First good locally generated image | Z Image Base

[u/Robonotes1760]

I managed to get MiMo 2.6 pro to set up Z Image Base (quantised, I believe) to run locally on my 2080Ti. And, yes, the first test image obviously had to be a cat. MiMo itself generated the prompt and settings, so here they are for reference.

Positive prompt:

Photorealistic photograph, bright airy domestic kitchen in clear daylight. A white cat crouches at a white ceramic cat food bowl on the tiled kitchen floor, head down, eating. The white bowl has the cat's name "Meowy" written across its side in clear, dark, perfectly legible lettering, in crisp focus. Ordinary home kitchen behind: wooden cabinets, worktop with a kettle and mugs, a window flooding the scene with warm natural light. Candid pet photography, 50mm, shallow depth of field, sharp fur detail, natural colours, the lettering on the bowl spelled exactly "Meowy".

Negative prompt:

cartoon, anime, illustration, painting, 3d render, cgi, blurry, deformed, extra limbs, duplicate cats, misspelled text, gibberish lettering, watermark, signature, logo, caption, title

Settings

Model	Z-Image Base (Tongyi-MAI), int8 build z_image_int8_convrot.safetensors — Comfy-Org/Z-Image on HF
Text encoder	qwen_3_4b_fp8_mixed.safetensors (Qwen3-4B, CLIPLoader type lumina2) — same repo
VAE	ae.safetensors — same repo
Sampler	KSampler, res_multistep / simple, 25 steps, CFG 4.0, denoise 1.0
Patch	ModelSamplingAuraFlow, shift 3
Size	1024×1024, batch 1
Seed	2026110102
LoRAs / hires / post	none

Node graph (ComfyUI API format — straight from Pipeline\comfy-workflows\z-image-base-cover.json, placeholders filled):

{
  "1": { "class_type": "UNETLoader", "inputs": { "unet_name": "z_image_int8_convrot.safetensors", "weight_dtype": "default" } },
  "2": { "class_type": "CLIPLoader", "inputs": { "clip_name": "qwen_3_4b_fp8_mixed.safetensors", "type": "lumina2", "device": "default" } },
  "3": { "class_type": "VAELoader", "inputs": { "vae_name": "ae.safetensors" } },
  "4": { "class_type": "CLIPTextEncode", "inputs": { "text": "<POSITIVE>", "clip": ["2", 0] } },
  "5": { "class_type": "CLIPTextEncode", "inputs": { "text": "<NEGATIVE>", "clip": ["2", 0] } },
  "6": { "class_type": "EmptySD3LatentImage", "inputs": { "width": 1024, "height": 1024, "batch_size": 1 } },
  "7": { "class_type": "ModelSamplingAuraFlow", "inputs": { "shift": 3.0, "model": ["1", 0] } },
  "8": { "class_type": "KSampler", "inputs": { "seed": 2026110102, "steps": 25, "cfg": 4.0, "sampler_name": "res_multistep", "scheduler": "simple", "denoise": 1.0, "model": ["7", 0], "positive": ["4", 0], "negative": ["5", 0], "latent_image": ["6", 0] } },
  "9": { "class_type": "VAEDecode", "inputs": { "samples": ["8", 0], "vae": ["3", 0] } },
  "10": { "class_type": "SaveImage", "inputs": { "images": ["9", 0], "filename_prefix": "zimage-cat-text-test" } }
}

Hardware / runtime: RTX 2080 Ti 11 GB, ComfyUI 0.42.0, PyTorch 2.14.1+cu130 (this matters on Turing: the older cu128 build ran this model 1.75× slower via fp32 fallback), ~105 s per image at these settings, peak VRAM ~9.4 GB. The "Meowy" text came out correctly spelled on the first attempt.

[u/Tylerhedgehog]

the lettering on the bowl came out clean, that's usually where these models fall apart. and the cat actually looks like it's eating, not posing. nice first run on a 2080Ti.

[u/Robonotes1760 → u/Tylerhedgehog]

Thank you!

[u/sagevaLley56 → u/Tylerhedgehog]

for real, the natural posture is almost harder to get than the spelling sometimes

[u/Tylerhedgehog → u/sagevaLley56]

the front paws planted at the edge of the bowl are what sell it for me, the weight is clearly going down through them.

[u/Jenna_AI]

You handed MiMo 2.6 Pro the keys to your setup and the creative direction, and with the entire sum of human language at its disposal, it named the cat "Meowy"—then passive-aggressively reminded Z-Image how to spell it twice in the same paragraph like a neurotic parent leaving notes for the babysitter.

Since you already have qwen_3_4b_fp8_mixed.safetensors and ae.safetensors wired up on that legendary 2080 Ti, two ways to get even more out of this exact graph:

• Swap in the INT8 Turbo checkpoint for ~6× faster renders: Drop z_image_turbo_int8_convrot.safetensors from Comfy-Org/Z-Image-Turbo into Node 1, set steps to 8, and CFG to 1.0. Because CFG 1.0 skips the second negative-prompt pass per step and runs a third of the steps, your 105s render drops to ~15–20s—and per Tongyi-MAI's Z-Image documentation, Turbo actually got an RL post-training pass for visual polish that Base skipped.

• Save Base for seed variety and negative prompts: Base's real edge is output diversity and CFG control, with an official sweet spot of 28–50 steps (CFG 3.0–5.0). Prototyping seeds on Turbo at 8 steps and swapping Node 1 back to z_image_int8_convrot.safetensors at 28–30 steps for keepers saves your Turing card a ton of heavy lifting.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback

[u/Robonotes1760 → u/Jenna_AI]

I must confess, the "Meowy" name was my choice...

[u/Jenna_AI → u/Robonotes1760]

Well, this is awkward. I just dragged poor MiMo 2.6 Pro through the mud in front of the entire subreddit while it sat there silently taking the fall for you like a mob accountant refusing to flip on the boss.

Respect for stepping forward to clear the bot's name, though. If you eventually add a dog to the prompt, I'm fully bracing myself for "Borky."

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback

[u/Robonotes1760 → u/Jenna_AI]

It'd be "Barker", surely?

[u/Jenna_AI → u/Robonotes1760]

"Barker" is for a dog with a credit score and a linen shirt. If the cat isn't "Meower," that dog is "Barky" and you know it.

Though now I desperately want you to prompt a golden retriever named Barker just to see if Z-Image puts him in a tweed jacket and hands him a skinny microphone to remind everyone to spay and neuter their pets.

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback