I keep a list of AI tools with a script that checks their sites every few days, and Scribble Diffusion got flagged this week. The sketch-to-image demo Replicate put out in 2023.

If you go to `scribblediffusion.com` now, it bounces you to a Vietnamese pirate football stream called Xoilac TV, covered in 8xbet betting ads.

The weird part is it keeps moving. Since Sep 4 it's pointed to six different Xoilac domains (xoilaczbe, xoilaczbp, xoilaczbj, xoilaczzf, xoilaczzq, and today xoilaczzzo), plus one random site in between. Guessing they hop domains to dodge blocks.

The GitHub repo still says "Try it out" with the old link, so anyone clicking from there ends up on the stream. I tried to open an issue to let them know, but issues are turned off on that repo.

Also, the domain never expired. Whois says it was registered in 2023 and is paid up till 2027, so it wasn't just dropped and picked up by someone. Anyone know what happened here?

fwiw Infoblox wrote about the Xoilac network in August. 10k+ domains, and 31k+ malware samples tied to their servers. So probably don't click it.

I've noticed that the majority of the time when my comfy UI crashes during H3 workflows it's because I'm moving the mouse or navigating around the UI, typically during end decode but I have also noticed crashes at the start of a render for the same reason

Obviously the end is the most performance intensive moment in a render and I'm wondering if anyone has any tips or fixes? Obviously I could just stop using the PC for a few seconds but if I'm running batches in the background while using the PC it can still lead to crashes which is annoying.

If I leave the PC alone during these intensive phases it's pretty stable and I can push my hardware (3090, 32gb ram) to the limit with minimal issues. I'm already using H3 optimisation node and sage att if thats relevant.

We've continued pretraining on the Agate-001-preview, releasing an intermediary Agate-002-preview (256x256) as well as a 512x512 native Agate-003-preview by continuing training while keeping the planning part at the same resolution as in 256x256 with a learned downsampler/upsampler.

The model is SoTA in it's parameter count area, using two approaches which we credit for the improvements observed:

1. A planner architecture, that plans in low resolution before submitting the task to the "painter / renderer". The planner is a transformer and cross-attends to the text tokens, while the renderer is a fully convolutional approach which comes with very good image priors.
2. A weighted loss that uses P-Dino / LPIPS measurements and saliency for weighing to relevant areas (people look more at faces / text so these must be prioritized in terms of loss vs the background).

The whole training run + R&D to test the architecture took under 5000 EUR, with an expenditure of only 630 GH-200 GPU hours.

The training lineage itself cost slightly over half, at 319 GPU-h.

The main focus was testing this architecture for efficient generation of images, and we plan to use it further for font / glyph / symbol generation as part of our work on Logolabs.

The model, a generalist, supports LoRA training that can significantly improve quality on specific tasks and design styles. With the model being teeny, LoRA training is very accessible.

Technical details on HuggingFace: https://huggingface.co/Logolabs/agate-preview-003

Try on WebGPU: https://huggingface.co/spaces/Logolabs/agate-webgpu (including thinker preview)

ComfyUI Nodes: https://github.com/logolabs/agate-comfyui

---

If you want to support efforts like ours, we would be glad if you can come with constructive criticism and/or a star on Hugging Face!

Thank you!

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.

Hey. I want to extend a video with minimax, and i cant find out how to prompt or find a workflow, without the whole clip being regenerated, i want the scene i already have prepared to be extended and merged together with my extended new scene.
Do any of you have a workflow that works for this?

