You can deploy this model and start earning money today!

WaveCut/
Cosmos3-Super-Text2Image-ModelOpt-FP8-Transformer

Name: Cosmos3-Super-Text2Image-ModelOpt-FP8-Transformer
Availability: InStock

nvidia/Cosmos3-Super-Text2Image

finetune

other

Views

No views yet

Cosmos3-Super-Text2Image NVIDIA ModelOpt FP8 Transformer

This repository contains a transformer-only NVIDIA ModelOpt FP8 quantization for nvidia/Cosmos3-Super-Text2Image.

It does not repeat the original model card. Read NVIDIA's model card, prompt-format guidance, license, and safety notes here: nvidia/Cosmos3-Super-Text2Image.

Only transformer/ is provided as a weight artifact. The VAE, scheduler, tokenizers, safety checker, and other components are loaded from the base model.

Recipe

Setting	Value
Quantizer	NVIDIA ModelOpt
ModelOpt version	`0.44.0`
Quant type	`FP8_DEFAULT_CFG`
Weight-only	`True`
Compressed	`True`
Quantized modules inserted	`2709`
Quantization time	1.34s
Compress time	0.45s
Save time	65.99s
Transformer checkpoint size	61.06 GiB

The checkpoint includes ModelOpt state in transformer/modelopt_state.pth.

Assemble The Pipeline

Install ModelOpt in the same environment as Diffusers:

pip install "nvidia_modelopt[hf]"

The current tested runtime requires a small compatibility helper for ModelOpt QTensorWrapper restoration with Diffusers and Accelerate. Important: load the quantized transformer without passing torch_dtype; otherwise Diffusers casts FP8 tensors back to BF16 during state-dict loading.

python

import json
import torch
from diffusers import Cosmos3OmniPipeline, Cosmos3OmniTransformer
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
from modelopt.torch.quantization.qtensor.base_qtensor import QTensorWrapper
import modelopt.torch.opt as mto


def patch_modelopt_qtensor_loader():
    import accelerate.utils.modeling as accelerate_modeling
    import diffusers.models.model_loading_utils as diffusers_loading

    original = accelerate_modeling.set_module_tensor_to_device
    if getattr(original, "_cosmos3_modelopt_patch", False):
        return

    def patched(module, tensor_name, device, value=None, dtype=None, fp16_statistics=None,
                tied_params_map=None, non_blocking=False, clear_cache=True):
        leaf_module = module
        leaf_name = tensor_name
        if "." in tensor_name:
            parts = tensor_name.split(".")
            for part in parts[:-1]:
                leaf_module = getattr(leaf_module, part)
            leaf_name = parts[-1]
        old_value = getattr(leaf_module, leaf_name) if hasattr(leaf_module, leaf_name) else None
        if isinstance(old_value, QTensorWrapper) and value is not None:
            leaf_module._parameters[leaf_name] = QTensorWrapper(
                value.to(device, non_blocking=non_blocking),
                metadata=old_value.metadata,
            )
            return
        return original(module, tensor_name, device, value, dtype, fp16_statistics,
                        tied_params_map, non_blocking, clear_cache)

    patched._cosmos3_modelopt_patch = True
    accelerate_modeling.set_module_tensor_to_device = patched
    diffusers_loading.set_module_tensor_to_device = patched


def cast_modelopt_runtime_tensors(model, dtype=torch.bfloat16):
    for module in model.modules():
        for name, param in list(module._parameters.items()):
            if isinstance(param, QTensorWrapper):
                param.metadata["dtype"] = dtype
            elif param is not None and param.is_floating_point():
                module._parameters[name] = torch.nn.Parameter(
                    param.detach().to(dtype),
                    requires_grad=param.requires_grad,
                )
        for name, buf in list(module._buffers.items()):
            if buf is not None and buf.is_floating_point():
                module._buffers[name] = buf.to(dtype)
    return model


patch_modelopt_qtensor_loader()
mto.enable_huggingface_checkpointing()

