Instructions to use RunDiffusion/Juggernaut-Z-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RunDiffusion/Juggernaut-Z-Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RunDiffusion/Juggernaut-Z-Image", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Document Diffusers format and add usage snippet
Browse files
README.md
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
---
|
| 2 |
-
license:
|
| 3 |
language:
|
| 4 |
- en
|
| 5 |
pipeline_tag: text-to-image
|
|
@@ -23,7 +23,7 @@ tags:
|
|
| 23 |
<p><i>A cinematic fine-tune of Z-Image Base — tuned for presentation-ready output.</i></p>
|
| 24 |
|
| 25 |
<p>
|
| 26 |
-
<a href="https://www.rundiffusion.com/juggernaut-z?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=cta_primary"><img alt="Try Juggernaut Z" src="https://img.shields.io/badge/%E2%96%B6%20Try%20Juggernaut%20Z-7C3AED?style=for-the-badge&labelColor=7C3AED"></a> <a href="https://www.rundiffusion.com/juggernaut-z-prompt-guide?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=prompt_guide_badge"><img alt="Prompt Guide" src="https://img.shields.io/badge/Prompt%20Guide-1f1f23?style=for-the-badge"></a> <a href="https://huggingface.co/Tongyi-MAI/Z-Image"><img alt="Base Model: Z-Image" src="https://img.shields.io/badge/%F0%9F%A4%97%20Base%20Model-Z--Image-FFD21E?style=for-the-badge&labelColor=1f1f23"></a> <img alt="License:
|
| 27 |
</p>
|
| 28 |
|
| 29 |
</div>
|
|
@@ -131,8 +131,33 @@ Cleaner structural lines and more coherent material rendering.
|
|
| 131 |
| `Juggernaut_Z_V1_by_RunDiffusion_q5_k_s-005.gguf` | GGUF · q5_k_s | |
|
| 132 |
| `Juggernaut_Z_V1_by_RunDiffusion_q4_k_m-002.gguf` | GGUF · q4_k_m | |
|
| 133 |
| `Juggernaut_Z_V1_by_RunDiffusion_q4_k_s-001.gguf` | GGUF · q4_k_s | Smallest footprint |
|
|
|
|
| 134 |
|
| 135 |
-
Use the `.safetensors` variants with the workflow that matches your local inference stack. Use the `.gguf` variants with a GGUF-compatible runtime.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 136 |
|
| 137 |
## Links
|
| 138 |
|
|
@@ -144,13 +169,6 @@ Use the `.safetensors` variants with the workflow that matches your local infere
|
|
| 144 |
|
| 145 |
Juggernaut Z is built on Z-Image Base — credit for the upstream base model belongs to the Z-Image team. This fine-tuned release is by **Team Juggernaut**, with training by **KandooAI**, published by **RunDiffusion**.
|
| 146 |
|
| 147 |
-
## License
|
| 148 |
-
|
| 149 |
-
Juggernaut Z is released under **[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)**:
|
| 150 |
-
|
| 151 |
-
- **BY** — attribute RunDiffusion / Team Juggernaut / KandooAI when sharing output.
|
| 152 |
-
- **NC** — **non-commercial use only**. You may not use the model — or its outputs in a workflow — for commercial purposes without a license.
|
| 153 |
-
|
| 154 |
-
You are free to fine-tune, merge, build LoRAs, and otherwise modify the model for non-commercial purposes.
|
| 155 |
|
| 156 |
-
|
|
|
|
| 1 |
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
language:
|
| 4 |
- en
|
| 5 |
pipeline_tag: text-to-image
|
|
|
|
| 23 |
<p><i>A cinematic fine-tune of Z-Image Base — tuned for presentation-ready output.</i></p>
|
| 24 |
|
| 25 |
<p>
|
| 26 |
+
<a href="https://www.rundiffusion.com/juggernaut-z?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=cta_primary"><img alt="Try Juggernaut Z" src="https://img.shields.io/badge/%E2%96%B6%20Try%20Juggernaut%20Z-7C3AED?style=for-the-badge&labelColor=7C3AED"></a> <a href="https://www.rundiffusion.com/juggernaut-z-prompt-guide?utm_source=huggingface&utm_medium=model_card&utm_campaign=juggernaut_z_v1&utm_content=prompt_guide_badge"><img alt="Prompt Guide" src="https://img.shields.io/badge/Prompt%20Guide-1f1f23?style=for-the-badge"></a> <a href="https://huggingface.co/Tongyi-MAI/Z-Image"><img alt="Base Model: Z-Image" src="https://img.shields.io/badge/%F0%9F%A4%97%20Base%20Model-Z--Image-FFD21E?style=for-the-badge&labelColor=1f1f23"></a> <img alt="License: Apache 2.0" src="https://img.shields.io/badge/License-Apache%202.0-2ea44f?style=for-the-badge">
|
| 27 |
</p>
|
| 28 |
|
| 29 |
</div>
|
|
|
|
| 131 |
| `Juggernaut_Z_V1_by_RunDiffusion_q5_k_s-005.gguf` | GGUF · q5_k_s | |
|
| 132 |
| `Juggernaut_Z_V1_by_RunDiffusion_q4_k_m-002.gguf` | GGUF · q4_k_m | |
|
| 133 |
| `Juggernaut_Z_V1_by_RunDiffusion_q4_k_s-001.gguf` | GGUF · q4_k_s | Smallest footprint |
|
| 134 |
+
| `diffusers/` (subfolder) | 🤗 Diffusers format | Load with `DiffusionPipeline.from_pretrained(..., subfolder="diffusers")` |
|
| 135 |
|
| 136 |
+
Use the `.safetensors` variants with the workflow that matches your local inference stack. Use the `.gguf` variants with a GGUF-compatible runtime. Use the `diffusers/` subfolder with the 🤗 Diffusers library — see below.
|
| 137 |
+
|
| 138 |
+
## Use with 🤗 Diffusers
|
| 139 |
+
|
| 140 |
+
The `diffusers/` subfolder contains the model in standard 🤗 Diffusers format (`ZImagePipeline`) and can be loaded directly:
|
| 141 |
+
|
| 142 |
+
```python
|
| 143 |
+
from diffusers import DiffusionPipeline
|
| 144 |
+
import torch
|
| 145 |
+
|
| 146 |
+
pipe = DiffusionPipeline.from_pretrained(
|
| 147 |
+
"RunDiffusion/Juggernaut-Z-Image",
|
| 148 |
+
subfolder="diffusers",
|
| 149 |
+
torch_dtype=torch.bfloat16,
|
| 150 |
+
).to("cuda")
|
| 151 |
+
|
| 152 |
+
image = pipe(
|
| 153 |
+
"a cinematic portrait, dramatic lighting",
|
| 154 |
+
guidance_scale=6.0,
|
| 155 |
+
num_inference_steps=35,
|
| 156 |
+
).images[0]
|
| 157 |
+
image.save("output.png")
|
| 158 |
+
```
|
| 159 |
+
|
| 160 |
+
Requires a version of `diffusers` that includes `ZImagePipeline` support (the format was exported against `diffusers` 0.37.1).
|
| 161 |
|
| 162 |
## Links
|
| 163 |
|
|
|
|
| 169 |
|
| 170 |
Juggernaut Z is built on Z-Image Base — credit for the upstream base model belongs to the Z-Image team. This fine-tuned release is by **Team Juggernaut**, with training by **KandooAI**, published by **RunDiffusion**.
|
| 171 |
|
| 172 |
+
## License
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 173 |
|
| 174 |
+
Released under the **Apache 2.0** license.
|