fix

Co-authored-by: Will Lin <wlsaidhi@gmail.com>

.github/workflows/fastvideo-kernel-publish.yml

@@ -67,6 +67,9 @@ jobs:
             - torch-version: '2.9.1'
               cuda-version: '12.8.0'
               torch-cuda-short: 'cu128'
+            # - torch-version: '2.10.0'
+            #   cuda-version: '12.8.0'
+            #   torch-cuda-short: 'cu128'
 
     steps:
       - name: Free up disk space
@@ -220,3 +223,6 @@ jobs:
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           packages-dir: fastvideo-kernel/dist/
+          # PyPI does not allow replacing an existing file with the same name.
+          # This makes re-runs idempotent by skipping files already uploaded.
+          skip-existing: true

.gitignore

@@ -18,6 +18,7 @@ venv/
 .venv/
 runs/
 samples/
+Miniconda3-latest-Linux-x86_64.sh
 *validation/
 data/
 outputs/
@@ -32,6 +33,11 @@ env
 **.txt
 *.log
 weights/
+official_weights/
+converted_weights/
+
+# SSIM test outputs
+fastvideo/tests/ssim/generated_videos/
 
 # Distribution / packaging
 build/
@@ -69,6 +75,11 @@ docs/distillation/examples/
 !docs/assets/images/**/*.png
 !comfyui/assets/**/*.png
 !comfyui/assets/**/*.gif
+!assets/images/**/*.png
+!assets/images/**/*.jpg
+!assets/images/**/*.jpeg
+!assets/images/**/*.gif
+!assets/videos/**/*.mp4
 
 dmd_t2v_output/
 preprocess_output_text/

.pre-commit-config.yaml

@@ -10,7 +10,7 @@ exclude: |
     demo/.*|
     predict\.py|
     scripts/.*|
-    prompts/.*|
+    assets/prompts/.*|
     fastvideo/data_preprocess/.*|
     fastvideo/dataset/.*|
     fastvideo/models/.*|

AGENTS.md

@@ -8,7 +8,7 @@
   - `tests/local_tests/` for additional local/component checks.
 - Docs and guides: `docs/` (MkDocs source), with contributor docs in `docs/contributing/`.
 - Runnable examples and scripts: `examples/` and `scripts/`.
-- Static assets: `assets/`, `images/`, `videos/`, and `comfyui/assets/`.
+- Static assets: `assets/` (including `assets/images/`, `assets/videos/`, and `assets/prompts/`) and `comfyui/assets/`.
 
 ## Build, Test, and Development Commands
 - `uv pip install -e .[dev]`: editable install with lint/test extras.

README.md

@@ -3,7 +3,7 @@
 </div>
 
 <p align="center">
-     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/sv3MMKyv" target="_blank"> <b> WeChat </b> </a> |
+     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://github.com/hao-ai-lab/FastVideo/discussions/1097" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
 **FastVideo is a unified post-training and inference framework for accelerated video generation.**

assets/prompts/mixkit_i2v.jsonl

@@ -1,110 +1,110 @@
 [
     {
         "prompt": "Young man skating with a skateboard on the ramps with graffiti of a park with trees, on a sunny day.", 
-        "image_path": "images/mixkit-boy-skating-with-a-skateboard-in-a-park-with-ramps-34389.png"
+        "image_path": "assets/images/mixkit-boy-skating-with-a-skateboard-in-a-park-with-ramps-34389.png"
     },
     {
         "prompt": "In the midst of the joyous New Year's Eve celebration, the cheerful group of friends, their spirits lifted by the festivities, decides to immortalize the moment with a vibrant snapshot", 
-        "image_path": "images/mixkit-a-cheerful-group-of-friends-celebrate-new-years-eve-and-51525.png"
+        "image_path": "assets/images/mixkit-a-cheerful-group-of-friends-celebrate-new-years-eve-and-51525.png"
     },
     {
         "prompt": "A man and a woman playing in a field with grass, during a bright afternoon, while cars pass by in the distance.", 
-        "image_path": "images/mixkit-a-cute-couple-playing-on-the-grass-4688.png"
+        "image_path": "assets/images/mixkit-a-cute-couple-playing-on-the-grass-4688.png"
     },
     {
         "prompt": "Aerial view of a rocky mountain in the forest at a sunny day drone flight footage", 
-        "image_path": "images/mixkit-aerial-view-of-a-rocky-mountain-in-the-forest-50589.png"
+        "image_path": "assets/images/mixkit-aerial-view-of-a-rocky-mountain-in-the-forest-50589.png"
     },
     {
         "prompt": "A little girl wearing a pink security helmet and denim overall discovers the art of cycling amidst the serene park, as the camera captures her graceful progress.", 
-        "image_path": "images/mixkit-a-little-girl-cruises-through-the-forest-path-on-her-50088.png"
+        "image_path": "assets/images/mixkit-a-little-girl-cruises-through-the-forest-path-on-her-50088.png"
     },
     {
         "prompt": "Aerial shot of a beach shore with sea waves. Big rocks on the sand at an alone beach.", 
-        "image_path": "images/mixkit-aerial-shot-of-a-beach-with-sea-waves-1087.png"
+        "image_path": "assets/images/mixkit-aerial-shot-of-a-beach-with-sea-waves-1087.png"
     },
     {
         "prompt": "Young woman cleaning her house decorated with plants and decorations, while dancing happily to music in her headphones.", 
-        "image_path": "images/mixkit-woman-cleaning-her-house-dancing-happy-43379.png"
+        "image_path": "assets/images/mixkit-woman-cleaning-her-house-dancing-happy-43379.png"
     },
     {
         "prompt": "Aerial tour in a meadow surrounded by hills on the horizon, while some birds fly low over a lake.", 
-        "image_path": "images/mixkit-birds-flying-low-over-a-lake-in-a-meadow-41417.png"
+        "image_path": "assets/images/mixkit-birds-flying-low-over-a-lake-in-a-meadow-41417.png"
     },
     {
         "prompt": "In the video, a lone rider guides a majestic horse across an expansive, open field as the sun sets in the background. The rider, dressed in a classic blue shirt and wide-brimmed hat, sits confidently in the saddle, silhouetted against the warm glow of the evening sky. The horse moves gracefully, its mane and tail flowing with each step, creating a sense of harmony between horse and rider. Surrounding the pair, towering trees form a natural border, their leaves gently rustling in the breeze. The shadows lengthen on the ground, accentuating the serene and timeless feel of the scene. The distant hills and wooden fences frame the horizon, adding depth to the tranquil landscape. A few horses graze peacefully in the background, blending into the pastoral setting. The overall ambiance evokes a sense of calmness and quietude, capturing a perfect moment in the golden light of dusk.", 
-        "image_path": "images/mixkit-a-rancher-riding-a-horse-at-sunset-1143.png"
+        "image_path": "assets/images/mixkit-a-rancher-riding-a-horse-at-sunset-1143.png"
     },
     {
         "prompt": "In the video, a martial artist dressed in a traditional white uniform with a black belt demonstrates a series of precise movements against a stark black background. The individual gracefully transitions between stances, embodying a sense of focused discipline and control. Each motion is executed with a deliberate pace, showcasing the fluidity of martial arts techniques. The soft lighting creates subtle highlights on the uniform, adding depth to the figure as it moves. The practitioner begins with an open-hand pose, feet firmly grounded, gradually shifting to a powerful forward punch. The fluidity of the sequence displays a mastery of balance and poise. Every trajectory of the limbs is precise and deliberate, capturing the elegance and strength of martial arts. The serene, isolated setting enhances the intensity and concentration of the practitioner. This visual presentation is an elegant interplay of motion and stillness, displaying the art form's discipline and grace.", 
-        "image_path": "images/mixkit-a-young-man-practicing-his-karate-moves-49635.png"
+        "image_path": "assets/images/mixkit-a-young-man-practicing-his-karate-moves-49635.png"
     },
     {
         "prompt": "In a serene and softly lit yoga studio, three individuals engage in a yoga session, each performing an upward-facing stretch. The central figure is a woman with shoulder-length brown hair, dressed in a light cropped top and green leggings, her posture reflecting grace and concentration. To her right, another participant, a woman in a purple outfit, mirrors the pose with equal poise. On her left, a person with a bun focuses intently, supported slightly by yoga blocks beneath their hands. The warm-colored wooden floor contrasts soothingly with the soft pastel mural on the back wall, featuring an abstract design and partial visage of a serene face. Natural light floods the space from a large window on the right, where lush greens peek through, adding an element of tranquility. In the corner of the room, a collection of meditation instruments, including a gong and a Buddha statue, subtly frame the peaceful setting. The mood is calm yet focused, as all three participants are deeply engaged in their practice. The scene combines elements of balance, harmony, and a shared journey towards mindfulness. This depiction captures the essence of a yoga session that blends personal growth with collective experience.", 
-        "image_path": "images/mixkit-small-group-of-people-doing-yoga-together-43730.png"
+        "image_path": "assets/images/mixkit-small-group-of-people-doing-yoga-together-43730.png"
     },
     {
         "prompt": "In the deep blue expanse of the ocean, two dolphins glide effortlessly, their sleek bodies reflecting the sunlight filtering through the water. The prominent shadows and caustics create a shimmering effect on their skin, capturing the beauty of their natural habitat. Each dolphin moves with a fluid grace, occasionally interacting with gentle nudges, showcasing their playful and social nature. The scene is vibrant and dynamic, with the clear blue background accentuating the dolphins' movements, making it an ideal subject for AI recreation.", 
-        "image_path": "images/mixkit-dolphins-underwater-4133.png"
+        "image_path": "assets/images/mixkit-dolphins-underwater-4133.png"
     },
     {
         "prompt": "A bustling ski slope comes alive with skiers descending a pristine, snow-covered hill, surrounded by towering, snow-draped evergreens. Several figures stand atop the slope, silhouetted against a clear blue sky, preparing to embark on their ski run. The chair lift on the right continuously drops off eager adventurers, adding to the excitement at the hilltop. Each skier, clad in colorful winter gear, carves distinct paths into the textured snow as they weave their way down. The interplay of sunlight and shadows accentuates the myriad tracks etched into the slope, creating a dynamic visual rhythm. The scene captures a vibrant winter wonderland, full of action and the thrill of a perfect ski day.", 
-        "image_path": "images/mixkit-skiers-on-a-snowy-slope-3327.png"
+        "image_path": "assets/images/mixkit-skiers-on-a-snowy-slope-3327.png"
     },
     {
         "prompt": "A determined climber is scaling a massive rock face, showcasing exceptional strength and skill. The person, clad in a teal shirt and dark pants, climbs with precision, their movements measured and deliberate. They are secured by climbing gear, which includes ropes and a harness, emphasizing their commitment to safety. The rugged texture of the sandy-colored rock provides an imposing backdrop, adding drama and scale to the climb. In the distance, other large rock formations and sparse vegetation can be seen under a bright, overcast sky, contributing to the natural and adventurous atmosphere. The scene captures a moment of focus and challenge, highlighting the climber's tenacity and the breathtaking environment.",
-        "image_path": "images/mixkit-alpinist-climbing-a-huge-rock-in-a-desert-43306.png"
+        "image_path": "assets/images/mixkit-alpinist-climbing-a-huge-rock-in-a-desert-43306.png"
     },
     {
         "prompt": "A silver SUV drives along a winding, snow-covered mountain road, with dense pine trees blanketed in snow lining both sides. The scene is serene, with the vehicle moving smoothly, possibly on a winter journey or vacation. As the SUV disappears around the bend, another, darker SUV follows, creating a sense of motion and perspective on the snow-dusted asphalt. The towering, snow-laden rock formation to the right contrasts with the dark green of the pines, highlighting the peacefulness of the wintry landscape.",
-        "image_path": "images/mixkit-curve-on-a-snowy-forest-road-3317.png"
+        "image_path": "assets/images/mixkit-curve-on-a-snowy-forest-road-3317.png"
     },
     {
         "prompt": "A solitary boat glides across the expansive, tranquil expanse of a serene lake. The vessel leaves a gentle wake behind, creating delicate ripples across the mirror-like surface. The water appears a rich shade of teal, seamlessly blending with the sky at the horizon. Silhouettes of distant trees are faintly visible, creating a picturesque backdrop that enhances the solitary journey of the boat. The sky is a calm gradient, shifting from soft oranges near the shore to the pale blues above. In the distance, a few slender poles emerge from the water, remnants of an old structure or natural formation. The mood of the scene is one of peace and solitude, with the boat journeying steadily through the quiet landscape. There is a sense of endless possibilities as the boat moves toward the unseen beyond the frame. The simplicity and stillness of the scene invite contemplation and reflection, encapsulating a perfect moment of quietude on the water.",
-        "image_path": "images/mixkit-motorboat-on-a-large-lake-with-turquoise-blue-waters-4996.png"
+        "image_path": "assets/images/mixkit-motorboat-on-a-large-lake-with-turquoise-blue-waters-4996.png"
     },
     {
         "prompt": "A man wearing grey shorts jumps rope in a gym, weights and gym equipment in the background.",
-        "image_path": "images/gray_short_man.jpg"
+        "image_path": "assets/images/gray_short_man.jpg"
     },
     {
         "prompt": "Flying over a peninsula covered in bushy trees, while discovering the sea around it, painted a beautiful turquoise blue, on a sunny day.",
-        "image_path": "images/peninsula.jpg"
+        "image_path": "assets/images/peninsula.jpg"
     },
     {
         "prompt": "Skillful cyclist doing a wheelie on a bike while riding through a forest, on a dirt road, surrounded by many trees, in the morning.",
-        "image_path": "images/cyclist.jpg"
+        "image_path": "assets/images/cyclist.jpg"
     },
     {
         "prompt": "Some friends dancing and having fun together in circles, at a party surrounded by colored lights at a party, in a fancy old place, in a view from below them.",
-        "image_path": "images/friends.jpg"
+        "image_path": "assets/images/friends.jpg"
     },
     {
         "prompt": "A saxophonist wearing a blazer dances while playing a song in a park.",
-        "image_path": "images/saxophonist.jpg"
+        "image_path": "assets/images/saxophonist.jpg"
     },
     {
         "prompt": "Romantic couple embracing and looking at each other in the middle of a forest, during a break on a road trip through nature.",
-        "image_path": "images/romance.jpg"
+        "image_path": "assets/images/romance.jpg"
     },
     {
         "prompt": "Man dressed in 80's style dances very happily in his kitchen while listening to music on his radio and drinking wine.",
-        "image_path": "images/80s_dance.jpg"
+        "image_path": "assets/images/80s_dance.jpg"
     },
     {
         "prompt": "Pair of jazz musicians performing a song with their saxophone and trombone on an abandoned train.",
-        "image_path": "images/jazz.jpg"
+        "image_path": "assets/images/jazz.jpg"
     },
     {
         "prompt": "A young woman with short hair wearing pink sunglasses chews gum and makes a bubble gum with the city in the background.",
-        "image_path": "images/pink.jpg"
+        "image_path": "assets/images/pink.jpg"
     },
     {
         "prompt": "Natural aerial landscape with a relief covered with abundant trees and vegetation and a thick layer of mist.",
-        "image_path": "images/natural.jpg"
+        "image_path": "assets/images/natural.jpg"
     },
     {
         "prompt": "Loving couple sitting on a log on the shore of a lake outside, sharing an affectionate hug.",
-        "image_path": "images/couple.jpg"
+        "image_path": "assets/images/couple.jpg"
     }
 ]

assets/videos/README.md

@@ -1,4 +1,4 @@
-# FastVideo/videos
+# FastVideo/assets/videos
 
 This folder is used to store **video assets for examples**, primarily **input videos** consumed by scripts under `FastVideo/examples/`.
 

demo/gradio_web_demo.py

@@ -1,195 +0,0 @@
-import argparse
-import os
-import tempfile
-
-import gradio as gr
-import torch
-from diffusers import FlowMatchEulerDiscreteScheduler
-from diffusers.utils import export_to_video
-
-from fastvideo.distill.solver import PCMFMScheduler
-from fastvideo.models.mochi_hf.modeling_mochi import MochiTransformer3DModel
-from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
-
-
-def init_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--prompts", nargs="+", default=[])
-    parser.add_argument("--num_frames", type=int, default=25)
-    parser.add_argument("--height", type=int, default=480)
-    parser.add_argument("--width", type=int, default=848)
-    parser.add_argument("--num_inference_steps", type=int, default=8)
-    parser.add_argument("--guidance_scale", type=float, default=4.5)
-    parser.add_argument("--model_path", type=str, default="data/mochi")
-    parser.add_argument("--seed", type=int, default=12345)
-    parser.add_argument("--transformer_path", type=str, default=None)
-    parser.add_argument("--scheduler_type", type=str, default="pcm_linear_quadratic")
-    parser.add_argument("--lora_checkpoint_dir", type=str, default=None)
-    parser.add_argument("--shift", type=float, default=8.0)
-    parser.add_argument("--num_euler_timesteps", type=int, default=50)
-    parser.add_argument("--linear_threshold", type=float, default=0.1)
-    parser.add_argument("--linear_range", type=float, default=0.75)
-    parser.add_argument("--cpu_offload", action="store_true")
-    return parser.parse_args()
-
-
-def load_model(args):
-    if args.scheduler_type == "euler":
-        scheduler = FlowMatchEulerDiscreteScheduler()
-    else:
-        linear_quadratic = True if "linear_quadratic" in args.scheduler_type else False
-        scheduler = PCMFMScheduler(
-            1000,
-            args.shift,
-            args.num_euler_timesteps,
-            linear_quadratic,
-            args.linear_threshold,
-            args.linear_range,
-        )
-
-    if args.transformer_path:
-        transformer = MochiTransformer3DModel.from_pretrained(args.transformer_path)
-    else:
-        transformer = MochiTransformer3DModel.from_pretrained(args.model_path, subfolder="transformer/")
-
-    pipe = MochiPipeline.from_pretrained(args.model_path, transformer=transformer, scheduler=scheduler)
-    pipe.enable_vae_tiling()
-    # pipe.to(device)
-    # if args.cpu_offload:
-    pipe.enable_sequential_cpu_offload()
-    return pipe
-
-
-def generate_video(
-    prompt,
-    negative_prompt,
-    use_negative_prompt,
-    seed,
-    guidance_scale,
-    num_frames,
-    height,
-    width,
-    num_inference_steps,
-    randomize_seed=False,
-):
-    if randomize_seed:
-        seed = torch.randint(0, 1000000, (1, )).item()
-
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-
-    if not use_negative_prompt:
-        negative_prompt = None
-
-    with torch.autocast("cuda", dtype=torch.bfloat16):
-        output = pipe(
-            prompt=[prompt],
-            negative_prompt=negative_prompt,
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).frames[0]
-
-    output_path = os.path.join(tempfile.mkdtemp(), "output.mp4")
-    export_to_video(output, output_path, fps=30)
-    return output_path, seed
-
-
-examples = [
-    "A hand enters the frame, pulling a sheet of plastic wrap over three balls of dough placed on a wooden surface. The plastic wrap is stretched to cover the dough more securely. The hand adjusts the wrap, ensuring that it is tight and smooth over the dough. The scene focuses on the hand’s movements as it secures the edges of the plastic wrap. No new objects appear, and the camera remains stationary, focusing on the action of covering the dough.",
-    "A vintage train snakes through the mountains, its plume of white steam rising dramatically against the jagged peaks. The cars glint in the late afternoon sun, their deep crimson and gold accents lending a touch of elegance. The tracks carve a precarious path along the cliffside, revealing glimpses of a roaring river far below. Inside, passengers peer out the large windows, their faces lit with awe as the landscape unfolds.",
-    "A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background. Strings of fairy lights hang above, casting a warm, golden glow over the scene. Groups of people gather around high tables, their laughter blending with the soft rhythm of live jazz. The aroma of freshly mixed cocktails and charred appetizers wafts through the air, mingling with the cool night breeze.",
-]
-
-args = init_args()
-pipe = load_model(args)
-print("load model successfully")
-with gr.Blocks() as demo:
-    gr.Markdown("# Fastvideo Mochi Video Generation Demo")
-
-    with gr.Group():
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0)
-        result = gr.Video(label="Result", show_label=False)
-
-    with gr.Accordion("Advanced options", open=False):
-        with gr.Group():
-            with gr.Row():
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=1024,
-                    step=32,
-                    value=args.height,
-                )
-                width = gr.Slider(label="Width", minimum=256, maximum=1024, step=32, value=args.width)
-
-            with gr.Row():
-                num_frames = gr.Slider(
-                    label="Number of Frames",
-                    minimum=21,
-                    maximum=163,
-                    value=args.num_frames,
-                )
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=12,
-                    value=args.guidance_scale,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Inference Steps",
-                    minimum=4,
-                    maximum=100,
-                    value=args.num_inference_steps,
-                )
-
-            with gr.Row():
-                use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(label="Seed", minimum=0, maximum=1000000, step=1, value=args.seed)
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            seed_output = gr.Number(label="Used Seed")
-
-    gr.Examples(examples=examples, inputs=prompt)
-
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt,
-    )
-
-    run_button.click(
-        fn=generate_video,
-        inputs=[
-            prompt,
-            negative_prompt,
-            use_negative_prompt,
-            seed,
-            guidance_scale,
-            num_frames,
-            height,
-            width,
-            num_inference_steps,
-            randomize_seed,
-        ],
-        outputs=[result, seed_output],
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=7860)

demo/qualitative_results.md

@@ -1,15 +0,0 @@
-Fast-Hunyuan comparison with original Hunyuan, achieving an 8X diffusion speed boost with the FastVideo framework.
-
-https://github.com/user-attachments/assets/064ac1d2-11ed-4a0c-955b-4d412a96ef30
-
-Fast-Mochi comparison with original Mochi, achieving an 8X diffusion speed boost with the FastVideo framework.
-
-https://github.com/user-attachments/assets/5fbc4596-56d6-43aa-98e0-da472cf8e26c
-
-Comparison between OpenAI Sora, original Hunyuan and FastHunyuan
-
-https://github.com/user-attachments/assets/d323b712-3f68-42b2-952b-94f6a49c4836
-
-Comparison between original FastHunyuan, LLM-INT8 quantized FastHunyuan and NF4 quantized FastHunyuan
-
-https://github.com/user-attachments/assets/cf89efb5-5f68-4949-a085-f41c1ef26c94

examples/inference/basic/basic_cosmos2_5_i2w.py

@@ -20,7 +20,7 @@ def main():
     sampling_param = SamplingParam.from_pretrained(model_path)
 
     # image2world example from official repo
-    image_path = "images/bus_terminal.jpg"
+    image_path = "assets/images/bus_terminal.jpg"
 
     prompt = (
         "A nighttime city bus terminal gradually shifts from stillness to subtle movement. "
@@ -48,4 +48,3 @@ def main():
 if __name__ == "__main__":
     main()
 
-

examples/inference/basic/basic_cosmos2_5_v2w.py

@@ -20,7 +20,7 @@ def main():
     sampling_param = SamplingParam.from_pretrained(model_path)
 
     # video2world example from official repo
-    video_path = "videos/robot_pouring.mp4"
+    video_path = "assets/videos/robot_pouring.mp4"
 
     prompt = (
         "A robotic arm, primarily white with black joints and cables, is shown in a clean, modern indoor setting with a white tabletop. "
@@ -51,4 +51,3 @@ def main():
 if __name__ == "__main__":
     main()
 
-

examples/inference/basic/basic_gamecraft.py

@@ -0,0 +1,119 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Basic inference script for HunyuanGameCraft video generation.
+
+HunyuanGameCraft generates game-like videos with camera/action control.
+It takes an optional image input and generates video with camera motion
+based on simple action commands (forward, left, right, backward, rotations).
+
+Available actions:
+    - forward (w): Move camera forward
+    - backward (s): Move camera backward  
+    - left (a): Move camera left (strafe)
+    - right (d): Move camera right (strafe)
+    - left_rot: Rotate camera left (pan)
+    - right_rot: Rotate camera right (pan)
+    - up_rot: Rotate camera up (tilt)
+    - down_rot: Rotate camera down (tilt)
+
+T2V vs I2V:
+    - Default: I2V (uses a default reference image). Set GAMECRAFT_I2V_IMAGE to a
+      URL or path to use a different image.
+    - T2V only (no reference image): run with GAMECRAFT_I2V_IMAGE= (empty).
+"""
+import os
+
+import torch
+
+from fastvideo import VideoGenerator
+from fastvideo.models.camera import create_camera_trajectory
+
+# Model configuration (use GAMECRAFT_MODEL_PATH for local weights)
+MODEL_PATH = os.environ.get("GAMECRAFT_MODEL_PATH", "FastVideo/HunyuanGameCraft-Diffusers")
+
+# Default prompts for demo
+DEFAULT_PROMPTS = {
+    "village": "A charming medieval village with cobblestone streets, thatched-roof houses, and vibrant flower gardens under a bright blue sky.",
+    "temple": "A majestic ancient temple stands under a clear blue sky, its grandeur highlighted by towering Doric columns and intricate architectural details.",
+    "forest": "A lush green forest with tall trees, dappled sunlight filtering through the leaves, and a winding dirt path.",
+    "beach": "A tropical beach with crystal clear turquoise water, white sand, and palm trees swaying in the breeze.",
+}
+
+# I2V: default reference image (URL). Can override with a local path.
+DEFAULT_I2V_IMAGE_URL = (
+    "https://huggingface.co/datasets/huggingface/documentation-images/"
+    "resolve/main/diffusers/astronaut.jpg"
+)
+DEFAULT_I2V_PROMPT = (
+    "An astronaut hatching from an egg, on the surface of the moon, "
+    "the darkness and depth of space realised in the background."
+)
+
+OUTPUT_PATH = "video_samples_gamecraft"
+
+
+def main():
+    # Initialize generator
+    # FastVideo will automatically download weights from HuggingFace
+    generator = VideoGenerator.from_pretrained(
+        MODEL_PATH,
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+    )
+
+    # Video parameters
+    height = 704
+    width = 1280
+    num_frames = 33
+    action = "forward"
+    action_speed = 0.2
+
+    # Create camera trajectory (Plücker coordinates)
+    camera_states = create_camera_trajectory(
+        action=action,
+        height=height,
+        width=width,
+        num_frames=num_frames,
+        action_speed=action_speed,
+        dtype=torch.bfloat16,
+    )
+    print(f"Camera states shape: {camera_states.shape}")
+
+    # I2V vs T2V: unset GAMECRAFT_I2V_IMAGE -> I2V (default image). Set to "" -> T2V.
+    env_image = os.environ.get("GAMECRAFT_I2V_IMAGE")
+    if env_image is None:
+        image_path = DEFAULT_I2V_IMAGE_URL  # default: I2V
+    elif env_image.strip() == "":
+        image_path = None  # T2V
+    else:
+        image_path = env_image.strip()  # I2V with given URL/path
+
+    is_i2v = image_path is not None
+    prompt = DEFAULT_I2V_PROMPT if is_i2v else DEFAULT_PROMPTS["temple"]
+    print(f"Mode: {'I2V' if is_i2v else 'T2V'}, prompt: {prompt[:60]}...")
+
+    gen_kw = dict(
+        prompt=prompt,
+        negative_prompt="",
+        camera_states=camera_states,
+        height=height,
+        width=width,
+        num_frames=num_frames,
+        num_inference_steps=50,
+        guidance_scale=6.0,
+        seed=42,
+        fps=24,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+    )
+    if is_i2v:
+        gen_kw["image_path"] = image_path
+    generator.generate_video(**gen_kw)
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_lingbotworld_base_cam.py