transformer = Cosmos3OmniTransformer.from_pretrained(
    "WaveCut/Cosmos3-Super-Text2Image-ModelOpt-FP8-Transformer",
    subfolder="transformer",
    use_safetensors=False,
)
transformer = cast_modelopt_runtime_tensors(transformer, torch.bfloat16)

pipe = Cosmos3OmniPipeline.from_pretrained(
    "nvidia/Cosmos3-Super-Text2Image",
    transformer=transformer,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
    enable_safety_checker=True,
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=3.0)
pipe.to("cuda")

json_caption = {
    "subjects": [],
    "background_setting": "A concise scene description.",
    "comprehensive_t2i_caption": "A detailed natural-language caption.",
    "resolution": {"H": 1024, "W": 1024},
    "aspect_ratio": "1,1",
}

with torch.autocast("cuda", dtype=torch.bfloat16):
    result = pipe(
        prompt=json.dumps(json_caption),
        negative_prompt="",
        num_frames=1,
        height=1024,
        width=1024,
        num_inference_steps=50,
        guidance_scale=4.0,
        generator=torch.Generator(device="cuda").manual_seed(1143),
    )
result.video[0].save("cosmos3_modelopt_fp8.png")

Benchmarks

Measured on one RunPod NVIDIA B200 instance with local container storage, cached model files, PyTorch 2.9.1+cu130, 1024x1024 image generation, 50 inference steps, guidance scale 4.0, flow_shift=3.0, system prompt enabled. The ModelOpt FP8 runtime uses BF16 autocast around the pipeline forward.

Transformer Component Load

Variant	Load to CUDA	VRAM after load	Torch allocated	Torch reserved	Transformer weights
BF16 base transformer	41.83s	122,758 MiB	122,121 MiB	122,132 MiB	119.21 GiB
NVIDIA ModelOpt FP8 transformer	21.95s	63,550 MiB	62,907 MiB	62,924 MiB	61.06 GiB

Full Pipeline Generation

The stress set is ten handwritten JSON-caption prompts designed to stress Cyrillic text, reflections, multi-object composition, anatomy, small details, and scene-following.

Variant	Full pipeline load	VRAM after load	Torch allocated after load	Avg generation time	Min / max generation time	Peak sampled VRAM	Images
BF16 base pipeline	31.31s	125,134 MiB	124,386 MiB	16.05s	15.51s / 17.97s	141,104 MiB	10
NVIDIA ModelOpt FP8 pipeline	35.49s	65,810 MiB	65,171 MiB	45.57s	45.07s / 47.28s	81,854 MiB	10

Original NVIDIA Example Caption

The original model repository provides assets/example_caption.json. The images below are generated locally with the same JSON-caption, seed 1143, 1024x1024, 50 steps, guidance scale 4.0.

Variant	Pipeline load	Generation time	Peak sampled VRAM
BF16 base pipeline	35.41s	18.01s	141,098 MiB
NVIDIA ModelOpt FP8 pipeline	35.28s	47.20s	71,470 MiB

BF16 reference output:

NVIDIA ModelOpt FP8 output:

Stress Prompt Outputs

Stress prompt	NVIDIA ModelOpt FP8 output
01 metro archive reading room	01 metro archive reading room
02 arctic greenhouse night shift	02 arctic greenhouse night shift
03 control room restoration	03 control room restoration
04 rain market cross section	04 rain market cross section
05 manuscript restoration table	05 manuscript restoration table
06 robotic assembly line signage	06 robotic assembly line signage
07 kitchen storm chess table	07 kitchen storm chess table
08 orbital cockpit cyrillic ui	08 orbital cockpit cyrillic ui
09 flood command center	09 flood command center
10 cyrillic newspaper press	10 cyrillic newspaper press

Notes

Treat this as an experimental ModelOpt FP8 transformer artifact. The upstream NVIDIA card documents BF16 as the tested precision.
Do not pass torch_dtype=torch.bfloat16 when loading this quantized transformer; cast runtime metadata after loading as shown above.
The safety checker is not included in this repository; load it from the base model if your use case requires it.
Text rendering, especially exact Cyrillic text, remains a hard case for this model family and should be evaluated visually for the target prompt distribution.