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Merged
merged 2 commits into from
Apr 16, 2025
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Hunyuan I2V fast tests fix #11341

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42c27db
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DN6 Apr 16, 2025
cb11196
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DN6 Apr 16, 2025
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2 changes: 1 addition & 1 deletion src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video_image2video.py
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Original file line number Diff line number Diff line change
Expand Up @@ -344,7 +344,7 @@ def _get_llama_prompt_embeds(
)
prompt_embeds = self.text_encoder(
**expanded_inputs,
pixel_value=image_embeds,
pixel_values=image_embeds,
output_hidden_states=True,
).hidden_states[-(num_hidden_layers_to_skip + 1)]
prompt_embeds = prompt_embeds.to(dtype=dtype)
Expand Down
34 changes: 25 additions & 9 deletions tests/pipelines/hunyuan_video/test_hunyuan_image2video.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,11 @@
CLIPTextModel,
CLIPTokenizer,
LlamaConfig,
LlamaModel,
LlamaTokenizer,
LlamaTokenizerFast,
LlavaConfig,
LlavaForConditionalGeneration,
)
from transformers.models.clip import CLIPVisionConfig

from diffusers import (
AutoencoderKLHunyuanVideo,
Expand Down Expand Up @@ -116,19 +118,29 @@ def get_dummy_components(self, num_layers: int = 1, num_single_layers: int = 1):
torch.manual_seed(0)
scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)

llama_text_encoder_config = LlamaConfig(
text_config = LlamaConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=16,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=2,
pad_token_id=1,
pad_token_id=100,
vocab_size=1000,
hidden_act="gelu",
projection_dim=32,
)
vision_config = CLIPVisionConfig(
hidden_size=8,
intermediate_size=37,
projection_dim=32,
num_attention_heads=4,
num_hidden_layers=2,
image_size=224,
)
llava_text_encoder_config = LlavaConfig(vision_config, text_config, pad_token_id=100, image_token_index=101)

clip_text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
Expand All @@ -144,23 +156,23 @@ def get_dummy_components(self, num_layers: int = 1, num_single_layers: int = 1):
)

torch.manual_seed(0)
text_encoder = LlamaModel(llama_text_encoder_config)
tokenizer = LlamaTokenizer.from_pretrained("finetrainers/dummy-hunyaunvideo", subfolder="tokenizer")
text_encoder = LlavaForConditionalGeneration(llava_text_encoder_config)
tokenizer = LlamaTokenizerFast.from_pretrained("finetrainers/dummy-hunyaunvideo", subfolder="tokenizer")

torch.manual_seed(0)
text_encoder_2 = CLIPTextModel(clip_text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

torch.manual_seed(0)
image_processor = CLIPImageProcessor(
crop_size=336,
crop_size=224,
do_center_crop=True,
do_normalize=True,
do_resize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
resample=3,
size=336,
size=224,
)

components = {
Expand Down Expand Up @@ -190,14 +202,18 @@ def get_dummy_inputs(self, device, seed=0):
"prompt_template": {
"template": "{}",
"crop_start": 0,
"image_emb_len": 49,
"image_emb_start": 5,
"image_emb_end": 54,
"double_return_token_id": 0,
},
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 4.5,
"height": image_height,
"width": image_width,
"num_frames": 9,
"max_sequence_length": 16,
"max_sequence_length": 64,
"output_type": "pt",
}
return inputs
Expand Down