StableDiffusion3 pipeline RuntimeError when using prompt_embeds

[pjjajal]

Describe the bug

StableDiffusion3 pipeline throws a RuntimeError when using prompt_embeds in lieu of prompt when using num_images_per_prompt > 1.

I am attempting to generate images using the StableDiffusion3 pipeline with some precomputed prompt embeddings. The prompt embeddings using the .encode_prompt(...) method of the pipeline and are passed to the call of the pipeline. Passing these encoded prompts to the pipeline leads to a Runtime error when:

• num_images_per_prompt >1 for both the .encode_prompt(...) and the __call__(...).
• num_images_per_prompt=1 for .encode_prompt(...) and num_images_per_prompt >1 for the __call__(...).

The StableDiffusionXL pipeline does not have these errors.

Reproduction

StableDiffusion3 Failing Cases

encode_prompt num_images_per_prompt>1 and call num_images_per_prompt>1

The code for this failing case is below:

import torch
from diffusers import DiffusionPipeline

model_name = "stabilityai/stable-diffusion-3.5-medium"
pipe = DiffusionPipeline.from_pretrained(
    model_name, torch_dtype=torch.float16
).to("cuda")

# encode the prompts
(
    prompt_embeds,
    negative_prompt_embeds,
    pooled_prompt_embeds,
    negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
    prompt="A painting of a cat",
    prompt_2=None,
    prompt_3=None,
    device="cuda",
    do_classifier_free_guidance=True,
    num_images_per_prompt=2, # NOTE
)

# sample (generate) from the diffusion model.
with torch.inference_mode():
    out = pipe(
        height=64, # this is set small for speeding up testing
        width=64, # this is set small for speeding up testing
        num_images_per_prompt=2,  # NOTE
        num_inference_steps=1,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
        generator=torch.Generator(0)
    )

encode_prompt num_images_per_prompt=1 and call num_images_per_prompt>1

import torch
from diffusers import DiffusionPipeline

model_name = "stabilityai/stable-diffusion-3.5-medium"
pipe = DiffusionPipeline.from_pretrained(
    model_name, torch_dtype=torch.float16
).to("cuda")

# encode the prompts
(
    prompt_embeds,
    negative_prompt_embeds,
    pooled_prompt_embeds,
    negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
    prompt="A painting of a cat",
    prompt_2=None,
    prompt_3=None,
    device="cuda",
    do_classifier_free_guidance=True,
    num_images_per_prompt=1, # NOTE
)

# sample (generate) from the diffusion model.
with torch.inference_mode():
    out = pipe(
        height=64, # this is set small for speeding up testing
        width=64, # this is set small for speeding up testing
        num_images_per_prompt=2,  # NOTE
        num_inference_steps=1,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
        generator=torch.Generator(0)
    )

Expected Behaviour (StableDiffusionXL pipeline)

encode_prompt num_images_per_prompt=1 and call num_images_per_prompt>1

import torch
from diffusers import DiffusionPipeline

model_name = "stabilityai/sdxl-turbo"
pipe = DiffusionPipeline.from_pretrained(
    model_name, torch_dtype=torch.float16
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()

(
    prompt_embeds,
    negative_prompt_embeds,
    pooled_prompt_embeds,
    negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
    prompt="A painting of a cat",
    device="cuda",
    do_classifier_free_guidance=True,
    num_images_per_prompt=1,
)

with torch.inference_mode():
    out = pipe(
        # prompt="Cat",
        height=64,
        width=64,
        num_images_per_prompt=2,
        num_inference_steps=1,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
        generator=torch.Generator(0)
    )
print(len(out.images)) # this returns 2.

encode_prompt num_images_per_prompt>1 and call num_images_per_prompt>1

import torch
from diffusers import DiffusionPipeline

model_name = "stabilityai/sdxl-turbo"
pipe = DiffusionPipeline.from_pretrained(
    model_name, torch_dtype=torch.float16
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()

(
    prompt_embeds,
    negative_prompt_embeds,
    pooled_prompt_embeds,
    negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
    prompt="A painting of a cat",
    device="cuda",
    do_classifier_free_guidance=True,
    num_images_per_prompt=2,
)

with torch.inference_mode():
    out = pipe(
        # prompt="Cat",
        height=64,
        width=64,
        num_images_per_prompt=2,
        num_inference_steps=1,
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
        generator=torch.Generator(0)
    )
print(len(out.images)) # this returns 4.

Logs

Traceback (most recent call last):
  File "/home/jajal/research/diffusion-trajectory/sd3.py", line 26, in <module>
    out = pipe(
          ^^^^^
  File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/jajal/research/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py", line 1060, in __call__
    noise_pred = self.transformer(
                 ^^^^^^^^^^^^^^^^^
  File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jajal/research/diffusers/src/diffusers/models/transformers/transformer_sd3.py", line 389, in forward
    temb = self.time_text_embed(timestep, pooled_projections)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jajal/research/diffusers/src/diffusers/models/embeddings.py", line 1606, in forward
    conditioning = timesteps_emb + pooled_projections
                   ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~
RuntimeError: The size of tensor a (8) must match the size of tensor b (4) at non-singleton dimension 0

System Info

• 🤗 Diffusers version: 0.33.0.dev0
• Platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.35
• Running on Google Colab?: No
• Python version: 3.12.8
• PyTorch version (GPU?): 2.6.0+cu124 (True)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Huggingface_hub version: 0.28.1
• Transformers version: 4.48.2
• Accelerate version: 1.3.0
• PEFT version: not installed
• Bitsandbytes version: not installed
• Safetensors version: 0.5.2
• xFormers version: 0.0.29.post2
• Accelerator: NVIDIA GeForce RTX 4070 Ti, 12282 MiB
• Using GPU in script?: yes
• Using distributed or parallel set-up in script?: no

Who can help?

@yiyixuxu @sayakpaul

[sayakpaul]

Cc: @yiyixuxu

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the contributing guidelines are likely to be ignored.

[FredyRivera-dev]

Any updates? I'm having a similar error with SD 3.5 when trying to do batch inference using the num_images_per_prompt parameter. Full log:

0%|          | 0/30 [00:00<?, ?it/s]

--------------------------------------------------------------------------------
ERROR: 'NoneType' object has no attribute 'ndim'
--------------------------------------------------------------------------------

ERROR: <class 'AttributeError'>
ERROR: Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/aquilesimage/runtime/batch_inf.py", line 294, in batch_infer
    return self.pipeline.generate_batch(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/aquilesimage/runtime/requestscopedpipeline.py", line 789, in generate_batch
    result = local_pipe(
             ^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py", line 1064, in __call__
    noise_pred = self.transformer(
                 ^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/diffusers/models/transformers/transformer_sd3.py", line 327, in forward
    encoder_hidden_states, hidden_states = block(
    ^^^^^^
  File "/usr/local/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/diffusers/models/attention.py", line 715, in forward
    attn_output2 = self.attn2(hidden_states=norm_hidden_states2, **joint_attention_kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/diffusers/models/attention_processor.py", line 605, in forward
    return self.processor(
           ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/diffusers/models/attention_processor.py", line 1849, in __call__
    context_input_ndim = encoder_hidden_states.ndim
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'ndim'

ERROR: 'NoneType' object has no attribute 'ndim'
ERROR: {'height': 1024, 'width': 1024, 'num_inference_steps': 30, 'device': 'cuda', 'num_images_per_prompt': 1}
ERROR: Full traceback:
ERROR: 'NoneType' object has no attribute 'ndim'