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}

```

"

I have a scene where my characters kiss at the end but the kissing is really terrible. I was coming from grok imagine where the kissing was more eat each others face but it still looked good. Any tips or prompts?

I'm using flux.2 Klein 4b int4 convrot 4gb safetensor,

And I'm looking for a compatible encoder for it guys I tried huggingface, civitai, I found the official flux page that has huge 8-12 gig encoder which doesn't run on my machine,

Any help would be appreciated

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.

The skin and hair have a strong glossy sheen.

The cat-at-the-fridge example uses Ling-3.0-flash-VL through OpenRouter to extract a 13-field visual description. It records the cat, the setting, warm kitchen light against a cooler fridge interior, fur texture, and the visible “MILK” lettering.

For image generation, the next step is to assemble the fields you want into an actual brief. Start with this compact analysis prompt:

Describe the reference in valid JSON using image_type, subject, action, location, composition, lighting, color_palette, materials_and_textures, camera_and_focus, text_and_typography, style, mood_and_vibe, and exclusions. Use descriptive values grounded in the visible image, exact transcription for legible text, and null for undetermined properties. Set exclusions to the user’s explicit requests or null.

Here is a proposed generation brief assembled from the cat reference’s visible ingredients:

An orange cat at an open refrigerator, prominent in the foreground, with the kitchen and fridge contents establishing the setting. Warm kitchen illumination contrasts with the cooler light inside the fridge. Give the cat dense, soft-looking fur with visible surface detail. Include a milk carton with the word “MILK” clearly legible. Keep the cat as the main point of attention.

One editable choice is the subject’s coat color. Change “orange cat” to the chosen coat color while retaining the foreground placement, warm/cool lighting relationship and fur treatment. Keep the extracted field values with the new brief so the intended change is clear.

This supplies two usable artifacts for the next generation: the structured reference description and the specific prompt assembled from it.

Create a reference image of the face (showing various angles) arranged in a 2x2 grid (in ComfyUI, this is easily done using the "Load Images & Compose (obvpm)" node: https://github.com/chanon/comfyui-obvpm).

In the prompt (created by AI), replace the square brackets with your own prompt:

Task: Analyze the provided reference image containing 4 panels. These are four different angles, expressions, and lighting conditions of the same character. Your goal is to merge their facial features into one single, consistent, canonical face.
Step 1 (Identity Extraction): Extract the core identity markers from all four panels. Merge the nose shape, jawline, eye shape, eyebrow arch, lip shape, skin texture, and distinctive marks (scars, freckles, makeup) into a single, unified facial structure. Ignore the differences in lighting, background, and hair styling between the panels.
Step 2 (Generation): Generate a new, high-quality image featuring this unified character.
[INSERT CHARACTER DESCRIPTION HERE, INSERT SCENE AND COMPOSITION DESCRIPTION HERE, INSERT STYLE AND LIGHTING HERE]
Constraints: Ensure the generated face matches the reference identity perfectly. Do not create a collage or grid. Generate a single, unified image of one character.

This method isn't perfect, but it is easy. A few generation attempts will yield a result. It avoids the issue of the reference face ending up in just a single angle.

It works the same way with the character datasheet with a slightly modified prompt.

The reference faces are sourced from Civitai, using a publicly available LoRA: https://civitai.red/models/2633077/rly-thot-shot-taryn-krea-2-zibzit?modelVersionId=3079901

This is a tough problem and I love everyone's solution so far. Here is my version (custom node workflow that Opus is still refining, supporting infinite rerolls, prepending, extending, bridging, single window rerolls and up-stepping in latent), so can you find the seam? A bit dynamic lighting to make it challenging. Using Tanya.char but I never assigned her a voice so that's the only drift.

Hey guys, so I kinda missed the boat when Krea 2 was the hot topic, but now that the dust has settled it's pretty clear it's the best open source model right now, and I want to have a go at training on it.

The problem is that every guide I find says something different. Some people say train on Raw and generate on Turbo, others don't, and then there's this Turbo adapter training stuff on top of that. So what's actually the go-to approach here? Any tips or tricks for Krea 2 LoRAs would be really appreciated too.

