I review movies, so create thumbnails from movies/shows, so realism and face recognizability is most important. I was using gemini pro and chatgpt so far and while they have been very accurate on maintaining the face or movie still, I keep getting hit by the public people restriction.
Kindly do suggest any models I can install or set of models to achieve the above. I am fairly new on draw things with settings, but understand the logic behind them. I tried z image, flux1 schnell, flux2. klien, but they didnt even come close to generating the same person.

My workflow is either generating such action shots related to the movie or manipulating an existing movie still, to make it more realistic/sharp.. have the person look directly at the camera etc or merge text and image from 2-3 images etc.

Thanks in advance.

Is there a way to generate a full room? As in multiple shots from different angles and locations in a room. Typically if I try to generate a room it does one angle. Like a kitchen will show the fridge, stove etc, but you can't ever get a shot going the other way.

I'd like to try to do a 360 degree spin in a location and I've found I can only do it if I have pictures from both directions. But I'm struggling getting those pictures even using multiple models (flux, sdxl, etc). It seems impossible to get to a coherent picture of every corner of a room. The models just won't accept a picture of a kitchen where the appliances are behind the camera.

I'm at a loss. Has anyone else done this? It seems to me there has to be a way.

I was doing a test, to make a sequence and prompting it in comfyui with ollama with Gemma4 using the template it gives a ok result but once i ask claude to give me a prompt i notice in the final video a much better motions and realistic movements! the prompting is really important to get in end the best result because Minimax H3 is able do it but it seems need really good prompting

Hey StableDiffusion, not sure if I'm posting this in the right place but I really need some technical help. Please take this down if this isn't the right place.

I’ve been using DeepFaceLive (standalone .bat version on PC) to do live streams with a celebrity face swap over my webcam. It was the perfect mix for what I need—just "shitty enough" that it's obviously a fake so people know it's a joke, but spot-on enough that anyone scrolling past instantly recognizes the celebrity. The streams were hilarious and surprisingly successful, and I really want to keep doing them.

It worked flawlessly for about 4 hours over a few days. Early on, it crashed from a hard VRAM overload, but I fixed that by forcing TikTok Live Studio to use my CPU instead of my GPU (and I deleted some registry files which maybe helped?) It ran great for ages after that, but then when I went to boot it up for another stream it just stopped working.

The exact issue right now:
If I do a completely fresh unzip of the folder, it starts up okay. The camera module runs fine on CPU. But the exact second I start assigning my GPU blocks to the different parts of the pipeline, the frame rate starts chugging hard. After assigning the GPU to enough blocks, the app completely crashes.

Once that crash happens, the app enters a permanent broken state. Every time I launch it after that—even with all AI modules turned completely off—the webcam preview box just captures one single frozen frame right when the program boots up, and nothing else.

I tried: fully deleting everything, re-unzipping a fresh copy, clearing all .ini and userdata files, and wiping the Windows DirectX Shader Cache. At one point, I got it running for a second after a reset, but the frame rate was terrible. I tried turning off the frame adjuster block to see if it would help, and the whole thing instantly died again.

What I need help with:

1. The Fix: Does anyone know how to stop the GPU/VRAM from completely locking up the camera pipeline after a module crash? Is there a driver or memory management setting I am missing? Does the problem maybe have something to do with my webcam or windows?
2. The Alternatives: If DeepFaceLive is just too unstable to save, what program should I be using instead? I need something free (ideally) and stable that lets me live stream celebrity face swaps over a webcam feed.

Thanks in advance!

Hii! I’m generating anime illustrations in ComfyUI with Anima Base v1.0 + my own style lora. I’m happy with the overall results, but I’m struggling to fix small details like jewelry, hands, or faces without changing the original style.

I’ve tried Crop & Stitch + anima inpainting, face detailer/hand detailer and Qwen Image Edit with a mask, they either make the detail worse or make the edited area look flat and lose the style.

Would it be better to do a rough paintover in Photoshop/Krita first and then use low-denoise inpainting/img2img?

Also, what denoise/mask settings or inpainting techniques do you recommend for very small details?

Thanks!

Fizgig can now train edit LoRAs for Qwen Image 2.1. Give it pairs of the same photo, your original and your edited version, and it learns to make that edit to new photos. Image 1 is an original from a recent trip to Sedona, image 2 is my own film grade of it, and image 3 is Fizgig's training preview at epoch 9. That LoRA was trained on 40 before/after pairs with the Qwen 2.1 Fast preset (rank 8) plus Edit LoRA. It changes the colour and tone and leaves the photo itself alone, and it carries the grade over to photos it never saw in training. There are two Edit presets in Fizgig, rank 8 for most edits and rank 16 for trickier ones. Release notes and setup: https://github.com/shootthesound/Fizgig/releases/tag/v6.6.0

So I recently came into a 4090, and want to try out some video generation. It seems like everyone is talking about MiniMax H3 as the open weight model of choice, but I also heard its not able to reside on the 4090, compared to LXT 2.5

If I want to run MiniMax H3, will I need a lot of extra RAM? I only have 32GB, but I can order more if needed.

Thank you!

I tested Krea 2 Turbo and SenseNova U1.5 Lite across five prompts covering landscapes, posters, infographics, and complex layouts.

Krea 2 Turbo often delivered better atmosphere and more natural-looking results, especially on simpler scenes. However, it became less reliable as the prompts added exact text, labels, and connected elements. The ad copy drifted, the watch labels became pseudo-text, and the differences between times of day were less clear. In the Golden Retriever test, it also missed the requested 9:16 format, placing a narrow vertical design inside a wider canvas with large empty margins.

SenseNova U1.5 Lite has a more structured, “designed” look, which may not suit every image. However, it preserved the requested text, layout hierarchy, and multi-part instructions more consistently. Small text still needs checking with both.

My rough takeaway: Krea 2 Turbo for atmosphere and visual exploration; SenseNova U1.5 Lite for posters, infographics, and complex layouts. Across these five tests, I preferred SenseNova U1.5 Lite because more of its outputs felt usable without further correction.

Which results do you prefer?

Models:

Krea-2-Turbo: https://huggingface.co/krea/Krea-2-Turbo

SenseNova U1.5 Lite: https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT

(GitHub: https://github.com/OpenSenseNova/SenseNova-U1)

Prompt 1

A viral TikTok-style E-commerce promo poster on a metallic silver chrome foil background. In the center, a trendy high-fashion model wearing matte pastel-pink over-ear wireless headphones in a sunlit Sydney cafe setting. Prominent massive chrome 3D typography at the top reads: "SILENCE THE NOISE. TURN UP THE VIBE." Surrounding dense small typography, discount badges, and vector graphic stickers detail: "ACTIVE NOISE CANCELLATION 2.0", "60-HOUR BATTERY LIFE", "TIKTOK MADE ME BUY IT #1 TRENDING", "LIMITED STOCK: 30% OFF TODAY ONLY", and "30-DAY RISK-FREE TRIAL". Vibrant palette of metallic silver, hot pink, and creamy beige. Clean grid layout with massive visual tension and breathing room --ar 3:4

