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Runtime error
Update app.py
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app.py
CHANGED
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@@ -256,22 +256,22 @@ def generate_60(
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pipe.text_encoder=text_encoder.to(device=device, dtype=torch.bfloat16)
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pipe.text_encoder_2=text_encoder_2.to(device=device, dtype=torch.bfloat16)
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text_inputs1 = tokenizer(
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prompt,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt",
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)
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text_inputs2 = tokenizer(
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prompt2,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt",
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)
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prompt_embedsa = text_encoder(text_input_ids.to(device), output_hidden_states=True)
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prompt_embedsb = text_encoder(text_input_ids.to(device), output_hidden_states=True)
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prompt_embeds = torch.cat([prompt_embedsa,prompt_embedsb]).mean(dim=-1)
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options = {
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@@ -319,22 +319,22 @@ def generate_90(
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pipe.text_encoder=text_encoder.to(device=device, dtype=torch.bfloat16)
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pipe.text_encoder_2=text_encoder_2.to(device=device, dtype=torch.bfloat16)
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text_inputs1 = tokenizer(
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prompt,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt",
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)
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text_inputs2 = tokenizer(
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prompt2,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt",
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)
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prompt_embedsa = text_encoder(text_input_ids.to(device), output_hidden_states=True)
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prompt_embedsb = text_encoder(text_input_ids.to(device), output_hidden_states=True)
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prompt_embeds = torch.cat([prompt_embedsa,prompt_embedsb]).mean(dim=-1)
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options = {
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pipe.text_encoder=text_encoder.to(device=device, dtype=torch.bfloat16)
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pipe.text_encoder_2=text_encoder_2.to(device=device, dtype=torch.bfloat16)
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text_inputs1 = pipe.tokenizer(
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prompt,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt",
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)
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text_inputs2 = pipe.tokenizer(
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prompt2,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt",
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)
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prompt_embedsa = pipe.text_encoder(text_input_ids.to(device), output_hidden_states=True)
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prompt_embedsb = pipe.text_encoder(text_input_ids.to(device), output_hidden_states=True)
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prompt_embeds = torch.cat([prompt_embedsa,prompt_embedsb]).mean(dim=-1)
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options = {
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pipe.text_encoder=text_encoder.to(device=device, dtype=torch.bfloat16)
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pipe.text_encoder_2=text_encoder_2.to(device=device, dtype=torch.bfloat16)
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text_inputs1 = pipe.tokenizer(
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prompt,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt",
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)
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text_inputs2 = pipe.tokenizer(
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prompt2,
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padding="max_length",
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max_length=77,
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truncation=True,
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return_tensors="pt",
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)
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prompt_embedsa = pipe.text_encoder(text_input_ids.to(device), output_hidden_states=True)
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prompt_embedsb = pipe.text_encoder(text_input_ids.to(device), output_hidden_states=True)
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prompt_embeds = torch.cat([prompt_embedsa,prompt_embedsb]).mean(dim=-1)
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options = {
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