@@ -0,0 +1,49 @@
+from fastvideo import VideoGenerator
+from fastvideo.models.dits.lingbotworld.cam_utils import prepare_camera_embedding
+
+# from fastvideo.configs.sample import SamplingParam
+OUTPUT_PATH = "video_samples_lingbotworld"
+def main():
+    # FastVideo will automatically use the optimal default arguments for the
+    # model.
+    # If a local path is provided, FastVideo will make a best effort
+    # attempt to identify the optimal arguments.
+    generator = VideoGenerator.from_pretrained(
+         "FastVideo/LingBot-World-Base-Cam-Diffusers",
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
+        dit_cpu_offload=True, # DiT need to be offloaded for MoE
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
+        pin_cpu_memory=True,
+        # image_encoder_cpu_offload=False,
+    )
+
+    num_frames = 81
+    prompt = "The video presents a soaring journey through a fantasy jungle. The wind whips past the rider's blue hands gripping the reins, causing the leather straps to vibrate. The ancient gothic castle approaches steadily, its stone details becoming clearer against the backdrop of floating islands and distant waterfalls."
+    image_path = "https://raw.githubusercontent.com/Robbyant/lingbot-world/main/examples/00/image.jpg"
+    action_path = "examples/inference/basic/lingbotworld_examples/00"
+    c2ws_plucker_emb, num_frames = prepare_camera_embedding(
+        action_path=action_path,
+        num_frames=num_frames,
+        height=480,
+        width=832,
+        spatial_scale=8,
+    )
+
+    generator.generate_video(
+        prompt,
+        image_path=image_path,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+        num_frames=num_frames,
+        height=480,
+        width=832,
+        c2ws_plucker_emb=c2ws_plucker_emb,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_ltx2.py

@@ -20,10 +20,10 @@ def main() -> None:
     # Uses FastVideo default sampling settings for LTX2 base.
     generator = VideoGenerator.from_pretrained(
         "Davids048/LTX2-Base-Diffusers",
-        num_gpus=1,
+        num_gpus=8,
     )
 
-    output_path = "outputs_video/ltx2_basic/output_ltx2_base_t2v_1088_1920_1.1.mp4"
+    output_path = "outputs_video/ltx2_basic/output_ltx2_base_t2v_1088_1920_1.4.mp4"
     generator.generate_video(
         prompt=PROMPT,
         output_path=output_path,
@@ -31,6 +31,12 @@ def main() -> None:
         num_frames=121,
         height=1088,
         width=1920,
+        # LTX2 uses these parameters for multi-modal CFG instead of guidance_scale
+        # ltx2_cfg_scale_video=3.0,
+        # ltx2_cfg_scale_audio=7.0,
+        # ltx2_modality_scale_video=3.0,
+        # ltx2_modality_scale_audio=3.0,
+        # ltx2_rescale_scale=0.7,
     )
     generator.shutdown()
 

examples/inference/basic/basic_sd35_t2i.py

@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import argparse
+import os
+import re
+from typing import List
+
+
+DEFAULT_PROMPTS = [
+    "a photo of a cat",
+    "a cinematic photo of a red panda wearing a tiny backpack, standing on a rainy neon-lit street at night, shallow depth of field, sharp focus, 35mm, bokeh",
+]
+
+
+def _safe_filename(text: str, max_len: int = 100) -> str:
+    """
+    Make a stable, filesystem-friendly filename base.
+    VideoGenerator uses prompt[:100].strip() internally, so we mirror that,
+    but also remove path separators and other problematic characters.
+    """
+    s = text[:max_len].strip()
+    s = s.replace(os.sep, "_")
+    if os.altsep:
+        s = s.replace(os.altsep, "_")
+    s = re.sub(r"\s+", " ", s)
+    s = re.sub(r"[^A-Za-z0-9 .,_-]", "_", s)
+    s = s.strip(" .")
+    return s or "prompt"
+
+
+def _remove_existing_outputs(out_dir: str, filename_base: str) -> None:
+    """
+    Ensure deterministic naming by deleting any existing mp4s that would
+    cause VideoGenerator to append suffixes like _1, _2, etc.
+    """
+    if not os.path.isdir(out_dir):
+        return
+
+    pattern = re.compile(rf"^{re.escape(filename_base)}(_\d+)?\.mp4$")
+    for fn in os.listdir(out_dir):
+        if pattern.match(fn):
+            try:
+                os.remove(os.path.join(out_dir, fn))
+            except FileNotFoundError:
+                pass
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Run SD3.5 Medium text-to-image with FastVideo VideoGenerator.")
+    p.add_argument("--model-path", default="stabilityai/stable-diffusion-3.5-medium", help="Path to local diffusers-format SD3.5 weights directory.")
+    p.add_argument(
+        "--out-dir",
+        "--outdir",
+        default="outputs/sd35/samples",
+        help="Output directory for generated mp4 files.",
+    )
+    p.add_argument(
+        "--prompt",
+        action="append",
+        default=None,
+        help="Prompt text. Repeat --prompt multiple times to generate multiple samples.",
+    )
+    p.add_argument("--negative", default="lowres, blurry, jpeg artifacts, watermark, text", help="Negative prompt.")
+    p.add_argument(
+        "--backend",
+        default=None,
+        help="Set FASTVIDEO_ATTENTION_BACKEND (e.g. TORCH_SDPA). If omitted, respects the existing env var.",
+    )
+    p.add_argument("--seed", type=int, default=42, help="Base seed. Each prompt uses seed + prompt_idx.")
+    p.add_argument("--height", type=int, default=768, help="Output height.")
+    p.add_argument("--width", type=int, default=768, help="Output width.")
+    p.add_argument("--steps", type=int, default=28, help="Number of inference steps.")
+    p.add_argument("--guidance", type=float, default=6.0, help="Guidance scale.")
+    p.add_argument("--num-gpus", type=int, default=1, help="Number of GPUs to use.")
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    prompts: List[str] = args.prompt if args.prompt else DEFAULT_PROMPTS
+
+    if args.backend:
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = args.backend
+
+    from fastvideo import VideoGenerator
+
+    os.makedirs(args.out_dir, exist_ok=True)
+
+    init_kwargs = {
+        "num_gpus": args.num_gpus,
+        "workload_type": "t2i",
+        "sp_size": 1,
+        "tp_size": 1,
+        "dit_cpu_offload": False,
+        "dit_layerwise_offload": False,
+        "text_encoder_cpu_offload": False,
+        "vae_cpu_offload": False,
+        "image_encoder_cpu_offload": False,
+        "pin_cpu_memory": False,
+        "use_fsdp_inference": False,
+    }
+
+    generator = VideoGenerator.from_pretrained(model_path=args.model_path, **init_kwargs)
+    try:
+        for i, prompt in enumerate(prompts):
+            seed = args.seed + i
+
+            filename_base = f"sd35_{i:02d}_seed{seed}_{_safe_filename(prompt, max_len=80)}"
+            _remove_existing_outputs(args.out_dir, filename_base)
+
+            output_path = os.path.join(args.out_dir, f"{filename_base}.png")
+            print(f"[sd35] prompt_idx={i} seed={seed} output_path={output_path}")
+
+            generation_kwargs = {
+                "output_path": output_path,
+                "height": args.height,
+                "width": args.width,
+                "num_frames": 1,
+                "fps": 1,
+                "num_inference_steps": args.steps,
+                "guidance_scale": args.guidance,
+                "seed": seed,
+                "negative_prompt": args.negative,
+                "save_video": True,
+            }
+
+            generator.generate_video(prompt, **generation_kwargs)
+
+        print(f"[sd35] done. outputs written to: {args.out_dir}")
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py

@@ -31,7 +31,7 @@ def main():
     sampling_param.height = 480
     sampling_param.seed = 1000
 
-    with open("prompts/mixkit_i2v.jsonl", "r") as f:
+    with open("assets/prompts/mixkit_i2v.jsonl", "r") as f:
         prompt_image_pairs = json.load(f)
 
     for prompt_image_pair in prompt_image_pairs:

examples/inference/gradio/serving/gradio_frontend.py

@@ -188,8 +188,9 @@ def load_example_prompts():
     prompt_to_image = {}
     # Try to find the JSON file relative to project root
     possible_json_paths = [
-        Path("prompts/mixkit_i2v.jsonl"),
-        Path(__file__).parent.parent.parent.parent / "prompts" / "mixkit_i2v.jsonl",
+        Path("assets/prompts/mixkit_i2v.jsonl"),
+        Path(__file__).resolve().parents[4] / "assets" / "prompts" /
+        "mixkit_i2v.jsonl",
     ]
     json_path = None
     for path in possible_json_paths:
@@ -201,8 +202,8 @@ def load_example_prompts():
         try:
             with open(json_path, "r", encoding='utf-8') as f:
                 data = json.load(f)
-                # Get the project root directory (parent of prompts directory)
-                project_root = json_path.parent.parent
+                # Resolve paths relative to repository root.
+                project_root = Path(__file__).resolve().parents[4]
                 for item in data:
                     prompt_text = item.get("prompt", "").strip()
                     image_path = item.get("image_path", "")
@@ -736,8 +737,8 @@ def main():
         allowed_paths=[
             os.path.abspath("outputs"), 
             os.path.abspath("fastvideo-logos"),
-            os.path.abspath("prompts"),
-            os.path.abspath("images"),
+            os.path.abspath("assets/prompts"),
+            os.path.abspath("assets/images"),
             os.path.abspath(tempfile.gettempdir()),
             os.path.abspath(os.path.join(tempfile.gettempdir(), "gradio")),
         ]
@@ -747,4 +748,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main() 
+    main() 

fastvideo-kernel/pyproject.toml

@@ -9,7 +9,7 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "fastvideo-kernel"
-version = "0.2.5"
+version = "0.2.6"
 description = "Unified CUDA kernels for FastVideo"
 readme = "README.md"
 requires-python = ">=3.10"

fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn.py

@@ -287,9 +287,8 @@ def block_sparse_attn(
     block_sparse_fwd, block_sparse_bwd = _get_sm90_ops()
     if (not _force_triton()) and _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
         return block_sparse_attn_sm90(q, k, v, block_map, variable_block_sizes)
-    # Triton path: generally assumes q/k/v share the same padded length
-    if q.shape[2] != k.shape[2] or q.shape[2] != v.shape[2]:
-        raise RuntimeError("Triton fallback requires q/k/v to have the same padded length.")
+    # Triton path: supports q_seq_len != kv_seq_len as long as both are padded
+    # to a multiple of the block size (64 tokens).
     return block_sparse_attn_triton(q, k, v, block_map, variable_block_sizes)
 
 

fastvideo-kernel/python/fastvideo_kernel/ops.py

@@ -141,12 +141,6 @@ def video_sparse_attn(
         # Use autograd-enabled wrapper so backward works (and still uses SM90 kernel when available)
         out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]
     else:
-        if q_seq_len != kv_seq_len:
-            raise RuntimeError(
-                "q/k have different lengths, but the compiled CUDA kernel (block_sparse_fwd) "
-                "is not available. The Triton fallback currently requires q and k/v to have "
-                "the same padded length."
-            )
         # Triton-only forward (kept for environments without the wrapper deps)
         out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num, variable_block_sizes)
 

fastvideo-kernel/python/fastvideo_kernel/triton_kernels/block_sparse_attn_triton.py

@@ -29,7 +29,7 @@ configs = [
 
 
 # ──────────────────────────── SPARSE ADDITION BEGIN ───────────────────────────
-@triton.autotune(configs, key=["N_CTX", "HEAD_DIM"])
+@triton.autotune(configs, key=["N_CTX_Q", "HEAD_DIM"])
 @triton.jit
 def _attn_fwd_sparse(
         Q,
@@ -60,7 +60,8 @@ def _attn_fwd_sparse(
         stride_on,
         Z,
         H,
-        N_CTX,  #
+        N_CTX_Q,  #
+        N_CTX_KV,  #
         HEAD_DIM: tl.constexpr,  #
         BLOCK_M: tl.constexpr,
         BLOCK_N: tl.constexpr,
@@ -75,24 +76,29 @@ def _attn_fwd_sparse(
     off_hz = tl.program_id(1)  # fused (batch, head)
     b = off_hz // H
     h = off_hz % H
-    q_tiles = N_CTX // BLOCK_M
+    q_tiles = N_CTX_Q // BLOCK_M
     meta_base = ((b * H + h) * q_tiles + q_blk)
 
     kv_blocks = tl.load(q2k_num + meta_base)  # int32
     kv_ptr = q2k_index + meta_base * max_kv_blks  # ptr to list
 
     # ----- base pointers -----
-    qvk_off = (b.to(tl.int64) * stride_qz + h.to(tl.int64) * stride_qh)
+    # Note: when q and kv have different sequence lengths, their per-(batch,head)
+    # strides differ, so we must compute separate base offsets.
+    q_off = (b.to(tl.int64) * stride_qz + h.to(tl.int64) * stride_qh)
+    k_off = (b.to(tl.int64) * stride_kz + h.to(tl.int64) * stride_kh)
+    v_off = (b.to(tl.int64) * stride_vz + h.to(tl.int64) * stride_vh)
+    o_off = (b.to(tl.int64) * stride_oz + h.to(tl.int64) * stride_oh)
 
-    Q_ptr = tl.make_block_ptr(base=Q + qvk_off,
-                              shape=(N_CTX, HEAD_DIM),
+    Q_ptr = tl.make_block_ptr(base=Q + q_off,
+                              shape=(N_CTX_Q, HEAD_DIM),
                               strides=(stride_qm, stride_qk),
                               offsets=(q_blk * BLOCK_M, 0),
                               block_shape=(BLOCK_M, HEAD_DIM),
                               order=(1, 0))
 
-    K_base = tl.make_block_ptr(base=K + qvk_off,
-                               shape=(HEAD_DIM, N_CTX),
+    K_base = tl.make_block_ptr(base=K + k_off,
+                               shape=(HEAD_DIM, N_CTX_KV),
                                strides=(stride_kk, stride_kn),
                                offsets=(0, 0),
                                block_shape=(HEAD_DIM, BLOCK_N),
@@ -100,15 +106,15 @@ def _attn_fwd_sparse(
 
     v_order: tl.constexpr = (0, 1) if V.dtype.element_ty == tl.float8e5 else (1,
                                                                               0)
-    V_base = tl.make_block_ptr(base=V + qvk_off,
-                               shape=(N_CTX, HEAD_DIM),
+    V_base = tl.make_block_ptr(base=V + v_off,
+                               shape=(N_CTX_KV, HEAD_DIM),
                                strides=(stride_vk, stride_vn),
                                offsets=(0, 0),
                                block_shape=(BLOCK_N, HEAD_DIM),
                                order=v_order)
 
-    O_ptr = tl.make_block_ptr(base=Out + qvk_off,
-                              shape=(N_CTX, HEAD_DIM),
+    O_ptr = tl.make_block_ptr(base=Out + o_off,
+                              shape=(N_CTX_Q, HEAD_DIM),
                               strides=(stride_om, stride_on),
                               offsets=(q_blk * BLOCK_M, 0),
                               block_shape=(BLOCK_M, HEAD_DIM),
@@ -150,7 +156,7 @@ def _attn_fwd_sparse(
     # ----- epilogue -----
     m_i += tl.math.log2(l_i)
     acc = acc / l_i[:, None]
-    tl.store(M + off_hz * N_CTX + offs_m, m_i)
+    tl.store(M + off_hz * N_CTX_Q + offs_m, m_i)
     tl.store(O_ptr, acc.to(Out.type.element_ty))
 
 
@@ -201,7 +207,7 @@ def _attn_bwd_dkdv(
         stride_tok,
         stride_d,  #
         H,
-        N_CTX,
+        N_CTX_KV,
         BLOCK_M1: tl.constexpr,  #
         BLOCK_N1: tl.constexpr,  #
         HEAD_DIM: tl.constexpr,  #
@@ -221,8 +227,8 @@ def _attn_bwd_dkdv(
     off_hz = tl.program_id(2)  # fused (batch, head)
     b = off_hz // H
     h = off_hz % H
-    q_tiles = N_CTX // BLOCK_N1
-    meta_base = ((b * H + h) * q_tiles + kv_blk)
+    kv_tiles = N_CTX_KV // BLOCK_N1
+    meta_base = ((b * H + h) * kv_tiles + kv_blk)
 
     q_blocks = tl.load(k2q_num + meta_base)  # int32
     q_ptr = k2q_index + meta_base * max_q_blks  # ptr to list
@@ -302,16 +308,21 @@ def _attn_bwd_dq(
 
     kv_blocks = tl.load(q2k_num + meta_base)  # int32
     kv_ptr = q2k_index + meta_base * max_kv_blks  # ptr to list
-    block_size = tl.load(variable_block_sizes + q_blk)
 
     for blk_idx in range(kv_blocks * 2):
-        block_sparse_offset = (tl.load(kv_ptr + blk_idx // 2).to(tl.int32) * 2 +
-                               blk_idx % 2) * step_n * stride_tok
+        kv_idx = tl.load(kv_ptr + blk_idx // 2).to(tl.int32)
+        # variable_block_sizes is defined per KV block (tile). Mask must therefore
+        # use kv_idx (not q_blk). Also, because we split each 64-token block into
+        # two 32-token halves, the mask must account for the half-block offset.
+        block_size = tl.load(variable_block_sizes + kv_idx).to(tl.int32)
+        half = (blk_idx % 2).to(tl.int32)
+        block_sparse_offset = (kv_idx * 2 + half) * step_n * stride_tok
         kT = tl.load(kT_ptrs + block_sparse_offset)
         vT = tl.load(vT_ptrs + block_sparse_offset)
         qk = tl.dot(q, kT)
         p = tl.math.exp2(qk - m)
-        mask = tl.arange(0, BLOCK_N2) < block_size.to(tl.int32)
+        offs_in_block = half * step_n + tl.arange(0, BLOCK_N2)
+        mask = offs_in_block < block_size
         p = tl.where(mask[None, :], p, 0.0)
         # Compute dP and dS.
         dp = tl.dot(do, vT).to(tl.float32)
@@ -467,19 +478,235 @@ def _attn_bwd(
     tl.store(dq_ptrs, dq)
 
 
+@triton.jit
+def _attn_bwd_dkdv_kernel(
+        Q,
+        K,
+        V,
+        sm_scale,  #
+        DO,  #
+        DK,
+        DV,  #
+        M,
+        D,
+        k2q_index,
+        k2q_num,
+        max_q_blks,
+        variable_block_sizes,
+        # shared token/dim strides (assumed contiguous along token and dim)
+        stride_tok,
+        stride_d,  #
+        # batch/head strides (may differ between Q and KV)
+        stride_qz,
+        stride_qh,
+        stride_kz,
+        stride_kh,
+        stride_vz,
+        stride_vh,
+        stride_doz,
+        stride_doh,
+        stride_dkz,
+        stride_dkh,
+        stride_dvz,
+        stride_dvh,
+        H,
+        N_CTX_Q,
+        N_CTX_KV,
+        BLOCK_M1: tl.constexpr,  #
+        BLOCK_N1: tl.constexpr,  #
+        HEAD_DIM: tl.constexpr):
+    """
+    Backward kernel that computes dK and dV for each KV block (64 tokens).
+    Grid:
+      pid0: kv_blk in [0, N_CTX_KV/BLOCK_N1)
+      pid2: fused (batch, head) in [0, B*H)
+    """
+    bhid = tl.program_id(2)
+    b = bhid // H
+    h = bhid % H
+    kv_blk = tl.program_id(0)
+
+    q_adj = (b.to(tl.int64) * stride_qz + h.to(tl.int64) * stride_qh)
+    kv_adj_k = (b.to(tl.int64) * stride_kz + h.to(tl.int64) * stride_kh)
+    kv_adj_v = (b.to(tl.int64) * stride_vz + h.to(tl.int64) * stride_vh)
+    do_adj = (b.to(tl.int64) * stride_doz + h.to(tl.int64) * stride_doh)
+    dk_adj = (b.to(tl.int64) * stride_dkz + h.to(tl.int64) * stride_dkh)
+    dv_adj = (b.to(tl.int64) * stride_dvz + h.to(tl.int64) * stride_dvh)
+
+    Q = Q + q_adj
+    K = K + kv_adj_k
+    V = V + kv_adj_v
+    DO = DO + do_adj
+    DK = DK + dk_adj
+    DV = DV + dv_adj
+
+    # M and D (delta) are always sized by Q length.
+    M = M + (bhid * N_CTX_Q).to(tl.int64)
+    D = D + (bhid * N_CTX_Q).to(tl.int64)
+
+    offs_k = tl.arange(0, HEAD_DIM)
+    start_n = kv_blk * BLOCK_N1
+    offs_n = start_n + tl.arange(0, BLOCK_N1)
+
+    # load K and V: they stay in SRAM throughout the inner loop.
+    k = tl.load(K + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d)
+    v = tl.load(V + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d)
+
+    dv_acc = tl.zeros([BLOCK_N1, HEAD_DIM], dtype=tl.float32)
+    dk_acc = tl.zeros([BLOCK_N1, HEAD_DIM], dtype=tl.float32)
+
+    num_steps = N_CTX_Q // BLOCK_M1
+    dk_acc, dv_acc = _attn_bwd_dkdv(
+        dk_acc,
+        dv_acc,
+        Q,
+        k,
+        v,
+        sm_scale,
+        DO,
+        M,
+        D,
+        k2q_index,
+        k2q_num,
+        max_q_blks,
+        variable_block_sizes,
+        stride_tok,
+        stride_d,
+        H,
+        N_CTX_KV,
+        BLOCK_M1=BLOCK_M1,
+        BLOCK_N1=BLOCK_N1,
+        HEAD_DIM=HEAD_DIM,
+        start_n=start_n,
+        start_m=0,
+        num_steps=num_steps,
+    )
+
+    dv_ptrs = DV + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d
+    tl.store(dv_ptrs, dv_acc)
+
+    dk_acc *= sm_scale
+    dk_ptrs = DK + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d
+    tl.store(dk_ptrs, dk_acc)
+
+
+@triton.jit
+def _attn_bwd_dq_kernel(
+        Q,
+        K,
+        V,
+        DO,  #
+        DQ,
+        M,
+        D,
+        q2k_index,
+        q2k_num,
+        max_kv_blks,
+        variable_block_sizes,
+        # shared token/dim strides (assumed contiguous along token and dim)
+        stride_tok,
+        stride_d,  #
+        # batch/head strides (may differ between Q and KV)
+        stride_qz,
+        stride_qh,
+        stride_kz,
+        stride_kh,
+        stride_vz,
+        stride_vh,
+        stride_doz,
+        stride_doh,
+        stride_dqz,
+        stride_dqh,
+        H,
+        N_CTX_Q,
+        BLOCK_M2: tl.constexpr,  #
+        BLOCK_N2: tl.constexpr,  #
+        HEAD_DIM: tl.constexpr):
+    """
+    Backward kernel that computes dQ for each Q block (64 tokens).
+    Grid:
+      pid0: q_blk in [0, N_CTX_Q/BLOCK_M2)
+      pid2: fused (batch, head) in [0, B*H)
+    """
+    LN2 = 0.6931471824645996  # = ln(2)
+    bhid = tl.program_id(2)
+    b = bhid // H
+    h = bhid % H
+    q_blk = tl.program_id(0)
+
+    q_adj = (b.to(tl.int64) * stride_qz + h.to(tl.int64) * stride_qh)
+    kv_adj_k = (b.to(tl.int64) * stride_kz + h.to(tl.int64) * stride_kh)
+    kv_adj_v = (b.to(tl.int64) * stride_vz + h.to(tl.int64) * stride_vh)
+    do_adj = (b.to(tl.int64) * stride_doz + h.to(tl.int64) * stride_doh)
+    dq_adj = (b.to(tl.int64) * stride_dqz + h.to(tl.int64) * stride_dqh)
+
+    Q = Q + q_adj
+    K = K + kv_adj_k
+    V = V + kv_adj_v
+    DO = DO + do_adj
+    DQ = DQ + dq_adj
+
+    M = M + (bhid * N_CTX_Q).to(tl.int64)
+    D = D + (bhid * N_CTX_Q).to(tl.int64)
+
+    offs_k = tl.arange(0, HEAD_DIM)
+    start_m = q_blk * BLOCK_M2
+    offs_m = start_m + tl.arange(0, BLOCK_M2)
+
+    q = tl.load(Q + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d)
+    do = tl.load(DO + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d)
+    m = tl.load(M + offs_m)[:, None]
+
+    dq_acc = tl.zeros([BLOCK_M2, HEAD_DIM], dtype=tl.float32)
+    num_steps = 0  # unused in _attn_bwd_dq
+    dq_acc = _attn_bwd_dq(
+        dq_acc,
+        q,
+        K,
+        V,
+        do,
+        m,
+        D,
+        q2k_index,
+        q2k_num,
+        max_kv_blks,
+        variable_block_sizes,
+        stride_tok,
+        stride_d,
+        H,
+        N_CTX_Q,
+        BLOCK_M2=BLOCK_M2,
+        BLOCK_N2=BLOCK_N2,
+        HEAD_DIM=HEAD_DIM,
+        start_m=start_m,
+        start_n=0,
+        num_steps=num_steps,
+    )
+
+    dq_ptrs = DQ + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d
+    dq_acc *= LN2
+    tl.store(dq_ptrs, dq_acc)
+
+
 # ──────────────────────────── SPARSE ADDITION BEGIN ───────────────────────────
 def triton_block_sparse_attn_forward(q, k, v, q2k_index, q2k_num,
                                      variable_block_sizes):
-    B, H, T, D = q.shape
+    B, H, Tq, D = q.shape
+    Tkv = k.shape[2]
     sm_scale = 1.0 / math.sqrt(D)
     max_kv_blks = q2k_index.shape[-1]
-    assert T % 64 == 0, f"T must be a multiple of 64, but got {T}"
+    assert Tq % 64 == 0, f"q length must be a multiple of 64, but got {Tq}"
+    assert Tkv % 64 == 0, f"kv length must be a multiple of 64, but got {Tkv}"
     assert q2k_num.shape[
-        -1] == T // 64, f"shape mismatch, T // 64 = {T // 64}, q2k_num.shape[-2] = {q2k_num.shape[-2]}"
+        -1] == Tq // 64, f"shape mismatch, Tq // 64 = {Tq // 64}, q2k_num.shape[-2] = {q2k_num.shape[-2]}"
+    assert variable_block_sizes.numel() == Tkv // 64, (
+        f"shape mismatch, variable_block_sizes must have length {Tkv // 64}, "
+        f"got {variable_block_sizes.numel()}"
+    )
     o = torch.empty_like(q)
-    M = torch.empty((B, H, T), dtype=torch.float32, device=q.device)
+    M = torch.empty((B, H, Tq), dtype=torch.float32, device=q.device)
 
-    grid = lambda _: (triton.cdiv(T, 64), B * H, 1)
+    grid = lambda _: (triton.cdiv(Tq, 64), B * H, 1)
     _attn_fwd_sparse[grid](q,
                            k,
                            v,
@@ -508,7 +735,8 @@ def triton_block_sparse_attn_forward(q, k, v, q2k_index, q2k_num,
                            o.stride(3),
                            B,
                            H,
-                           T,
+                           Tq,
+                           Tkv,
                            HEAD_DIM=D,
                            STAGE=3)
 
@@ -518,21 +746,21 @@ def triton_block_sparse_attn_forward(q, k, v, q2k_index, q2k_num,
 def triton_block_sparse_attn_backward(do, q, k, v, o, M, q2k_index, q2k_num,
                                       k2q_index, k2q_num, variable_block_sizes):
     assert do.is_contiguous()
-    assert q.stride() == k.stride() == v.stride() == o.stride() == do.stride()
 