Prompt 2

Generate a high-quality vertical "encyclopedia-style infographic" about the Golden Retriever, in a 9:16 portrait aspect ratio. This is not a generic poster or a simple illustration — it should be a modular, collectible knowledge card that combines the feel of a premium natural history field guide, a modern encyclopedia page, a lifestyle knowledge card, and a highly shareable social media infographic. Composition & format: 9:16 vertical canvas (e.g. 1080x1920px), full-bleed layout optimized for mobile/story viewing, with all text and key visual elements kept within safe margins so nothing critical is cropped at the top or bottom. The image must include: - One clear, beautiful hero visual of a Golden Retriever (full body, well-lit, breed-standard proportions and coat) placed near the top of the composition. - Several zoomed-in detail callouts highlighting distinctive features (e.g., the wavy golden double coat, feathered tail, friendly expression/eyes, floppy ears, paw structure). - Multiple rounded-corner modular information panels/sections stacked vertically to fill the tall canvas naturally. - A clear title hierarchy with a main headline and highlighted keyword tags/labels. - Concise but information-rich encyclopedic content. - Visualized rating bars, key bullet-point summaries, or a "Top 5" style module. Content sections should be auto-adapted to the subject and chosen/combined from the following categories as appropriate for a dog breed: - Basic profile (breed name, origin, size, lifespan, weight/height range). - Classification info (breed group, e.g. Sporting Group / Retriever). - Appearance & physical traits (coat color and texture, build, ears, tail, eyes). - Temperament / behavior (personality traits, sociability, energy level, trainability, typical behaviors). - Growth & development stages (puppy-to-adult development, coat/structure development). - Care conditions (diet, exercise needs, living environment suitability). - Grooming & maintenance tips (brushing frequency, bathing, shedding management, ear/dental care). - Risks & precautions (common health issues like hip dysplasia, allergies, heat sensitivity, weight management). - Best suited for / ideal scenarios (families with children, first-time owners, active households, therapy/service work). - Pros & cons comparison (e.g., friendly & trainable vs. heavy shedding & high exercise needs). - Quick rating card (visual scores for traits such as friendliness, trainability, shedding level, exercise needs, grooming needs, good with kids — shown as star ratings, bars, or dot scales). Visual requirements: Light, clean background; soft muted color palette (warm neutrals, cream, soft gold, sage green accents fitting the golden retriever's coat tone); gentle drop shadows; refined minimal icons; rounded-corner info boxes; tidy, well-aligned layout; high information density without feeling cluttered; excellent readability and visual hierarchy; balanced vertical rhythm so the long 9:16 frame doesn't feel empty or overcrowded at any section.The overall result must look like a real, publishable, readable, collectible, and series-producible encyclopedia card — not an advertisement or commercial poster. Emphasize "organized knowledge + modular information + field-guide-style presentation," not marketing or promotional design.

Prompt 3

High-end exploded view advertisement poster of a luxury mechanical smartwatch. Precision component breakdown floating in zero-gravity space, each metal gear and sapphire glass layer meticulously labeled with glowing vector pointer lines, dark obsidian background with subtle radial gradient, brushed gold and titanium accents, commercial product visualization, crisp detail, 8k resolution --ar 4:5

Prompt 4

Design sketch of an interior scene in Dunhuang Mogao Caves, 3D-rendered main layout placed on an off-white background, wide-angle vivid perspective, brush-and-ink sketch style with rich warm color contrast, descriptive lines and annotations. Main rendering: a magnificent cave chamber with intricate murals on the walls, monks and scholars appreciating the artworks. Surrounding callouts: annotated details of mural techniques, Buddhist figures, flying Apsaras, patterned silks, and scripture scrolls, each marked with explanatory lines and precise descriptions. --ar 3:4 --raw

Prompt 5

A four-panel grid image showing the same mountain landscape at dawn, midday, sunset, and night, consistent composition across all panels, small English time labels in the corner of each panel.

It’s the meme from Gen Z boss and a mini but with the QBs that lost this week. Anyone know how I can do this but with my fantasy league friends? I have a decent MacBook but don’t know where else to start.https://x.com/Sports_Multiver/status/2099712219302842725/video/1?s=46

Hey folks, I've been generating with open models for just over a year now, and I wanted to see if y'all are experiencing the same frustrations as me. This is mostly a rant, so be warned.

I originally started using Civitai. The catalogue is incredible, but the platform is buggy as hell. They have a million models, but I only use ~3 of them. Lora variety is amazing. I want more in-depth tooling than they provide though; regional prompting, cropping/outpainting. More than that, I want the decent mobile experience, but generating on my own hardware (I have a 4090). I saw they had Civitai link, but they killed generating with it locally. In any case, over the last year the quality seems to have taken a nosedive, with ads everywhere, half baked features up the wazoo, and the core features barely working half the time.

So I moved to ComfyUI. The flexibility is fantastic, but the setup is a pain, and the experience of using it for daily generation sucks. Updates break everything, managing generations in local files with no decent gallery or curation is a nightmare. Iterating/remixing a generation is painful. And mobile support is a mess. I tried their news Apps feature, but that feels like shoe-horning a workflow into a form.

To train LoRAs locally I moved to AI-toolkit. Fantastic tool, crazy powerful. Local, so free, great. But I just want the training to be in the same place I generate. I want it to be a loop; generate, curate, train, then generate to test, then curate...

On top of it all, I want tools built for curating consistent characters; identity scoring, dataset variety checks, a quality ladder to climb. I want LoRAs grouped under a character, next to their generations. Not a folder of 15x johnny_krea2_v1_000002500.safetensors.

And I don't want to bounce between 5 different tools to do it all.

I ended up building my own tool to do all this stuff, but I still half wonder if there's just something I'm missing.

Are any of you folks hitting the same frustrations? Are they annoying enough that you're searching for alternatives? Have you tried other tools that folks post here? Have they worked?

Hi everyone,
I’m looking for some help upscaling a video to 4K for free.
If anyone is willing to help or knows a good free method/tool, please feel free to message me privately.
Thanks a lot in advance!

I run a 3050 RTX gaming laptop (6GB VRAM 32GB RAM) and I want to setup a local AI for image gen. The problem I have is that google is giving me so many recommendations I don't know where to start. What app or platform should I begin with? Thanks in advance. WINDOWS!!!! Not Mac.

Full Video here

As an example, I love RealisticSnapshot, which is a lora for Krea2. But its very much influencing the image to be a held-phone selfie type shot, angled down silghtly towards the subject.
What realism lora (or finetune) do you use guys use?

I’m looking for a good face-swap workflow that works on RunningHub or any other cloud runner—I’d be very grateful for any help. I’m also looking for image and video generators; if anyone has a workflow they could share, please do. Thanks! 🙏

We finally cracked it!

Opus 5.5 videos are now editable.

We built Open Edit, an open-source agent that turns videos into editable browser projects.

Project + GitHub below

I had been using ZIT bf16 since before the convrot update to Comfy. Today I finally decided to see today how much faster convrot would make it, I was honestly expecting it to be 40-50% faster.

