
Juggernaut X (v10) is a ground-up retrain of the Juggernaut SDXL line. Not a fine-tune of v9 — the entire dataset was re-captioned with GPT-4 Vision, image classifications were rebuilt from scratch, and the model was trained fresh against an expanded, higher-quality image set. The result: noticeably better prompt adherence, cleaner shot-type control, and native text-rendering capability.
| Style | Best for | Example |
|---|---|---|
| Natural language | Storytelling, scenes, complex instructions | "A photographer on a windswept cliff at golden hour, holding a vintage Leica, the ocean churning below" |
| Tagging | Quick iteration, established workflows, LoRA stacking | photograph, photographer, cliff, golden hour, vintage Leica camera, ocean, waves, cinematic lighting |
1import torch
2from diffusers import DiffusionPipeline
3
4pipe = DiffusionPipeline.from_pretrained(
5 "RunDiffusion/Juggernaut-X-v10",
6 torch_dtype=torch.float16,
7 use_safetensors=True,
8).to("cuda")
9
10prompt = "A photographer on a windswept cliff at golden hour, holding a vintage Leica, the ocean churning below, cinematic lighting, hyperdetailed photography"
11
12image = pipe(prompt, width=832, height=1216, num_inference_steps=35, guidance_scale=5.0).images[0]
13image.save("juggernaut_x_v10.png")models/checkpoints/ directory.| Parameter | Value |
|---|---|
| Resolution | 832 × 1216 (portrait) · 1216 × 832 (landscape) |
| Sampler | DPM++ 2M Karras |
| Steps | 30 – 40 |
| CFG scale | 3 – 7 (lower = more realistic) |
| VAE | Already baked in — no external VAE required |
| Model | Why pick it |
|---|---|
| Juggernaut XL v9 | The most popular Juggernaut — proven SDXL photorealism workhorse. 6M+ downloads. |
| Juggernaut XI v11 | Continuation of the SDXL line, post-X refinement |
| Juggernaut Z | Lumina-Image-2 architecture — cinematic, presentation-ready |
| Juggernaut Pro Flux | FLUX.1 backbone — top-tier photo quality with strong consistency |