-    B, H, T, D = q.shape
+    B, H, Tq, D = q.shape
+    Tkv = k.shape[2]
     sm_scale = 1.0 / math.sqrt(D)
     dq = torch.empty_like(q)
     dk = torch.empty_like(k)
     dv = torch.empty_like(v)
-    BATCH, N_HEAD, N_CTX = q.shape[:3]
+    BATCH, N_HEAD = q.shape[:2]
     BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 64, 64, 32
     RCP_LN2 = 1.4426950408889634  # = 1.0 / ln(2)
     arg_k = k
     arg_k = arg_k * (sm_scale * RCP_LN2)
     PRE_BLOCK = 64
-    assert N_CTX % PRE_BLOCK == 0
-    pre_grid = (N_CTX // PRE_BLOCK, BATCH * N_HEAD)
+    assert Tq % PRE_BLOCK == 0
+    pre_grid = (Tq // PRE_BLOCK, BATCH * N_HEAD)
     delta = torch.empty_like(M)
     _attn_bwd_preprocess[pre_grid](
         o,
@@ -540,7 +768,7 @@ def triton_block_sparse_attn_backward(do, q, k, v, o, M, q2k_index, q2k_num,
         delta,  #
         BATCH,
         N_HEAD,
-        N_CTX,  #
+        Tq,  #
         BLOCK_M=PRE_BLOCK,
         HEAD_DIM=D  #
     )
@@ -548,36 +776,75 @@ def triton_block_sparse_attn_backward(do, q, k, v, o, M, q2k_index, q2k_num,
     max_q_blks = k2q_index.shape[-1]
     max_kv_blks = q2k_index.shape[-1]
 
-    grid = (N_CTX // BLOCK_N1, 1, BATCH * N_HEAD)
-    _attn_bwd[grid](
+    # dK/dV kernel: grid over KV blocks
+    grid_kv = (Tkv // BLOCK_N1, 1, BATCH * N_HEAD)
+    _attn_bwd_dkdv_kernel[grid_kv](
         q,
         arg_k,
         v,
         sm_scale,
         do,
-        dq,
         dk,
-        dv,  #
+        dv,
         M,
-        delta,  #
-        q2k_index,
-        q2k_num,
-        max_kv_blks,
+        delta,
         k2q_index,
         k2q_num,
         max_q_blks,
         variable_block_sizes,
+        q.stride(2),
+        q.stride(3),
         q.stride(0),
         q.stride(1),
-        q.stride(2),
-        q.stride(3),  #
+        arg_k.stride(0),
+        arg_k.stride(1),
+        v.stride(0),
+        v.stride(1),
+        do.stride(0),
+        do.stride(1),
+        dk.stride(0),
+        dk.stride(1),
+        dv.stride(0),
+        dv.stride(1),
         N_HEAD,
-        N_CTX,  #
+        Tq,
+        Tkv,
         BLOCK_M1=BLOCK_M1,
-        BLOCK_N1=BLOCK_N1,  #
+        BLOCK_N1=BLOCK_N1,
+        HEAD_DIM=D,
+    )
+
+    # dQ kernel: grid over Q blocks
+    grid_q = (Tq // BLOCK_M2, 1, BATCH * N_HEAD)
+    _attn_bwd_dq_kernel[grid_q](
+        q,
+        arg_k,
+        v,
+        do,
+        dq,
+        M,
+        delta,
+        q2k_index,
+        q2k_num,
+        max_kv_blks,
+        variable_block_sizes,
+        q.stride(2),
+        q.stride(3),
+        q.stride(0),
+        q.stride(1),
+        arg_k.stride(0),
+        arg_k.stride(1),
+        v.stride(0),
+        v.stride(1),
+        do.stride(0),
+        do.stride(1),
+        dq.stride(0),
+        dq.stride(1),
+        N_HEAD,
+        Tq,
         BLOCK_M2=BLOCK_M2,
-        BLOCK_N2=BLOCK_N2,  #
-        HEAD_DIM=D  #
+        BLOCK_N2=BLOCK_N2,
+        HEAD_DIM=D,
     )
 
     return dq, dk, dv

fastvideo-kernel/python/fastvideo_kernel/version.py

@@ -1 +1 @@
-__version__ = "0.2.5"
+__version__ = "0.2.6"

fastvideo/configs/models/dits/__init__.py

@@ -1,5 +1,6 @@
 from fastvideo.configs.models.dits.cosmos import CosmosVideoConfig
 from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
+from fastvideo.configs.models.dits.hunyuangamecraft import HunyuanGameCraftConfig
 from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
 from fastvideo.configs.models.dits.longcat import LongCatVideoConfig
@@ -9,7 +10,8 @@ from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
 from fastvideo.configs.models.dits.hyworld import HYWorldConfig
 
 __all__ = [
-    "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
-    "StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
-    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig"
+    "HunyuanVideoConfig", "HunyuanVideo15Config", "HunyuanGameCraftConfig",
+    "WanVideoConfig", "StepVideoConfig", "CosmosVideoConfig",
+    "Cosmos25VideoConfig", "LongCatVideoConfig", "LTX2VideoConfig",
+    "HYWorldConfig"
 ]

fastvideo/configs/models/dits/hunyuangamecraft.py

@@ -0,0 +1,163 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Configuration for HunyuanGameCraft transformer model.
+
+HunyuanGameCraft extends HunyuanVideo with:
+1. CameraNet for camera/action conditioning
+2. 33 input channels (16 latent + 16 gt_latent + 1 mask)
+3. Mask-based conditioning for autoregressive generation
+"""
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_double_block(n: str, m) -> bool:
+    return "double" in n and str.isdigit(n.split(".")[-1])
+
+
+def is_single_block(n: str, m) -> bool:
+    return "single" in n and str.isdigit(n.split(".")[-1])
+
+
+def is_refiner_block(n: str, m) -> bool:
+    return "refiner" in n and str.isdigit(n.split(".")[-1])
+
+
+def is_txt_in(n: str, m) -> bool:
+    return n.split(".")[-1] == "txt_in"
+
+
+def is_camera_net(n: str, m) -> bool:
+    return "camera_net" in n
+
+
+@dataclass
+class HunyuanGameCraftArchConfig(DiTArchConfig):
+    """Architecture config for HunyuanGameCraft transformer."""
+
+    # Version field for compatibility with saved config.json
+    _fastvideo_version: str = "0.1.0"
+
+    # Camera net flag (for config.json compatibility)
+    camera_net: bool = True
+
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda:
+        [is_double_block, is_single_block, is_refiner_block, is_camera_net])
+
+    _compile_conditions: list = field(
+        default_factory=lambda: [is_double_block, is_single_block, is_txt_in])
+
+    # Parameter names mapping from official checkpoint to FastVideo naming
+    # GameCraft weights are already close to FastVideo format with minor adjustments
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            # MLP naming: fc1 -> fc_in, fc2 -> fc_out
+            r"^(.*)\.img_mlp\.fc1\.(.*)$":
+            r"\1.img_mlp.fc_in.\2",
+            r"^(.*)\.img_mlp\.fc2\.(.*)$":
+            r"\1.img_mlp.fc_out.\2",
+            r"^(.*)\.txt_mlp\.fc1\.(.*)$":
+            r"\1.txt_mlp.fc_in.\2",
+            r"^(.*)\.txt_mlp\.fc2\.(.*)$":
+            r"\1.txt_mlp.fc_out.\2",
+
+            # Single block MLP naming
+            r"^single_blocks\.(\d+)\.mlp\.fc1\.(.*)$":
+            r"single_blocks.\1.mlp.fc_in.\2",
+            r"^single_blocks\.(\d+)\.mlp\.fc2\.(.*)$":
+            r"single_blocks.\1.mlp.fc_out.\2",
+
+            # Token refiner naming
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.(.*)$":
+            r"txt_in.refiner_blocks.\1.\2",
+
+            # Vector in naming
+            r"^vector_in\.in_layer\.(.*)$":
+            r"vector_in.fc_in.\1",
+            r"^vector_in\.out_layer\.(.*)$":
+            r"vector_in.fc_out.\1",
+
+            # Time embedder naming
+            r"^time_in\.mlp\.0\.(.*)$":
+            r"time_in.mlp.fc_in.\1",
+            r"^time_in\.mlp\.2\.(.*)$":
+            r"time_in.mlp.fc_out.\1",
+
+            # Guidance embedder naming (if present)
+            r"^guidance_in\.mlp\.0\.(.*)$":
+            r"guidance_in.mlp.fc_in.\1",
+            r"^guidance_in\.mlp\.2\.(.*)$":
+            r"guidance_in.mlp.fc_out.\1",
+
+            # Final layer adaLN modulation
+            r"^final_layer\.adaLN_modulation\.1\.(.*)$":
+            r"final_layer.adaLN_modulation.linear.\1",
+
+            # Refiner block MLP naming
+            r"^txt_in\.refiner_blocks\.(\d+)\.mlp\.fc1\.(.*)$":
+            r"txt_in.refiner_blocks.\1.mlp.fc_in.\2",
+            r"^txt_in\.refiner_blocks\.(\d+)\.mlp\.fc2\.(.*)$":
+            r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
+
+            # Camera net weights are already correctly named
+        })
+
+    # Reverse mapping for saving checkpoints
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    # Model architecture parameters
+    # patch_size can be int or tuple - if tuple, it's [T, H, W]
+    patch_size: int | tuple[int, int, int] = 2
+    patch_size_t: int = 1
+    in_channels: int = 33  # 16 latent + 16 gt_latent + 1 mask
+    out_channels: int = 16
+    num_attention_heads: int = 24
+    attention_head_dim: int = 128
+    mlp_ratio: float = 4.0
+    num_layers: int = 20  # Double stream blocks
+    num_single_layers: int = 40  # Single stream blocks
+    num_refiner_layers: int = 2
+    rope_axes_dim: tuple[int, int, int] = (16, 56, 56)
+    guidance_embeds: bool = False  # GameCraft doesn't use guidance
+    dtype: torch.dtype | None = None
+    text_embed_dim: int = 4096  # LLaMA-3 hidden size
+    pooled_projection_dim: int = 768  # CLIP pooled output dim
+    rope_theta: int = 256
+    qk_norm: str = "rms_norm"
+
+    # Camera net parameters
+    camera_in_channels: int = 6  # Plücker coordinates
+    camera_downscale_coef: int = 8
+    camera_out_channels: int = 16
+
+    # Layers to exclude from LoRA
+    exclude_lora_layers: list[str] = field(
+        default_factory=lambda:
+        ["img_in", "txt_in", "time_in", "vector_in", "camera_net"])
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.hidden_size: int = self.attention_head_dim * self.num_attention_heads
+        self.num_channels_latents: int = 16  # Output is 16 channels
+
+        # Convert patch_size list to tuple if needed (from JSON deserialization)
+        if isinstance(self.patch_size, list):
+            self.patch_size = tuple(self.patch_size)
+
+        # Convert rope_axes_dim list to tuple if needed
+        if isinstance(self.rope_axes_dim, list):
+            self.rope_axes_dim = tuple(self.rope_axes_dim)
+
+
+@dataclass
+class HunyuanGameCraftConfig(DiTConfig):
+    """Full config for HunyuanGameCraft model."""
+
+    arch_config: DiTArchConfig = field(
+        default_factory=HunyuanGameCraftArchConfig)
+
+    prefix: str = "HunyuanGameCraft"

fastvideo/configs/models/dits/lingbotworld.py

@@ -0,0 +1,110 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_blocks(n: str, m) -> bool:
+    return "blocks" in n and str.isdigit(n.split(".")[-1])
+
+
+@dataclass
+class LingBotWorldArchConfig(DiTArchConfig):
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^patch_embedding\.(.*)$": r"patch_embedding.proj.\1",
+            r"^patch_embedding_wancamctrl\.(.*)$":
+            r"patch_embedding_wancamctrl.proj.\1",
+            r"^c2ws_hidden_states_layer1\.(.*)$": r"c2ws_mlp.fc_in.\1",
+            r"^c2ws_hidden_states_layer2\.(.*)$": r"c2ws_mlp.fc_out.\1",
+            r"^text_embedding\.0\.(.*)$":
+            r"condition_embedder.text_embedder.fc_in.\1",
+            r"^text_embedding\.2\.(.*)$":
+            r"condition_embedder.text_embedder.fc_out.\1",
+            r"^time_embedding\.0\.(.*)$":
+            r"condition_embedder.time_embedder.mlp.fc_in.\1",
+            r"^time_embedding\.2\.(.*)$":
+            r"condition_embedder.time_embedder.mlp.fc_out.\1",
+            r"^time_projection\.1\.(.*)$":
+            r"condition_embedder.time_modulation.linear.\1",
+            r"^blocks\.(\d+)\.modulation$": r"blocks.\1.scale_shift_table",
+            r"^blocks\.(\d+)\.self_attn\.q\.(.*)$": r"blocks.\1.to_q.\2",
+            r"^blocks\.(\d+)\.self_attn\.k\.(.*)$": r"blocks.\1.to_k.\2",
+            r"^blocks\.(\d+)\.self_attn\.v\.(.*)$": r"blocks.\1.to_v.\2",
+            r"^blocks\.(\d+)\.self_attn\.o\.(.*)$": r"blocks.\1.to_out.\2",
+            r"^blocks\.(\d+)\.self_attn\.norm_q\.(.*)$": r"blocks.\1.norm_q.\2",
+            r"^blocks\.(\d+)\.self_attn\.norm_k\.(.*)$": r"blocks.\1.norm_k.\2",
+            r"^blocks\.(\d+)\.norm3\.(.*)$":
+            r"blocks.\1.self_attn_residual_norm.norm.\2",
+            r"^blocks\.(\d+)\.cross_attn\.q\.(.*)$": r"blocks.\1.attn2.to_q.\2",
+            r"^blocks\.(\d+)\.cross_attn\.k\.(.*)$": r"blocks.\1.attn2.to_k.\2",
+            r"^blocks\.(\d+)\.cross_attn\.v\.(.*)$": r"blocks.\1.attn2.to_v.\2",
+            r"^blocks\.(\d+)\.cross_attn\.o\.(.*)$":
+            r"blocks.\1.attn2.to_out.\2",
+            r"^blocks\.(\d+)\.cross_attn\.norm_q\.(.*)$":
+            r"blocks.\1.attn2.norm_q.\2",
+            r"^blocks\.(\d+)\.cross_attn\.norm_k\.(.*)$":
+            r"blocks.\1.attn2.norm_k.\2",
+            r"^blocks\.(\d+)\.ffn\.0\.(.*)$": r"blocks.\1.ffn.fc_in.\2",
+            r"^blocks\.(\d+)\.ffn\.2\.(.*)$": r"blocks.\1.ffn.fc_out.\2",
+            r"^blocks\.(\d+)\.cam_injector_layer1\.(.*)$":
+            r"blocks.\1.cam_conditioner.cam_injector.fc_in.\2",
+            r"^blocks\.(\d+)\.cam_injector_layer2\.(.*)$":
+            r"blocks.\1.cam_conditioner.cam_injector.fc_out.\2",
+            r"^blocks\.(\d+)\.cam_scale_layer\.(.*)$":
+            r"blocks.\1.cam_conditioner.cam_scale_layer.\2",
+            r"^blocks\.(\d+)\.cam_shift_layer\.(.*)$":
+            r"blocks.\1.cam_conditioner.cam_shift_layer.\2",
+            r"^head\.modulation$": r"scale_shift_table",
+            r"^head\.head\.(.*)$": r"proj_out.\1",
+        })
+
+    # Reverse mapping for saving checkpoints: custom -> hf
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    # Some LoRA adapters use the original official layer names instead of hf layer names,
+    # so apply this before the param_names_mapping
+    lora_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    patch_size: tuple[int, int, int] = (1, 2, 2)
+    text_len: int = 512
+    num_attention_heads: int = 40
+    attention_head_dim: int = 128
+    in_channels: int = 16
+    out_channels: int = 16
+    text_dim: int = 4096
+    freq_dim: int = 256
+    ffn_dim: int = 13824
+    num_layers: int = 40
+    cross_attn_norm: bool = True
+    qk_norm: str = "rms_norm_across_heads"
+    eps: float = 1e-6
+    image_dim: int | None = None
+    added_kv_proj_dim: int | None = None
+    rope_max_seq_len: int = 1024
+    pos_embed_seq_len: int | None = None
+    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])
+
+    # Wan MoE
+    boundary_ratio: float | None = None
+
+    # Causal Wan
+    local_attn_size: int = -1  # Window size for temporal local attention (-1 indicates global attention)
+    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
+    num_frames_per_block: int = 3
+    sliding_window_num_frames: int = 21
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.out_channels = self.out_channels or self.in_channels
+        self.hidden_size = self.num_attention_heads * self.attention_head_dim
+        self.num_channels_latents = self.out_channels
+
+
+@dataclass
+class LingBotWorldVideoConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=LingBotWorldArchConfig)
+
+    prefix: str = "Wan"

fastvideo/configs/models/dits/sd3.py

@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+@dataclass
+class SD3Transformer2DArchConfig(DiTArchConfig):
+    # Diffusers SD3Transformer2DModel config fields.
+    sample_size: int = 128
+    patch_size: int = 2
+    num_layers: int = 24
+    attention_head_dim: int = 64
+    joint_attention_dim: int = 4096
+    caption_projection_dim: int = 1536
+    pooled_projection_dim: int = 2048
+    pos_embed_max_size: int = 384
+    dual_attention_layers: list[int] = field(
+        default_factory=lambda: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
+    qk_norm: str = "rms_norm"
+    in_channels: int = 16
+    out_channels: int = 16
+    num_attention_heads = 24
+
+
+@dataclass
+class SD3DiTConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(
+        default_factory=SD3Transformer2DArchConfig)
+    prefix: str = "sd3"

fastvideo/configs/models/vaes/__init__.py

@@ -1,5 +1,6 @@
 from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
 from fastvideo.configs.models.vaes.cosmos2_5vae import Cosmos25VAEConfig
+from fastvideo.configs.models.vaes.gamecraftvae import GameCraftVAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
 from fastvideo.configs.models.vaes.ltx2vae import LTX2VAEConfig
@@ -7,6 +8,7 @@ from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
 from fastvideo.configs.models.vaes.wanvae import WanVAEConfig
 
 __all__ = [
+    "GameCraftVAEConfig",
     "HunyuanVAEConfig",
     "WanVAEConfig",
     "StepVideoVAEConfig",

fastvideo/configs/models/vaes/autoencoder_kl.py

@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
+
+
+@dataclass
+class AutoencoderKLArchConfig(VAEArchConfig):
+    _name_or_path: str = ""
+    act_fn: str = "silu"
+    block_out_channels: tuple[int, ...] | list[int] = field(
+        default_factory=list)
+    down_block_types: tuple[str, ...] | list[str] = field(default_factory=list)
+    up_block_types: tuple[str, ...] | list[str] = field(default_factory=list)
+    force_upcast: bool = True
+    in_channels: int = 3
+    latent_channels: int = 4
+    latents_mean: tuple[float, ...] | list[float] | None = None
+    latents_std: tuple[float, ...] | list[float] | None = None
+    layers_per_block: int = 1
+    mid_block_add_attention: bool = True
+    norm_num_groups: int = 32
+    out_channels: int = 3
+    sample_size: int = 32
+    scaling_factor: float | torch.Tensor = 0.18215
+    shift_factor: float | None = None
+    use_post_quant_conv: bool = True
+    use_quant_conv: bool = True
+
+    temporal_compression_ratio: int = 1
+    spatial_compression_ratio: int = 8
+
+
+@dataclass
+class AutoencoderKLVAEConfig(VAEConfig):
+    arch_config: VAEArchConfig = field(default_factory=AutoencoderKLArchConfig)

fastvideo/configs/models/vaes/gamecraftvae.py

@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+GameCraft VAE config - matches official config.json from Hunyuan-GameCraft-1.0.
+"""
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
+
+
+@dataclass
+class GameCraftVAEArchConfig(VAEArchConfig):
+    """Architecture config matching official AutoencoderKLCausal3D config.json."""
+
+    in_channels: int = 3
+    out_channels: int = 3
+    latent_channels: int = 16
+    down_block_types: tuple[str, ...] = (
+        "DownEncoderBlockCausal3D",
+        "DownEncoderBlockCausal3D",
+        "DownEncoderBlockCausal3D",
+        "DownEncoderBlockCausal3D",
+    )
+    up_block_types: tuple[str, ...] = (
+        "UpDecoderBlockCausal3D",
+        "UpDecoderBlockCausal3D",
+        "UpDecoderBlockCausal3D",
+        "UpDecoderBlockCausal3D",
+    )
+    block_out_channels: tuple[int, ...] = (128, 256, 512, 512)
+    layers_per_block: int = 2
+    act_fn: str = "silu"
+    norm_num_groups: int = 32
+    scaling_factor: float = 0.476986
+    spatial_compression_ratio: int = 8
+    temporal_compression_ratio: int = 4
+    time_compression_ratio: int = 4  # alias for DecoderCausal3D
+    mid_block_add_attention: bool = True
+    mid_block_causal_attn: bool = True
+    sample_size: int = 256  # from config.json
+    sample_tsize: int = 64  # from config.json
+
+    def __post_init__(self):
+        self.spatial_compression_ratio = 2**(len(self.block_out_channels) - 1)
+
+
+@dataclass
+class GameCraftVAEConfig(VAEConfig):
+    """Full config for GameCraft VAE."""
+
+    arch_config: VAEArchConfig = field(default_factory=GameCraftVAEArchConfig)

fastvideo/configs/pipelines/__init__.py

@@ -4,6 +4,7 @@ from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
+from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
 from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
 from fastvideo.registry import get_pipeline_config_cls_from_name
@@ -13,10 +14,10 @@ from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig,
                                              WanT2V480PConfig, WanT2V720PConfig)
 
 __all__ = [
-    "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig",
-    "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig", "SlidingTileAttnConfig",
-    "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig",
-    "WanI2V720PConfig", "StepVideoT2VConfig", "SelfForcingWanT2V480PConfig",
-    "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig", "HYWorldConfig",
-    "get_pipeline_config_cls_from_name"
+    "HunyuanConfig", "FastHunyuanConfig", "HunyuanGameCraftPipelineConfig",
+    "PipelineConfig", "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig",
+    "SlidingTileAttnConfig", "WanT2V480PConfig", "WanI2V480PConfig",
+    "WanT2V720PConfig", "WanI2V720PConfig", "StepVideoT2VConfig",
+    "SelfForcingWanT2V480PConfig", "CosmosConfig", "Cosmos25Config",
+    "LTX2T2VConfig", "HYWorldConfig", "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/hunyuangamecraft.py

@@ -0,0 +1,122 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Pipeline configuration for HunyuanGameCraft.
+
+HunyuanGameCraft extends HunyuanVideo with:
+1. CameraNet for camera/action conditioning (Plücker coordinates)
+2. Mask-based conditioning for autoregressive generation
+3. 33 input channels (16 latent + 16 gt_latent + 1 mask)
+
+Text encoders are the same as HunyuanVideo:
+- LLaVA-LLaMA-3-8B for primary text encoding (4096 dim)
+- CLIP ViT-L/14 for secondary pooled embeddings (768 dim)
+"""
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import TypedDict
+
+import torch
+
+from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.configs.models.dits import HunyuanGameCraftConfig
+from fastvideo.configs.models.encoders import (
+    BaseEncoderOutput,
+    CLIPTextConfig,
+    LlamaConfig,
+)
+from fastvideo.configs.models.vaes import GameCraftVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig
+
+# GameCraft uses the same prompt template as HunyuanVideo
+PROMPT_TEMPLATE_ENCODE_VIDEO = (
+    "<|start_header_id|>system<|end_header_id|>\n\nDescribe 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:<|eot_id|>"
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>")
+
+
+class PromptTemplate(TypedDict):
+    template: str
+    crop_start: int
+
+
+prompt_template_video: PromptTemplate = {
+    "template": PROMPT_TEMPLATE_ENCODE_VIDEO,
+    "crop_start": 95,
+}
+
+
+def llama_preprocess_text(prompt: str) -> str:
+    """Apply prompt template for LLaMA encoder."""
+    return prompt_template_video["template"].format(prompt)
+
+
+def llama_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
+    """Extract hidden states from LLaMA output, skipping instruction tokens."""
+    hidden_state_skip_layer = 2
+    assert outputs.hidden_states is not None
+    hidden_states: tuple[torch.Tensor, ...] = outputs.hidden_states
+    last_hidden_state: torch.Tensor = hidden_states[-(hidden_state_skip_layer +
+                                                      1)]
+    crop_start = prompt_template_video.get("crop_start", -1)
+    last_hidden_state = last_hidden_state[:, crop_start:]
+    return last_hidden_state
+
+
+def clip_preprocess_text(prompt: str) -> str:
+    """No preprocessing for CLIP encoder."""
+    return prompt
+
+
+def clip_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
+    """Extract pooled output from CLIP encoder."""
+    pooler_output: torch.Tensor = outputs.pooler_output
+    return pooler_output
+
+
+@dataclass
+class HunyuanGameCraftPipelineConfig(PipelineConfig):
+    """Configuration for HunyuanGameCraft pipeline.
+    
+    Inherits text encoding from HunyuanVideo but uses:
+    - GameCraft DiT with CameraNet
+    - Same VAE (HunyuanVAE)
+    - Same text encoders (LLaMA + CLIP)
+    """
+
+    # DiT config - uses GameCraft config (33 input channels)
+    dit_config: DiTConfig = field(default_factory=HunyuanGameCraftConfig)
+
+    # VAE config - GameCraft VAE (mid_block_causal_attn=True, etc.)
+    vae_config: VAEConfig = field(default_factory=GameCraftVAEConfig)
+
+    # Denoising parameters
+    # Official GameCraft does NOT use embedded guidance (passes guidance=None)
+    # It uses standard CFG with guidance_scale=6.0 instead
+    embedded_cfg_scale = None
+    flow_shift: int = 5  # Official GameCraft uses flow_shift=5.0
+
+    # Text encoding stage - same as HunyuanVideo
+    # Uses LLaMA-3-8B (via LLaVA) + CLIP
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (LlamaConfig(), CLIPTextConfig()))
+    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
+        default_factory=lambda: (llama_preprocess_text, clip_preprocess_text))
+    postprocess_text_funcs: tuple[
+        Callable[[BaseEncoderOutput], torch.Tensor],
+        ...] = field(default_factory=lambda:
+                     (llama_postprocess_text, clip_postprocess_text))
+
+    # Precision for each component
+    dit_precision: str = "bf16"
+    vae_precision: str = "fp16"
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("fp16", "fp16"))
+
+    def __post_init__(self):
+        # VAE only needs decoder for inference
+        self.vae_config.load_encoder = False
+        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/lingbotworld.py

