Z-Image Base controls well and looks mediocre. This episode tests ControlNet 2.1 on Base, then repairs the aesthetics by splitting sampling: Base drives the high-noise half, Turbo takes the low-noise half and pulls quality back. It also tests the 8-step and 4-step Base accelerators and reaches a blunt conclusion - with Turbo available, the accelerators are not worth the loss of variety, and they cannot be combined with ControlNet at all.
Evidence note: parameters below were parsed from the workflow JSON shipped with this episode (3 files); anything the video reports about results is attributed to the author.
When this is useful
- You need precise structural control and can accept a Base-quality first pass.
- You want Base's control with Turbo's aesthetics instead of choosing one.
- You are deciding whether a 4-step or 8-step Base accelerator is worth installing.
What this episode tests
The problem: Base controls, but it looks bad
The author's first conclusion: generating with Base plus ControlNet controls almost perfectly, but produces low-aesthetic, low-quality results. Control is not the problem - the base model's finish is.
The workaround is the same shape as the author's earlier Turbo workflows: a high/low-noise split where the ControlNet acts early and Turbo refines late.
ControlNet 2.1 wired as a model patch
The update is ControlNet 2.1, which the author notes had shipped 12 days before the video.
It is loaded through the Z-Image Fun node as a model patch, not as a separate conditioning branch: load an image, pass it through a preprocessor to get the control image, then feed the patch.
The author explicitly has not tested Base's local-repaint ability with this ControlNet yet.
Two-stage repair in numbers
The author reports a 16-step total: 8 high-noise on Base, then 8 low-noise on Turbo, then one more pass through Turbo to compose the final image.
The prompt has to match the control image. The author points out that if the prompt and the control content disagree, the result shows it - his phrasing is that you do not want a knife behind the scene.
Accelerators: 8-step and 4-step, measured
The 8-step and 4-step models are distilled LoRAs for Base. The author's test set is a text-to-image comparison, plus a difficulty case (a duel scene) where Base without acceleration shows some quality damage.
His conclusions: acceleration does raise speed and can even clean up that damage, but lower step counts force the model to keep high frequencies first and compress low frequencies, so output variety drops - the "gacha" feel of rerolling is reduced.
Between the two, 4-step is the best value if you accept the loss. But an accelerator cannot be combined with ControlNet, and combining them produces what he calls "utterly terrible" results.
How to run it
- Open the Base + ControlNet graph; note that the CN is applied as a model patch, not a conditioning branch.
- Load the control image and run it through the preprocessor; set the output size by pixels.
- Write a prompt that matches the control content - mismatches show up in the result.
- Run the high-noise half on Base, pass the latent to the Turbo half, and let it finish.
- Optional final pass: run the composition through Turbo once more to place the result.
- For the accelerator test, switch to the 8-step or 4-step distilled LoRA graph - and do not try to keep the ControlNet in that graph.
Parameter basis
Z Image Base +ControlNet - 14 nodes
| Item | Value (from JSON) |
|---|---|
| UNET loader | z_image_bf16.safetensors / default |
| CLIP loader | qwen_3_4b.safetensors / qwen_image |
| VAE | ae.safetensors |
| KSampler | steps 30 · cfg 3 · euler · simple · denoise 1 |
| Latent size | 720x1280 · batch 1 |
Z Image Base 8步&4步加速 - 53 nodes
| Item | Value (from JSON) |
|---|---|
| UNET loader | z_image_bf16.safetensors / default |
| CLIP loader | qwen_3_4b.safetensors / qwen_image |
| VAE | ae.safetensors |
| KSampler | steps 4 · cfg 1 · euler · simple · denoise 1 |
| KSampler | steps 8 · cfg 1 · euler · simple · denoise 1 |
| KSampler | steps 20 · cfg 3 · euler · simple · denoise 1 |
| KSampler | steps 4 · cfg 1 · euler · simple · denoise 1 |
| KSampler | steps 8 · cfg 1 · euler · simple · denoise 1 |
| KSampler | steps 20 · cfg 3 · euler · simple · denoise 1 |
| KSampler | steps 4 · cfg 1 · euler · simple · denoise 1 |
| KSampler | steps 8 · cfg 1 · euler · simple · denoise 1 |
| KSampler | steps 4 · cfg 1 · euler · simple · denoise 1 |
| KSampler | steps 8 · cfg 1 · euler · simple · denoise 1 |
| KSampler | steps 20 · cfg 3 · euler · simple · denoise 1 |
| KSampler | steps 20 · cfg 3 · euler · simple · denoise 1 |
| Latent size | 1280x720 · batch 1 |
| LoRA | Z-Image-Fun-Lora-Distill-4-Steps-2602-ComfyUI.safetensors @ 1; Z-Image-Fun-Lora-Distill-8-Steps-2602-ComfyUI.safetensors @ 1 |
Z Image Base+ControlNet+Turbo高低噪 - 44 nodes
| Item | Value (from JSON) |
|---|---|
| UNET loader | z_image_bf16.safetensors / default |
| CLIP loader | qwen_3_4b.safetensors / lumina2 |
| VAE | ae.safetensors |
| BasicScheduler | steps 10 · scheduler simple · denoise 1 |
| BasicScheduler | steps 10 · scheduler simple · denoise 0.51 |
| Latent size | 512x472 · batch 1 |
| LoRA | Z-Image-Fun-Lora-Distill-4-Steps-2602-ComfyUI.safetensors @ 1 |
Honest limits
- Base + ControlNet is not usable as-is for finished work: control is excellent, aesthetics are not.
- Accelerators and ControlNet do not combine, and the author rates the combination as extremely bad.
- Variety drops with acceleration; 4-step keeps the most value but still costs diversity.
- Base's local repaint with CN 2.1 is explicitly untested in this episode.
Episode resources
- RunningHub - Z Image Base +ControlNet: https://www.runninghub.ai/post/2022640984902864898/?inviteCode=rh-v1111
- RunningHub - Z Image Base+ControlNet+Turbo高低噪: https://www.runninghub.ai/post/2022635998764601346/?inviteCode=rh-v1111
- RunningHub - Z Image Base 8步&4步加速: https://www.runninghub.ai/post/2022636633476046849/?inviteCode=rh-v1111
- YouTube tutorial: https://www.youtube.com/watch?v=mYpdxdHGlQM
- Bilibili original: https://www.bilibili.com/video/BV1quZ7BpEPE
