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
padding=True,
).to(device)
embeds = self.text_encoder(
input_ids=inputs["input_ids"],
return_dict=True,
output_hidden_states=True,
)["hidden_states"][-1][:, crop_start:]
embeds = self.text_encoder(
input_ids=inputs["input_ids"],
return_dict=True,
output_hidden_states=True,
)["hidden_states"][-1][:, crop_start:]
batch_size = len(prompt)
attention_mask = inputs["attention_mask"][:, crop_start:]
cu_seqlens = torch.cumsum(attention_mask.sum(1), dim=0)
cu_seqlens = torch.cat([torch.zeros_like(cu_seqlens)[:1], cu_seqlens]).to(dtype=torch.int32)
# cu_seqlens = torch.cat([torch.zeros_like(cu_seqlens)[:1], cu_seqlens]).to(dtype=torch.int32)
cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0).to(dtype=torch.int32)
embeds = torch.cat([embeds[i].unsqueeze(dim=0).repeat(num_videos_per_prompt, 1, 1) for i in range(batch_size)], dim=0)
return embeds.to(dtype), cu_seqlens
@@ -354,8 +357,7 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
return_tensors="pt",
).to(device)
with torch.no_grad():
pooled_embed = self.text_encoder_2(**inputs)["pooler_output"]
pooled_embed = self.text_encoder_2(**inputs)["pooler_output"]
# duplicate for each generation per prompt
batch_size = len(prompt)