@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models import DiTConfig
+from fastvideo.configs.pipelines.wan import Wan2_2_I2V_A14B_Config
+from fastvideo.configs.models.dits.lingbotworld import LingBotWorldVideoConfig
+
+
+@dataclass
+class LingBotWorldI2V480PConfig(Wan2_2_I2V_A14B_Config):
+    dit_config: DiTConfig = field(default_factory=LingBotWorldVideoConfig)
+    flow_shift: float | None = 10.0
+    boundary_ratio: float | None = 0.947

fastvideo/configs/pipelines/sd35.py

@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models import EncoderConfig
+from fastvideo.configs.models.encoders import (
+    BaseEncoderOutput,
+    CLIPTextConfig,
+    T5Config,
+)
+from fastvideo.configs.models.dits.sd3 import SD3DiTConfig
+from fastvideo.configs.models.vaes.autoencoder_kl import AutoencoderKLVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
+
+
+def _sd35_text_postprocess(outputs: BaseEncoderOutput) -> torch.Tensor:
+    assert outputs.last_hidden_state is not None
+    return outputs.last_hidden_state
+
+
+@dataclass
+class SD35Config(PipelineConfig):
+
+    scheduler_arch: str = "FlowMatchEulerDiscreteScheduler"
+    transformer_arch: str = "SD3Transformer2DModel"
+    vae_arch: str = "AutoencoderKL"
+    text_encoder_archs: tuple[str, ...] = (
+        "CLIPTextModelWithProjection",
+        "CLIPTextModelWithProjection",
+        "T5EncoderModel",
+    )
+    tokenizer_archs: tuple[str, ...] = (
+        "CLIPTokenizer",
+        "CLIPTokenizer",
+        "T5TokenizerFast",
+    )
+
+    dit_config: SD3DiTConfig = field(default_factory=SD3DiTConfig)
+    vae_config: AutoencoderKLVAEConfig = field(
+        default_factory=AutoencoderKLVAEConfig)
+
+    embedded_cfg_scale: float = 0.0
+    flow_shift: float | None = None
+
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda:
+        (CLIPTextConfig(), CLIPTextConfig(), T5Config()))
+    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
+        default_factory=lambda:
+        (preprocess_text, preprocess_text, preprocess_text))
+    postprocess_text_funcs: tuple[
+        Callable[[BaseEncoderOutput], torch.Tensor],
+        ...] = field(default_factory=lambda:
+                     (_sd35_text_postprocess, _sd35_text_postprocess,
+                      _sd35_text_postprocess))
+
+    dit_precision: str = "bf16"
+    vae_precision: str = "fp32"
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("fp32", "fp32", "bf16"))

fastvideo/configs/sample/__init__.py

@@ -1,3 +1,13 @@
 from fastvideo.configs.sample.base import SamplingParam
+from fastvideo.configs.sample.hunyuangamecraft import (
+    HunyuanGameCraftSamplingParam,
+    HunyuanGameCraft65FrameSamplingParam,
+    HunyuanGameCraft129FrameSamplingParam,
+)
 
-__all__ = ["SamplingParam"]
+__all__ = [
+    "SamplingParam",
+    "HunyuanGameCraftSamplingParam",
+    "HunyuanGameCraft65FrameSamplingParam",
+    "HunyuanGameCraft129FrameSamplingParam",
+]

fastvideo/configs/sample/base.py

@@ -31,6 +31,9 @@ class SamplingParam:
     # Camera control inputs (HYWorld)
     pose: str | None = None  # Camera trajectory: pose string (e.g., 'w-31') or JSON file path
 
+    # Camera control inputs (LingBotWorld)
+    c2ws_plucker_emb: Any | None = None  # Plucker embedding: [B, C, F_lat, H_lat, W_lat]
+
     # Refine inputs (LongCat 480p->720p upscaling)
     # Path-based refine (load stage1 video from disk, e.g. MP4)
     refine_from: str | None = None  # Path to stage1 video (480p output from distill)

fastvideo/configs/sample/hunyuangamecraft.py

@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Sampling parameters for HunyuanGameCraft video generation.
+
+GameCraft generates game-like videos with camera/action control.
+Default parameters are based on the official implementation.
+"""
+from dataclasses import dataclass, field
+from typing import Any
+
+from fastvideo.configs.sample.base import SamplingParam
+from fastvideo.configs.sample.teacache import TeaCacheParams
+
+
+@dataclass
+class HunyuanGameCraftSamplingParam(SamplingParam):
+    """Sampling parameters for HunyuanGameCraft video generation.
+    
+    Supports camera/action conditioning via:
+    - camera_trajectory: Plücker coordinates for camera motion
+    - action_list: List of actions (e.g., ["forward", "left", "right"])
+    - action_speed_list: Speed multipliers for each action
+    
+    Default resolution is 704x1280 (same as HunyuanVideo).
+    Default frame count is 33 video frames -> 9 latent frames.
+    """
+
+    # Number of denoising steps
+    num_inference_steps: int = 50
+
+    # Video dimensions
+    # 33 video frames -> 9 latent frames (4x temporal compression)
+    num_frames: int = 33
+    height: int = 704
+    width: int = 1280
+    fps: int = 24
+
+    # Guidance scale - official GameCraft uses CFG with guidance_scale=6.0
+    guidance_scale: float = 6.0
+
+    # Negative prompt for CFG (empty string = unconditional)
+    negative_prompt: str = ""
+
+    # Camera/Action conditioning
+    # Camera states as Plücker coordinates [B, T_video, 6, H, W]
+    camera_states: Any | None = None
+
+    # Camera trajectory file/identifier (alternative to camera_states)
+    camera_trajectory: str | None = None
+
+    # Action list for camera motion (e.g., ["forward", "left"])
+    action_list: list[str] | None = None
+
+    # Speed multipliers for each action
+    action_speed_list: list[float] | None = None
+
+    # History frame conditioning (for autoregressive generation)
+    # Ground truth latents for conditioning [B, 16, T, H, W]
+    gt_latents: Any | None = None
+
+    # Mask for conditioning (1=use gt, 0=generate) [B, 1, T, H, W]
+    conditioning_mask: Any | None = None
+
+    # Number of conditioning frames (for autoregressive) - maps to num_cond_frames
+    num_cond_frames: int = 0
+
+    # TeaCache parameters (if enabled)
+    teacache_params: TeaCacheParams = field(
+        default_factory=lambda: TeaCacheParams(
+            teacache_thresh=0.15,
+            coefficients=[
+                7.33226126e+02, -4.01131952e+02, 6.75869174e+01,
+                -3.14987800e+00, 9.61237896e-02
+            ],
+        ))
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        # Validate action lists
+        if (self.action_list is not None and self.action_speed_list is not None
+                and len(self.action_list) != len(self.action_speed_list)):
+            raise ValueError(
+                f"action_list length ({len(self.action_list)}) must match "
+                f"action_speed_list length ({len(self.action_speed_list)})")
+
+
+@dataclass
+class HunyuanGameCraft65FrameSamplingParam(HunyuanGameCraftSamplingParam):
+    """Sampling parameters for 65-frame GameCraft generation.
+    
+    65 video frames -> 17 latent frames (with first frame as key frame).
+    This is useful for longer video generation.
+    """
+    num_frames: int = 65
+
+
+@dataclass
+class HunyuanGameCraft129FrameSamplingParam(HunyuanGameCraftSamplingParam):
+    """Sampling parameters for 129-frame GameCraft generation.
+    
+    129 video frames -> 33 latent frames.
+    This is the maximum supported by the official implementation.
+    """
+    num_frames: int = 129

fastvideo/configs/sample/lingbotworld.py

@@ -0,0 +1,21 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+from fastvideo.configs.sample.wan import Wan2_2_I2V_A14B_SamplingParam
+
+
+@dataclass
+class LingBotWorld_SamplingParam(Wan2_2_I2V_A14B_SamplingParam):
+    guidance_scale: float = 5.0  # high_noise
+    guidance_scale_2: float = 5.0  # low_noise
+    num_inference_steps: int = 70
+    boundary_ratio: float | None = 0.947
+    negative_prompt: str | None = (
+        "画面突变，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，"
+        "最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，"
+        "畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走，"
+        "镜头晃动，画面闪烁，模糊，噪点，水印，签名，文字，变形，扭曲，液化，不合逻辑的结构，卡顿，"
+        "PPT幻灯片感，过暗，欠曝，低对比度，霓虹灯光感，过度锐化，3D渲染感，人物，行人，游客，身体，"
+        "皮肤，肢体，面部特征，汽车，电线")
+    fps: int = 16
+    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
+    # can be overridden during sampling

fastvideo/configs/sample/ltx2.py

@@ -9,6 +9,7 @@ class LTX2BaseSamplingParam(SamplingParam):
     """Default sampling parameters for LTX-2 base one-stage T2V.
 
     Values follow the official LTX-2 one-stage defaults.
+    Multi-modal CFG params are read by ``LTX2DenoisingStage``.
     """
 
     seed: int = 10
@@ -37,6 +38,12 @@ class LTX2BaseSamplingParam(SamplingParam):
         "awkward pauses, incorrect timing, unnatural transitions, "
         "inconsistent framing, tilted camera, flat lighting, inconsistent "
         "tone, cinematic oversaturation, stylized filters, or AI artifacts.")
+    # Official LTX-2 multi-modal CFG defaults.
+    ltx2_cfg_scale_video: float = 3.0
+    ltx2_cfg_scale_audio: float = 7.0
+    ltx2_modality_scale_video: float = 3.0
+    ltx2_modality_scale_audio: float = 3.0
+    ltx2_rescale_scale: float = 0.7
 
 
 @dataclass

fastvideo/configs/sample/sd35.py

@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class SD35SamplingParam(SamplingParam):
+
+    prompt: str | None = "a photo of a cat"
+    negative_prompt: str = ""
+
+    num_videos_per_prompt: int = 1
+    seed: int = 0
+
+    num_frames: int = 1
+    height: int = 512
+    width: int = 512
+    fps: int = 1
+
+    num_inference_steps: int = 28
+    guidance_scale: float = 6.0

fastvideo/entrypoints/video_generator.py

@@ -223,64 +223,82 @@ class VideoGenerator:
                                            sampling_param=sampling_param,
                                            **kwargs)
 
+    def _is_image_workload(self) -> bool:
+        """Return True when the workload produces a single image (t2i, i2i …)."""
+        args = getattr(self, "fastvideo_args", None)
+        if args is None:
+            return False
+        return args.workload_type.value.endswith("2i")
+
     def _prepare_output_path(
         self,
         output_path: str,
         prompt: str,
     ) -> str:
-        """Build a unique, sanitized .mp4 output file path.
+        """Build a unique, sanitized output file path.
 
-        - If `output_path` ends with .mp4 (case-insensitive), treat it as a file path.
-        - Otherwise, treat `output_path` as a directory and derive the filename
-          from the prompt.
+        The file extension is chosen automatically based on the workload type:
+        ``.png`` for image workloads (``t2i``, ``i2i``, …) and ``.mp4`` for
+        video workloads.
+
+        - If ``output_path`` already carries the correct extension, treat it
+          as a file path.
+        - Otherwise, treat ``output_path`` as a directory and derive the
+          filename from the prompt.
         - Invalid filename characters are removed; if the name changes, a
           warning is logged.
         - If the target path already exists, a numeric suffix is appended.
         """
+        target_ext = ".png" if self._is_image_workload() else ".mp4"
 
         def _sanitize_filename_component(name: str) -> str:
             # Remove characters invalid on common filesystems, strip spaces/dots
             sanitized = re.sub(r'[\\/:*?"<>|]', '', name)
             sanitized = sanitized.strip().strip('.')
             sanitized = re.sub(r'\s+', ' ', sanitized)
-            return sanitized or "video"
+            return sanitized or "output"
 
         base_path, extension = os.path.splitext(output_path)
         extension_lower = extension.lower()
 
-        if extension_lower == ".mp4":
+        if extension_lower == target_ext:
             output_dir = os.path.dirname(output_path)
             base_name = os.path.basename(
                 base_path)  # filename without extension
             sanitized_base = _sanitize_filename_component(base_name)
             if sanitized_base != base_name:
                 logger.warning(
-                    "The video name '%s' contained invalid characters. It has been renamed to '%s.mp4'",
+                    "The output name '%s' contained invalid characters. "
+                    "It has been renamed to '%s%s'",
                     os.path.basename(output_path),
                     sanitized_base,
+                    target_ext,
                 )
-            video_name = f"{sanitized_base}.mp4"
+            out_name = f"{sanitized_base}{target_ext}"
         else:
-            # Treat as directory; inform if an unexpected extension was provided.
+            # Treat as directory; inform if an unexpected extension was
+            # provided.
             if extension:
                 logger.info(
-                    "Output path '%s' has non-mp4 extension '%s'; treating it as a directory and using a .mp4 filename derived from the prompt",
+                    "Output path '%s' has extension '%s' which does not "
+                    "match the target '%s'; treating it as a directory",
                     output_path,
                     extension,
+                    target_ext,
                 )
             output_dir = output_path
             prompt_component = _sanitize_filename_component(prompt[:100])
-            video_name = f"{prompt_component}.mp4"
+            out_name = f"{prompt_component}{target_ext}"
 
         if output_dir:
             os.makedirs(output_dir, exist_ok=True)
 
-        new_output_path = os.path.join(output_dir, video_name)
+        new_output_path = os.path.join(output_dir, out_name)
         counter = 1
         while os.path.exists(new_output_path):
-            name_part, ext_part = os.path.splitext(video_name)
-            new_video_name = f"{name_part}_{counter}{ext_part}"
-            new_output_path = os.path.join(output_dir, new_video_name)
+            name_part, ext_part = os.path.splitext(out_name)
+            new_name = f"{name_part}_{counter}{ext_part}"
+            new_output_path = os.path.join(output_dir, new_name)
             counter += 1
         return new_output_path
 
@@ -426,15 +444,25 @@ class VideoGenerator:
             x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
             frames.append((x * 255).numpy().astype(np.uint8))
 
-        # Save video if requested
+        # Save output if requested
         if batch.save_video:
-            imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
-            logger.info("Saved video to %s", output_path)
-            audio = output_batch.extra.get("audio")
-            audio_sample_rate = output_batch.extra.get("audio_sample_rate")
-            if (audio is not None and audio_sample_rate is not None and
-                    not self._mux_audio(output_path, audio, audio_sample_rate)):
-                logger.warning("Audio mux failed; saved video without audio.")
+            if self._is_image_workload():
+                # Image workloads (t2i, i2i, …): save the first frame as PNG.
+                imageio.imwrite(output_path, frames[0])
+                logger.info("Saved image to %s", output_path)
+            else:
+                imageio.mimsave(output_path,
+                                frames,
+                                fps=batch.fps,
+                                format="mp4")
+                logger.info("Saved video to %s", output_path)
+                audio = output_batch.extra.get("audio")
+                audio_sample_rate = output_batch.extra.get("audio_sample_rate")
+                if (audio is not None and audio_sample_rate is not None
+                        and not self._mux_audio(output_path, audio,
+                                                audio_sample_rate)):
+                    logger.warning(
+                        "Audio mux failed; saved video without audio.")
 
         if batch.return_frames:
             return frames

fastvideo/layers/rotary_embedding.py

@@ -109,26 +109,45 @@ def _apply_rotary_emb(
     """
     Args:
         x: [num_tokens, num_heads, head_size]
-        cos: [num_tokens, head_size // 2]
-        sin: [num_tokens, head_size // 2]
+        cos: [num_tokens, head_size] or [num_tokens, head_size // 2]
+        sin: [num_tokens, head_size] or [num_tokens, head_size // 2]
         is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
             positional embeddings.
+    
+    The function auto-detects whether cos/sin are full or half head_size:
+    - If cos/sin have head_size: use rotate_half style (for HunyuanVideo/GameCraft)
+    - If cos/sin have head_size // 2: use Neox/GPT-J style
     """
-    # cos = cos.unsqueeze(-2).to(x.dtype)
-    # sin = sin.unsqueeze(-2).to(x.dtype)
-    cos = cos.unsqueeze(-2)
-    sin = sin.unsqueeze(-2)
-    if is_neox_style:
-        x1, x2 = torch.chunk(x, 2, dim=-1)
+    head_size = x.shape[-1]
+    rope_dim = cos.shape[-1]
+
+    # Check if cos/sin are full head_dim (rotate_half style) or half (traditional style)
+    if rope_dim == head_size:
+        # Full head_dim - use rotate_half style (HunyuanVideo, GameCraft)
+        # x * cos + rotate_half(x) * sin
+        cos = cos.unsqueeze(-2)  # [num_tokens, 1, head_size]
+        sin = sin.unsqueeze(-2)  # [num_tokens, 1, head_size]
+        # rotate_half: split into pairs, negate and swap
+        x_real, x_imag = x.float().reshape(*x.shape[:-1], -1,
+                                           2).unbind(-1)  # [B, H, D//2] each
+        x_rotated = torch.stack([-x_imag, x_real],
+                                dim=-1).flatten(-2)  # [B, H, D]
+        return (x.float() * cos + x_rotated * sin).type_as(x)
     else:
-        x1 = x[..., ::2]
-        x2 = x[..., 1::2]
-    o1 = (x1.float() * cos - x2.float() * sin).type_as(x)
-    o2 = (x2.float() * cos + x1.float() * sin).type_as(x)
-    if is_neox_style:
-        return torch.cat((o1, o2), dim=-1)
-    else:
-        return torch.stack((o1, o2), dim=-1).flatten(-2)
+        # Half head_dim - use traditional Neox/GPT-J style
+        cos = cos.unsqueeze(-2)
+        sin = sin.unsqueeze(-2)
+        if is_neox_style:
+            x1, x2 = torch.chunk(x, 2, dim=-1)
+        else:
+            x1 = x[..., ::2]
+            x2 = x[..., 1::2]
+        o1 = (x1.float() * cos - x2.float() * sin).type_as(x)
+        o2 = (x2.float() * cos + x1.float() * sin).type_as(x)
+        if is_neox_style:
+            return torch.cat((o1, o2), dim=-1)
+        else:
+            return torch.stack((o1, o2), dim=-1).flatten(-2)
 
 
 @CustomOp.register("rotary_embedding")
@@ -278,6 +297,7 @@ def get_1d_rotary_pos_embed(
     theta_rescale_factor: float = 1.0,
     interpolation_factor: float = 1.0,
     dtype: torch.dtype = torch.float32,
+    use_real: bool = True,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
     Precompute the frequency tensor for complex exponential (cis) with given dimensions.
@@ -292,9 +312,12 @@ def get_1d_rotary_pos_embed(
         theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
         theta_rescale_factor (float, optional): Rescale factor for theta. Defaults to 1.0.
         interpolation_factor (float, optional): Factor to scale positions. Defaults to 1.0.
+        use_real (bool, optional): If True, output full head_dim with repeated cos/sin for 
+            rotate_half style RoPE. If False, output half head_dim for complex style. Defaults to True.
 
     Returns:
-        freqs_cos, freqs_sin: Precomputed frequency tensor with real and imaginary parts separately. [S, D]
+        freqs_cos, freqs_sin: Precomputed frequency tensor with real and imaginary parts separately. 
+            Shape is [S, D] if use_real=True, [S, D/2] if use_real=False.
     """
     if isinstance(pos, int):
         pos = torch.arange(pos).float()
@@ -309,6 +332,16 @@ def get_1d_rotary_pos_embed(
     freqs = torch.outer(pos * interpolation_factor, freqs)  # [S, D/2]
     freqs_cos = freqs.cos()  # [S, D/2]
     freqs_sin = freqs.sin()  # [S, D/2]
+
+    if use_real:
+        # For rotate_half style RoPE (used by HunyuanVideo, GameCraft),
+        # we need to expand cos/sin to full head_dim using repeat_interleave.
+        # The rotate_half operation works on consecutive PAIRS: (x0,x1), (x2,x3)...
+        # so cos/sin must be interleaved: [c0,c0,c1,c1,...] to match the pairing.
+        # Using torch.cat would produce [c0,c1,...,c0,c1,...] which is WRONG.
+        freqs_cos = freqs_cos.repeat_interleave(2, dim=-1)  # [S, D]
+        freqs_sin = freqs_sin.repeat_interleave(2, dim=-1)  # [S, D]
+
     return freqs_cos, freqs_sin
 
 
@@ -324,6 +357,7 @@ def get_nd_rotary_pos_embed(
     sp_world_size: int = 1,
     dtype: torch.dtype = torch.float32,
     start_frame: int = 0,
+    use_real: bool = True,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
     This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure.
@@ -341,9 +375,10 @@ def get_nd_rotary_pos_embed(
         shard_dim (int): Which dimension to shard for sequence parallelism. Defaults to 0.
         sp_rank (int): Rank in the sequence parallel group. Defaults to 0.
         sp_world_size (int): World size of the sequence parallel group. Defaults to 1.
+        use_real (bool): If True, output full head_dim for rotate_half style. Defaults to True.
 
     Returns:
-        Tuple[torch.Tensor, torch.Tensor]: (cos, sin) tensors of shape [HW, D/2]
+        Tuple[torch.Tensor, torch.Tensor]: (cos, sin) tensors of shape [HW, D] if use_real, [HW, D/2] otherwise
     """
     # Get the full grid
     full_grid = get_meshgrid_nd(
@@ -412,11 +447,12 @@ def get_nd_rotary_pos_embed(
             theta_rescale_factor=theta_rescale_factor[i],
             interpolation_factor=interpolation_factor[i],
             dtype=dtype,
-        )  # 2 x [WHD, rope_dim_list[i]]
+            use_real=use_real,
+        )  # 2 x [WHD, rope_dim_list[i]] or 2 x [WHD, rope_dim_list[i]*2] if use_real
         embs.append(emb)
 
-    cos = torch.cat([emb[0] for emb in embs], dim=1)  # (WHD, D/2)
-    sin = torch.cat([emb[1] for emb in embs], dim=1)  # (WHD, D/2)
+    cos = torch.cat([emb[0] for emb in embs], dim=1)  # (WHD, D) or (WHD, D/2)
+    sin = torch.cat([emb[1] for emb in embs], dim=1)  # (WHD, D) or (WHD, D/2)
     return cos, sin
 
 
@@ -432,6 +468,7 @@ def get_rotary_pos_embed(
     do_sp_sharding: bool = False,
     dtype: torch.dtype = torch.float32,
     start_frame: int = 0,
+    use_real: bool = True,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
     Generate rotary positional embeddings for the given sizes.
@@ -446,9 +483,10 @@ def get_rotary_pos_embed(
         interpolation_factor: Factor to scale positions. Defaults to 1.0
         shard_dim: Which dimension to shard for sequence parallelism. Defaults to 0.
         do_sp_sharding: Whether to shard the positional embeddings for sequence parallelism. Defaults to False.
+        use_real: If True, output full head_dim for rotate_half style RoPE. Defaults to True.
         
     Returns:
-        Tuple of (cos, sin) tensors for rotary embeddings
+        Tuple of (cos, sin) tensors for rotary embeddings. Shape [S, D] if use_real, [S, D/2] otherwise.
     """
 
     target_ndim = 3
@@ -481,6 +519,7 @@ def get_rotary_pos_embed(
         sp_world_size=sp_world_size,
         dtype=dtype,
         start_frame=start_frame,
+        use_real=use_real,
     )
     return freqs_cos, freqs_sin
 

fastvideo/layers/visual_embedding.py

@@ -60,6 +60,61 @@ class PatchEmbed(nn.Module):
         return x
 
 
+class WanCamControlPatchEmbedding(nn.Module):
+    """Lingbot World Patch embedding for Plucker features."""
+
+    def __init__(
+            self,
+            patch_size=(1, 2, 2),
+            in_chans=384,  # 6 * 64
+            embed_dim=2048,
+            bias=True,
+            dtype=None,
+            prefix: str = ""):
+        super().__init__()
+        # must be 3-tuple
+        if isinstance(patch_size, list | tuple):
+            if len(patch_size) != 3:
+                raise ValueError(
+                    f"patch_size must have length 3, got {len(patch_size)}")
+        else:
+            raise ValueError(f"Unsupported patch_size type: {type(patch_size)}")
+
+        self.patch_size = patch_size
+        pt, ph, pw = self.patch_size
+        self.in_features = in_chans * pt * ph * pw
+        self.proj = nn.Linear(self.in_features,
+                              embed_dim,
+                              bias=bias,
+                              dtype=dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if x.dim() != 5:
+            raise ValueError(
+                f"Expected camera embedding shape [B, C, F, H, W], got {x.shape}"
+            )
+        bsz, channels, frames, height, width = x.shape
+        pt, ph, pw = self.patch_size
+        if (frames % pt) != 0 or (height % ph) != 0 or (width % pw) != 0:
+            raise ValueError(
+                f"Input shape {x.shape} must be divisible by patch_size {self.patch_size}"
+            )
+
+        # '1 c (f c1) (h c2) (w c3) -> 1 (f h w) (c c1 c2 c3)',
+        x = x.view(
+            bsz,
+            channels,
+            frames // pt,
+            pt,
+            height // ph,
+            ph,
+            width // pw,
+            pw,
+        )
+        x = x.permute(0, 2, 4, 6, 1, 3, 5, 7).reshape(bsz, -1, self.in_features)
+        return self.proj(x)
+
+
 class TimestepEmbedder(nn.Module):
     """
     Embeds scalar timesteps into vector representations.
@@ -252,4 +307,4 @@ class Timesteps(nn.Module):
             downscale_freq_shift=self.downscale_freq_shift,
             scale=self.scale,
         )
-        return t_emb
+        return t_emb

fastvideo/models/camera/__init__.py

@@ -0,0 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Camera trajectory utilities for video generation models."""
+
+from fastvideo.models.camera.trajectory import create_camera_trajectory
+
+__all__ = ["create_camera_trajectory"]