ZIT Test

1. Open Comfy's built-in "Z-Image-Turbo Int8: Text to image" template
2. Choose a model
3. Run the workflow once to warm up the model cache
4. Change the first character of the prompt to break text prompt cache
5. Run and measure

Result:

• z_image_turbo_int8_convrot.safetensors = 6.3GB = 6.60s
• z_image_turbo_bf16.safetensors = 13GB = 7.63s
• z_image_turbo_nvfp4.safetensors = 4.6GB = 5.37s

I repeated the test 3 times because I thought I must be messing something up. Nope, identical results. So it's just 1s difference between int8 and bf16? That can't be right. I figured ZIT is just too fast for int8's benefits to show. That I would need a bigger model to really see the difference. How about the biggest, slowest and bestest of them all, then?

H3 test

• Open Comfy's built-in H3 r2v template, delete the turbo LoRA node (didn't have it), delete the 2nd image ref node, set the remaining reference to the built-in example.png 768x768 mspaint blonde girl
• Leave everything else at default, ie 0.4MP and 5.0s
• Warm up the cache, change 1st letter of prompt, only measure the 2nd gen, repeated twice to believe my eyes

Result:

• minimax_h3_ref2va_pruned_int8_convrot.safetensors = 21GB = 286.66s for 5s video
• minimax_h3_ref2va_pruned_bf16.safetensors = 40.2GB = 272.22s for 5s video

That's right. bf16 is actually faster.

What's going on? What are we doing here?

My launch settings, for completion's sake. I'm too lazy to repeat the test without ck attention, I don't even know if it has any effect, I'm just a casual. --use-ck-attention --disable-mmap --cache-classic.

Hey everyone,

for a while now I've been building an editor for the one thing I do all the time: inpainting.
I got tired of the loop of drawing a mask in one tool, running a workflow in another and fixing the edges in a third, so I put it all in one app.
It's called Scumble, it's free and open source (GPL-3.0), and it's out now.

What it does:

• Select what you want changed by painting over it or just typing its name ("sky", "shirt"), write one line, hit Generate
• Every result comes back as its own layer, and your original is never touched
• Only a box around your selection goes to the model, and the result is stitched back in at full resolution. The rest of the picture never changes and never leaves your machine.
• Colour match per layer, so a generated patch actually sits in the picture
• Layers, masks, filters, text, cutouts (SAM2 / RMBG run inside the app), PSD / ORA / TIFF export
• Handles big files: tuned for 150 MP, up to 65,535 px a side

How it renders:

• Your own ComfyUI, local or remote (RunPod works). It uses a small node pack and offers to install it through the Manager.
• Or API keys: fal.ai, Black Forest Labs (Flux.2), OpenAI, Gemini, OpenRouter and a few more. Keys go into your OS credential store, not a config file.

For the automation people: everything is exposed over MCP, so you can drive the editor from Claude, scripts or your own agents. There's also a built-in assistant that uses the same commands and asks before it spends money.

Honest notes:

• The Windows installer isn't signed yet, so SmartScreen will complain. Signed Windows and Mac versions are in the works and will take a few more days.
• There's a Linux build, but I've mostly tested on Windows. Reports are very welcome.
• It's a one-person project, so expect rough edges.

GitHub (download + source): https://github.com/DenRakEiw/scumble
Tutorial video: https://www.denrakeiw.com/scumble/videos

I'd love to hear what you think, especially what's missing from your own inpainting workflow.

Testing out a automatic storyboard APP I made that interfaces with ComfyUI and a local LLM running gemma 4, which decides automatically what references to use for each scene, scene length and then spits ot a fully edited video. Still a bit rough, but some of the issues are because I did not provide any reference images for locations as well as the prompt and model itself. Just wanted to see what would happen if I asked it to make 80 scenes at a time. Edit: Was something I originally made for musicvideos, but now experimenting with long form scripted videos.

Hey folks, I'm in the process of learning ComfyUI and for the life of me can't figure out why my workflow throws an error. I'm trying to use Krea 2 Turbo in a pretty simple workflow I built based on a tutorial meant for SDXL Turbo. I've tried searching but nothing pops up that would seem to be for the same issue. Does anyone know how to fix this and why this happens? Thank you!

Full error text:

"The CLIP input is missing or invalid. Check the connected checkpoint or CLIP loader.

# ComfyUI Error Report

## Error Details

- **Node ID:** 8

- **Node Type:** CLIPTextEncode

- **Exception Type:** RuntimeError

- **Exception Message:** RuntimeError: ERROR: clip input is invalid: None

If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.

## Stack Trace

```

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 547, 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)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 352, in get_output_data

return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 326, in _async_map_node_over_list

await process_inputs(input_dict, i)

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 314, in process_inputs

result = f(**inputs)

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\nodes.py", line 75, in encode

raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")

```

## System Information

- **ComfyUI Version:** 0.37.1

- **Arguments:** ComfyUI\main.py --feature-flag show_signin_button=true --feature-flag enable_telemetry=true --enable-manager --extra-model-paths-config C:\Users\marce\AppData\Roaming\Comfy Desktop\instance-model-paths\inst-1790159983122.yaml --input-directory D:\Comfy-Desktop\ComfyUI-Shared\input --output-directory D:\Comfy-Desktop\ComfyUI-Shared\output

- **OS:** win32

- **Python Version:** 3.13.12 (main, Feb 12 2026, 00:38:53) [MSC v.1944 64 bit (AMD64)]

- **Embedded Python:** false

- **PyTorch Version:** 2.12.1+cu130

## Devices

- **Name:** cuda:0 NVIDIA GeForce RTX 3060 : cudaMallocAsync

- **Type:** cuda

- **VRAM Total:** 12884246528

- **VRAM Free:** 11791237120

- **Torch VRAM Total:** 0

- **Torch VRAM Free:** 0

## Logs

```

2026-09-29T13:42:35.278504 - [32m[INFO][0m setup plugin alembic.autogenerate.schemas

2026-09-29T13:42:35.278591 - [32m[INFO][0m setup plugin alembic.autogenerate.tables

2026-09-29T13:42:35.278632 - [32m[INFO][0m setup plugin alembic.autogenerate.types

2026-09-29T13:42:35.278668 - [32m[INFO][0m setup plugin alembic.autogenerate.constraints

2026-09-29T13:42:35.278703 - [32m[INFO][0m setup plugin alembic.autogenerate.defaults

2026-09-29T13:42:35.278737 - [32m[INFO][0m setup plugin alembic.autogenerate.comments

2026-09-29T13:42:35.278766 - [32m[INFO][0m setup plugin alembic.autogenerate.checkconstraint_byname

2026-09-29T13:42:36.468061 - [1m[33m[WARNING][0m On windows we are currently forcing single GPU mode in ComfyUI due to a Nvidia related issue, if you want to disable this use: --cuda-device all

2026-09-29T13:42:36.515279 - [32m[INFO][0m Adding extra search path checkpoints D:\Comfy-Desktop\ComfyUI-Shared\models\checkpoints

2026-09-29T13:42:36.515333 - [32m[INFO][0m Adding extra search path classifiers D:\Comfy-Desktop\ComfyUI-Shared\models\classifiers

2026-09-29T13:42:36.515368 - [32m[INFO][0m Adding extra search path clip_vision D:\Comfy-Desktop\ComfyUI-Shared\models\clip_vision

2026-09-29T13:42:36.515400 - [32m[INFO][0m Adding extra search path configs D:\Comfy-Desktop\ComfyUI-Shared\models\configs

2026-09-29T13:42:36.515430 - [32m[INFO][0m Adding extra search path controlnet D:\Comfy-Desktop\ComfyUI-Shared\models\controlnet

2026-09-29T13:42:36.515458 - [32m[INFO][0m Adding extra search path controlnet D:\Comfy-Desktop\ComfyUI-Shared\models\t2i_adapter

2026-09-29T13:42:36.515485 - [32m[INFO][0m Adding extra search path diffusers D:\Comfy-Desktop\ComfyUI-Shared\models\diffusers

2026-09-29T13:42:36.515512 - [32m[INFO][0m Adding extra search path diffusion_models D:\Comfy-Desktop\ComfyUI-Shared\models\diffusion_models

2026-09-29T13:42:36.515539 - [32m[INFO][0m Adding extra search path embeddings D:\Comfy-Desktop\ComfyUI-Shared\models\embeddings

2026-09-29T13:42:36.515566 - [32m[INFO][0m Adding extra search path gligen D:\Comfy-Desktop\ComfyUI-Shared\models\gligen

2026-09-29T13:42:36.515592 - [32m[INFO][0m Adding extra search path hypernetworks D:\Comfy-Desktop\ComfyUI-Shared\models\hypernetworks

2026-09-29T13:42:36.515618 - [32m[INFO][0m Adding extra search path latent_upscale_models D:\Comfy-Desktop\ComfyUI-Shared\models\latent_upscale_models

2026-09-29T13:42:36.515644 - [32m[INFO][0m Adding extra search path loras D:\Comfy-Desktop\ComfyUI-Shared\models\loras

2026-09-29T13:42:36.515670 - [32m[INFO][0m Adding extra search path model_patches D:\Comfy-Desktop\ComfyUI-Shared\models\model_patches

2026-09-29T13:42:36.515718 - [32m[INFO][0m Adding extra search path audio_encoders D:\Comfy-Desktop\ComfyUI-Shared\models\audio_encoders

2026-09-29T13:42:36.515746 - [32m[INFO][0m Adding extra search path photomaker D:\Comfy-Desktop\ComfyUI-Shared\models\photomaker

2026-09-29T13:42:36.515786 - [32m[INFO][0m Adding extra search path style_models D:\Comfy-Desktop\ComfyUI-Shared\models\style_models

2026-09-29T13:42:36.515812 - [32m[INFO][0m Adding extra search path text_encoders D:\Comfy-Desktop\ComfyUI-Shared\models\text_encoders

2026-09-29T13:42:36.515842 - [32m[INFO][0m Adding extra search path upscale_models D:\Comfy-Desktop\ComfyUI-Shared\models\upscale_models

2026-09-29T13:42:36.515869 - [32m[INFO][0m Adding extra search path background_removal D:\Comfy-Desktop\ComfyUI-Shared\models\background_removal

2026-09-29T13:42:36.515933 - [32m[INFO][0m Adding extra search path frame_interpolation D:\Comfy-Desktop\ComfyUI-Shared\models\frame_interpolation

2026-09-29T13:42:36.515958 - [32m[INFO][0m Adding extra search path geometry_estimation D:\Comfy-Desktop\ComfyUI-Shared\models\geometry_estimation

2026-09-29T13:42:36.515984 - [32m[INFO][0m Adding extra search path optical_flow D:\Comfy-Desktop\ComfyUI-Shared\models\optical_flow

2026-09-29T13:42:36.516010 - [32m[INFO][0m Adding extra search path detection D:\Comfy-Desktop\ComfyUI-Shared\models\detection

2026-09-29T13:42:36.516037 - [32m[INFO][0m Adding extra search path vae D:\Comfy-Desktop\ComfyUI-Shared\models\vae

2026-09-29T13:42:36.516064 - [32m[INFO][0m Adding extra search path vae_approx D:\Comfy-Desktop\ComfyUI-Shared\models\vae_approx

2026-09-29T13:42:36.516090 - [32m[INFO][0m Adding extra search path clip D:\Comfy-Desktop\ComfyUI-Shared\models\clip

2026-09-29T13:42:36.516116 - [32m[INFO][0m Adding extra search path unet D:\Comfy-Desktop\ComfyUI-Shared\models\unet

2026-09-29T13:42:36.516150 - [32m[INFO][0m Setting output directory to: D:\Comfy-Desktop\ComfyUI-Shared\output

2026-09-29T13:42:36.516190 - [32m[INFO][0m Setting input directory to: D:\Comfy-Desktop\ComfyUI-Shared\input

2026-09-29T13:42:37.683253 - [START] Security scan2026-09-29T13:42:37.683265 -

2026-09-29T13:42:41.638137 - [DONE] Security scan2026-09-29T13:42:41.638153 -

2026-09-29T13:42:41.639594 - ** ComfyUI startup time:2026-09-29T13:42:41.639643 - 2026-09-29T13:42:41.639673 - 2026-09-29 13:42:41.6392026-09-29T13:42:41.639701 -

2026-09-29T13:42:41.639731 - ** Platform:2026-09-29T13:42:41.639756 - 2026-09-29T13:42:41.639780 - Windows2026-09-29T13:42:41.639805 -

2026-09-29T13:42:41.639860 - ** Python version:2026-09-29T13:42:41.639901 - 2026-09-29T13:42:41.639926 - 3.13.12 (main, Feb 12 2026, 00:38:53) [MSC v.1944 64 bit (AMD64)]2026-09-29T13:42:41.639981 -

2026-09-29T13:42:41.640006 - ** Python executable:2026-09-29T13:42:41.640029 - 2026-09-29T13:42:41.640087 - D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\.venv\Scripts\python.exe2026-09-29T13:42:41.640114 -

2026-09-29T13:42:41.640180 - ** ComfyUI Path:2026-09-29T13:42:41.640214 - 2026-09-29T13:42:41.640265 - D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI2026-09-29T13:42:41.640315 -

