AI/diffusers
1
0
Fork 0
mirror of https://github.com/huggingface/diffusers.git synced 2026-01-27 17:22:53 +03:00
Code Activity

update docs.

Browse Source
This commit is contained in:
sayakpaul
2026-01-06 21:02:18 +05:30
parent c39f1b87a4
commit bdcf23ec17
2 changed files with 45 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
src/diffusers/pipelines/ltx2/pipeline_ltx2.py
View File

@@ -47,24 +47,36 @@ EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import LTXPipeline
>>> from diffusers.utils import export_to_video
>>> from diffusers import LTX2Pipeline
>>> from diffusers.pipelines.ltx2.export_utils import encode_video
>>> pipe = LTXPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")
>>> pipe = LTX2Pipeline.from_pretrained("Lightricks/LTX-2", torch_dtype=torch.bfloat16)
>>> pipe.enable_model_cpu_offload()
>>> prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"
>>> negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
>>> video = pipe(
>>> frame_rate = 24.0
>>> video, audio = pipe(
... prompt=prompt,
... negative_prompt=negative_prompt,
... width=704,
... height=480,
... num_frames=161,
... num_inference_steps=50,
... ).frames[0]
>>> export_to_video(video, "output.mp4", fps=24)
... width=768,
... height=512,
... frame_rate=frame_rate,
... num_frames=121,
... output_type="np",
... return_dict=False,
... )
>>> video = (video * 255).round().astype("uint8")
>>> video = torch.from_numpy(video)
>>> encode_video(
... video[0],
... fps=frame_rate,
... audio=audio[0].float().cpu(),
... audio_sample_rate=pipe.vocoder.config.output_sampling_rate, # should be 24000
... output_path="video.mp4",
... )
```
"""

31
src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py
View File

@@ -48,11 +48,12 @@ EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import LTX2ImageToVideoPipeline
>>> from diffusers.utils import export_to_video, load_image
>>> from diffusers import LTX2Pipeline
>>> from diffusers.pipelines.ltx2.export_utils import encode_video
>>> from diffusers.utils import load_image
>>> pipe = LTX2ImageToVideoPipeline.from_pretrained("Lightricks/LTX-Video-2", torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")
>>> pipe = LTX2ImageToVideoPipeline.from_pretrained("Lightricks/LTX-2", torch_dtype=torch.bfloat16)
>>> pipe.enable_model_cpu_offload()
>>> image = load_image(
... "https://huggingface.co/datasets/a-r-r-o-w/tiny-meme-dataset-captioned/resolve/main/images/8.png"
@@ -60,16 +61,28 @@ EXAMPLE_DOC_STRING = """
>>> prompt = "A young girl stands calmly in the foreground, looking directly at the camera, as a house fire rages in the background."
>>> negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
>>> frame_rate = 24.0
>>> video = pipe(
... image=image,
... prompt=prompt,
... negative_prompt=negative_prompt,
... width=704,
... height=480,
... width=768,
... height=512,
... num_frames=121,
... num_inference_steps=40,
... ).frames[0]
>>> export_to_video(video, "output.mp4", fps=24)
... frame_rate=frame_rate,
... output_type="np",
... return_dict=False,
... )
>>> video = (video * 255).round().astype("uint8")
>>> video = torch.from_numpy(video)
>>> encode_video(
... video[0],
... fps=frame_rate,
... audio=audio[0].float().cpu(),
... audio_sample_rate=pipe.vocoder.config.output_sampling_rate, # should be 24000
... output_path="video.mp4",
... )
```
"""