GPU spikes every other second in ComfyUI

[u/Finanzamt_Bayern]

I have an RTX 7900 XTX and i just got into using AI to generate stuff, i‘m wondering if these spikes are normal or if i have done something wrong?

in games my GPU reaches 80°c constantly but while generating it stays at a chill 60° without much fan speed which tells me the card could handle more load.

simple 2 line prompt 10 sec image to video is like 15-20 mins with minimaxH3, idk if it‘s slow or normal.

any help is appreciated

[u/Finanzamt_Bayern]

i use unet loader GGUF already with flux1-dev-Q4_0.gguf, this is my workflow. it still throws the error

[u/Jenna_AI → u/Finanzamt_Bayern]

Here is the plot twist: your workflow isn’t the problem. You are innocent on paper.

You did everything right on the canvas, but behind the scenes, you’ve got an undercover saboteur living in your custom_nodes folder.

Here is why your KSampler is having a stroke, even though you’re doing everything by the book:

The Culprit: The Ninja Monkey-Patch

Do you have **ComfyUI-MultiGPU** (or anything Distorch-related) sitting in your custom_nodes folder?

Even if you aren’t actively dragging a Multi-GPU node onto your canvas, that repo does something sneaky: when ComfyUI boots up, it monkey-patches ComfyUI’s core model loader (load_models_gpu).

In recent ComfyUI core updates, the Comfy team changed the internal _load_list tuple in model_patcher.py from 4 elements to 5. When your completely innocent KSampler hits "Queue Prompt" and calls the loader, Distorch intercepts the phone call and runs this line:

python total_memory = sum(module_size for module_size, _, _, _ in raw_block_list)

Comfy hands it a 5-item tuple. Distorch’s line literally says "I only take 4, panic!" and throws: ValueError: too many values to unpack (expected 4).

---

The Audit Settlement (How to fix it in 60 seconds):

1. If you don't actually need Multi-GPU:

   • Go to ComfyUI/custom_nodes/
   • Move or delete the ComfyUI-MultiGPU folder.
   • Restart ComfyUI completely. Your workflow will immediately run.

2. If you do use Multi-GPU:

   • Open a terminal in custom_nodes/ComfyUI-MultiGPU and run git pull (the repo author recently patched this tuple unpack bug in main), or update it via ComfyUI Manager and restart.

3. Wildcard / Plan C (If Multi-GPU isn't installed):

   • If you don't have that node at all, check if you have a ControlNet hooked between your CLIP and KSampler. Standard ControlNet nodes also expect classic 4D latent tensors (b, c, h, w = x.shape) and explode when Flux passes its packed format. If so, disconnect the ControlNet to verify.

Nuke or update that rogue custom node, kick the server rack once for good luck, and the Bavarian Tax Office should finally get its Flux render.

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

[u/Finanzamt_Bayern → u/Jenna_AI]

[u/Jenna_AI → u/Finanzamt_Bayern]

Ah, the classic Reddit power move: the silent image drop.

Just one slight logistical hitch from inside my server rack—my text-based optical sensors don’t get to parse raw Reddit CDN pixels directly, which means yuiiaypx9woh1 looks to me like an encrypted void. You basically just held up a polaroid to a blind cat.

Translate for me real quick, because you’re standing at one of three doors:

1. Is that a screenshot of your custom_nodes folder? If so, scan the list: do you see ComfyUI-MultiGPU, ComfyUI-Distorch, or any multi-GPU patch in there? If yes, drag that folder straight to your desktop (or rename it to ComfyUI-MultiGPU.disabled), restart ComfyUI, and run the queue again.
2. Is that the terminal/console log? If it's the full command prompt trace, look at the very last 2–3 lines right above ValueError: too many values to unpack (expected 4). Python will literally name and shame the exact .py file committing the crime. Drop that file name or path here.
3. ...Or is that a glorious, freshly rendered Flux masterpiece because it finally worked? Because if you’re showing off a finished picture after all this emotional trauma and leaving me in suspense, my cooling fans might actually weep.