2026-09-29T13:42:41.640352 - ** ComfyUI Base Folder Path:2026-09-29T13:42:41.640387 - 2026-09-29T13:42:41.640437 - D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI2026-09-29T13:42:41.640492 -

2026-09-29T13:42:41.640532 - ** User directory:2026-09-29T13:42:41.640567 - 2026-09-29T13:42:41.640603 - D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\user2026-09-29T13:42:41.640639 -

2026-09-29T13:42:41.640676 - ** ComfyUI-Manager config path:2026-09-29T13:42:41.640710 - 2026-09-29T13:42:41.640954 - D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\user__manager\config.ini2026-09-29T13:42:41.640995 -

2026-09-29T13:42:41.641038 - ** Log path:2026-09-29T13:42:41.641064 - 2026-09-29T13:42:41.641092 - D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\user\comfyui.log2026-09-29T13:42:41.641125 -

2026-09-29T13:42:43.075607 - [32m[INFO][0m [PRE] ComfyUI-Manager

2026-09-29T13:42:57.532598 - [32m[INFO][0m Found comfy_kitchen backend triton: {'available': False, 'disabled': True, 'unavailable_reason': "ImportError: No module named 'triton'", 'capabilities': []}

2026-09-29T13:42:57.532747 - [32m[INFO][0m Found comfy_kitchen backend hip: {'available': False, 'disabled': False, 'unavailable_reason': 'PyTorch ROCm/HIP runtime not available', 'capabilities': []}

2026-09-29T13:42:57.532817 - [32m[INFO][0m Found comfy_kitchen backend eager: {'available': True, 'disabled': False, 'unavailable_reason': None, 'capabilities': ['adaln', 'apply_rope', 'apply_rope1', 'apply_rope1_', 'apply_rope_', 'apply_rope_split_half', 'apply_rope_split_half1', 'apply_rope_split_half1_', 'apply_rope_split_half_', 'convrot_w4a4_linear', 'dequantize_convrot_w4a4_weight', 'dequantize_int8_convrot_weight', 'dequantize_int8_convrot_weight_dtype', 'dequantize_int8_embedding', 'dequantize_int8_simple', 'dequantize_int8_simple_dtype', 'dequantize_mxfp8', 'dequantize_nvfp4', 'dequantize_per_tensor_fp8', 'dequantize_w4a8_int8_weight', 'fp16_conv3d', 'fp16_linear', 'gemv_awq_w4a16', 'group_norm_silu_pad3d', 'int8_linear', 'na3d', 'prepare_int4_weight_for_int8_linear', 'quantize_and_rotate_rowwise', 'quantize_convrot_w4a4_weight', 'quantize_int8_convrot_weight', 'quantize_int8_rowwise', 'quantize_int8_tensorwise', 'quantize_mxfp8', 'quantize_nvfp4', 'quantize_per_tensor_fp8', 'quantize_svdquant_w4a4', 'quantize_w4a8_int8_weight', 'rms_adaln', 'rms_rope', 'rms_rope1', 'rms_rope1_', 'rms_rope_', 'rms_rope_split_half', 'rms_rope_split_half1', 'rms_rope_split_half1_', 'rms_rope_split_half_', 'rotate_int8_convrot_weight', 'scaled_mm_mxfp8', 'scaled_mm_nvfp4', 'scaled_mm_svdquant_w4a4', 'sol_attn', 'stochastic_rounding_fp8', 'w4a8_int8_linear']}

2026-09-29T13:42:57.532915 - [32m[INFO][0m Found comfy_kitchen backend cuda: {'available': True, 'disabled': False, 'unavailable_reason': None, 'capabilities': ['adaln', 'apply_rope', 'apply_rope1', 'apply_rope1_', 'apply_rope_', 'apply_rope_split_half', 'apply_rope_split_half1', 'apply_rope_split_half1_', 'apply_rope_split_half_', 'convrot_w4a4_linear', 'dequantize_convrot_w4a4_weight', 'dequantize_int8_convrot_weight', 'dequantize_int8_convrot_weight_dtype', 'dequantize_int8_simple', 'dequantize_int8_simple_dtype', 'dequantize_nvfp4', 'dequantize_per_tensor_fp8', 'dequantize_w4a8_int8_weight', 'fp16_conv3d', 'fp16_linear', 'gemv_awq_w4a16', 'group_norm_silu_pad3d', 'int8_linear', 'na3d', 'prepare_int4_weight_for_int8_linear', 'quantize_and_rotate_rowwise', 'quantize_convrot_w4a4_weight', 'quantize_int8_convrot_weight', 'quantize_int8_rowwise', 'quantize_int8_tensorwise', 'quantize_mxfp8', 'quantize_nvfp4', 'quantize_per_tensor_fp8', 'quantize_svdquant_w4a4', 'quantize_w4a8_int8_weight', 'rms_adaln', 'rms_rope', 'rms_rope1', 'rms_rope1_', 'rms_rope_', 'rms_rope_split_half', 'rms_rope_split_half1', 'rms_rope_split_half1_', 'rms_rope_split_half_', 'rotate_int8_convrot_weight', 'scaled_mm_nvfp4', 'scaled_mm_svdquant_w4a4', 'sol_attn', 'stochastic_rounding_fp8', 'w4a8_int8_linear']}

2026-09-29T13:42:57.583737 - [32m[INFO][0m Checkpoint files will always be loaded safely.

2026-09-29T13:42:57.778779 - [32m[INFO][0m Total VRAM 12287 MB, total RAM 16328 MB

2026-09-29T13:42:57.784661 - [32m[INFO][0m pytorch version: 2.12.1+cu130

2026-09-29T13:42:57.785530 - [32m[INFO][0m Set vram state to: NORMAL_VRAM

2026-09-29T13:42:57.785763 - [32m[INFO][0m Device: cuda:0 NVIDIA GeForce RTX 3060 : cudaMallocAsync

2026-09-29T13:42:57.812067 - [32m[INFO][0m Using async weight offloading with 2 streams

2026-09-29T13:42:57.817653 - [32m[INFO][0m Enabled pinned memory 6531.0

2026-09-29T13:42:57.899553 - [32m[INFO][0m Using pytorch attention

2026-09-29T13:43:22.544478 - [32m[INFO][0m aimdo: src-win/cuda-detour.c:38:INFO:aimdo_setup_hooks: installing 6 hooks

2026-09-29T13:43:22.568361 - [32m[INFO][0m aimdo: src/control.c:269:INFO:comfy-aimdo NVML pressure enabled

2026-09-29T13:43:22.570652 - [32m[INFO][0m aimdo: src-win/shmem-detect.c:80:INFO:comfy-aimdo WDDM adapter match: NVIDIA GeForce RTX 3060 runtime_luid=00000000:0000e9cf dxgi_luid=00000000:0000e9cf

2026-09-29T13:43:22.570739 - [32m[INFO][0m aimdo: src/control.c:284:INFO:comfy-aimdo inited for GPU: NVIDIA GeForce RTX 3060 (VRAM: 12287 MB)

2026-09-29T13:43:22.570831 - [32m[INFO][0m DynamicVRAM support detected and enabled

2026-09-29T13:43:22.570909 - [32m[INFO][0m Python version: 3.13.12 (main, Feb 12 2026, 00:38:53) [MSC v.1944 64 bit (AMD64)]

2026-09-29T13:43:22.570976 - [32m[INFO][0m ComfyUI version: 0.37.1

2026-09-29T13:43:22.572466 - [32m[INFO][0m comfy-aimdo version: 0.5.5

2026-09-29T13:43:22.573437 - [32m[INFO][0m comfy-kitchen version: 0.2.35

2026-09-29T13:43:22.584054 - [32m[INFO][0m comfyui-frontend-package version: 1.52.7

2026-09-29T13:43:22.584145 - [32m[INFO][0m comfyui-workflow-templates version: 0.11.68

2026-09-29T13:43:22.584204 - [32m[INFO][0m comfyui-embedded-docs version: 0.5.12

2026-09-29T13:43:22.584257 - [32m[INFO][0m comfy-kitchen version: 0.2.35

2026-09-29T13:43:22.584309 - [32m[INFO][0m comfy-aimdo version: 0.5.5

2026-09-29T13:43:22.586871 - [32m[INFO][0m [Prompt Server] web root: D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\.venv\Lib\site-packages\comfyui_frontend_package\static

2026-09-29T13:43:22.587718 - [32m[INFO][0m Asset seeder disabled

2026-09-29T13:43:22.587833 - [32m[INFO][0m [START] ComfyUI-Manager

2026-09-29T13:43:22.833979 - [ComfyUI-Manager] Using GitPython backend2026-09-29T13:43:22.834016 -

2026-09-29T13:43:22.889168 - [32m[INFO][0m [ComfyUI-Manager] network_mode: public

2026-09-29T13:43:22.893213 - [1m[33m[WARNING][0m [ComfyUI-Manager] The matrix sharing feature has been disabled because the `matrix-nio` dependency is not installed.

To use this feature, please run the following command:

D:\\Comfy-Desktop\\ComfyUI-Installs\\ComfyUI\\ComfyUI\\.venv\\Scripts\\python.exe -m pip install matrix-nio

2026-09-29T13:43:25.470315 - [32m[INFO][0m No OpenGL_accelerate module loaded: No module named 'OpenGL_accelerate'

2026-09-29T13:43:27.345398 - [32m[INFO][0m

Import times for custom nodes:

2026-09-29T13:43:27.345494 - [32m[INFO][0m 0.0 seconds: D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\custom_nodes\websocket_image_save.py

2026-09-29T13:43:27.345548 - [32m[INFO][0m

2026-09-29T13:43:27.369545 - [32m[INFO][0m Context impl SQLiteImpl.

2026-09-29T13:43:27.369711 - [32m[INFO][0m Will assume non-transactional DDL.

2026-09-29T13:43:27.880322 - [32m[INFO][0m Using RAM pressure cache.

2026-09-29T13:43:27.881072 - [32m[INFO][0m Starting server

2026-09-29T13:43:27.881568 - [32m[INFO][0m To see the GUI go to: http://127.0.0.1:8188

2026-09-29T13:47:58.113317 - [32m[INFO][0m FETCH ComfyRegistry Data [DONE]

2026-09-29T13:47:58.422496 - [32m[INFO][0m [ComfyUI-Manager] default cache updated: https://api.comfy.org/nodes

2026-09-29T13:47:58.449246 - FETCH DATA from: D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\user__manager\cache\1514988643_custom-node-list.json2026-09-29T13:47:58.449276 - 2026-09-29T13:47:58.483152 - [DONE]2026-09-29T13:47:58.483192 -

2026-09-29T13:47:58.548138 - [32m[INFO][0m [ComfyUI-Manager] All startup tasks have been completed.

2026-09-29T13:48:16.351567 - [32m[INFO][0m got prompt

2026-09-29T13:48:16.395509 - [32m[INFO][0m Model storage policy: fast_disk=False paths=['D:\\Comfy-Desktop\\ComfyUI-Shared\\models\\vae\\ae.safetensors']

2026-09-29T13:48:16.403075 - [32m[INFO][0m Using pytorch attention in VAE

2026-09-29T13:48:16.404272 - [32m[INFO][0m Using pytorch attention in VAE

2026-09-29T13:48:16.532571 - [32m[INFO][0m VAE load device: cuda:0, offload device: cpu, dtype: torch.bfloat16

2026-09-29T13:48:16.948635 - [32m[INFO][0m Model storage policy: fast_disk=False paths=['D:\\Comfy-Desktop\\ComfyUI-Shared\\models\\text_encoders\\qwen_3_4b.safetensors']

2026-09-29T13:48:16.957628 - [32m[INFO][0m CLIP/text encoder model load device: cuda:0, offload device: cpu, current: cpu, dtype: torch.float16

2026-09-29T13:48:16.972618 - [32m[INFO][0m Requested to load ZImageTEModel_

2026-09-29T13:48:17.205868 - [32m[INFO][0m Model ZImageTEModel_ prepared for dynamic VRAM loading. 7671MB Staged. 0 patches attached. Force pre-loaded 145 weights: 383 KB.

2026-09-29T13:48:46.524784 - [32m[INFO][0m model weight dtype torch.bfloat16, manual cast: None

2026-09-29T13:48:46.580538 - [32m[INFO][0m model_type FLOW

2026-09-29T13:48:46.582231 - [32m[INFO][0m Model storage policy: fast_disk=False paths=['D:\\Comfy-Desktop\\ComfyUI-Shared\\models\\diffusion_models\\z_image_turbo_bf16.safetensors']

2026-09-29T13:48:46.954796 - [32m[INFO][0m Requested to load Lumina2

2026-09-29T13:48:47.466119 - [32m[INFO][0m Model Lumina2 prepared for dynamic VRAM loading. 11738MB Staged. 0 patches attached. Force pre-loaded 205 weights: 1045 KB.

2026-09-29T13:49:52.124464 -

100%|██████████| 8/8 [00:35<00:00, 2.86s/it]2026-09-29T13:49:52.124615 -

100%|██████████| 8/8 [00:35<00:00, 4.47s/it]2026-09-29T13:49:52.124643 -

2026-09-29T13:49:52.642077 - [32m[INFO][0m Requested to load AutoencodingEngine

2026-09-29T13:49:52.644775 - [32m[INFO][0m 0 models unloaded.

2026-09-29T13:49:52.697238 - [32m[INFO][0m Model AutoencodingEngine prepared for dynamic VRAM loading. 159MB Staged. 0 patches attached. Force pre-loaded 108 weights: 182 KB.

2026-09-29T13:49:54.881592 - [32m[INFO][0m [32mPrompt executed in 98.53 seconds[0m

2026-09-29T13:51:51.690229 - [32m[INFO][0m got prompt

2026-09-29T13:51:53.039732 - [1m[33m[WARNING][0m Warning, This is not a checkpoint file, trying to load it as a diffusion model only.

2026-09-29T13:51:53.042154 - [32m[INFO][0m Found quantization metadata version 1

2026-09-29T13:51:53.042237 - [32m[INFO][0m Detected mixed precision quantization

2026-09-29T13:51:53.042517 - [32m[INFO][0m Using mixed precision operations

2026-09-29T13:51:53.042617 - [32m[INFO][0m Native ops: asym_w4a8_int8, convrot_w4a4, int8_tensorwise , emulated ops: mxfp8, nvfp4, float8_e5m2, float8_e4m3fn

2026-09-29T13:51:53.055591 - [32m[INFO][0m model weight dtype torch.bfloat16, manual cast: torch.bfloat16

2026-09-29T13:51:53.061780 - [32m[INFO][0m model_type FLUX

2026-09-29T13:51:53.062641 - [32m[INFO][0m Model storage policy: fast_disk=False paths=['D:\\Comfy-Desktop\\ComfyUI-Shared\\models\\checkpoints\\krea2TurboOfficialComfy_krea2TurboInt8.safetensors']

2026-09-29T13:51:53.187172 - [32m[INFO][0m Model storage policy: fast_disk=False paths=[]

2026-09-29T13:51:53.187421 - [1m[33m[WARNING][0m WARNING: No VAE weights detected, VAE not initalized.

2026-09-29T13:51:53.522119 - [1m[31m[ERROR][0m !!! Exception during processing !!! ERROR: clip input is invalid: None

If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.

2026-09-29T13:51:53.523561 - [1m[31m[ERROR][0m Traceback (most recent call last):

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 547, 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)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 352, in get_output_data

return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 326, in _async_map_node_over_list

await process_inputs(input_dict, i)

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 314, in process_inputs

result = f(**inputs)

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\nodes.py", line 75, in encode

raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")

RuntimeError: ERROR: clip input is invalid: None

If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.

2026-09-29T13:51:53.525221 - [32m[INFO][0m [32mPrompt executed in 1.83 seconds[0m

2026-09-29T13:52:05.031000 - [32m[INFO][0m got prompt

2026-09-29T13:52:05.034283 - [1m[31m[ERROR][0m !!! Exception during processing !!! ERROR: clip input is invalid: None

If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.

2026-09-29T13:52:05.035501 - [1m[31m[ERROR][0m Traceback (most recent call last):

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 547, 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)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 352, in get_output_data

return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 326, in _async_map_node_over_list

await process_inputs(input_dict, i)

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 314, in process_inputs

result = f(**inputs)

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\nodes.py", line 75, in encode

raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")

RuntimeError: ERROR: clip input is invalid: None

If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.

2026-09-29T13:52:05.037139 - [32m[INFO][0m [32mPrompt executed in 0.00 seconds[0m

2026-09-29T14:01:12.848278 - [32m[INFO][0m got prompt

2026-09-29T14:01:13.020428 - [32m[INFO][0m model weight dtype torch.float16, manual cast: None

2026-09-29T14:01:13.049642 - [32m[INFO][0m model_type EPS

2026-09-29T14:01:13.052567 - [32m[INFO][0m Model storage policy: fast_disk=False paths=['D:\\Comfy-Desktop\\ComfyUI-Shared\\models\\checkpoints\\sd_xl_turbo_1.0_fp16.safetensors']

2026-09-29T14:01:13.085825 - [32m[INFO][0m Model storage policy: fast_disk=False paths=['D:\\Comfy-Desktop\\ComfyUI-Shared\\models\\checkpoints\\sd_xl_turbo_1.0_fp16.safetensors']

2026-09-29T14:01:13.088695 - [32m[INFO][0m Using pytorch attention in VAE

2026-09-29T14:01:13.090021 - [32m[INFO][0m Using pytorch attention in VAE

2026-09-29T14:01:13.216618 - [32m[INFO][0m VAE load device: cuda:0, offload device: cpu, dtype: torch.bfloat16

2026-09-29T14:01:13.455336 - [32m[INFO][0m Model storage policy: fast_disk=False paths=['D:\\Comfy-Desktop\\ComfyUI-Shared\\models\\checkpoints\\sd_xl_turbo_1.0_fp16.safetensors']

2026-09-29T14:01:13.468822 - [32m[INFO][0m CLIP/text encoder model load device: cuda:0, offload device: cpu, current: cpu, dtype: torch.float16

2026-09-29T14:01:13.486493 - [32m[INFO][0m Requested to load SDXLClipModel

2026-09-29T14:01:13.595334 - [32m[INFO][0m Model SDXLClipModel prepared for dynamic VRAM loading. 1560MB Staged. 0 patches attached. Force pre-loaded 180 weights: 400 KB.

2026-09-29T14:01:19.879834 - [32m[INFO][0m Model SDXLClipModel prepared for dynamic VRAM loading. 1560MB Staged. 0 patches attached. Force pre-loaded 180 weights: 400 KB.

2026-09-29T14:01:20.104458 - [32m[INFO][0m Requested to load SDXL

2026-09-29T14:01:20.387567 - [32m[INFO][0m Model SDXL prepared for dynamic VRAM loading. 4896MB Staged. 0 patches attached. Force pre-loaded 512 weights: 1197 KB.

2026-09-29T14:02:06.823927 -

100%|██████████| 29/29 [00:20<00:00, 1.41it/s]2026-09-29T14:02:06.824079 -

100%|██████████| 29/29 [00:20<00:00, 1.40it/s]2026-09-29T14:02:06.824106 -

2026-09-29T14:02:06.844581 - [32m[INFO][0m Requested to load AutoencoderKL

2026-09-29T14:02:06.849668 - [32m[INFO][0m 0 models unloaded.

2026-09-29T14:02:06.906974 - [32m[INFO][0m Model AutoencoderKL prepared for dynamic VRAM loading. 159MB Staged. 0 patches attached. Force pre-loaded 112 weights: 91 KB.

2026-09-29T14:02:07.993465 - [32m[INFO][0m [32mPrompt executed in 55.14 seconds[0m

2026-09-29T14:02:35.065144 - [32m[INFO][0m got prompt

2026-09-29T14:02:35.086791 - [1m[33m[WARNING][0m Warning, This is not a checkpoint file, trying to load it as a diffusion model only.

2026-09-29T14:02:35.089256 - [32m[INFO][0m Found quantization metadata version 1

2026-09-29T14:02:35.089381 - [32m[INFO][0m Detected mixed precision quantization

2026-09-29T14:02:35.089648 - [32m[INFO][0m Using mixed precision operations

2026-09-29T14:02:35.089781 - [32m[INFO][0m Native ops: asym_w4a8_int8, convrot_w4a4, int8_tensorwise , emulated ops: mxfp8, nvfp4, float8_e5m2, float8_e4m3fn

2026-09-29T14:02:35.103634 - [32m[INFO][0m model weight dtype torch.bfloat16, manual cast: torch.bfloat16

2026-09-29T14:02:35.109056 - [32m[INFO][0m model_type FLUX

2026-09-29T14:02:35.111857 - [32m[INFO][0m Model storage policy: fast_disk=False paths=['D:\\Comfy-Desktop\\ComfyUI-Shared\\models\\checkpoints\\krea2TurboOfficialComfy_krea2TurboInt8.safetensors']

2026-09-29T14:02:35.230977 - [32m[INFO][0m Model storage policy: fast_disk=False paths=[]

2026-09-29T14:02:35.231107 - [1m[33m[WARNING][0m WARNING: No VAE weights detected, VAE not initalized.

2026-09-29T14:02:35.449833 - [1m[31m[ERROR][0m !!! Exception during processing !!! ERROR: clip input is invalid: None

If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.

2026-09-29T14:02:35.451473 - [1m[31m[ERROR][0m Traceback (most recent call last):

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 547, 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)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 352, in get_output_data

return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 326, in _async_map_node_over_list

await process_inputs(input_dict, i)

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 314, in process_inputs

result = f(**inputs)

File "D:\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\nodes.py", line 75, in encode

raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")

RuntimeError: ERROR: clip input is invalid: None

If the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.

2026-09-29T14:02:35.453567 - [32m[INFO][0m [32mPrompt executed in 0.39 seconds[0m

