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fix more, system prompt etc

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yiyixuxu
2025-11-23 05:43:01 +01:00
parent 5732d60db3
commit 76bb607bc0
1 changed files with 17 additions and 16 deletions
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33
src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5.py
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@@ -227,13 +227,14 @@ class HunyuanVideo15Pipeline(DiffusionPipeline):
self.video_processor = HunyuanVideo15ImageProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
self.target_size = self.transformer.config.target_size if getattr(self, "transformer", None) else 640
self.vision_states_dim = self.transformer.config.image_embed_dim if getattr(self, "transformer", None) else 1152
self.num_channels_latents = self.vae.latent_channels if hasattr(self, "vae") else 32
# fmt: off
self.system_message ="You are a helpful assistant. Describe the video by detailing the following aspects: \
1. The main content and theme of the video. \
2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \
4. background environment, light, style and atmosphere. \
5. camera angles, movements, and transitions used in the video."
self.system_message = "You are a helpful assistant. Describe the video by detailing the following aspects: \
1. The main content and theme of the video. \
2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \
4. background environment, light, style and atmosphere. \
5. camera angles, movements, and transitions used in the video."
# fmt: on
self.prompt_template_encode_start_idx = 108
self.tokenizer_max_length = 1000
@@ -253,11 +254,11 @@ class HunyuanVideo15Pipeline(DiffusionPipeline):
num_hidden_layers_to_skip: int = 2,
# fmt: off
system_message: str = "You are a helpful assistant. Describe the video by detailing the following aspects: \
1. The main content and theme of the video. \
2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \
4. background environment, light, style and atmosphere. \
5. camera angles, movements, and transitions used in the video.",
1. The main content and theme of the video. \
2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \
4. background environment, light, style and atmosphere. \
5. camera angles, movements, and transitions used in the video.",
# fmt: on
crop_start: int = 108,
) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -286,12 +287,13 @@ class HunyuanVideo15Pipeline(DiffusionPipeline):
attention_mask=prompt_attention_mask,
output_hidden_states=True,
).hidden_states[-(num_hidden_layers_to_skip + 1)]
prompt_embeds = prompt_embeds.to(dtype=dtype)
if crop_start is not None and crop_start > 0:
prompt_embeds = prompt_embeds[:, crop_start:]
prompt_attention_mask = prompt_attention_mask[:, crop_start:]
prompt_embeds = prompt_embeds.to(dtype=dtype)
return prompt_embeds, prompt_attention_mask
@@ -578,7 +580,7 @@ class HunyuanVideo15Pipeline(DiffusionPipeline):
negative_prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_frames: int = 129,
num_frames: int = 121,
num_inference_steps: int = 50,
sigmas: List[float] = None,
num_videos_per_prompt: Optional[int] = 1,
@@ -752,10 +754,9 @@ class HunyuanVideo15Pipeline(DiffusionPipeline):
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
# 5. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
num_channels_latents,
self.num_channels_latents,
height,
width,
num_frames,
@@ -877,7 +878,7 @@ class HunyuanVideo15Pipeline(DiffusionPipeline):
if not output_type == "latent":
latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
video = self.vae.decode(latents, return_dict=False, generator=generator)[0]
video = self.vae.decode(latents, return_dict=False)[0]
video = self.video_processor.postprocess_video(video, output_type=output_type)
else:
video = latents