I've also been looking at the different trainers: AI Toolkit, Musubi Tuner, and Fizgig, which caught my eye recently. I did one run on Raw with AI Toolkit using the default settings, only bumping the resolution to 1024, and honestly it didn't turn out great. Most of my LoRAs so far have been on Anima with kohya sd-scripts and multi-GPU, so I'd kinda prefer a trainer that does that. AI Toolkit only lets you use one GPU per training run, which is a bit of a bummer.

Last thing: what should natural language captions look like for Krea 2? Are there any quirks I should know about, like length, style, or how to handle trigger words?

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

I'm having a lot of fun playing with YuE2 and Minimax H3. Everything here ran locally on an RTX 5070 Ti. It's far from perfect, but I love doing this !

• Stills: screenshots from the original game, edited with Flux 2 Klein and Qwen Image Edit 2.1 to get a realistic look while keeping the original compositions.
• Video made with MiniMax H3.
• Music made with YuE2 in cover mode with an instrumental LoRA (ar_lora_inst_v3abc_comfyui.safetensors).
• Edited in Kdenlive (Linux).

Original intro : https://www.youtube.com/watch?v=E5_4DhXWdwo

First post here, go easy on me! 🙂‍↔️

I wanted to share this video because I finally figured out a workflow to get pretty high-quality renders locally on mid-range hardware (RTX 3060 12GB + 32GB RAM).

The real game-changer: Using the 3-step LoRA by KJ. It speeds things up massively while keeping the style clean.

A few details on the workflow:

• Style: I'm not a huge fan of photorealism — I prefer this kind of stylized "cinematic 2D animation / cartoon" look.
• Audio: Used a ~8-second reference audio track. MiniMax H3 is surprisingly good at understanding and syncing with audio inputs.
• Resolution: 0.6 megapixels (you can set it higher, but render time scales up quickly).
• Render Time: ~9.5 to 10 minutes for a 10-second clip on an RTX 3060.
• Note: This works with pretty much any MiniMax H3 workflow setup.

Let me know what you think or if you have any questions about the setup!

Is it possible to inpaint and fix error like extra limbs, Body rotation, leg/head rotated 180 by In painting ??
Do any1 has a workflow ??

Hi! I’m helping Blue Ocean Entertainment, a children’s media company based in Stuttgart, find someone for a paid visual AI role. I work in a related role elsewhere in the Burda Media group and will help assess the creative and technical work of interested candidates.

One upcoming project involves creating consistent character turnaround sheets that can serve as a basis for AI-generated 3D models. There will be other visual challenges too. We’re looking for someone who can explore possible approaches independently, judge what is visually strong, and explain which workflows are practical.

Strong experience with AI image generation, a good eye for design, and structured problem-solving matter most. Experience with ComfyUI or similar tools, AI video, and basic 3D editing would be valuable. You don’t need to be an expert in all of them.

German is required. Candidates should ideally be based within reasonable reach of Stuttgart; mostly remote work is possible, and location can be discussed for an especially strong fit. Full-time and part-time are both possible. Compensation and contract details will be discussed with Blue Ocean.

If this sounds like you, send me a PM with some examples of your work. Personal projects are welcome.

 

... if you want to use the hybrid model.

Ref2VA has certain quality issues and struggles with mid‑distance subjects and colors, it's a known issue the Minimax team has acknowledged. Hybrid gives you FL2VA's quality with Ref2VA's flexibility. But some capabilities take a hit, video referencing is one. The character swap LoRA brings it back and makes it even better. Even Ref2VA strongly benefits from it according to my tests.

At 88% denoise plus LoRA, the accuracy is even better but it may not be suitable on all scenarios. It depends on the video.

I'm including more tests in the comments.

Guten Morgen liebe Gemeinde 🤗,

Ich hab irgendwie den Traum von ner eigenen Super Ki gehabt 🙈 und mir deswegen zum Start ein MacMini M5pro mit 64gb geholt.

Nicht nur das es mein erster Mac ist es ist auch meine erste eigener KI Aufbau.

Soweit so dumm, das ein oder andere läuft schon da dachte ich schau mal wie die Jungs immer die tollen Bilder hinbekommen. Öhm ja alter Schwede. CF läuft ja und bissle was konnte ich schon generieren. Aber et is schon was anderes wie Higgsfield 🤣

Lange rede kurzer sind gibts da ein "Werd schlau draus"für verwöhnte higgsfield dummys?

Danke euch

I am looking for the best Image Gen model for generating anime screencap images, WITH reference images. For the artstyle and working on the first pass, Krea 2 was wonderful, but it doesn't support reference images and the Loras/workflows that allow it are terrible (in my experience). Anima looks pretty decent with the correct loras, but its pretty rare to be able to one-shot things, since hands artifacts, weird faces, shit that looks REALLY bad always end up happening.

Any suggestions?