add README

README.md

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+---
+library_name: diffusers
+pipeline_tag: text-to-video
+base_model: Lightricks/LTX-2.3
+tags:
+- video-generation
+- text-to-video
+- ltx
+- ltx-2
+- distilled
+license: other
+license_name: ltx-video-2-open-source-license
+license_link: https://huggingface.co/Lightricks/LTX-2.3/blob/main/LICENSE
+---
+
+# LTX-2.3 Distilled (Diffusers)
+
+Diffusers-format weights for the distilled LTX-2.3 model from [Lightricks/LTX-2.3](https://huggingface.co/Lightricks/LTX-2.3). Runs in **8 steps with CFG = 1**, trading some flexibility for substantially faster inference.
+
+The non-distilled base model is at [`diffusers/LTX-2.3-Diffusers`](https://huggingface.co/diffusers/LTX-2.3-Diffusers).
+
+## Usage
+
+Requires a recent build of `diffusers` with LTX-2 support:
+
+```bash
+pip install -U git+https://github.com/huggingface/diffusers
+```
+
+The distilled checkpoint uses a fixed sigma schedule. Always pass `sigmas=DISTILLED_SIGMA_VALUES`, `num_inference_steps=8`, and `guidance_scale=1.0`.
+
+### Text-to-video + audio
+
+```python
+import torch
+from diffusers import LTX2Pipeline
+from diffusers.pipelines.ltx2.export_utils import encode_video
+from diffusers.pipelines.ltx2.utils import DEFAULT_NEGATIVE_PROMPT, DISTILLED_SIGMA_VALUES
+
+pipe = LTX2Pipeline.from_pretrained(
+    "diffusers/LTX-2.3-Distilled-Diffusers", torch_dtype=torch.bfloat16
+)
+pipe.enable_model_cpu_offload()
+
+prompt = "A flowing river in a forest at golden hour, gentle wind in the leaves."
+frame_rate = 24.0
+
+video, audio = pipe(
+    prompt=prompt,
+    negative_prompt=DEFAULT_NEGATIVE_PROMPT,
+    width=768,
+    height=512,
+    num_frames=121,
+    frame_rate=frame_rate,
+    num_inference_steps=8,
+    sigmas=DISTILLED_SIGMA_VALUES,
+    guidance_scale=1.0,
+    output_type="np",
+    return_dict=False,
+)
+
+encode_video(
+    video[0],
+    fps=frame_rate,
+    audio=audio[0].float().cpu(),
+    audio_sample_rate=pipe.vocoder.config.output_sampling_rate,
+    output_path="ltx2_distilled_t2v.mp4",
+)
+```
+
+### First-last-frame-to-video (FLF2V)
+
+```python
+import torch
+from diffusers import LTX2ConditionPipeline
+from diffusers.pipelines.ltx2.pipeline_ltx2_condition import LTX2VideoCondition
+from diffusers.pipelines.ltx2.utils import DEFAULT_NEGATIVE_PROMPT, DISTILLED_SIGMA_VALUES
+from diffusers.utils import load_image
+
+pipe = LTX2ConditionPipeline.from_pretrained(
+    "diffusers/LTX-2.3-Distilled-Diffusers", torch_dtype=torch.bfloat16
+)
+pipe.enable_model_cpu_offload()
+
+first_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png")
+last_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png")
+
+conditions = [
+    LTX2VideoCondition(frames=first_image, index=0, strength=1.0),
+    LTX2VideoCondition(frames=last_image, index=-1, strength=1.0),
+]
+
+prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings."
+frame_rate = 24.0
+
+video = pipe(
+    conditions=conditions,
+    prompt=prompt,
+    negative_prompt=DEFAULT_NEGATIVE_PROMPT,
+    width=768,
+    height=512,
+    num_frames=121,
+    frame_rate=frame_rate,
+    num_inference_steps=8,
+    sigmas=DISTILLED_SIGMA_VALUES,
+    guidance_scale=1.0,
+    output_type="np",
+    return_dict=False,
+)
+```
+
+### HDR generation (IC-LoRA)
+
+```python
+import torch
+from safetensors import safe_open
+from diffusers import LTX2HDRPipeline
+from diffusers.pipelines.ltx2.export_utils import encode_hdr_tensor_to_mp4
+from diffusers.pipelines.ltx2.pipeline_ltx2_hdr_lora import LTX2HDRReferenceCondition
+from diffusers.pipelines.ltx2.utils import DISTILLED_SIGMA_VALUES
+from diffusers.utils import load_video
+
+pipe = LTX2HDRPipeline.from_pretrained(
+    "diffusers/LTX-2.3-Distilled-Diffusers", torch_dtype=torch.bfloat16
+)
+pipe.enable_model_cpu_offload()
+pipe.load_lora_weights(
+    "Lightricks/LTX-2.3-22b-IC-LoRA-HDR",
+    adapter_name="hdr_lora",
+    weight_name="ltx-2.3-22b-ic-lora-hdr-0.9.safetensors",
+)
+pipe.set_adapters("hdr_lora", 1.0)
+
+reference_video = load_video("input.mp4")
+ref_cond = LTX2HDRReferenceCondition(frames=reference_video, strength=1.0)
+
+with safe_open("ltx-2.3-22b-ic-lora-hdr-scene-emb.safetensors", framework="pt", device="cuda") as f:
+    connector_video_embeds = f.get_tensor("video_context")
+    connector_audio_embeds = f.get_tensor("audio_context")
+
+hdr_video = pipe(
+    reference_conditions=[ref_cond],
+    connector_video_embeds=connector_video_embeds,
+    connector_audio_embeds=connector_audio_embeds,
+    width=768,
+    height=512,
+    num_frames=121,
+    frame_rate=24.0,
+    num_inference_steps=8,
+    sigmas=DISTILLED_SIGMA_VALUES,
+    guidance_scale=1.0,
+    output_type="pt",
+    return_dict=False,
+)[0]
+
+encode_hdr_tensor_to_mp4(hdr_video[0], output_mp4="ltx2_hdr.mp4", frame_rate=24.0)
+```
+
+## Notes
+
+- `width` and `height` must be divisible by 32; `num_frames` must equal `8k + 1`.
+- See the [Diffusers LTX-2 docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx2) for multimodal guidance, prompt enhancement, and the upscaling/refinement pipeline.
+
+## License
+
+These weights are released under the [LTX Video 2 Open Source License](https://huggingface.co/Lightricks/LTX-2.3/blob/main/LICENSE).