fastvideo/models/camera/trajectory.py

@@ -0,0 +1,395 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Camera trajectory generation for HunyuanGameCraft.
+
+Generates Plücker coordinate embeddings from simple action commands
+(forward, backward, left, right, rotations) for camera conditioning.
+
+This is a self-contained implementation that does not depend on the
+official Hunyuan-GameCraft-1.0 repository.
+"""
+import math
+
+import numpy as np
+import torch
+from packaging import version as pver
+
+
+# Action name -> motion type mapping
+ACTION_DICT = {
+    "w": "forward",
+    "a": "left",
+    "d": "right",
+    "s": "backward",
+    "forward": "forward",
+    "backward": "backward",
+    "left": "left",
+    "right": "right",
+    "left_rot": "left_rot",
+    "right_rot": "right_rot",
+    "up_rot": "up_rot",
+    "down_rot": "down_rot",
+}
+
+
+def _custom_meshgrid(*args):
+    """Torch meshgrid with consistent indexing."""
+    if pver.parse(torch.__version__) < pver.parse("1.10"):
+        return torch.meshgrid(*args)
+    else:
+        return torch.meshgrid(*args, indexing="ij")
+
+
+def _generate_motion_segment(
+    current_pose: dict,
+    motion_type: str,
+    value: float,
+    duration: int = 30,
+) -> tuple[list, list, dict]:
+    """
+    Generate camera motion segment.
+
+    Args:
+        current_pose: Dict with 'position' (xyz) and 'rotation' (pitch, yaw, roll).
+        motion_type: One of 'forward', 'backward', 'left', 'right',
+                     'left_rot', 'right_rot', 'up_rot', 'down_rot'.
+        value: Translation (meters) or rotation (degrees).
+        duration: Number of frames.
+
+    Returns:
+        positions: List of position arrays.
+        rotations: List of rotation arrays.
+        current_pose: Updated pose dict.
+    """
+    positions = []
+    rotations = []
+
+    if motion_type in ["forward", "backward"]:
+        yaw_rad = np.radians(current_pose["rotation"][1])
+        pitch_rad = np.radians(current_pose["rotation"][0])
+
+        forward_vec = np.array([
+            -math.sin(yaw_rad) * math.cos(pitch_rad),
+            math.sin(pitch_rad),
+            -math.cos(yaw_rad) * math.cos(pitch_rad),
+        ])
+
+        direction = 1 if motion_type == "forward" else -1
+        total_move = forward_vec * value * direction
+        step = total_move / duration
+
+        for i in range(1, duration + 1):
+            new_pos = current_pose["position"] + step * i
+            positions.append(new_pos.copy())
+            rotations.append(current_pose["rotation"].copy())
+
+        current_pose["position"] = positions[-1]
+
+    elif motion_type in ["left", "right"]:
+        yaw_rad = np.radians(current_pose["rotation"][1])
+        right_vec = np.array([math.cos(yaw_rad), 0, -math.sin(yaw_rad)])
+
+        direction = -1 if motion_type == "right" else 1
+        total_move = right_vec * value * direction
+        step = total_move / duration
+
+        for i in range(1, duration + 1):
+            new_pos = current_pose["position"] + step * i
+            positions.append(new_pos.copy())
+            rotations.append(current_pose["rotation"].copy())
+
+        current_pose["position"] = positions[-1]
+
+    elif motion_type.endswith("rot"):
+        axis = motion_type.split("_")[0]
+        total_rotation = np.zeros(3)
+
+        if axis == "left":
+            total_rotation[0] = value
+        elif axis == "right":
+            total_rotation[0] = -value
+        elif axis == "up":
+            total_rotation[2] = -value
+        elif axis == "down":
+            total_rotation[2] = value
+
+        step = total_rotation / duration
+
+        for i in range(1, duration + 1):
+            positions.append(current_pose["position"].copy())
+            new_rot = current_pose["rotation"] + step * i
+            rotations.append(new_rot.copy())
+
+        current_pose["rotation"] = rotations[-1]
+
+    return positions, rotations, current_pose
+
+
+def _euler_to_quaternion(angles: np.ndarray) -> list[float]:
+    """Convert Euler angles (pitch, yaw, roll in degrees) to quaternion."""
+    pitch, yaw, roll = np.radians(angles)
+
+    cy = math.cos(yaw * 0.5)
+    sy = math.sin(yaw * 0.5)
+    cp = math.cos(pitch * 0.5)
+    sp = math.sin(pitch * 0.5)
+    cr = math.cos(roll * 0.5)
+    sr = math.sin(roll * 0.5)
+
+    qw = cy * cp * cr + sy * sp * sr
+    qx = cy * cp * sr - sy * sp * cr
+    qy = sy * cp * sr + cy * sp * cr
+    qz = sy * cp * cr - cy * sp * sr
+
+    return [qw, qx, qy, qz]
+
+
+def _quaternion_to_rotation_matrix(q: list[float]) -> np.ndarray:
+    """Convert quaternion to 3x3 rotation matrix."""
+    qw, qx, qy, qz = q
+    return np.array([
+        [1 - 2 * (qy**2 + qz**2), 2 * (qx * qy - qw * qz), 2 * (qx * qz + qw * qy)],
+        [2 * (qx * qy + qw * qz), 1 - 2 * (qx**2 + qz**2), 2 * (qy * qz - qw * qx)],
+        [2 * (qx * qz - qw * qy), 2 * (qy * qz + qw * qx), 1 - 2 * (qx**2 + qy**2)],
+    ])
+
+
+def _action_to_pose_list(action_id: str, value: float = 0.2, duration: int = 33) -> list[str]:
+    """
+    Convert an action ID to a list of pose strings.
+
+    Args:
+        action_id: Action identifier (e.g., 'w', 'forward', 'left_rot').
+        value: Motion magnitude (translation in meters, rotation in degrees).
+        duration: Number of frames.
+
+    Returns:
+        List of pose strings in the official GameCraft format.
+    """
+    all_positions = []
+    all_rotations = []
+    current_pose = {
+        "position": np.array([0.0, 0.0, 0.0]),
+        "rotation": np.array([0.0, 0.0, 0.0]),
+    }
+    intrinsic = [0.50505, 0.8979, 0.5, 0.5]
+
+    motion_type = ACTION_DICT.get(action_id, action_id)
+    positions, rotations, current_pose = _generate_motion_segment(
+        current_pose, motion_type, value, duration
+    )
+    all_positions.extend(positions)
+    all_rotations.extend(rotations)
+
+    pose_list = []
+
+    # First frame: identity pose
+    row = [0] + intrinsic + [0, 0] + [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]
+    first_row = " ".join(map(str, row))
+    pose_list.append(first_row)
+
+    for i, (pos, rot) in enumerate(zip(all_positions, all_rotations)):
+        quat = _euler_to_quaternion(rot)
+        R = _quaternion_to_rotation_matrix(quat)
+        extrinsic = np.hstack([R, pos.reshape(3, 1)])
+
+        row = [i] + intrinsic + [0, 0] + extrinsic.flatten().tolist()
+        pose_list.append(" ".join(map(str, row)))
+
+    return pose_list
+
+
+class _Camera:
+    """Camera parameters from a pose string."""
+
+    def __init__(self, entry: list[float]):
+        fx, fy, cx, cy = entry[1:5]
+        self.fx = fx
+        self.fy = fy
+        self.cx = cx
+        self.cy = cy
+        w2c_mat = np.array(entry[7:]).reshape(3, 4)
+        w2c_mat_4x4 = np.eye(4)
+        w2c_mat_4x4[:3, :] = w2c_mat
+        self.w2c_mat = w2c_mat_4x4
+        self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
+
+
+def _get_relative_pose(cam_params: list[_Camera]) -> np.ndarray:
+    """Convert camera parameters to relative poses."""
+    abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
+    abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
+    target_cam_c2w = np.array([
+        [1, 0, 0, 0],
+        [0, 1, 0, 0],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1],
+    ])
+    abs2rel = target_cam_c2w @ abs_w2cs[0]
+    ret_poses = [target_cam_c2w] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
+    for pose in ret_poses:
+        pose[:3, -1:] *= 10
+    ret_poses = np.array(ret_poses, dtype=np.float32)
+    return ret_poses
+
+
+def _get_c2w(w2cs: list[np.ndarray], transform_matrix: np.ndarray) -> np.ndarray:
+    """Convert w2c matrices to c2w with relative transform."""
+    target_cam_c2w = np.array([
+        [1, 0, 0, 0],
+        [0, 1, 0, 0],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1],
+    ])
+    abs2rel = target_cam_c2w @ w2cs[0]
+    ret_poses = [target_cam_c2w] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]]
+    for pose in ret_poses:
+        pose[:3, -1:] *= 2
+    ret_poses = [transform_matrix @ x for x in ret_poses]
+    return np.array(ret_poses, dtype=np.float32)
+
+
+def _ray_condition(
+    K: torch.Tensor,
+    c2w: torch.Tensor,
+    H: int,
+    W: int,
+    device: str | torch.device = "cpu",
+    flip_flag: torch.Tensor | None = None,
+) -> torch.Tensor:
+    """
+    Compute Plücker coordinates from camera intrinsics and extrinsics.
+
+    Args:
+        K: Intrinsics [B, V, 4] (fx, fy, cx, cy).
+        c2w: Camera-to-world matrices [B, V, 4, 4].
+        H: Image height.
+        W: Image width.
+        device: Torch device.
+        flip_flag: Optional flip flags [V].
+
+    Returns:
+        Plücker coordinates [B, V, H, W, 6].
+    """
+    B, V = K.shape[:2]
+
+    j, i = _custom_meshgrid(
+        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+    )
+    i = i.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5
+    j = j.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5
+
+    n_flip = torch.sum(flip_flag).item() if flip_flag is not None else 0
+    if n_flip > 0:
+        j_flip, i_flip = _custom_meshgrid(
+            torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+            torch.linspace(W - 1, 0, W, device=device, dtype=c2w.dtype),
+        )
+        i_flip = i_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5
+        j_flip = j_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5
+        i[:, flip_flag, ...] = i_flip
+        j[:, flip_flag, ...] = j_flip
+
+    fx, fy, cx, cy = K.chunk(4, dim=-1)
+
+    zs = torch.ones_like(i)
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+    zs = zs.expand_as(ys)
+
+    directions = torch.stack((xs, ys, zs), dim=-1)
+    directions = directions / directions.norm(dim=-1, keepdim=True)
+
+    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)
+    rays_o = c2w[..., :3, 3]
+    rays_o = rays_o[:, :, None].expand_as(rays_d)
+    rays_dxo = torch.linalg.cross(rays_o, rays_d)
+    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)
+    return plucker
+
+
+def create_camera_trajectory(
+    action: str,
+    height: int,
+    width: int,
+    num_frames: int,
+    action_speed: float = 0.2,
+    device: torch.device | str = "cpu",
+    dtype: torch.dtype = torch.bfloat16,
+) -> torch.Tensor:
+    """
+    Create Plücker coordinate embeddings from an action command.
+
+    Args:
+        action: One of 'forward', 'backward', 'left', 'right',
+                'left_rot', 'right_rot', 'up_rot', 'down_rot'
+                (or shorthand 'w', 'a', 's', 'd').
+        height: Video height in pixels.
+        width: Video width in pixels.
+        num_frames: Number of video frames.
+        action_speed: Speed of motion (default 0.2).
+        device: Torch device for output tensor.
+        dtype: Torch dtype for output tensor.
+
+    Returns:
+        camera_states: [1, num_frames, 6, height, width] Plücker embeddings.
+    """
+    # Generate pose list from action
+    poses = _action_to_pose_list(action, value=action_speed, duration=num_frames)
+
+    # Parse poses
+    poses_parsed = [pose.split(" ") for pose in poses]
+    start_idx = 0
+    sample_id = [start_idx + i for i in range(num_frames)]
+    poses_parsed = [poses_parsed[i] for i in sample_id]
+
+    # Convert to w2c matrices
+    w2cs = [np.asarray([float(p) for p in pose[7:]]).reshape(3, 4) for pose in poses_parsed]
+    transform_matrix = np.asarray(
+        [[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]
+    ).reshape(4, 4)
+    last_row = np.zeros((1, 4))
+    last_row[0, -1] = 1.0
+    w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs]
+    c2ws = _get_c2w(w2cs, transform_matrix)
+
+    # Parse camera parameters
+    cam_params = [[float(x) for x in pose] for pose in poses_parsed]
+    assert len(cam_params) == num_frames
+    cam_params = [_Camera(cam_param) for cam_param in cam_params]
+
+    # Compute scaled intrinsics
+    monst3r_w = cam_params[0].cx * 2
+    monst3r_h = cam_params[0].cy * 2
+    ratio_w, ratio_h = width / monst3r_w, height / monst3r_h
+    intrinsics = np.asarray(
+        [
+            [
+                cam_param.fx * ratio_w,
+                cam_param.fy * ratio_h,
+                cam_param.cx * ratio_w,
+                cam_param.cy * ratio_h,
+            ]
+            for cam_param in cam_params
+        ],
+        dtype=np.float32,
+    )
+    intrinsics = torch.as_tensor(intrinsics)[None]  # [1, n_frame, 4]
+
+    # Get relative poses
+    c2w_poses = _get_relative_pose(cam_params)
+    c2w = torch.as_tensor(c2w_poses)[None]  # [1, n_frame, 4, 4]
+
+    # Compute Plücker embeddings
+    flip_flag = torch.zeros(num_frames, dtype=torch.bool, device="cpu")
+    plucker_embedding = _ray_condition(intrinsics, c2w, height, width, device="cpu", flip_flag=flip_flag)
+    # [1, n_frame, H, W, 6] -> [1, n_frame, 6, H, W]
+    plucker_embedding = plucker_embedding[0].permute(0, 3, 1, 2).contiguous()
+
+    # Add batch dim and convert to target dtype/device
+    # Shape: [n_frame, 6, H, W] -> [1, n_frame, 6, H, W]
+    camera_states = plucker_embedding.unsqueeze(0).to(device=device, dtype=dtype)
+
+    return camera_states

fastvideo/models/dits/hunyuangamecraft.py

@@ -0,0 +1,363 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+HunyuanGameCraft Transformer model for FastVideo.
+
+Ported from official Hunyuan-GameCraft-1.0 implementation.
+"""
+from typing import Any, List, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+from fastvideo.configs.models.dits.hunyuangamecraft import (
+    HunyuanGameCraftArchConfig,
+    HunyuanGameCraftConfig,
+)
+from fastvideo.layers.linear import ReplicatedLinear
+from fastvideo.layers.mlp import MLP
+from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
+from fastvideo.layers.visual_embedding import ModulateProjection, PatchEmbed, TimestepEmbedder, unpatchify
+from fastvideo.models.dits.base import CachableDiT
+from fastvideo.models.dits.hunyuanvideo import (
+    MMDoubleStreamBlock,
+    MMSingleStreamBlock,
+    SingleTokenRefiner,
+)
+
+
+class GameCraftFinalLayer(nn.Module):
+    """
+    GameCraft-specific FinalLayer with correct shift/scale order.
+    
+    The official GameCraft implementation uses shift, scale order (not scale, shift).
+    This differs from the HunyuanVideo FinalLayer.
+    """
+
+    def __init__(self,
+                 hidden_size,
+                 patch_size,
+                 out_channels,
+                 dtype=None,
+                 prefix: str = "") -> None:
+        super().__init__()
+
+        self.norm_final = nn.LayerNorm(hidden_size,
+                                       eps=1e-6,
+                                       elementwise_affine=False,
+                                       dtype=dtype)
+
+        output_dim = patch_size[0] * patch_size[1] * patch_size[2] * out_channels
+
+        self.linear = ReplicatedLinear(hidden_size,
+                                       output_dim,
+                                       bias=True,
+                                       params_dtype=dtype,
+                                       prefix=f"{prefix}.linear")
+
+        self.adaLN_modulation = ModulateProjection(
+            hidden_size,
+            factor=2,
+            act_layer="silu",
+            dtype=dtype,
+            prefix=f"{prefix}.adaLN_modulation")
+
+    def forward(self, x, c):
+        # GameCraft uses shift, scale order (verified against official implementation)
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
+        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        x, _ = self.linear(x)
+        return x
+
+
+class CameraNet(nn.Module):
+    """
+    Camera state encoding network - ported from official GameCraft.
+    
+    Processes camera parameters (Plücker coordinates) into feature embeddings.
+    """
+    
+    def __init__(
+        self,
+        in_channels: int = 6,
+        downscale_coef: int = 8,
+        out_channels: int = 16,
+        patch_size: List[int] = [1, 2, 2],
+        hidden_size: int = 3072,
+        dtype: Optional[torch.dtype] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        _ = prefix  # Unused
+        
+        start_channels = in_channels * (downscale_coef ** 2)
+        input_channels = [start_channels, start_channels // 2, start_channels // 4]
+        self.input_channels = input_channels
+        
+        self.unshuffle = nn.PixelUnshuffle(downscale_coef)
+        
+        self.encode_first = nn.Sequential(
+            nn.Conv2d(input_channels[0], input_channels[1], kernel_size=1, stride=1, padding=0),
+            nn.GroupNorm(2, input_channels[1]),
+            nn.ReLU(),
+        )
+        self._initialize_weights(self.encode_first)
+        
+        self.encode_second = nn.Sequential(
+            nn.Conv2d(input_channels[1], input_channels[2], kernel_size=1, stride=1, padding=0),
+            nn.GroupNorm(2, input_channels[2]),
+            nn.ReLU(),
+        )
+        self._initialize_weights(self.encode_second)
+        
+        self.final_proj = nn.Conv2d(input_channels[2], out_channels, kernel_size=1)
+        self._zeros_init_linear(self.final_proj)
+        
+        self.scale = nn.Parameter(torch.ones(1))
+        
+        self.camera_in = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=out_channels,
+            embed_dim=hidden_size,
+        )
+    
+    def _zeros_init_linear(self, linear):
+        if hasattr(linear, "weight"):
+            nn.init.zeros_(linear.weight)
+        if hasattr(linear, "bias") and linear.bias is not None:
+            nn.init.zeros_(linear.bias)
+    
+    def _initialize_weights(self, block):
+        for m in block:
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
+                nn.init.normal_(m.weight, mean=0.0, std=np.sqrt(2.0 / n))
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+    
+    def compress_time(self, x: torch.Tensor, num_frames: int) -> torch.Tensor:
+        x = rearrange(x, '(b f) c h w -> b f c h w', f=num_frames)
+        batch_size, frames, channels, height, width = x.shape
+        x = rearrange(x, 'b f c h w -> (b h w) c f')
+        
+        if x.shape[-1] == 66 or x.shape[-1] == 34:
+            x_len = x.shape[-1]
+            x_clip1 = x[..., :x_len // 2]
+            x_clip1_first = x_clip1[..., 0].unsqueeze(-1)
+            x_clip1_rest = F.avg_pool1d(x_clip1[..., 1:], kernel_size=2, stride=2)
+            x_clip2 = x[..., x_len // 2:]
+            x_clip2_first = x_clip2[..., 0].unsqueeze(-1)
+            x_clip2_rest = F.avg_pool1d(x_clip2[..., 1:], kernel_size=2, stride=2)
+            x = torch.cat([x_clip1_first, x_clip1_rest, x_clip2_first, x_clip2_rest], dim=-1)
+        elif x.shape[-1] % 2 == 1:
+            x_first = x[..., 0]
+            x_rest = x[..., 1:]
+            if x_rest.shape[-1] > 0:
+                x_rest = F.avg_pool1d(x_rest, kernel_size=2, stride=2)
+            x = torch.cat([x_first[..., None], x_rest], dim=-1)
+        else:
+            x = F.avg_pool1d(x, kernel_size=2, stride=2)
+        
+        x = rearrange(x, '(b h w) c f -> (b f) c h w', b=batch_size, h=height, w=width)
+        return x
+    
+    def forward(self, camera_states: torch.Tensor) -> torch.Tensor:
+        batch_size, num_frames, channels, height, width = camera_states.shape
+        camera_states = rearrange(camera_states, 'b f c h w -> (b f) c h w')
+        camera_states = self.unshuffle(camera_states)
+        camera_states = self.encode_first(camera_states)
+        camera_states = self.compress_time(camera_states, num_frames=num_frames)
+        num_frames = camera_states.shape[0] // batch_size
+        camera_states = self.encode_second(camera_states)
+        camera_states = self.compress_time(camera_states, num_frames=num_frames)
+        camera_states = self.final_proj(camera_states)
+        camera_states = rearrange(camera_states, "(b f) c h w -> b c f h w", b=batch_size)
+        camera_states = self.camera_in(camera_states)
+        return camera_states * self.scale
+
+
+class HunyuanGameCraftTransformer3DModel(CachableDiT):
+    """
+    HunyuanGameCraft Transformer - ported from official implementation.
+    """
+    
+    _fsdp_shard_conditions = HunyuanGameCraftArchConfig()._fsdp_shard_conditions
+    _compile_conditions = HunyuanGameCraftArchConfig()._compile_conditions
+    _supported_attention_backends = HunyuanGameCraftArchConfig()._supported_attention_backends
+    param_names_mapping = HunyuanGameCraftConfig().param_names_mapping
+    reverse_param_names_mapping = HunyuanGameCraftConfig().reverse_param_names_mapping
+    
+    def __init__(self, config: HunyuanGameCraftConfig, hf_config: dict[str, Any]):
+        super().__init__(config=config, hf_config=hf_config)
+        
+        arch = config.arch_config
+        
+        if isinstance(arch.patch_size, (list, tuple)):
+            self.patch_size = list(arch.patch_size)
+        else:
+            self.patch_size = [arch.patch_size_t, arch.patch_size, arch.patch_size]
+        
+        self.in_channels = arch.in_channels
+        self.out_channels = arch.out_channels
+        self.unpatchify_channels = self.out_channels
+        self.num_channels_latents = self.out_channels  # Alias for latent_preparation stage
+        self.hidden_size = arch.hidden_size
+        self.num_heads = arch.num_attention_heads
+        self.num_attention_heads = arch.num_attention_heads  # Alias for compatibility
+        self.guidance_embeds = arch.guidance_embeds
+        self.rope_dim_list = list(arch.rope_axes_dim)
+        self.rope_theta = arch.rope_theta
+        self.text_states_dim = arch.text_embed_dim
+        self.text_states_dim_2 = arch.pooled_projection_dim
+        self.dtype = arch.dtype
+        
+        pe_dim = self.hidden_size // self.num_heads
+        if sum(self.rope_dim_list) != pe_dim:
+            raise ValueError(f"rope_axes_dim sum {sum(self.rope_dim_list)} != {pe_dim}")
+        
+        factory_kwargs = {'dtype': self.dtype}
+        
+        self.img_in = PatchEmbed(
+            patch_size=self.patch_size,
+            in_chans=self.in_channels,
+            embed_dim=self.hidden_size,
+            **factory_kwargs,
+        )
+        
+        self.txt_in = SingleTokenRefiner(
+            self.text_states_dim,
+            self.hidden_size,
+            self.num_heads,
+            depth=arch.num_refiner_layers,
+            **factory_kwargs,
+        )
+        
+        self.time_in = TimestepEmbedder(self.hidden_size, **factory_kwargs)
+        
+        self.vector_in = MLP(
+            self.text_states_dim_2,
+            self.hidden_size,
+            self.hidden_size,
+            act_type="silu",
+            **factory_kwargs,
+        )
+        
+        self.guidance_in = (
+            TimestepEmbedder(self.hidden_size, **factory_kwargs)
+            if self.guidance_embeds else None
+        )
+        
+        self.double_blocks = nn.ModuleList([
+            MMDoubleStreamBlock(
+                hidden_size=self.hidden_size,
+                num_attention_heads=self.num_heads,
+                mlp_ratio=arch.mlp_ratio,
+                supported_attention_backends=self._supported_attention_backends,
+                **factory_kwargs,
+            )
+            for _ in range(arch.num_layers)
+        ])
+        
+        self.single_blocks = nn.ModuleList([
+            MMSingleStreamBlock(
+                hidden_size=self.hidden_size,
+                num_attention_heads=self.num_heads,
+                mlp_ratio=arch.mlp_ratio,
+                supported_attention_backends=self._supported_attention_backends,
+                **factory_kwargs,
+            )
+            for _ in range(arch.num_single_layers)
+        ])
+        
+        self.final_layer = GameCraftFinalLayer(
+            self.hidden_size,
+            self.patch_size,
+            self.out_channels,
+            **factory_kwargs,
+        )
+        
+        self.camera_net = CameraNet(
+            in_channels=arch.camera_in_channels,
+            out_channels=16,
+            downscale_coef=arch.camera_downscale_coef,
+            patch_size=self.patch_size,
+            hidden_size=self.hidden_size,
+        )
+    
+    def forward(
+        self,
+        x: torch.Tensor,
+        encoder_hidden_states: List[torch.Tensor],
+        timestep: torch.Tensor,
+        camera_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[List[torch.Tensor]] = None,
+        guidance: Optional[torch.Tensor] = None,
+        return_dict: bool = False,
+    ) -> torch.Tensor:
+        img = x
+        _, _, ot, oh, ow = x.shape
+        tt = ot // self.patch_size[0]
+        th = oh // self.patch_size[1]
+        tw = ow // self.patch_size[2]
+        
+        text_states = encoder_hidden_states[0]
+        text_states_2 = encoder_hidden_states[1] if len(encoder_hidden_states) > 1 else None
+        text_mask = encoder_attention_mask[0] if encoder_attention_mask else None
+        
+        vec = self.time_in(timestep)
+        
+        if text_states_2 is not None:
+            vec = vec + self.vector_in(text_states_2)
+        
+        if self.guidance_in is not None and guidance is not None:
+            vec = vec + self.guidance_in(guidance)
+        
+        img = self.img_in(img)
+        
+        if camera_states is not None:
+            latent_len = ot
+            if latent_len == 18:
+                camera_latents = torch.cat([
+                    self.camera_net(torch.zeros_like(camera_states)),
+                    self.camera_net(camera_states)
+                ], dim=1)
+            elif latent_len == 9:
+                camera_latents = self.camera_net(camera_states)
+            elif latent_len == 10:
+                camera_latents = torch.cat([
+                    self.camera_net(torch.zeros_like(camera_states[:, 0:4, :, :, :])),
+                    self.camera_net(camera_states)
+                ], dim=1)
+            else:
+                camera_latents = self.camera_net(camera_states)
+            img = img + camera_latents
+        
+        txt = self.txt_in(text_states, timestep)
+        txt_seq_len = txt.shape[1]
+        
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (tt, th, tw),
+            self.hidden_size,
+            self.num_heads,
+            self.rope_dim_list,
+            self.rope_theta,
+        )
+        freqs_cos = freqs_cos.to(device=img.device, dtype=img.dtype)
+        freqs_sin = freqs_sin.to(device=img.device, dtype=img.dtype)
+        freqs_cis = (freqs_cos, freqs_sin)
+        
+        for block in self.double_blocks:
+            img, txt = block(img, txt, vec, freqs_cis)
+        
+        x = torch.cat([img, txt], dim=1)
+        
+        for block in self.single_blocks:
+            x = block(x, vec, txt_seq_len, freqs_cis)
+        
+        img = x[:, :-txt_seq_len, ...]
+        img = self.final_layer(img, vec)
+        img = unpatchify(img, tt, th, tw, self.patch_size, self.out_channels)
+        
+        return img

fastvideo/models/dits/lingbotworld/__init__.py

@@ -0,0 +1,8 @@
+from .model import LingBotWorldTransformer3DModel
+
+__all__ = [
+    "LingBotWorldTransformer3DModel",
+]
+
+# Entry point for model registry
+EntryClass = [LingBotWorldTransformer3DModel]