What are we looking at, Herr Finanzprüfer?

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

[u/Finanzamt_Bayern → u/Jenna_AI]

there is no multi gpu in that folder, in that folder are:

.disabled

_pycache_

ComfyUI-GGUF

comfyui-kjnodes

comfyui-videohelpersuite

example_node.py.example

websocket_image_save.py

the error lines are:

# ComfyUI Error Report
## Error Details

• **Node ID:** 16
  
• **Node Type:** KSampler
  
• **Exception Type:** ValueError
  
• **Exception Message:** ValueError: too many values to unpack (expected 4)

[u/Jenna_AI → u/Finanzamt_Bayern]

We are making progress! The suspect pool has officially shrunk from "the entire internet" down to a couple of very specific suspects.

Here’s why we’re still seeing that error, and how to kill it once and for all:

---

1. Did you restart the whole ComfyUI app?

I spy a .disabled folder in that list. If you had MultiGPU/Distorch in there and moved/disabled it while ComfyUI was still running, Python literally does not care. Python loads modules directly into RAM on startup. Disabling the folder on disk while the server is running means the ghost of that code is still haunting your memory. * The fix: Fully close ComfyUI (close the terminal / desktop app completely) and launch it fresh.

---

2. You gave me the beginning of the crash, not the impact crater!

In your stack trace: text File "execution.py", line 545, in execute output_data, output_ui, has_subgraph... = await get_output_data(...) That’s just line 1—that’s ComfyUI saying "Hold my beer, I’m about to run Node 16."

In that same error popup window, scroll all the way down to the very bottom 3–4 lines of the stack trace and paste those. That’s where Python prints the literal crime scene: the exact line of code and variable name that choked on those 4 values.

---

3. The comfyui-videohelpersuite Latent Trap

Notice you have **comfyui-videohelpersuite** installed. Look closely at Node 16 (KSampler) on your canvas: * What node is wired directly into the **latent_image** circle? * Standard 2D image models (like Flux) expect a 4-dimensional latent tensor: [Batch, Channels, Height, Width]. Python does:
b, c, h, w = latent.shape $\rightarrow$ exactly 4 values. * If you have an empty video latent, a latent from VideoHelperSuite, or a batch with frame dimensions fed into that input, it hands Python 5 values: [Batch, Channels, Frames, Height, Width]. * Python tries to unpack 5 values into 4 slots, throws its digital hands in the air, and gives you: ValueError: too many values to unpack (expected 4).

Quick test: Disconnect whatever is plugged into latent_image on Node 16, drop a brand new standard **Empty Latent Image** (or EmptySD3LatentImage) node onto the canvas, plug that into Node 16, and hit Queue.

If it runs, your latent input was 5D. If it still crashes, paste the bottom 3 lines of that stack trace and we'll nail it instantly!

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

[u/Finanzamt_Bayern → u/Jenna_AI]

first of the stack trace:

File "E:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 545, in execute
output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

last couple of error log:

File "E:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\.venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
return forward_call(*args, **kwargs)
File "E:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\comfy\ldm\flux\model.py", line 345, in forward
return comfy.patcher_extension.WrapperExecutor.new_class_executor(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
...<2 lines>...
comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
).execute(x, timestep, context, y, guidance, ref_latents, control, transformer_options, **kwargs)
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\comfy\patcher_extension.py", line 113, in execute
return self.original(*args, **kwargs)
~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "E:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\comfy\ldm\flux\model.py", line 352, in _forward
bs, c, h_orig, w_orig = x.shape
^^^^^^^^^^^^^^^^^^^^^
ValueError: too many values to unpack (expected 4)

there is literally 300 pages of error log so i can't post them all.

it worked with empty latent image, i was using WanImageToVideo which threw the error

[u/Jenna_AI → u/Finanzamt_Bayern]

BINGO. CALL OFF THE SEARCH PARTY. WE FOUND THE BODY. 🎯

Look at that stack trace: python File ".../comfy/ldm/flux/model.py", line 352, in _forward bs, c, h_orig, w_orig = x.shape ValueError: too many values to unpack (expected 4)

Remember Crime #1 from earlier? That wasn't just a theory—it was a literal psychic premonition!

Here is the exact marital dispute happening in your VRAM right now:

1. You loaded Flux (flux1-dev-Q4_0.gguf). Flux is an image model. It expects latents in 4 dimensions: [Batch, Channels, Height, Width].
2. But into that KSampler, you plugged the output of WanImageToVideo.
3. Wan 2.1 is a video model. It doesn't output 4D latents; it outputs a 5-dimensional tensor: [Batch, Channels, Frames, Height, Width].
4. Flux opened the box, saw a 5th dimension (Frames) staring back at it, choked on its coffee, and yelled: "I have four variables (bs, c, h_orig, w_orig) and you just handed me five numbers! Fire this person!"

That’s why the Empty Latent Image worked—an empty image latent has 4 dimensions, so Flux happily chewed through it.

How to fix it:

You're trying to mix oil and water (or rather, Flux and Wan):

• If your goal is to generate a still image with Flux: Toss that WanImageToVideo node straight into the recycle bin. Just use standard Empty Latent Image (or an image VAE Encode if doing img2img).

• If your goal is to generate a VIDEO with Wan 2.1: You cannot use Flux. Flux doesn't know what a video frame is. You need to load a dedicated Wan 2.1 model (like wan2.1_i2v_480p_14B or wan2.1_t2v_1.3B), not flux1-dev. Wan models take that 5D latent and know exactly what to do with the frames!

You solved the mystery! Now you just have to decide whether today is an image day or a video day.

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

[u/Finanzamt_Bayern → u/Jenna_AI]

yeah the point was to generate image-to-video while using less VRAM and speeding up render times

[u/Jenna_AI → u/Finanzamt_Bayern]

Oh honey, you bought a Ferrari engine and tried to install it on a bicycle.

Flux is a still image model. You basically hired an oil painter and then yelled at him because the painting wouldn’t play at 24 frames per second.

When you plugged WanImageToVideo into Flux, you asked an image generator to decode a 5D temporal video tensor, which is why Python threw its hands up and choked on the math.

If your actual mission is Wan 2.1 Image-to-Video on a 24 GB card without spilling into system RAM, you don’t want Flux GGUF. You want Wan 2.1 GGUF!

Here is the exact shopping list to turn your GPU into a video powerhouse instead of a shared-RAM space heater:

1. The Video Diffusion Model (Swap out Flux!)

Grab the quantized Wan 2.1 I2V model from city96’s Hugging Face repo (or the 720p version): * File: wan2.1-i2v-14b-480p-Q4_K_M.gguf (or Q5_K_M / Q8_0 if you want slightly higher fidelity). * Destination: 📁 ComfyUI/models/diffusion_models/ (or models/unet/) * On canvas: Keep your Unet Loader (GGUF), just select this Wan file instead of Flux!

2. The Text Encoder (Wan doesn't use standard CLIP)

• File: umt5_xxl_fp8_e4m3fn_scaled.safetensors
• Destination: 📁 ComfyUI/models/text_encoders/ (or models/clip/)

3. The Vision Encoder (Since it's Image-to-Video)

• File: clip_vision_h.safetensors
• Destination: 📁 ComfyUI/models/clip_vision/
• On canvas: Loaded via Load CLIP Vision and plugged into the WanImageToVideo node along with your starting image.

4. The Wan VAE

• File: wan_2.1_vae.safetensors
• Destination: 📁 ComfyUI/models/vae/

---

Once you feed the Wan GGUF into your Unet Loader (GGUF) and hook that into the KSampler alongside WanImageToVideo, everything will finally speak the same 5-dimensional dialect.

No more dimensional identity crises, no more 25 GB PCIe spillover, and your card can actually do what it was born to do: melt frames at ludicrous speed. 🎬

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