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fix long prompt for qwen-image

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mi804
2025-08-13 17:23:00 +08:00
parent 34766863f8
commit f7f5c07570
2 changed files with 35 additions and 2 deletions
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33
diffsynth/models/qwen_image_dit.py
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@@ -90,8 +90,39 @@ class QwenEmbedRope(nn.Module):
)
freqs = torch.polar(torch.ones_like(freqs), freqs)
return freqs
def _expand_pos_freqs_if_needed(self, video_fhw, txt_seq_lens):
if isinstance(video_fhw, list):
video_fhw = video_fhw[0]
_, height, width = video_fhw
if self.scale_rope:
max_vid_index = max(height // 2, width // 2)
else:
max_vid_index = max(height, width)
required_len = max_vid_index + max(txt_seq_lens)
cur_max_len = self.pos_freqs.shape[0]
if required_len <= cur_max_len:
return
new_max_len = math.ceil(required_len / 512) * 512
pos_index = torch.arange(new_max_len)
neg_index = torch.arange(new_max_len).flip(0) * -1 - 1
self.pos_freqs = torch.cat([
self.rope_params(pos_index, self.axes_dim[0], self.theta),
self.rope_params(pos_index, self.axes_dim[1], self.theta),
self.rope_params(pos_index, self.axes_dim[2], self.theta),
], dim=1)
self.neg_freqs = torch.cat([
self.rope_params(neg_index, self.axes_dim[0], self.theta),
self.rope_params(neg_index, self.axes_dim[1], self.theta),
self.rope_params(neg_index, self.axes_dim[2], self.theta),
], dim=1)
return
def forward(self, video_fhw, txt_seq_lens, device):
self._expand_pos_freqs_if_needed(video_fhw, txt_seq_lens)
if self.pos_freqs.device != device:
self.pos_freqs = self.pos_freqs.to(device)
self.neg_freqs = self.neg_freqs.to(device)

4
diffsynth/pipelines/qwen_image.py
View File

@@ -372,7 +372,9 @@ class QwenImageUnit_PromptEmbedder(PipelineUnit):
template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
drop_idx = 34
txt = [template.format(e) for e in prompt]
txt_tokens = pipe.tokenizer(txt, max_length=1024+drop_idx, padding=True, truncation=True, return_tensors="pt").to(pipe.device)
txt_tokens = pipe.tokenizer(txt, max_length=4096+drop_idx, padding=True, truncation=True, return_tensors="pt").to(pipe.device)
if txt_tokens.input_ids.shape[1] >= 1024:
print(f"Warning!!! QwenImage model was trained on prompts up to 512 tokens. Current prompt requires {txt_tokens['input_ids'].shape[1] - drop_idx} tokens, which may lead to unpredictable behavior.")
hidden_states = pipe.text_encoder(input_ids=txt_tokens.input_ids, attention_mask=txt_tokens.attention_mask, output_hidden_states=True,)[-1]
split_hidden_states = self.extract_masked_hidden(hidden_states, txt_tokens.attention_mask)