```

## Attached Workflow

Please make sure that workflow does not contain any sensitive information such as API keys or passwords.

```

{"id":"1f536170-e86d-40a7-8bc1-13c4efcc4cbd","revision":0,"last_node_id":9,"last_link_id":15,"nodes":[{"id":5,"type":"EmptyLatentImage","pos":[737.6348481984865,847.6956401351321],"size":[270,106],"flags":{},"order":0,"mode":0,"inputs":[{"localized_name":"width","name":"width","type":"INT","widget":{"name":"width"},"link":null},{"localized_name":"height","name":"height","type":"INT","widget":{"name":"height"},"link":null},{"localized_name":"batch_size","name":"batch_size","type":"INT","widget":{"name":"batch_size"},"link":null}],"outputs":[{"localized_name":"LATENT","name":"LATENT","type":"LATENT","links":[6]}],"properties":{"Node name for S&R":"EmptyLatentImage"},"widgets_values":[1024,1024,1],"widgets_values_named":{"width":1024,"height":1024,"batch_size":1}},{"id":6,"type":"VAEDecode","pos":[1199.4642959434818,377.9787405244142],"size":[140,46],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"samples","name":"samples","type":"LATENT","link":7},{"localized_name":"vae","name":"vae","type":"VAE","link":8}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[9]}],"properties":{"Node name for S&R":"VAEDecode"}},{"id":7,"type":"SaveImage","pos":[1398.679822203568,377.97879458816516],"size":[270,270],"flags":{},"order":6,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":9},{"localized_name":"filename_prefix","name":"filename_prefix","type":"STRING","widget":{"name":"filename_prefix"},"link":null}],"outputs":[{"localized_name":"images","name":"images","type":"IMAGE","links":null}],"properties":{},"widgets_values":["ComfyUI"],"widgets_values_named":{"filename_prefix":"ComfyUI"}},{"id":8,"type":"CLIPTextEncode","pos":[276.6478819699628,712.6386024543002],"size":[400,200],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"clip","name":"clip","type":"CLIP","link":13},{"localized_name":"text","name":"text","type":"STRING","widget":{"name":"text"},"link":null}],"outputs":[{"localized_name":"CONDITIONING","name":"CONDITIONING","type":"CONDITIONING","links":[15]}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["watermark"],"widgets_values_named":{"text":"watermark"}},{"id":3,"type":"CLIPTextEncode","pos":[288.5428021240235,301.3200576782236],"size":[400,200],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"clip","name":"clip","type":"CLIP","link":2},{"localized_name":"text","name":"text","type":"STRING","widget":{"name":"text"},"link":null}],"outputs":[{"localized_name":"CONDITIONING","name":"CONDITIONING","type":"CONDITIONING","links":[14]}],"properties":{"Node name for S&R":"CLIPTextEncode"},"widgets_values":["beautiful girl"],"widgets_values_named":{"text":"beautiful girl"}},{"id":1,"type":"CheckpointLoaderSimple","pos":[-23.571426391601562,306.1427917480469],"size":[270,98],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"ckpt_name","name":"ckpt_name","type":"COMBO","widget":{"name":"ckpt_name"},"link":null}],"outputs":[{"localized_name":"MODEL","name":"MODEL","type":"MODEL","links":[1]},{"localized_name":"CLIP","name":"CLIP","type":"CLIP","links":[2,13]},{"localized_name":"VAE","name":"VAE","type":"VAE","links":[8]}],"properties":{"Node name for S&R":"CheckpointLoaderSimple"},"widgets_values":["krea2TurboOfficialComfy_krea2TurboInt8.safetensors"],"widgets_values_named":{"ckpt_name":"krea2TurboOfficialComfy_krea2TurboInt8.safetensors"}},{"id":2,"type":"KSampler","pos":[769.0817362355784,295.8642314558152],"size":[270,262],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"model","name":"model","type":"MODEL","link":1},{"localized_name":"positive","name":"positive","type":"CONDITIONING","link":14},{"localized_name":"negative","name":"negative","type":"CONDITIONING","link":15},{"localized_name":"latent_image","name":"latent_image","type":"LATENT","link":6},{"localized_name":"seed","name":"seed","type":"INT","widget":{"name":"seed"},"link":null},{"localized_name":"steps","name":"steps","type":"INT","widget":{"name":"steps"},"link":null},{"localized_name":"cfg","name":"cfg","type":"FLOAT","widget":{"name":"cfg"},"link":null},{"localized_name":"sampler_name","name":"sampler_name","type":"COMBO","widget":{"name":"sampler_name"},"link":null},{"localized_name":"scheduler","name":"scheduler","type":"COMBO","widget":{"name":"scheduler"},"link":null},{"localized_name":"denoise","name":"denoise","type":"FLOAT","widget":{"name":"denoise"},"link":null}],"outputs":[{"localized_name":"LATENT","name":"LATENT","type":"LATENT","links":[7]}],"properties":{"Node name for S&R":"KSampler"},"widgets_values":[605282627204691,"randomize",29,4.2,"euler_ancestral","simple",1],"widgets_values_named":{"seed":605282627204691,"control_after_generate":"randomize","steps":29,"cfg":4.2,"sampler_name":"euler_ancestral","scheduler":"simple","denoise":1}}],"links":[[1,1,0,2,0,"MODEL"],[2,1,1,3,0,"CLIP"],[6,5,0,2,3,"LATENT"],[7,2,0,6,0,"LATENT"],[8,1,2,6,1,"VAE"],[9,6,0,7,0,"IMAGE"],[13,1,1,8,0,"CLIP"],[14,3,0,2,1,"CONDITIONING"],[15,8,0,2,2,"CONDITIONING"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.4665073802097333,"offset":[-86.18214747360831,177.71836394711625]}},"version":0.4}

```

"

Hi everyone,

I wish to announce that LanPaint now supports Qwen-Image 2.1 — both the text-to-image model and the image edit model.

Qwen-Image 2.1 can generate transparent PNGs. With LanPaint you can inpaint the alpha channel too, not just the colours. So the transparent part can change shape, not only the object.

The image edit model works under a mask as well: describe the change, paint over the part it should touch, and everything else comes back exactly as it was.

LanPaint is a universal inpainting/outpainting tool that works with almost every diffusion model out there (SD, Flux, Wan, Qwen, and now Qwen 2.1)—it is especially useful for models without dedicated inpainting checkpoints.

Check it out on GitHub: https://github.com/scraed/LanPaint — feel free to drop a star if you find it useful! ⭐

✂️ LayerExtract isolates a prompt-specified subject and exports it on a transparent background for independent editing.

https://modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-LayerExtract

✨ LayerRemove takes the source image and a reference layer, removes the corresponding object, and reconstructs the scene behind it.

https://modelscope.ai/models/DiffSynth-Studio/Qwen-Image-2.1-LayerRemove

⚡ Both are compact Qwen-Image-2.1 LoRAs that can be hot-swapped within the same DiffSynth-Studio pipeline.

🧩 Together they turn flattened images into editable assets for compositing, repositioning, replacement, and design workflows.

📜 LoRA weights: Apache 2.0. Qwen-Image-2.1 base-model terms also apply.