diffusers-workflow/workflows/models/z-image.json at master · dkackman/diffusers-workflow · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
{
    "cost_drivers": ["num_images_per_prompt", "num_inference_steps", "width", "height"],
    "variables": {
        "prompt": "prompt:zimage/kidney_trade_in",
        "negative_prompt": "colorful, photorealistic faces, modern UI, flat design, white background, sharp clean edges, high saturation, lens flare, bloom overexposed",
        "num_images_per_prompt": 4,
        "num_inference_steps": 9,
        "guidance_scale": 0.0,
        "width": 960,
        "height": 544,
        "seed": 42
    },
    "seed": "variable:seed",
    "id": "ZImage",
    "description": "Text-to-image with Z-Image Turbo - photorealistic output in nine steps.",
    "cost": [
        {"device": "cuda", "name": "RTX 3090", "vram_gb": 24, "minutes": 1.4}
    ],
    "vram_estimate": {
        "base_gb": 11.8,
        "bytes_per_voxel": 1031,
        "voxel_variables": ["num_images_per_prompt", "width", "height"],
        "reason": "peak reserved VRAM on an RTX 3090 with offload: model, bf16, 9 steps (#945): 12.2 GiB at 1x512x512, 12.9 at 1x1024x1024, 13.8 at 4x960x544, 15.8 at 4x1344x768 - batch size multiplies the latent and VAE decode tensors as pixels do. Calibrated WITH offload: model; a workflow that omits it keeps the whole pipeline resident (~22.9 GiB at 4x1344x768 on lem) and runs out of memory well below this projection"
    },
    "configures": "templates/text-to-image",
    "steps": [
        {
            "name": "txt2img",
            "pipeline": {
                "configuration": {
                    "component_type": "ZImagePipeline",
                    "offload": "model"
                },
                "from_pretrained_arguments": {
                    "model_name": "Tongyi-MAI/Z-Image-Turbo",
                    "torch_dtype": "torch.bfloat16",
                    "low_cpu_mem_usage": true
                },
                "arguments": {
                    "prompt": "variable:prompt",
                    "negative_prompt": "variable:negative_prompt",
                    "num_inference_steps": "variable:num_inference_steps",
                    "num_images_per_prompt": "variable:num_images_per_prompt",
                    "width": "variable:width",
                    "height": "variable:height",
                    "guidance_scale": "variable:guidance_scale"
                }
            },
            "result": {
                "content_type": "image/jpeg",
                "embed_metadata": true
            }
        }
    ]
}