fastvideo/models/dits/lingbotworld/cam_utils.py

@@ -0,0 +1,203 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapted from LingBot World: https://github.com/Robbyant/lingbot-world/blob/main/wan/utils/cam_utils.py
+
+import numpy as np
+import os
+import torch
+from scipy.interpolate import interp1d
+from scipy.spatial.transform import Rotation, Slerp
+
+
+# --- Official Code (Leave Unchanged) ---
+ 
+def interpolate_camera_poses(
+    src_indices: np.ndarray, 
+    src_rot_mat: np.ndarray, 
+    src_trans_vec: np.ndarray, 
+    tgt_indices: np.ndarray,
+) -> torch.Tensor:
+    # interpolate translation
+    interp_func_trans = interp1d(
+        src_indices, 
+        src_trans_vec, 
+        axis=0, 
+        kind='linear', 
+        bounds_error=False,
+        fill_value="extrapolate",
+    )
+    interpolated_trans_vec = interp_func_trans(tgt_indices)
+
+    # interpolate rotation
+    src_quat_vec = Rotation.from_matrix(src_rot_mat)
+    # ensure there is no sudden change in qw
+    quats = src_quat_vec.as_quat().copy()  # [N, 4]
+    for i in range(1, len(quats)):
+        if np.dot(quats[i], quats[i-1]) < 0:
+            quats[i] = -quats[i]
+    src_quat_vec = Rotation.from_quat(quats)
+    slerp_func_rot = Slerp(src_indices, src_quat_vec)
+    interpolated_rot_quat = slerp_func_rot(tgt_indices)
+    interpolated_rot_mat = interpolated_rot_quat.as_matrix()
+
+    poses = np.zeros((len(tgt_indices), 4, 4))
+    poses[:, :3, :3] = interpolated_rot_mat
+    poses[:, :3, 3] = interpolated_trans_vec
+    poses[:, 3, 3] = 1.0
+    return torch.from_numpy(poses).float()
+
+
+def SE3_inverse(T: torch.Tensor) -> torch.Tensor:
+    Rot = T[:, :3, :3] # [B,3,3]
+    trans = T[:, :3, 3:] # [B,3,1]
+    R_inv = Rot.transpose(-1, -2)
+    t_inv = -torch.bmm(R_inv, trans)
+    T_inv = torch.eye(4, device=T.device, dtype=T.dtype)[None, :, :].repeat(T.shape[0], 1, 1)
+    T_inv[:, :3, :3] = R_inv
+    T_inv[:, :3, 3:] = t_inv
+    return T_inv
+
+
+def compute_relative_poses(
+    c2ws_mat: torch.Tensor, 
+    framewise: bool = False, 
+    normalize_trans: bool = True, 
+) -> torch.Tensor:
+    ref_w2cs = SE3_inverse(c2ws_mat[0:1])
+    relative_poses = torch.matmul(ref_w2cs, c2ws_mat)
+    # ensure identity matrix for 1st frame
+    relative_poses[0] = torch.eye(4, device=c2ws_mat.device, dtype=c2ws_mat.dtype)
+    if framewise:
+        # compute pose between i and i+1
+        relative_poses_framewise = torch.bmm(SE3_inverse(relative_poses[:-1]), relative_poses[1:])
+        relative_poses[1:] = relative_poses_framewise
+    if normalize_trans: # note refer to camctrl2: "we scale the coordinate inputs to roughly 1 standard deviation to simplify model learning."
+        translations = relative_poses[:, :3, 3] # [f, 3]
+        max_norm = torch.norm(translations, dim=-1).max()
+        # only normlaize when moving
+        if max_norm > 0:
+            relative_poses[:, :3, 3] = translations / max_norm
+    return relative_poses
+
+
+@torch.no_grad()
+def create_meshgrid(n_frames: int, height: int, width: int, bias: float = 0.5, device='cuda', dtype=torch.float32) -> torch.Tensor:
+    x_range = torch.arange(width, device=device, dtype=dtype)
+    y_range = torch.arange(height, device=device, dtype=dtype)
+    grid_y, grid_x = torch.meshgrid(y_range, x_range, indexing='ij')
+    grid_xy = torch.stack([grid_x, grid_y], dim=-1).view([-1, 2]) + bias # [h*w, 2]
+    grid_xy = grid_xy[None, ...].repeat(n_frames, 1, 1) # [f, h*w, 2]
+    return grid_xy
+
+
+def get_plucker_embeddings(
+    c2ws_mat: torch.Tensor,
+    Ks: torch.Tensor,
+    height: int,
+    width: int,
+):
+    n_frames = c2ws_mat.shape[0]
+    grid_xy = create_meshgrid(n_frames, height, width, device=c2ws_mat.device, dtype=c2ws_mat.dtype) # [f, h*w, 2]
+    fx, fy, cx, cy = Ks.chunk(4, dim=-1) # [f, 1]
+
+    i = grid_xy[..., 0] # [f, h*w]
+    j = grid_xy[..., 1] # [f, h*w]
+    zs = torch.ones_like(i) # [f, h*w]
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+
+    directions = torch.stack([xs, ys, zs], dim=-1) # [f, h*w, 3]
+    directions = directions / directions.norm(dim=-1, keepdim=True) # [f, h*w, 3]
+
+    rays_d = directions @ c2ws_mat[:, :3, :3].transpose(-1, -2) # [f, h*w, 3]
+    rays_o = c2ws_mat[:, :3, 3] # [f, 3]
+    rays_o = rays_o[:, None, :].expand_as(rays_d) # [f, h*w, 3]
+    # rays_dxo = torch.cross(rays_o, rays_d, dim=-1) # [f, h*w, 3]
+    # note refer to: apt2
+    plucker_embeddings = torch.cat([rays_o, rays_d], dim=-1) # [f, h*w, 6]
+    plucker_embeddings = plucker_embeddings.view([n_frames, height, width, 6]) # [f*h*w, 6]
+    return plucker_embeddings
+
+
+def get_Ks_transformed(
+    Ks: torch.Tensor,
+    height_org: int,
+    width_org: int,
+    height_resize: int,
+    width_resize: int,
+    height_final: int,
+    width_final: int,
+):
+    fx, fy, cx, cy = Ks.chunk(4, dim=-1) # [f, 1]
+
+    scale_x = width_resize / width_org
+    scale_y = height_resize / height_org
+
+    fx_resize = fx * scale_x
+    fy_resize = fy * scale_y
+    cx_resize = cx * scale_x
+    cy_resize = cy * scale_y
+
+    crop_offset_x = (width_resize - width_final) / 2
+    crop_offset_y = (height_resize - height_final) / 2
+
+    cx_final = cx_resize - crop_offset_x
+    cy_final = cy_resize - crop_offset_y
+    
+    Ks_transformed = torch.zeros_like(Ks)
+    Ks_transformed[:, 0:1] = fx_resize
+    Ks_transformed[:, 1:2] = fy_resize
+    Ks_transformed[:, 2:3] = cx_final
+    Ks_transformed[:, 3:4] = cy_final
+
+    return Ks_transformed
+
+
+# --- Custom ---
+
+def prepare_camera_embedding(
+    action_path: str,
+    num_frames: int,
+    height: int,
+    width: int,
+    spatial_scale: int = 8,
+) -> tuple[torch.Tensor, int]:
+    c2ws = np.load(os.path.join(action_path, "poses.npy"))
+    len_c2ws = ((len(c2ws) - 1) // 4) * 4 + 1
+    num_frames = min(num_frames, len_c2ws)
+    c2ws = c2ws[:num_frames]
+
+    Ks = torch.from_numpy(
+        np.load(os.path.join(action_path, "intrinsics.npy"))
+    ).float()
+    Ks = get_Ks_transformed(
+        Ks,
+        height_org=480,
+        width_org=832,
+        height_resize=height,
+        width_resize=width,
+        height_final=height,
+        width_final=width,
+    )
+    Ks = Ks[0]  # use first frame
+
+    len_c2ws = len(c2ws)
+    num_latent_frames = (len_c2ws - 1) // 4 + 1
+    c2ws_infer = interpolate_camera_poses(
+        src_indices=np.linspace(0, len_c2ws - 1, len_c2ws),
+        src_rot_mat=c2ws[:, :3, :3],
+        src_trans_vec=c2ws[:, :3, 3],
+        tgt_indices=np.linspace(0, len_c2ws - 1, num_latent_frames),
+    )
+    c2ws_infer = compute_relative_poses(c2ws_infer, framewise=True)
+    Ks = Ks.repeat(num_latent_frames, 1)
+    plucker = get_plucker_embeddings(c2ws_infer, Ks, height, width)  # [F, H, W, 6]
+
+    # reshpae
+    latent_height = height // spatial_scale
+    latent_width = width // spatial_scale
+    plucker = plucker.view(num_latent_frames, latent_height, spatial_scale, latent_width, spatial_scale, 6)
+    plucker = plucker.permute(0, 1, 3, 5, 2, 4).contiguous()
+    plucker = plucker.view(num_latent_frames, latent_height, latent_width, 6 * spatial_scale * spatial_scale)
+    c2ws_plucker_emb = plucker.permute(3, 0, 1, 2).contiguous().unsqueeze(0)
+
+    return c2ws_plucker_emb, num_frames

fastvideo/models/dits/lingbotworld/model.py

@@ -0,0 +1,569 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+from contextlib import nullcontext
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from fastvideo.attention import DistributedAttention
+from fastvideo.configs.models.dits.lingbotworld import LingBotWorldVideoConfig
+from fastvideo.configs.sample.wan import WanTeaCacheParams
+from fastvideo.distributed.communication_op import (
+    sequence_model_parallel_all_gather_with_unpad,
+    sequence_model_parallel_shard)
+from fastvideo.forward_context import get_forward_context
+from fastvideo.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
+                                        RMSNorm, ScaleResidual,
+                                        ScaleResidualLayerNormScaleShift)
+from fastvideo.layers.linear import ReplicatedLinear
+# from torch.nn import RMSNorm
+# TODO: RMSNorm ....
+from fastvideo.layers.mlp import MLP
+from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
+from fastvideo.layers.visual_embedding import (PatchEmbed, WanCamControlPatchEmbedding)
+from fastvideo.logger import init_logger
+from fastvideo.models.dits.base import CachableDiT
+from fastvideo.models.dits.wanvideo import (
+    WanI2VCrossAttention,
+    WanT2VCrossAttention,
+    WanTimeTextImageEmbedding,
+)
+from fastvideo.platforms import AttentionBackendEnum, current_platform
+
+from fastvideo.distributed.parallel_state import get_sp_world_size
+from fastvideo.distributed.utils import create_attention_mask_for_padding
+
+logger = init_logger(__name__)
+
+
+class LingBotWorldCamConditioner(nn.Module):
+
+    def __init__(self, dim: int) -> None:
+        super().__init__()
+        self.cam_injector = MLP(dim, dim, dim, bias=True, act_type="silu")
+        self.cam_scale_layer = nn.Linear(dim, dim)
+        self.cam_shift_layer = nn.Linear(dim, dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        c2ws_plucker_emb: torch.Tensor | None,
+    ) -> torch.Tensor:
+        if c2ws_plucker_emb is None:
+            return hidden_states
+        assert c2ws_plucker_emb.shape == hidden_states.shape, (
+            f"c2ws_plucker_emb shape must match hidden_states shape, got "
+            f"{tuple(c2ws_plucker_emb.shape)} vs {tuple(hidden_states.shape)}"
+        )
+        c2ws_hidden_states = self.cam_injector(c2ws_plucker_emb)
+        c2ws_hidden_states = c2ws_hidden_states + c2ws_plucker_emb
+        cam_scale = self.cam_scale_layer(c2ws_hidden_states)
+        cam_shift = self.cam_shift_layer(c2ws_hidden_states)
+        return (1.0 + cam_scale) * hidden_states + cam_shift
+
+
+class LingBotWorldTransformerBlock(nn.Module):
+
+    def __init__(self,
+                 dim: int,
+                 ffn_dim: int,
+                 num_heads: int,
+                 qk_norm: str = "rms_norm_across_heads",
+                 cross_attn_norm: bool = False,
+                 eps: float = 1e-6,
+                 added_kv_proj_dim: int | None = None,
+                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
+                 | None = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        # 1. Self-attention
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.to_q = ReplicatedLinear(dim, dim, bias=True)
+        self.to_k = ReplicatedLinear(dim, dim, bias=True)
+        self.to_v = ReplicatedLinear(dim, dim, bias=True)
+
+        self.to_out = ReplicatedLinear(dim, dim, bias=True)
+        self.attn1 = DistributedAttention(
+            num_heads=num_heads,
+            head_size=dim // num_heads,
+            causal=False,
+            supported_attention_backends=supported_attention_backends,
+            prefix=f"{prefix}.attn1")
+        self.hidden_dim = dim
+        self.num_attention_heads = num_heads
+        dim_head = dim // num_heads
+        if qk_norm == "rms_norm":
+            self.norm_q = RMSNorm(dim_head, eps=eps)
+            self.norm_k = RMSNorm(dim_head, eps=eps)
+        elif qk_norm == "rms_norm_across_heads":
+            # LTX applies qk norm across all heads
+            self.norm_q = RMSNorm(dim, eps=eps)
+            self.norm_k = RMSNorm(dim, eps=eps)
+        else:
+            raise NotImplementedError(
+                f"QK Norm type '{qk_norm}' not supported")
+        assert cross_attn_norm is True
+        self.self_attn_residual_norm = ScaleResidualLayerNormScaleShift(
+            dim,
+            norm_type="layer",
+            eps=eps,
+            elementwise_affine=True,
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
+
+        # 2. Cross-attention
+        if added_kv_proj_dim is not None:
+            # I2V
+            self.attn2 = WanI2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
+                                              eps=eps)
+        else:
+            # T2V
+            self.attn2 = WanT2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
+                                              eps=eps)
+        self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(
+            dim,
+            norm_type="layer",
+            eps=eps,
+            elementwise_affine=False,
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
+
+        # 3. Feed-forward
+        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
+        self.mlp_residual = ScaleResidual()
+
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+        self.cam_conditioner = LingBotWorldCamConditioner(dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        freqs_cis: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        c2ws_plucker_emb: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if hidden_states.dim() == 4:
+            hidden_states = hidden_states.squeeze(1)
+        bs, seq_length, _ = hidden_states.shape
+        orig_dtype = hidden_states.dtype
+        # assert orig_dtype != torch.float32
+
+        if temb.dim() == 4:
+            # temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+                self.scale_shift_table.unsqueeze(0) + temb.float()
+            ).chunk(6, dim=2)
+            # batch_size, seq_len, 1, inner_dim
+            shift_msa = shift_msa.squeeze(2)
+            scale_msa = scale_msa.squeeze(2)
+            gate_msa = gate_msa.squeeze(2)
+            c_shift_msa = c_shift_msa.squeeze(2)
+            c_scale_msa = c_scale_msa.squeeze(2)
+            c_gate_msa = c_gate_msa.squeeze(2)
+        else:
+            # temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
+            e = self.scale_shift_table + temb.float()
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
+                6, dim=1)
+        assert shift_msa.dtype == torch.float32
+
+        # 1. Self-attention
+        norm_hidden_states = (self.norm1(hidden_states.float()) *
+                              (1 + scale_msa) + shift_msa).to(orig_dtype)
+        query, _ = self.to_q(norm_hidden_states)
+        key, _ = self.to_k(norm_hidden_states)
+        value, _ = self.to_v(norm_hidden_states)
+
+        if self.norm_q is not None:
+            query = self.norm_q(query)
+        if self.norm_k is not None:
+            key = self.norm_k(key)
+
+        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+
+        attn_output, _ = self.attn1(query, key, value, freqs_cis=freqs_cis, attention_mask=attention_mask)
+        attn_output = attn_output.flatten(2)
+        attn_output, _ = self.to_out(attn_output)
+        attn_output = attn_output.squeeze(1)
+
+        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
+        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
+            hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
+        # Inject camera condition
+        # must be applied after the self-attention residual update.
+        hidden_states = self.cam_conditioner(hidden_states, c2ws_plucker_emb)
+        norm_hidden_states = self.self_attn_residual_norm.norm(hidden_states)
+        norm_hidden_states = norm_hidden_states.to(orig_dtype)
+
+        # 2. Cross-attention
+        attn_output = self.attn2(norm_hidden_states,
+                                 context=encoder_hidden_states,
+                                 context_lens=None)
+        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
+            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
+
+        # 3. Feed-forward
+        ff_output = self.ffn(norm_hidden_states)
+        hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)
+
+        return hidden_states
+
+
+class LingBotWorldTransformer3DModel(CachableDiT):
+    _fsdp_shard_conditions = LingBotWorldVideoConfig()._fsdp_shard_conditions
+    _compile_conditions = LingBotWorldVideoConfig()._compile_conditions
+    _supported_attention_backends = LingBotWorldVideoConfig(
+    )._supported_attention_backends
+    param_names_mapping = LingBotWorldVideoConfig().param_names_mapping
+    reverse_param_names_mapping = LingBotWorldVideoConfig().reverse_param_names_mapping
+    lora_param_names_mapping = LingBotWorldVideoConfig().lora_param_names_mapping
+
+    def __init__(self, config: LingBotWorldVideoConfig, hf_config: dict[str,
+                                                               Any]) -> None:
+        super().__init__(config=config, hf_config=hf_config)
+
+        inner_dim = config.num_attention_heads * config.attention_head_dim
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.in_channels = config.in_channels
+        self.out_channels = config.out_channels
+        self.num_channels_latents = config.num_channels_latents
+        self.patch_size = config.patch_size
+        self.text_len = config.text_len
+
+        assert config.num_attention_heads % get_sp_world_size() == 0, f"The number of attention heads ({config.num_attention_heads}) must be divisible by the sequence parallel size ({get_sp_world_size()})"
+
+        # 1. Patch & position embedding
+        self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
+                                          embed_dim=inner_dim,
+                                          patch_size=config.patch_size,
+                                          flatten=False)
+        self.patch_embedding_wancamctrl = WanCamControlPatchEmbedding(in_chans=6 * 64,
+                                                                      embed_dim=inner_dim,
+                                                                      patch_size=config.patch_size)
+        self.c2ws_mlp = MLP(inner_dim, inner_dim, inner_dim, bias=True, act_type="silu")
+
+        # 2. Condition embeddings
+        self.condition_embedder = WanTimeTextImageEmbedding(
+            dim=inner_dim,
+            time_freq_dim=config.freq_dim,
+            text_embed_dim=config.text_dim,
+            image_embed_dim=config.image_dim,
+        )
+
+        # 3. Transformer blocks
+        transformer_block = LingBotWorldTransformerBlock
+        self.blocks = nn.ModuleList([
+            transformer_block(inner_dim,
+                              config.ffn_dim,
+                              config.num_attention_heads,
+                              config.qk_norm,
+                              config.cross_attn_norm,
+                              config.eps,
+                              config.added_kv_proj_dim,
+                              self._supported_attention_backends,
+                              prefix=f"{config.prefix}.blocks.{i}")
+            for i in range(config.num_layers)
+        ])
+
+        # 4. Output norm & projection
+        self.norm_out = LayerNormScaleShift(inner_dim,
+                                            norm_type="layer",
+                                            eps=config.eps,
+                                            elementwise_affine=False,
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
+        self.proj_out = nn.Linear(
+            inner_dim, config.out_channels * math.prod(config.patch_size))
+        self.scale_shift_table = nn.Parameter(
+            torch.randn(1, 2, inner_dim) / inner_dim**0.5)
+
+        self.gradient_checkpointing = False
+        self._logged_attention_mask = False
+
+        # For type checking
+        self.previous_e0_even = None
+        self.previous_e0_odd = None
+        self.previous_residual_even = None
+        self.previous_residual_odd = None
+        self.is_even = True
+        self.should_calc_even = True
+        self.should_calc_odd = True
+        self.accumulated_rel_l1_distance_even = 0
+        self.accumulated_rel_l1_distance_odd = 0
+        self.cnt = 0
+        self.__post_init__()
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+                timestep: torch.LongTensor,
+                encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
+                | None = None,
+                guidance=None,
+                c2ws_plucker_emb: torch.Tensor | None = None,
+                **kwargs) -> torch.Tensor:
+        forward_batch = get_forward_context().forward_batch
+        enable_teacache = forward_batch is not None and forward_batch.enable_teacache
+
+        orig_dtype = hidden_states.dtype
+        if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor):
+            encoder_hidden_states = encoder_hidden_states[0]
+        if isinstance(encoder_hidden_states_image,
+                      list) and len(encoder_hidden_states_image) > 0:
+            encoder_hidden_states_image = encoder_hidden_states_image[0]
+        else:
+            encoder_hidden_states_image = None
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        # Get rotary embeddings
+        d = self.hidden_size // self.num_attention_heads
+        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (post_patch_num_frames, post_patch_height,
+             post_patch_width),
+            self.hidden_size,
+            self.num_attention_heads,
+            rope_dim_list,
+            dtype=torch.float32 if current_platform.is_mps() else torch.float64,
+            rope_theta=10000)
+        freqs_cis = (freqs_cos.to(hidden_states.device).float(),
+                     freqs_sin.to(hidden_states.device).float())
+
+        hidden_states = self.patch_embedding(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+        c2ws_hidden_states = None
+        if c2ws_plucker_emb is not None:
+            c2ws_plucker_emb = self.patch_embedding_wancamctrl(
+                c2ws_plucker_emb.to(device=hidden_states.device, dtype=hidden_states.dtype)
+            )
+            c2ws_hidden_states = self.c2ws_mlp(c2ws_plucker_emb)
+            c2ws_plucker_emb = c2ws_plucker_emb + c2ws_hidden_states
+
+        # Shard with padding support - returns (sharded_tensor, original_seq_len)
+        hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
+        
+        # Shard c2ws_plucker_emb
+        if c2ws_plucker_emb is not None:
+            c2ws_plucker_emb, _ = sequence_model_parallel_shard(c2ws_plucker_emb, dim=1)
+        
+        # Create attention mask for padded tokens if padding was applied
+        current_seq_len = hidden_states.shape[1]
+        sp_world_size = get_sp_world_size()
+        padded_seq_len = current_seq_len * sp_world_size
+        
+        if padded_seq_len > original_seq_len:
+            if not self._logged_attention_mask:
+                logger.info(f"Padding applied, original seq len: {original_seq_len}, padded seq len: {padded_seq_len}")
+                self._logged_attention_mask = True
+            attention_mask = create_attention_mask_for_padding(
+                seq_len=original_seq_len,
+                padded_seq_len=padded_seq_len,
+                batch_size=batch_size,
+                device=hidden_states.device,
+            )
+        else:
+            if not self._logged_attention_mask:
+                logger.info(f"Padding not applied")
+                self._logged_attention_mask = True
+            attention_mask = None
+
+        # timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
+        if timestep.dim() == 2:
+            ts_seq_len = timestep.shape[1]
+            timestep = timestep.flatten()  # batch_size * seq_len
+        else:
+            ts_seq_len = None
+
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep, encoder_hidden_states, encoder_hidden_states_image, timestep_seq_len=ts_seq_len)
+        if ts_seq_len is not None:
+            # batch_size, seq_len, 6, inner_dim
+            timestep_proj = timestep_proj.unflatten(2, (6, -1))
+        else:
+            # batch_size, 6, inner_dim
+            timestep_proj = timestep_proj.unflatten(1, (6, -1))
+
+        if encoder_hidden_states_image is not None:
+            if encoder_hidden_states is not None:
+                encoder_hidden_states = torch.concat(
+                    [encoder_hidden_states_image, encoder_hidden_states], dim=1)
+            else:
+                encoder_hidden_states = encoder_hidden_states_image
+
+        if current_platform.is_mps() or current_platform.is_npu():
+            encoder_hidden_states = encoder_hidden_states.to(orig_dtype)
+        else:
+            encoder_hidden_states = encoder_hidden_states # cast to orig_dtype for MPS & NPU
+
+        assert encoder_hidden_states.dtype == orig_dtype
+
+        # 4. Transformer blocks
+        # if caching is enabled, we might be able to skip the forward pass
+        should_skip_forward = self.should_skip_forward_for_cached_states(
+            timestep_proj=timestep_proj, temb=temb)
+
+        if should_skip_forward:
+            print("skipping forward, cached")
+            hidden_states = self.retrieve_cached_states(hidden_states)
+        else:
+            # if teacache is enabled, we need to cache the original hidden states
+            if enable_teacache:
+                original_hidden_states = hidden_states.clone()
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                for block in self.blocks:
+                    hidden_states = self._gradient_checkpointing_func(
+                        block, hidden_states, encoder_hidden_states,
+                        timestep_proj, freqs_cis, attention_mask, c2ws_plucker_emb)
+            else:
+                for block in self.blocks:
+                    hidden_states = block(hidden_states, encoder_hidden_states,
+                                              timestep_proj, freqs_cis, attention_mask, c2ws_plucker_emb)
+            # if teacache is enabled, we need to cache the original hidden states
+
+            if enable_teacache:
+                self.maybe_cache_states(hidden_states, original_hidden_states)
+        # 5. Output norm, projection & unpatchify
+        if temb.dim() == 3:
+            # batch_size, seq_len, inner_dim (wan 2.2 ti2v)
+            shift, scale = (self.scale_shift_table.unsqueeze(0) + temb.unsqueeze(2)).chunk(2, dim=2)
+            shift = shift.squeeze(2)
+            scale = scale.squeeze(2)
+        else:
+            # batch_size, inner_dim
+            shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
+            
+
+        hidden_states = self.norm_out(hidden_states, shift, scale)
+
+        # Gather and unpad in one operation
+        hidden_states = sequence_model_parallel_all_gather_with_unpad(
+            hidden_states, original_seq_len, dim=1)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        return output
+
+    def maybe_cache_states(self, hidden_states: torch.Tensor,
+                           original_hidden_states: torch.Tensor) -> None:
+        if self.is_even:
+            self.previous_residual_even = hidden_states.squeeze(
+                0) - original_hidden_states
+        else:
+            self.previous_residual_odd = hidden_states.squeeze(
+                0) - original_hidden_states
+
+    def should_skip_forward_for_cached_states(self, **kwargs) -> bool:
+
+        forward_context = get_forward_context()
+        forward_batch = forward_context.forward_batch
+        if forward_batch is None or not forward_batch.enable_teacache:
+            return False
+        teacache_params = forward_batch.teacache_params
+        assert teacache_params is not None, "teacache_params is not initialized"
+        assert isinstance(
+            teacache_params,
+            WanTeaCacheParams), "teacache_params is not a WanTeaCacheParams"
+        current_timestep = forward_context.current_timestep
+        num_inference_steps = forward_batch.num_inference_steps
+
+        # initialize the coefficients, cutoff_steps, and ret_steps
+        coefficients = teacache_params.coefficients
+        use_ret_steps = teacache_params.use_ret_steps
+        cutoff_steps = teacache_params.get_cutoff_steps(num_inference_steps)
+        ret_steps = teacache_params.ret_steps
+        teacache_thresh = teacache_params.teacache_thresh
+
+        if current_timestep == 0:
+            self.cnt = 0
+
+        timestep_proj = kwargs["timestep_proj"]
+        temb = kwargs["temb"]
+        modulated_inp = timestep_proj if use_ret_steps else temb
+
+        if self.cnt % 2 == 0:  # even -> condition
+            self.is_even = True
+            if self.cnt < ret_steps or self.cnt >= cutoff_steps:
+                self.should_calc_even = True
+                self.accumulated_rel_l1_distance_even = 0
+            else:
+                assert self.previous_e0_even is not None, "previous_e0_even is not initialized"
+                assert self.accumulated_rel_l1_distance_even is not None, "accumulated_rel_l1_distance_even is not initialized"
+                rescale_func = np.poly1d(coefficients)
+                self.accumulated_rel_l1_distance_even += rescale_func(
+                    ((modulated_inp - self.previous_e0_even).abs().mean() /
+                     self.previous_e0_even.abs().mean()).cpu().item())
+                if self.accumulated_rel_l1_distance_even < teacache_thresh:
+                    self.should_calc_even = False
+                else:
+                    self.should_calc_even = True
+                    self.accumulated_rel_l1_distance_even = 0
+            self.previous_e0_even = modulated_inp.clone()
+
+        else:  # odd -> unconditon
+            self.is_even = False
+            if self.cnt < ret_steps or self.cnt >= cutoff_steps:
+                self.should_calc_odd = True
+                self.accumulated_rel_l1_distance_odd = 0
+            else:
+                assert self.previous_e0_odd is not None, "previous_e0_odd is not initialized"
+                assert self.accumulated_rel_l1_distance_odd is not None, "accumulated_rel_l1_distance_odd is not initialized"
+                rescale_func = np.poly1d(coefficients)
+                self.accumulated_rel_l1_distance_odd += rescale_func(
+                    ((modulated_inp - self.previous_e0_odd).abs().mean() /
+                     self.previous_e0_odd.abs().mean()).cpu().item())
+                if self.accumulated_rel_l1_distance_odd < teacache_thresh:
+                    self.should_calc_odd = False
+                else:
+                    self.should_calc_odd = True
+                    self.accumulated_rel_l1_distance_odd = 0
+            self.previous_e0_odd = modulated_inp.clone()
+        self.cnt += 1
+        should_skip_forward = False
+        if self.is_even:
+            if not self.should_calc_even:
+                should_skip_forward = True
+        else:
+            if not self.should_calc_odd:
+                should_skip_forward = True
+
+        return should_skip_forward
+
+    def retrieve_cached_states(self,
+                               hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.is_even:
+            return hidden_states + self.previous_residual_even
+        else:
+            return hidden_states + self.previous_residual_odd
+
+# Entry point for model registry
+EntryClass = LingBotWorldTransformer3DModel

fastvideo/models/dits/ltx2.py

@@ -1534,6 +1534,7 @@ class BasicAVTransformerBlock(torch.nn.Module):
         audio: TransformerArgs | None,
         video_attention_mask: torch.Tensor | None = None,
         audio_attention_mask: torch.Tensor | None = None,
+        skip_cross_modal_attn: bool = False,
     ) -> tuple[TransformerArgs | None, TransformerArgs | None]:
         """Forward pass for transformer block.
 
@@ -1542,6 +1543,8 @@ class BasicAVTransformerBlock(torch.nn.Module):
             audio: Audio transformer args
             video_attention_mask: SP padding attention mask for video [B, padded_seq_len]
             audio_attention_mask: SP padding attention mask for audio [B, padded_seq_len]
+            skip_cross_modal_attn: If True, skip A2V and V2A cross-modal
+                attention (used for the modality-isolated CFG pass).
         """
         vx = video.x if video is not None else None
         ax = audio.x if audio is not None else None
@@ -1585,7 +1588,7 @@ class BasicAVTransformerBlock(torch.nn.Module):
                 mask=audio.context_mask,
             )
 
-        if run_a2v or run_v2a:
+        if (run_a2v or run_v2a) and not skip_cross_modal_attn:
             vx_norm3 = torch.nn.functional.rms_norm(vx, (vx.shape[-1],), eps=self.norm_eps)
             ax_norm3 = torch.nn.functional.rms_norm(ax, (ax.shape[-1],), eps=self.norm_eps)
 
@@ -2006,6 +2009,7 @@ class LTXModel(torch.nn.Module):
         audio: TransformerArgs | None,
         video_attention_mask: torch.Tensor | None = None,
         audio_attention_mask: torch.Tensor | None = None,
+        skip_cross_modal_attn: bool = False,
     ) -> tuple[TransformerArgs | None, TransformerArgs | None]:
         for block in self.transformer_blocks:
             video, audio = block(
@@ -2013,6 +2017,7 @@ class LTXModel(torch.nn.Module):
                 audio=audio,
                 video_attention_mask=video_attention_mask,
                 audio_attention_mask=audio_attention_mask,
+                skip_cross_modal_attn=skip_cross_modal_attn,
             )
         return video, audio
 
@@ -2039,6 +2044,7 @@ class LTXModel(torch.nn.Module):
         audio: Modality | None,
         video_attention_mask: torch.Tensor | None = None,
         audio_attention_mask: torch.Tensor | None = None,
+        skip_cross_modal_attn: bool = False,
     ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
         """Forward pass through the LTX model.
 
@@ -2047,6 +2053,9 @@ class LTXModel(torch.nn.Module):
             audio: Audio modality input
             video_attention_mask: SP padding attention mask for video [B, padded_seq_len]
             audio_attention_mask: SP padding attention mask for audio [B, padded_seq_len]
+            skip_cross_modal_attn: If True, skip A2V and V2A cross-modal
+                attention in all transformer blocks (modality-isolated
+                CFG pass).
         """
         if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") == "1":
             _debug_block_log_line(
@@ -2070,6 +2079,7 @@ class LTXModel(torch.nn.Module):
             audio_args,
             video_attention_mask=video_attention_mask,
             audio_attention_mask=audio_attention_mask,
+            skip_cross_modal_attn=skip_cross_modal_attn,
         )
 
         vx = (
@@ -2238,6 +2248,7 @@ class LTX2Transformer3DModel(CachableDiT):
         audio_encoder_hidden_states: torch.Tensor | None = None,
         audio_timestep: torch.Tensor | None = None,
         audio_encoder_attention_mask: torch.Tensor | None = None,
+        skip_cross_modal_attn: bool = False,
         **kwargs,
     ) -> torch.Tensor:
         if isinstance(encoder_hidden_states, list):
@@ -2394,6 +2405,7 @@ class LTX2Transformer3DModel(CachableDiT):
             audio=audio_modality,
             video_attention_mask=video_attention_mask,
             audio_attention_mask=audio_attention_mask,
+            skip_cross_modal_attn=skip_cross_modal_attn,
         )
 
         # Denoised prediction

fastvideo/models/dits/sd3.py

@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from typing import Any
+
+from diffusers import SD3Transformer2DModel as _DiffusersSD3Transformer2DModel
+
+from fastvideo.configs.models import DiTConfig
+
+
+class SD3Transformer2DModel(_DiffusersSD3Transformer2DModel):
+
+    _fsdp_shard_conditions: list = []
+    _compile_conditions: list = []
+    param_names_mapping: dict[str, Any] = {}
+    reverse_param_names_mapping: dict[str, Any] = {}
+    lora_param_names_mapping: dict[str, Any] = {}
+
+    def __init__(self, config: DiTConfig, hf_config: dict[str, Any], **kwargs):
+        self.fastvideo_config = config
+        self.hf_config = hf_config
+
+        arch = config.arch_config
+        dual_layers = getattr(arch, "dual_attention_layers", ())
+        if isinstance(dual_layers, list):
+            dual_layers = tuple(dual_layers)
+
+        super().__init__(
+            sample_size=arch.sample_size,
+            patch_size=arch.patch_size,
+            in_channels=arch.in_channels,
+            num_layers=arch.num_layers,
+            attention_head_dim=arch.attention_head_dim,
+            num_attention_heads=arch.num_attention_heads,
+            joint_attention_dim=arch.joint_attention_dim,
+            caption_projection_dim=arch.caption_projection_dim,
+            pooled_projection_dim=arch.pooled_projection_dim,
+            out_channels=arch.out_channels,
+            pos_embed_max_size=arch.pos_embed_max_size,
+            dual_attention_layers=dual_layers,
+            qk_norm=arch.qk_norm,
+            **kwargs,
+        )

fastvideo/models/encoders/clip.py

@@ -491,6 +491,43 @@ class CLIPTextModel(TextEncoder):
         return loaded_params
 
 
+class CLIPTextModelWithProjection(CLIPTextModel):
+
+    def __init__(self, config: CLIPTextConfig) -> None:
+        super().__init__(config)
+        self.text_projection = nn.Linear(
+            config.hidden_size, config.projection_dim, bias=False
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        position_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        output_hidden_states: bool | None = None,
+        **kwargs,
+    ) -> BaseEncoderOutput:
+        outputs = super().forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            output_hidden_states=output_hidden_states,
+            **kwargs,
+        )
+        pooled = outputs.pooler_output
+        if pooled is not None:
+            pooled = self.text_projection(pooled)
+        return BaseEncoderOutput(
+            last_hidden_state=outputs.last_hidden_state,
+            pooler_output=pooled,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            attention_mask=outputs.attention_mask,
+        )
+
+
 class CLIPVisionTransformer(nn.Module):
 
     def __init__(

fastvideo/models/encoders/t5_hf.py

@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+
+from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
+from fastvideo.models.encoders.base import TextEncoder
+
+
+class T5EncoderModel(TextEncoder):
+    
+    supports_hf_from_pretrained: bool = True
+
+    def __init__(self, config: T5Config, hf_model: Any | None = None) -> None:
+        super().__init__(config)
+        self.hf_model = hf_model
+
+    @classmethod
+    def from_pretrained_local(
+        cls,
+        model_path: str,
+        config: T5Config,
+        *,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> "T5EncoderModel":
+        from transformers import T5EncoderModel as HFT5EncoderModel
+
+        kwargs = {
+            "local_files_only": True,
+            "low_cpu_mem_usage": True,
+        }
+        try:
+            hf = HFT5EncoderModel.from_pretrained(model_path, dtype=dtype, **kwargs)
+        except TypeError:
+            # Backward-compatible fallback for older Transformers versions.
+            hf = HFT5EncoderModel.from_pretrained(
+                model_path, torch_dtype=dtype, **kwargs
+            )
+
+        hf = hf.eval().to(device=device, dtype=dtype)
+        return cls(config=config, hf_model=hf).eval()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        position_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        output_hidden_states: bool | None = None,
+        **kwargs,
+    ) -> BaseEncoderOutput:
+        if self.hf_model is None:
+            raise RuntimeError(
+                "T5EncoderModel(HF) is not initialized. Use "
+                "`from_pretrained_local(...)` to construct a loaded instance."
+            )
+
+        out = self.hf_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            output_hidden_states=bool(output_hidden_states)
+            if output_hidden_states is not None
+            else False,
+            return_dict=True,
+        )
+        return BaseEncoderOutput(
+            last_hidden_state=out.last_hidden_state,
+            hidden_states=out.hidden_states if output_hidden_states else None,
+            attention_mask=attention_mask,
+        )

fastvideo/models/loader/component_loader.py

@@ -86,8 +86,10 @@ class ComponentLoader(ABC):
             "vocoder": (VocoderLoader, "diffusers"),
             "text_encoder": (TextEncoderLoader, "transformers"),
             "text_encoder_2": (TextEncoderLoader, "transformers"),
+            "text_encoder_3": (TextEncoderLoader, "transformers"),
             "tokenizer": (TokenizerLoader, "transformers"),
             "tokenizer_2": (TokenizerLoader, "transformers"),
+            "tokenizer_3": (TokenizerLoader, "transformers"),
             "image_processor": (ImageProcessorLoader, "transformers"),
             "feature_extractor": (ImageProcessorLoader, "transformers"),
             "image_encoder": (ImageEncoderLoader, "transformers"),
@@ -292,23 +294,26 @@ class TextEncoderLoader(ComponentLoader):
                 pass
         logger.info("HF Model config: %s", model_config)
 
-        # @TODO(Wei): Better way to handle this?
-        try:
-            encoder_config = (
-                fastvideo_args.pipeline_config.text_encoder_configs[0]
+        base = os.path.basename(os.path.normpath(model_path))
+        idx = 0
+        if base.startswith("text_encoder_"):
+            try:
+                idx = int(base.split("_")[-1]) - 1
+            except Exception:
+                idx = 0
+        encoder_configs = fastvideo_args.pipeline_config.text_encoder_configs
+        encoder_precisions = fastvideo_args.pipeline_config.text_encoder_precisions
+        if idx < 0 or idx >= len(encoder_configs):
+            raise IndexError(
+                f"text encoder index {idx} out of range for text_encoder_configs (len={len(encoder_configs)}), model_path={model_path}"
             )
-            encoder_config.update_model_arch(model_config)
-            encoder_precision = (
-                fastvideo_args.pipeline_config.text_encoder_precisions[0]
-            )
-        except Exception:
-            encoder_config = (
-                fastvideo_args.pipeline_config.text_encoder_configs[1]
-            )
-            encoder_config.update_model_arch(model_config)
-            encoder_precision = (
-                fastvideo_args.pipeline_config.text_encoder_precisions[1]
+        encoder_config = encoder_configs[idx]
+        encoder_config.update_model_arch(model_config)
+        if idx < 0 or idx >= len(encoder_precisions):
+            raise IndexError(
+                f"text encoder index {idx} out of range for text_encoder_precisions (len={len(encoder_precisions)}), model_path={model_path}"
             )
+        encoder_precision = encoder_precisions[idx]
 
         target_device = get_local_torch_device()
         # TODO(will): add support for other dtypes
@@ -362,7 +367,16 @@ class TextEncoderLoader(ComponentLoader):
             with target_device:
                 architectures = getattr(model_config, "architectures", [])
                 model_cls, _ = ModelRegistry.resolve_model_cls(architectures)
-                model: TextEncoder = model_cls(model_config)  # type: ignore
+                if getattr(model_cls, "supports_hf_from_pretrained", False):
+                    model = model_cls.from_pretrained_local(  # type: ignore[attr-defined]
+                        model_path,
+                        model_config,  # type: ignore[arg-type]
+                        dtype=PRECISION_TO_TYPE[dtype],
+                        device=target_device,
+                    )
+                    return model.eval()
+
+                model = model_cls(model_config)  # type: ignore
 
             weights_to_load = {name for name, _ in model.named_parameters()}
             if (
@@ -659,7 +673,10 @@ class VAELoader(ComponentLoader):
                         break
             loaded = remapped
 
-        vae.load_state_dict(loaded, strict=False)
+        # Diffusers-format AutoencoderKL checkpoints should match exactly; load
+        # strictly so missing/unexpected keys are surfaced early.
+        strict_load = class_name == "AutoencoderKL"
+        vae.load_state_dict(loaded, strict=strict_load)
 
         return vae.eval()
 
@@ -1005,4 +1022,4 @@ class PipelineComponentLoader:
         )
 
         # Load the module
-        return loader.load(component_model_path, fastvideo_args)
+        return loader.load(component_model_path, fastvideo_args)

fastvideo/models/registry.py

@@ -25,6 +25,8 @@ logger = init_logger(__name__)
 _TEXT_TO_VIDEO_DIT_MODELS = {
     "HunyuanVideoTransformer3DModel":
     ("dits", "hunyuanvideo", "HunyuanVideoTransformer3DModel"),
+    "HunyuanGameCraftTransformer3DModel":
+    ("dits", "hunyuangamecraft", "HunyuanGameCraftTransformer3DModel"),
     "HunyuanVideo15Transformer3DModel":
     ("dits", "hunyuanvideo15", "HunyuanVideo15Transformer3DModel"),
     "HYWorldTransformer3DModel":
@@ -37,6 +39,8 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
     "LongCatVideoTransformer3DModel": ("dits", "longcat_video_dit", "LongCatVideoTransformer3DModel"),  # Wrapper (Phase 1)
     "LongCatTransformer3DModel": ("dits", "longcat", "LongCatTransformer3DModel"),  # Native (Phase 2)
     "LTX2Transformer3DModel": ("dits", "ltx2", "LTX2Transformer3DModel"),
+    "SD3Transformer2DModel": ("dits", "sd3", "SD3Transformer2DModel"),
+    "LingBotWorldTransformer3DModel": ("dits", "lingbotworld", "LingBotWorldTransformer3DModel"),
 }
 
 _IMAGE_TO_VIDEO_DIT_MODELS = {
@@ -49,9 +53,11 @@ _IMAGE_TO_VIDEO_DIT_MODELS = {
 
 _TEXT_ENCODER_MODELS = {
     "CLIPTextModel": ("encoders", "clip", "CLIPTextModel"),
+    "CLIPTextModelWithProjection":
+    ("encoders", "clip", "CLIPTextModelWithProjection"),
     "LlamaModel": ("encoders", "llama", "LlamaModel"),
     "UMT5EncoderModel": ("encoders", "t5", "UMT5EncoderModel"),
-    "T5EncoderModel": ("encoders", "t5", "T5EncoderModel"),
+    "T5EncoderModel": ("encoders", "t5_hf", "T5EncoderModel"),
     "STEP1TextEncoder": ("encoders", "stepllm", "STEP1TextEncoder"),
     "BertModel": ("encoders", "clip", "CLIPTextModel"),
     "Qwen2_5_VLTextModel": ("encoders", "qwen2_5", "Qwen2_5_VLTextModel"),
@@ -71,10 +77,12 @@ _IMAGE_ENCODER_MODELS: dict[str, tuple] = {
 _VAE_MODELS = {
     "AutoencoderKLHunyuanVideo":
     ("vaes", "hunyuanvae", "AutoencoderKLHunyuanVideo"),
+    "AutoencoderKLCausal3D": ("vaes", "gamecraftvae", "GameCraftVAE"),
     "AutoencoderKLHYWorld": ("vaes", "hyworldvae", "AutoencoderKLHYWorld"),
     "AutoencoderKLHunyuanVideo15": ("vaes", "hunyuan15vae", "AutoencoderKLHunyuanVideo15"),
     "AutoencoderKLWan": ("vaes", "wanvae", "AutoencoderKLWan"),
     "AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo"),
+    "AutoencoderKL": ("vaes", "autoencoder_kl", "AutoencoderKL"),
     "CausalVideoAutoencoder": ("vaes", "ltx2vae", "LTX2CausalVideoAutoencoder"),
 }
 
@@ -446,4 +454,4 @@ ModelRegistry = _ModelRegistry({
     )
     for model_arch, (component_name, mod_relname,
                      cls_name) in _FAST_VIDEO_MODELS.items()
-})
+})

fastvideo/models/vaes/autoencoder_kl.py

@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from typing import Any
+
+from diffusers import AutoencoderKL as _DiffusersAutoencoderKL
+
+from fastvideo.configs.models import VAEConfig
+
+
+class AutoencoderKL(_DiffusersAutoencoderKL):
+    
+    def __init__(self, config: VAEConfig, **kwargs: Any) -> None:
+        self.fastvideo_config = config
+        arch = config.arch_config
+
+        down_block_types = arch.down_block_types
+        if isinstance(down_block_types, list):
+            down_block_types = tuple(down_block_types)
+        up_block_types = arch.up_block_types
+        if isinstance(up_block_types, list):
+            up_block_types = tuple(up_block_types)
+        block_out_channels = arch.block_out_channels
+        if isinstance(block_out_channels, list):
+            block_out_channels = tuple(block_out_channels)
+
+        latents_mean = arch.latents_mean
+        if isinstance(latents_mean, list):
+            latents_mean = tuple(latents_mean)
+        latents_std = arch.latents_std
+        if isinstance(latents_std, list):
+            latents_std = tuple(latents_std)
+
+        super().__init__(
+            in_channels=arch.in_channels,
+            out_channels=arch.out_channels,
+            down_block_types=down_block_types,
+            up_block_types=up_block_types,
+            block_out_channels=block_out_channels,
+            layers_per_block=arch.layers_per_block,
+            act_fn=arch.act_fn,
+            latent_channels=arch.latent_channels,
+            norm_num_groups=arch.norm_num_groups,
+            sample_size=arch.sample_size,
+            scaling_factor=arch.scaling_factor,
+            shift_factor=arch.shift_factor,
+            latents_mean=latents_mean,
+            latents_std=latents_std,
+            force_upcast=arch.force_upcast,
+            use_quant_conv=arch.use_quant_conv,
+            use_post_quant_conv=arch.use_post_quant_conv,
+            mid_block_add_attention=arch.mid_block_add_attention,
+            **kwargs,
+        )

fastvideo/models/vaes/gamecraftvae.py

@@ -0,0 +1,462 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+GameCraft VAE - ported from official Hunyuan-GameCraft-1.0/hymm_sp/vae/.
+
+Matches the official AutoencoderKLCausal3D structure exactly for weight loading.
+"""
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from fastvideo.configs.models.vaes.gamecraftvae import GameCraftVAEConfig
+from fastvideo.models.vaes.common import DiagonalGaussianDistribution
+from fastvideo.models.vaes.gamecraftvae_blocks import (
+    CausalConv3d,
+    DownEncoderBlockCausal3D,
+    UpDecoderBlockCausal3D,
+    UNetMidBlockCausal3D,
+)
+
+
+@dataclass
+class AutoencoderKLOutput:
+    """Matches official AutoencoderKLOutput interface."""
+
+    latent_dist: DiagonalGaussianDistribution
+
+
+@dataclass
+class DecoderOutput:
+    """Matches official DecoderOutput interface."""
+
+    sample: torch.Tensor
+
+
+class EncoderCausal3D(nn.Module):
+    """Encoder - ported from official vae.py. Structure matches for weight loading."""
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 16,
+        down_block_types: Tuple[str, ...] = ("DownEncoderBlockCausal3D",),
+        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
+        layers_per_block: int = 2,
+        norm_num_groups: int = 32,
+        act_fn: str = "silu",
+        double_z: bool = True,
+        mid_block_add_attention: bool = True,
+        time_compression_ratio: int = 4,
+        spatial_compression_ratio: int = 8,
+        disable_causal: bool = False,
+        mid_block_causal_attn: bool = False,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+
+        self.conv_in = CausalConv3d(
+            in_channels, block_out_channels[0], kernel_size=3, stride=1, disable_causal=disable_causal
+        )
+        self.down_blocks = nn.ModuleList([])
+
+        output_channel = block_out_channels[0]
+        for i, _ in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            num_spatial = int(np.log2(spatial_compression_ratio))
+            num_time = int(np.log2(time_compression_ratio))
+
+            if time_compression_ratio == 4:
+                add_spatial = bool(i < num_spatial)
+                add_time = bool(
+                    i >= (len(block_out_channels) - 1 - num_time) and not is_final_block
+                )
+            elif time_compression_ratio == 8:
+                add_spatial = bool(i < num_spatial)
+                add_time = bool(i < num_time)
+            else:
+                raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}")
+
+            downsample_stride_HW = (2, 2) if add_spatial else (1, 1)
+            downsample_stride_T = (2,) if add_time else (1,)
+            downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)
+
+            down_block = DownEncoderBlockCausal3D(
+                in_channels=input_channel,
+                out_channels=output_channel,
+                num_layers=layers_per_block,
+                resnet_eps=1e-6,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                add_downsample=bool(add_spatial or add_time),
+                downsample_stride=downsample_stride,
+                downsample_padding=0,
+                disable_causal=disable_causal,
+            )
+            self.down_blocks.append(down_block)
+
+        self.mid_block = UNetMidBlockCausal3D(
+            in_channels=block_out_channels[-1],
+            temb_channels=None,
+            num_layers=1,
+            resnet_eps=1e-6,
+            resnet_act_fn=act_fn,
+            resnet_groups=norm_num_groups,
+            add_attention=mid_block_add_attention,
+            attention_head_dim=block_out_channels[-1],
+            disable_causal=disable_causal,
+            causal_attention=mid_block_causal_attn,
+        )
+
+        self.conv_norm_out = nn.GroupNorm(
+            num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6
+        )
+        self.conv_act = nn.SiLU()
+        conv_out_channels = 2 * out_channels if double_z else out_channels
+        self.conv_out = CausalConv3d(
+            block_out_channels[-1],
+            conv_out_channels,
+            kernel_size=3,
+            disable_causal=disable_causal,
+        )
+
+    def forward(self, sample: torch.Tensor) -> torch.Tensor:
+        sample = self.conv_in(sample)
+        for down_block in self.down_blocks:
+            sample = down_block(sample, scale=1.0)
+        sample = self.mid_block(sample, temb=None)
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+        return sample
+
+
+class DecoderCausal3D(nn.Module):
+    """Decoder - ported from official vae.py. Structure matches for weight loading."""
+
+    def __init__(
+        self,
+        in_channels: int = 16,
+        out_channels: int = 3,
+        up_block_types: Tuple[str, ...] = ("UpDecoderBlockCausal3D",),
+        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
+        layers_per_block: int = 2,
+        norm_num_groups: int = 32,
+        act_fn: str = "silu",
+        mid_block_add_attention: bool = True,
+        time_compression_ratio: int = 4,
+        spatial_compression_ratio: int = 8,
+        disable_causal: bool = False,
+        mid_block_causal_attn: bool = False,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+
+        self.conv_in = CausalConv3d(
+            in_channels,
+            block_out_channels[-1],
+            kernel_size=3,
+            stride=1,
+            disable_causal=disable_causal,
+        )
+
+        self.mid_block = UNetMidBlockCausal3D(
+            in_channels=block_out_channels[-1],
+            temb_channels=None,
+            num_layers=1,
+            resnet_eps=1e-6,
+            resnet_act_fn=act_fn,
+            resnet_groups=norm_num_groups,
+            add_attention=mid_block_add_attention,
+            attention_head_dim=block_out_channels[-1],
+            disable_causal=disable_causal,
+            causal_attention=mid_block_causal_attn,
+        )
+
+        self.up_blocks = nn.ModuleList([])
+        reversed_channels = list(reversed(block_out_channels))
+        output_channel = reversed_channels[0]
+        for i, _ in enumerate(up_block_types):
+            prev_output_channel = output_channel
+            output_channel = reversed_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            num_spatial = int(np.log2(spatial_compression_ratio))
+            num_time = int(np.log2(time_compression_ratio))
+
+            if time_compression_ratio == 4:
+                add_spatial = bool(i < num_spatial)
+                add_time = bool(
+                    i >= len(block_out_channels) - 1 - num_time and not is_final_block
+                )
+            elif time_compression_ratio == 8:
+                add_spatial = bool(i >= len(block_out_channels) - num_spatial)
+                add_time = bool(i >= len(block_out_channels) - num_time)
+            else:
+                raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}")
+
+            upsample_HW = (2, 2) if add_spatial else (1, 1)
+            upsample_T = (2,) if add_time else (1,)
+            upsample_scale_factor = tuple(upsample_T + upsample_HW)
+
+            up_block = UpDecoderBlockCausal3D(
+                in_channels=prev_output_channel,
+                out_channels=output_channel,
+                num_layers=layers_per_block + 1,
+                resnet_eps=1e-6,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                add_upsample=bool(add_spatial or add_time),
+                upsample_scale_factor=upsample_scale_factor,
+                temb_channels=None,
+                disable_causal=disable_causal,
+            )
+            self.up_blocks.append(up_block)
+            output_channel = output_channel
+
+        self.conv_norm_out = nn.GroupNorm(
+            num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6
+        )
+        self.conv_act = nn.SiLU()
+        self.conv_out = CausalConv3d(
+            block_out_channels[0], out_channels, kernel_size=3, disable_causal=disable_causal
+        )
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        latent_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        sample = self.conv_in(sample)
+        sample = self.mid_block(sample, temb=latent_embeds)
+        for up_block in self.up_blocks:
+            sample = up_block(sample, temb=latent_embeds, scale=1.0)
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+        return sample
+
+
+class GameCraftVAE(nn.Module):
+    """
+    GameCraft VAE - ported from official AutoencoderKLCausal3D.
+    Structure matches exactly for loading official weights.
+    """
+
+    def __init__(self, config: GameCraftVAEConfig):
+        super().__init__()
+        self.config = config
+        arch = config.arch_config
+
+        time_ratio = getattr(arch, "time_compression_ratio", arch.temporal_compression_ratio)
+        self.encoder = EncoderCausal3D(
+            in_channels=arch.in_channels,
+            out_channels=arch.latent_channels,
+            down_block_types=tuple(arch.down_block_types),
+            block_out_channels=tuple(arch.block_out_channels),
+            layers_per_block=arch.layers_per_block,
+            norm_num_groups=arch.norm_num_groups,
+            act_fn=arch.act_fn,
+            double_z=True,
+            time_compression_ratio=time_ratio,
+            spatial_compression_ratio=arch.spatial_compression_ratio,
+            disable_causal=getattr(arch, "disable_causal_conv", False),
+            mid_block_add_attention=arch.mid_block_add_attention,
+            mid_block_causal_attn=getattr(arch, "mid_block_causal_attn", False),
+        )
+
+        self.decoder = DecoderCausal3D(
+            in_channels=arch.latent_channels,
+            out_channels=arch.out_channels,
+            up_block_types=tuple(arch.up_block_types),
+            block_out_channels=tuple(arch.block_out_channels),
+            layers_per_block=arch.layers_per_block,
+            norm_num_groups=arch.norm_num_groups,
+            act_fn=arch.act_fn,
+            time_compression_ratio=time_ratio,
+            spatial_compression_ratio=arch.spatial_compression_ratio,
+            disable_causal=getattr(arch, "disable_causal_conv", False),
+            mid_block_add_attention=arch.mid_block_add_attention,
+            mid_block_causal_attn=getattr(arch, "mid_block_causal_attn", False),
+        )
+
+        self.quant_conv = nn.Conv3d(
+            2 * arch.latent_channels, 2 * arch.latent_channels, kernel_size=1
+        )
+        self.post_quant_conv = nn.Conv3d(
+            arch.latent_channels, arch.latent_channels, kernel_size=1
+        )
+        
+        # Scaling factor for latent normalization (required for decoding stage)
+        self.scaling_factor = arch.scaling_factor
+        
+        # Tiling support - matches official GameCraft VAE settings
+        self._tiling_enabled = False
+        self.tile_overlap_factor = 0.25
+        
+        # Temporal tiling params (for >64 output frames)
+        self.tile_sample_min_tsize = 64  # Minimum sample temporal size (video frames)
+        self.tile_latent_min_tsize = 16  # = 64 // 4 (time_compression_ratio)
+        
+        # Spatial tiling params - use small tiles to reduce memory
+        self.tile_sample_min_size = 256  # Minimum spatial tile size in pixel space
+        self.tile_latent_min_size = 32   # = 256 // 8 (spatial_compression_ratio)
+
+    def encode(self, x: torch.Tensor) -> AutoencoderKLOutput:
+        """Encode to latent distribution."""
+        h = self.encoder(x)
+        moments = self.quant_conv(h)
+        posterior = DiagonalGaussianDistribution(moments)
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    def decode(self, z: torch.Tensor) -> torch.Tensor:
+        """Decode from latents.
+        
+        Args:
+            z: Latent tensor [B, C, T, H, W]
+            
+        Returns:
+            Decoded tensor [B, C, T_out, H_out, W_out]
+        """
+        # Use tiled decode for memory efficiency when enabled
+        if self._tiling_enabled:
+            # Check if temporal tiling needed (>64 output frames)
+            if z.shape[2] > self.tile_latent_min_tsize:
+                return self._temporal_tiled_decode(z)
+            # Check if spatial tiling needed (large H or W)
+            if z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size:
+                return self._spatial_tiled_decode(z)
+        
+        z = self.post_quant_conv(z)
+        dec = self.decoder(z, latent_embeds=None)
+        return dec
+
+    def _temporal_tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
+        """Decode latents in temporal tiles with overlapping and blending.
+        
+        Based on official GameCraft temporal_tiled_decode implementation.
+        Only used when T > tile_latent_min_tsize (16).
+        """
+        B, C, T, H, W = z.shape
+        
+        # Use the pre-configured tiling parameters
+        overlap_size = int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_sample_min_tsize * self.tile_overlap_factor)
+        t_limit = self.tile_sample_min_tsize - blend_extent
+        
+        row = []
+        for i in range(0, T, overlap_size):
+            tile = z[:, :, i : i + self.tile_latent_min_tsize + 1, :, :]
+            tile = self.post_quant_conv(tile)
+            decoded = self.decoder(tile, latent_embeds=None)
+            if i > 0:
+                decoded = decoded[:, :, 1:, :, :]  # Skip first frame for non-first tiles
+            row.append(decoded)
+        
+        # Blend overlapping regions
+        result_row = []
+        for i, tile in enumerate(row):
+            if i > 0:
+                tile = self._blend_t(row[i - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :t_limit, :, :])
+            else:
+                result_row.append(tile[:, :, :t_limit+1, :, :])
+        
+        return torch.cat(result_row, dim=2)
+    
+    def _spatial_tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
+        """Decode latents in spatial tiles with overlapping and blending.
+        
+        Based on official GameCraft spatial_tiled_decode implementation.
+        """
+        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
+        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
+        row_limit = self.tile_sample_min_size - blend_extent
+        
+        # Split z into overlapping tiles and decode them separately
+        rows = []
+        for i in range(0, z.shape[-2], overlap_size):
+            row = []
+            for j in range(0, z.shape[-1], overlap_size):
+                tile = z[:, :, :, i: i + self.tile_latent_min_size, j: j + self.tile_latent_min_size]
+                tile = self.post_quant_conv(tile)
+                decoded = self.decoder(tile, latent_embeds=None)
+                row.append(decoded)
+            rows.append(row)
+        
+        # Blend overlapping regions
+        result_rows = []
+        for i, row in enumerate(rows):
+            result_row = []
+            for j, tile in enumerate(row):
+                # Blend with above tile and left tile
+                if i > 0:
+                    tile = self._blend_v(rows[i - 1][j], tile, blend_extent)
+                if j > 0:
+                    tile = self._blend_h(row[j - 1], tile, blend_extent)
+                result_row.append(tile[:, :, :, :row_limit, :row_limit])
+            result_rows.append(torch.cat(result_row, dim=-1))
+        
+        return torch.cat(result_rows, dim=-2)
+    
+    def _blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        """Blend two tensors along temporal dimension."""
+        blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
+        if blend_extent == 0:
+            return b
+        
+        a_region = a[..., -blend_extent:, :, :]
+        b_region = b[..., :blend_extent, :, :]
+        
+        weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
+        weights = weights.view(1, 1, blend_extent, 1, 1)
+        
+        blended = a_region * (1 - weights) + b_region * weights
+        b[..., :blend_extent, :, :] = blended
+        return b
+    
+    def _blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        """Blend two tensors along vertical (height) dimension."""
+        blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
+        if blend_extent == 0:
+            return b
+        
+        a_region = a[..., -blend_extent:, :]
+        b_region = b[..., :blend_extent, :]
+        
+        weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
+        weights = weights.view(1, 1, 1, blend_extent, 1)
+        
+        blended = a_region * (1 - weights) + b_region * weights
+        b[..., :blend_extent, :] = blended
+        return b
+    
+    def _blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
+        """Blend two tensors along horizontal (width) dimension."""
+        blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
+        if blend_extent == 0:
+            return b
+        
+        a_region = a[..., -blend_extent:]
+        b_region = b[..., :blend_extent]
+        
+        weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
+        weights = weights.view(1, 1, 1, 1, blend_extent)
+        
+        blended = a_region * (1 - weights) + b_region * weights
+        b[..., :blend_extent] = blended
+        return b
+
+    def enable_tiling(self) -> None:
+        """Enable tiling for large inputs."""
+        self._tiling_enabled = True
+
+    def disable_tiling(self) -> None:
+        """Disable tiling."""
+        self._tiling_enabled = False
+
+
+EntryClass = GameCraftVAE

fastvideo/models/vaes/gamecraftvae_blocks.py

@@ -0,0 +1,446 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+GameCraft VAE building blocks - ported from official Hunyuan-GameCraft-1.0/hymm_sp/vae/unet_causal_3d_blocks.py.
+
+Matches the official structure exactly for weight loading.
+"""
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+def prepare_causal_attention_mask(
+    n_frame: int, n_hw: int, dtype, device, batch_size: Optional[int] = None
+):
+    seq_len = n_frame * n_hw
+    mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
+    for i in range(seq_len):
+        i_frame = i // n_hw
+        mask[i, : (i_frame + 1) * n_hw] = 0
+    if batch_size is not None:
+        mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
+    return mask
+
+
+class CausalConv3d(nn.Module):
+    """Causal 3D convolution - matches official structure (has .conv)."""
+
+    def __init__(
+        self,
+        chan_in: int,
+        chan_out: int,
+        kernel_size: Union[int, Tuple[int, int, int]] = 3,
+        stride: Union[int, Tuple[int, int, int]] = 1,
+        dilation: Union[int, Tuple[int, int, int]] = 1,
+        pad_mode: str = "replicate",
+        disable_causal: bool = False,
+        **kwargs,
+    ):
+        super().__init__()
+        self.pad_mode = pad_mode
+        if isinstance(kernel_size, int):
+            k = kernel_size
+        else:
+            k = kernel_size[0]
+        if disable_causal:
+            padding = (k // 2, k // 2, k // 2, k // 2, k // 2, k // 2)
+        else:
+            padding = (k // 2, k // 2, k // 2, k // 2, k - 1, 0)
+        self.time_causal_padding = padding
+        self.conv = nn.Conv3d(
+            chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(x)
+
+
+class DownsampleCausal3D(nn.Module):
+    """Causal 3D downsampling - matches official (has .conv)."""
+
+    def __init__(
+        self,
+        channels: int,
+        out_channels: Optional[int] = None,
+        padding: int = 1,
+        stride: Union[int, Tuple[int, int, int]] = 2,
+        kernel_size: int = 3,
+        bias: bool = True,
+        disable_causal: bool = False,
+    ):
+        super().__init__()
+        self.out_channels = out_channels or channels
+        self.conv = CausalConv3d(
+            channels,
+            self.out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=0,
+            disable_causal=disable_causal,
+            bias=bias,
+        )
+
+    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
+        return self.conv(hidden_states)
+
+
+class UpsampleCausal3D(nn.Module):
+    """Causal 3D upsampling - matches official (has .conv when use_conv=True)."""
+
+    def __init__(
+        self,
+        channels: int,
+        out_channels: Optional[int] = None,
+        kernel_size: int = 3,
+        upsample_factor: Tuple[int, int, int] = (2, 2, 2),
+        disable_causal: bool = False,
+        bias: bool = True,
+    ):
+        super().__init__()
+        self.out_channels = out_channels or channels
+        self.upsample_factor = upsample_factor
+        self.disable_causal = disable_causal
+        self.conv = CausalConv3d(
+            channels,
+            self.out_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            disable_causal=disable_causal,
+            bias=bias,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        output_size: Optional[int] = None,
+        scale: float = 1.0,
+    ) -> torch.Tensor:
+        B, C, T, H, W = hidden_states.shape
+        dtype = hidden_states.dtype
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(torch.float32)
+
+        if not self.disable_causal and T > 1:
+            first_h, other_h = hidden_states.split((1, T - 1), dim=2)
+            other_h = F.interpolate(
+                other_h, scale_factor=self.upsample_factor, mode="nearest"
+            )
+            first_h = F.interpolate(
+                first_h.squeeze(2), scale_factor=self.upsample_factor[1:], mode="nearest"
+            ).unsqueeze(2)
+            hidden_states = torch.cat((first_h, other_h), dim=2)
+        else:
+            hidden_states = F.interpolate(
+                hidden_states, scale_factor=self.upsample_factor, mode="nearest"
+            )
+
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(dtype)
+        return self.conv(hidden_states)
+
+
+class GameCraftVAEAttention(nn.Module):
+    """Attention block matching official diffusers Attention structure (group_norm, to_q, to_k, to_v, to_out)."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        heads: int,
+        dim_head: int,
+        eps: float = 1e-6,
+        norm_num_groups: Optional[int] = 32,
+        bias: bool = True,
+    ):
+        super().__init__()
+        self.heads = heads
+        self.dim_head = dim_head
+        inner_dim = heads * dim_head
+        self.group_norm = nn.GroupNorm(
+            norm_num_groups or in_channels, in_channels, eps=eps
+        )
+        self.to_q = nn.Linear(in_channels, inner_dim, bias=bias)
+        self.to_k = nn.Linear(in_channels, inner_dim, bias=bias)
+        self.to_v = nn.Linear(in_channels, inner_dim, bias=bias)
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, in_channels, bias=bias))
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        batch_size, seq_len, _ = hidden_states.shape
+
+        hidden_states = self.group_norm(
+            hidden_states.permute(0, 2, 1)
+        ).permute(0, 2, 1)
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(hidden_states)
+        v = self.to_v(hidden_states)
+
+        q = q.view(batch_size, seq_len, self.heads, self.dim_head).transpose(1, 2)
+        k = k.view(batch_size, seq_len, self.heads, self.dim_head).transpose(1, 2)
+        v = v.view(batch_size, seq_len, self.heads, self.dim_head).transpose(1, 2)
+
+        scale = self.dim_head**-0.5
+        attn = torch.matmul(q, k.transpose(-2, -1)) * scale
+        if attention_mask is not None:
+            attn = attn + attention_mask
+        # Official uses upcast_softmax=True: compute softmax in fp32 for numerical stability
+        attn = F.softmax(attn.float(), dim=-1).to(q.dtype)
+        hidden_states = torch.matmul(attn, v)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, seq_len, -1)
+        hidden_states = self.to_out(hidden_states) + residual
+        return hidden_states
+
+
+class ResnetBlockCausal3D(nn.Module):
+    """ResNet block - matches official structure (conv1.conv, conv2.conv, norm1, norm2)."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        temb_channels: Optional[int] = None,
+        eps: float = 1e-6,
+        groups: int = 32,
+        dropout: float = 0.0,
+        non_linearity: str = "swish",
+        disable_causal: bool = False,
+    ):
+        super().__init__()
+        out_channels = out_channels or in_channels
+        self.norm1 = nn.GroupNorm(groups, in_channels, eps=eps)
+        self.conv1 = CausalConv3d(in_channels, out_channels, 3, 1, disable_causal=disable_causal)
+        self.norm2 = nn.GroupNorm(groups, out_channels, eps=eps)
+        self.conv2 = CausalConv3d(out_channels, out_channels, 3, 1, disable_causal=disable_causal)
+        self.dropout = nn.Dropout(dropout)
+        self.conv_shortcut = (
+            CausalConv3d(in_channels, out_channels, 1, 1, disable_causal=disable_causal)
+            if in_channels != out_channels
+            else None
+        )
+        self.nonlinearity = getattr(F, non_linearity, F.silu)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        scale: float = 1.0,
+    ) -> torch.Tensor:
+        h = self.norm1(x)
+        h = self.nonlinearity(h)
+        h = self.conv1(h)
+        h = self.norm2(h)
+        h = self.nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+        if self.conv_shortcut is not None:
+            x = self.conv_shortcut(x)
+        return (x + h) / 1.0
+
+
+class UNetMidBlockCausal3D(nn.Module):
+    """Mid block with resnets and optional attention - matches official structure."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: Optional[int] = None,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        add_attention: bool = True,
+        attention_head_dim: int = 1,
+        disable_causal: bool = False,
+        causal_attention: bool = False,
+    ):
+        super().__init__()
+        self.add_attention = add_attention
+        self.causal_attention = causal_attention
+
+        self.resnets = nn.ModuleList()
+        self.attentions = nn.ModuleList()
+
+        self.resnets.append(
+            ResnetBlockCausal3D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=0.0,
+                non_linearity=resnet_act_fn,
+                disable_causal=disable_causal,
+            )
+        )
+
+        for _ in range(num_layers):
+            if add_attention:
+                self.attentions.append(
+                    GameCraftVAEAttention(
+                        in_channels=in_channels,
+                        heads=in_channels // attention_head_dim,
+                        dim_head=attention_head_dim,
+                        eps=resnet_eps,
+                        norm_num_groups=resnet_groups,
+                        bias=True,
+                    )
+                )
+            else:
+                self.attentions.append(None)
+            self.resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=0.0,
+                    non_linearity=resnet_act_fn,
+                    disable_causal=disable_causal,
+                )
+            )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.resnets[0](hidden_states, temb)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if attn is not None:
+                B, C, T, H, W = hidden_states.shape
+                hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c")
+                if self.causal_attention:
+                    mask = prepare_causal_attention_mask(
+                        T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B
+                    )
+                else:
+                    mask = None
+                hidden_states = attn(hidden_states, attention_mask=mask)
+                hidden_states = rearrange(
+                    hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W
+                )
+            hidden_states = resnet(hidden_states, temb)
+        return hidden_states
+
+
+class DownEncoderBlockCausal3D(nn.Module):
+    """Encoder down block - matches official structure."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 2,
+        resnet_eps: float = 1e-6,
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        add_downsample: bool = True,
+        downsample_stride: Union[int, Tuple[int, int, int]] = 2,
+        downsample_padding: int = 0,
+        disable_causal: bool = False,
+    ):
+        super().__init__()
+        self.resnets = nn.ModuleList()
+        for i in range(num_layers):
+            inc = in_channels if i == 0 else out_channels
+            self.resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=inc,
+                    out_channels=out_channels,
+                    temb_channels=None,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=0.0,
+                    non_linearity=resnet_act_fn,
+                    disable_causal=disable_causal,
+                )
+            )
+
+        self.downsamplers = None
+        if add_downsample:
+            self.downsamplers = nn.ModuleList([
+                DownsampleCausal3D(
+                    out_channels,
+                    out_channels=out_channels,
+                    padding=downsample_padding,
+                    stride=downsample_stride,
+                    disable_causal=disable_causal,
+                )
+            ])
+
+    def forward(
+        self, hidden_states: torch.Tensor, scale: float = 1.0
+    ) -> torch.Tensor:
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states, temb=None, scale=scale)
+        if self.downsamplers is not None:
+            for ds in self.downsamplers:
+                hidden_states = ds(hidden_states, scale)
+        return hidden_states
+
+
+class UpDecoderBlockCausal3D(nn.Module):
+    """Decoder up block - matches official structure."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 3,
+        resnet_eps: float = 1e-6,
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        add_upsample: bool = True,
+        upsample_scale_factor: Tuple[int, int, int] = (2, 2, 2),
+        temb_channels: Optional[int] = None,
+        disable_causal: bool = False,
+    ):
+        super().__init__()
+        self.resnets = nn.ModuleList()
+        for i in range(num_layers):
+            inc = in_channels if i == 0 else out_channels
+            self.resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=inc,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=0.0,
+                    non_linearity=resnet_act_fn,
+                    disable_causal=disable_causal,
+                )
+            )
+
+        self.upsamplers = None
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([
+                UpsampleCausal3D(
+                    out_channels,
+                    out_channels=out_channels,
+                    upsample_factor=upsample_scale_factor,
+                    disable_causal=disable_causal,
+                )
+            ])
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        scale: float = 1.0,
+    ) -> torch.Tensor:
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states, temb=temb, scale=scale)
+        if self.upsamplers is not None:
+            for us in self.upsamplers:
+                hidden_states = us(hidden_states)
+        return hidden_states

fastvideo/pipelines/basic/gamecraft/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+"""HunyuanGameCraft pipeline implementations."""

fastvideo/pipelines/basic/gamecraft/gamecraft_pipeline.py

@@ -0,0 +1,101 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+HunyuanGameCraft video diffusion pipeline implementation.
+
+This module implements the HunyuanGameCraft pipeline for camera/action-conditioned
+video generation with the modular pipeline architecture.
+"""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages import (
+    ConditioningStage,
+    DecodingStage,
+    InputValidationStage,
+    LatentPreparationStage,
+    TextEncodingStage,
+    TimestepPreparationStage,
+)
+from fastvideo.pipelines.stages.gamecraft_denoising import GameCraftDenoisingStage
+
+logger = init_logger(__name__)
+
+
+class HunyuanGameCraftPipeline(ComposedPipelineBase):
+    """
+    Pipeline for HunyuanGameCraft video generation.
+    
+    This pipeline supports:
+    - Text-to-video generation with camera/action conditioning
+    - Autoregressive generation with history frames
+    - 33-channel input (16 latent + 16 gt_latent + 1 mask)
+    - CameraNet for encoding Plücker coordinates
+    """
+
+    _required_config_modules = [
+        "text_encoder",
+        "text_encoder_2",
+        "tokenizer",
+        "tokenizer_2",
+        "vae",
+        "transformer",
+        "scheduler",
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+
+        self.add_stage(
+            stage_name="input_validation_stage",
+            stage=InputValidationStage(),
+        )
+
+        self.add_stage(
+            stage_name="prompt_encoding_stage_primary",
+            stage=TextEncodingStage(
+                text_encoders=[
+                    self.get_module("text_encoder"),
+                    self.get_module("text_encoder_2"),
+                ],
+                tokenizers=[
+                    self.get_module("tokenizer"),
+                    self.get_module("tokenizer_2"),
+                ],
+            ),
+        )
+
+        self.add_stage(
+            stage_name="conditioning_stage",
+            stage=ConditioningStage(),
+        )
+
+        self.add_stage(
+            stage_name="timestep_preparation_stage",
+            stage=TimestepPreparationStage(
+                scheduler=self.get_module("scheduler")),
+        )
+
+        self.add_stage(
+            stage_name="latent_preparation_stage",
+            stage=LatentPreparationStage(
+                scheduler=self.get_module("scheduler"),
+                transformer=self.get_module("transformer"),
+            ),
+        )
+
+        self.add_stage(
+            stage_name="denoising_stage",
+            stage=GameCraftDenoisingStage(
+                transformer=self.get_module("transformer"),
+                scheduler=self.get_module("scheduler"),
+            ),
+        )
+
+        self.add_stage(
+            stage_name="decoding_stage",
+            stage=DecodingStage(vae=self.get_module("vae")),
+        )
+
+
+EntryClass = HunyuanGameCraftPipeline

fastvideo/pipelines/basic/lingbotworld/__init__.py

@@ -0,0 +1 @@
+

fastvideo/pipelines/basic/lingbotworld/lingbotworld_pipeline.py

@@ -0,0 +1,16 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Wan video diffusion pipeline implementation.
+
+This module contains an implementation of the Wan video diffusion pipeline
+using the modular pipeline architecture.
+"""
+
+from fastvideo.pipelines.basic.wan.wan_i2v_pipeline import WanImageToVideoPipeline
+
+
+class LingBotWorldImageToVideoPipeline(WanImageToVideoPipeline):
+    pass
+
+
+EntryClass = LingBotWorldImageToVideoPipeline

fastvideo/pipelines/basic/sd35/sd35_pipeline.py

@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages.input_validation import InputValidationStage
+from fastvideo.pipelines.stages.text_encoding import TextEncodingStage
+from fastvideo.pipelines.stages.timestep_preparation import (
+    TimestepPreparationStage, )
+from fastvideo.pipelines.stages.sd35_conditioning import (
+    SD35ConditioningStage,
+    SD35DecodingStage,
+    SD35DenoisingStage,
+    SD35LatentPreparationStage,
+)
+
+logger = init_logger(__name__)
+
+
+class SD35Pipeline(ComposedPipelineBase):
+    """Minimal SD3.5 Medium text-to-image pipeline (treat as num_frames=1)."""
+
+    _required_config_modules = [
+        "scheduler",
+        "transformer",
+        "vae",
+        "text_encoder",
+        "text_encoder_2",
+        "text_encoder_3",
+        "tokenizer",
+        "tokenizer_2",
+        "tokenizer_3",
+    ]
+
+    def initialize_pipeline(self, fastvideo_args: FastVideoArgs) -> None:
+        te_cfgs = list(fastvideo_args.pipeline_config.text_encoder_configs)
+        if len(te_cfgs) >= 2:
+            for i in (0, 1):
+                te_cfgs[i].tokenizer_kwargs.setdefault("padding", "max_length")
+                te_cfgs[i].tokenizer_kwargs.setdefault("max_length", 77)
+                te_cfgs[i].tokenizer_kwargs.setdefault("truncation", True)
+                te_cfgs[i].tokenizer_kwargs.setdefault("return_tensors", "pt")
+        if len(te_cfgs) >= 3:
+            te_cfgs[2].tokenizer_kwargs["max_length"] = min(
+                int(te_cfgs[2].tokenizer_kwargs.get("max_length", 256)), 256)
+            te_cfgs[2].tokenizer_kwargs.setdefault("padding", "max_length")
+            te_cfgs[2].tokenizer_kwargs.setdefault("truncation", True)
+            te_cfgs[2].tokenizer_kwargs.setdefault("return_tensors", "pt")
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        self.add_stage(
+            stage_name="text_encoding_stage",
+            stage=TextEncodingStage(
+                text_encoders=[
+                    self.get_module("text_encoder"),
+                    self.get_module("text_encoder_2"),
+                    self.get_module("text_encoder_3"),
+                ],
+                tokenizers=[
+                    self.get_module("tokenizer"),
+                    self.get_module("tokenizer_2"),
+                    self.get_module("tokenizer_3"),
+                ],
+            ),
+        )
+
+        self.add_stage(
+            stage_name="timestep_preparation_stage",
+            stage=TimestepPreparationStage(
+                scheduler=self.get_module("scheduler")),
+        )
+
+        self.add_stage(
+            stage_name="latent_preparation_stage",
+            stage=SD35LatentPreparationStage(
+                scheduler=self.get_module("scheduler"), ),
+        )
+
+        self.add_stage(
+            stage_name="sd35_conditioning_stage",
+            stage=SD35ConditioningStage(
+                text_encoders=[
+                    self.get_module("text_encoder"),
+                    self.get_module("text_encoder_2"),
+                    self.get_module("text_encoder_3"),
+                ],
+                tokenizers=[
+                    self.get_module("tokenizer"),
+                    self.get_module("tokenizer_2"),
+                    self.get_module("tokenizer_3"),
+                ],
+            ),
+        )
+
+        self.add_stage(
+            stage_name="denoising_stage",
+            stage=SD35DenoisingStage(
+                transformer=self.get_module("transformer"),
+                scheduler=self.get_module("scheduler"),
+            ),
+        )
+
+        self.add_stage(
+            stage_name="decoding_stage",
+            stage=SD35DecodingStage(vae=self.get_module("vae"), ),
+        )
+
+
+class StableDiffusion3Pipeline(SD35Pipeline):
+    """Alias name to match SD3.5 diffusers `model_index.json` _class_name."""
+
+
+EntryClass = [SD35Pipeline, StableDiffusion3Pipeline]

fastvideo/pipelines/pipeline_batch_info.py

@@ -136,6 +136,17 @@ class ForwardBatch:
     # Camera control inputs (HYWorld)
     pose: str | None = None  # Camera trajectory: pose string (e.g., 'w-31') or JSON file path
 
+    # Camera/action control inputs (GameCraft)
+    camera_states: torch.Tensor | None = None  # Plücker coordinates [B, T, 6, H, W]
+    gt_latents: torch.Tensor | None = None  # Ground truth latents for conditioning [B, 16, T, H, W]
+    conditioning_mask: torch.Tensor | None = None  # Mask for conditioning [B, 1, T, H, W]
+    camera_trajectory: str | None = None  # Camera trajectory file/identifier
+    action_list: list[
+        str] | None = None  # List of actions (e.g., ['forward', 'left'])
+    action_speed_list: list[float] | None = None  # Speed for each action
+    # Camera control inputs (LingBotWorld)
+    c2ws_plucker_emb: torch.Tensor | None = None  # Plucker embedding: [B, C, F_lat, H_lat, W_lat]
+
     # Latent dimensions
     height_latents: list[int] | int | None = None
     width_latents: list[int] | int | None = None
@@ -164,6 +175,13 @@ class ForwardBatch:
     eta: float = 0.0
     sigmas: list[float] | None = None
 
+    # LTX-2 multi-modal CFG parameters
+    ltx2_cfg_scale_video: float = 1.0
+    ltx2_cfg_scale_audio: float = 1.0
+    ltx2_modality_scale_video: float = 1.0
+    ltx2_modality_scale_audio: float = 1.0
+    ltx2_rescale_scale: float = 0.0
+
     n_tokens: int | None = None
 
     # Other parameters that may be needed by specific schedulers

fastvideo/pipelines/stages/__init__.py

@@ -20,6 +20,8 @@ from fastvideo.pipelines.stages.image_encoding import (
     ImageEncodingStage, MatrixGameImageEncodingStage, RefImageEncodingStage,
     ImageVAEEncodingStage, VideoVAEEncodingStage, Hy15ImageEncodingStage,
     HYWorldImageEncodingStage)
+from fastvideo.pipelines.stages.gamecraft_image_encoding import (
+    GameCraftImageVAEEncodingStage)
 from fastvideo.pipelines.stages.input_validation import InputValidationStage
 from fastvideo.pipelines.stages.latent_preparation import (
     Cosmos25LatentPreparationStage, CosmosLatentPreparationStage,
@@ -33,6 +35,7 @@ from fastvideo.pipelines.stages.ltx2_text_encoding import LTX2TextEncodingStage
 from fastvideo.pipelines.stages.matrixgame_denoising import (
     MatrixGameCausalDenoisingStage)
 from fastvideo.pipelines.stages.hyworld_denoising import HYWorldDenoisingStage
+from fastvideo.pipelines.stages.gamecraft_denoising import GameCraftDenoisingStage
 from fastvideo.pipelines.stages.stepvideo_encoding import (
     StepvideoPromptEncodingStage)
 from fastvideo.pipelines.stages.text_encoding import (Cosmos25TextEncodingStage,
@@ -64,6 +67,7 @@ __all__ = [
     "CausalDMDDenosingStage",
     "MatrixGameCausalDenoisingStage",
     "HYWorldDenoisingStage",
+    "GameCraftDenoisingStage",
     "CosmosDenoisingStage",
     "Cosmos25DenoisingStage",
     "Cosmos25T2WDenoisingStage",
@@ -81,6 +85,7 @@ __all__ = [
     "RefImageEncodingStage",
     "ImageVAEEncodingStage",
     "VideoVAEEncodingStage",
+    "GameCraftImageVAEEncodingStage",
     "TextEncodingStage",
     "Cosmos25TextEncodingStage",
     "StepvideoPromptEncodingStage",