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Build Flux txt2img workflows — Flux.1 Dev (SRPO), Flux 2 Klein 9B, Turbo LoRAs, FluxGuidance, and DualCLIPLoader patterns
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
Drive the LTX Director (Timeline) node — its Add Image/Text/Audio buttons are DOM-only and cannot be clicked by an agent; edit the hidden timeline_data JSON widget instead. Load when a workflow contains LTXDirector / LTXDirectorGuide / PromptRelayEncodeTimeline, or when asked to add, move, retime, or remove timeline segments (image / text / audio / motion).
Train a character/identity LoRA locally on FLUX.1-dev via the comfyui-mcp train_* tools (GPU Docker + ostris ai-toolkit). Use when the user wants to train a LoRA of a person/character from their photos on the local GPU — covers dataset prep, launch, monitoring, and using the result in ComfyUI. For WAN/Z-Image training via the ai-toolkit UI see ai-toolkit-trainer.
Core ComfyUI knowledge — workflow format, node types, pipeline patterns, and MCP tool usage
Discover Civitai models with the BUILT-IN search_civitai_models tool and install/generate them locally — find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).
Run the ComfyUI agent locally for FREE — no subscription, no API key, fully offline — using our gemma4 models fine-tuned on the comfyui-mcp tool suite via Ollama. Use when the user asks about running locally, running for free, offline use, avoiding API costs, Ollama setup, or which local model to pick.
Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node — render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video comes out wrong — artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, a two-stage refiner or upscale degrading the result — rather than the run failing with an error (for errors/OOM/missing nodes use the troubleshooting skill).
| name | flux-txt2img |
| description | Build Flux txt2img workflows — Flux.1 Dev (SRPO), Flux 2 Klein 9B, Turbo LoRAs, FluxGuidance, and DualCLIPLoader patterns |
| globs | ["**/*.json"] |
Flux is a guidance-distilled diffusion model family from Black Forest Labs. It uses a separate FluxGuidance node instead of KSampler CFG (which must always be 1.0). Three variants are available locally:
flux2-vae.safetensors. Fast 4-step generation.| Component | Node | Model | Notes |
|---|---|---|---|
| UNET | UNETLoader | flux.1-dev-SRPO-BFL-bf16.safetensors | 22.7GB, BF16 only — FP8 produces broken results |
| CLIP | DualCLIPLoader (type=flux) | clip_name1: t5xxl_fp8_e4m3fn.safetensors, clip_name2: clip_l.safetensors | T5XXL (4.7GB) + CLIP-L (235MB) |
| VAE | VAELoader | ae.safetensors | Standard Flux VAE (320MB). Z-Image uses the same VAE architecture but different weights — its VAE is a separate file (z-image-ae.safetensors), not this one |
| Component | Node | Model | Notes |
|---|---|---|---|
| UNET | UNETLoader | bigLove_klein1.safetensors | 17.3GB, Klein 9B variant |
| CLIP | CLIPLoader (type=flux) | qwen_3_8b_fp8mixed.safetensors | Qwen3-8B in text_encoders/ (8.3GB) |
| VAE | VAELoader | flux2-vae.safetensors | Flux 2 specific VAE (321MB) |
Klein 9B vs Flux.1 Dev: Klein uses Qwen3-8B text encoder (not T5XXL + CLIP-L). It has a different VAE (flux2-vae.safetensors). 9B distilled runs in 4 steps; 9B base needs ~50 steps at CFG 5.0. Fits in ~20GB VRAM with FP8.
| Component | Node | Model | Notes |
|---|---|---|---|
| LoRA | LoraLoaderModelOnly | flux2-turbo-lora.safetensors | 2.6GB, strength 1.0 |
| Alt LoRA | LoraLoaderModelOnly | Flux2TurboComfyv2.safetensors | Community variant, same size |
Provides separate prompt fields for each text encoder:
{
"class_type": "CLIPTextEncodeFlux",
"inputs": {
"clip": ["<dual_clip>", 0],
"clip_l": "short prompt for CLIP-L",
"t5xxl": "detailed description for T5XXL",
"guidance": 3.5
}
}
clip_l captures key semantic features. t5xxl expands and refines descriptions. For simple use, put the same prompt in both fields. Guidance is built into this node — no separate FluxGuidance needed.
If using standard CLIPTextEncode instead of CLIPTextEncodeFlux, apply guidance separately:
{
"class_type": "FluxGuidance",
"inputs": {
"conditioning": ["<clip_text_encode>", 0],
"guidance": 3.5
}
}
| Scenario | Guidance | Notes |
|---|---|---|
| Short prompts | 3.5–4.0 | Tighter prompt adherence |
| Long/complex prompts | 1.0–1.5 | More creative freedom |
| Realism | 2.5 | Less glossy skin, richer detail |
| Standard | 3.5 | Default for most use cases |
Flux does NOT support traditional negative prompts (guidance-distilled, CFG=1.0). Use ConditioningZeroOut:
{
"class_type": "ConditioningZeroOut",
"inputs": { "conditioning": ["<positive_cond>", 0] }
}
Or simply use an empty CLIPTextEncode for the negative input.
| Parameter | Standard | Notes |
|---|---|---|
| steps | 20 | Range: 20–28 |
| cfg | 1.0 | Always 1.0 — guidance is via FluxGuidance |
| sampler_name | ipndm | Author-recommended for SRPO |
| scheduler | beta | Author-recommended for SRPO |
| guidance | 3.5 | Via CLIPTextEncodeFlux or FluxGuidance |
| denoise | 1.0 |
SRPO note: The ipndm/beta combo is specifically recommended by the SRPO author. Standard Flux settings (euler/simple) also work but ipndm/beta gives better results with this fine-tune.
| Parameter | Value | Notes |
|---|---|---|
| steps | 4 | Distilled model, 4 steps is optimal |
| cfg | 1.0 | Always 1.0 |
| sampler_name | euler | |
| scheduler | simple | |
| denoise | 1.0 |
| Parameter | Value | Notes |
|---|---|---|
| steps | 50 | Full quality |
| cfg | 5.0 | Higher CFG for base model |
| sampler_name | euler | |
| scheduler | simple |
| Parameter | Value | Notes |
|---|---|---|
| steps | 4 | Turbo-distilled |
| cfg | 1.0 | |
| sampler_name | euler | |
| scheduler | simple | |
| lora_strength | 1.0 |
| Aspect | Resolution | Megapixels |
|---|---|---|
| Square | 1024x1024 | 1.0MP |
| Portrait 3:4 | 896x1152 | 1.0MP |
| Landscape 4:3 | 1152x896 | 1.0MP |
| Landscape 16:9 | 1344x768 | 1.0MP |
| Portrait 9:16 | 768x1344 | 1.0MP |
Flux operates at ~1 megapixel natively. Dimensions should be multiples of 8.
Natural language descriptions. No quality tags needed (unlike SDXL/Illustrious). Detailed, descriptive prompts work best.
Good: "A young woman with auburn hair sits at a sunlit cafe in Paris, wearing a cream linen blazer, soft bokeh background, shot on Sony A7III 85mm f/1.4"
Bad: "masterpiece, best quality, 1girl, cafe, paris"
{
"1": { "class_type": "UNETLoader", "inputs": { "unet_name": "flux.1-dev-SRPO-BFL-bf16.safetensors", "weight_dtype": "default" }},
"2": { "class_type": "DualCLIPLoader", "inputs": { "clip_name1": "t5xxl_fp8_e4m3fn.safetensors", "clip_name2": "clip_l.safetensors", "type": "flux" }},
"3": { "class_type": "VAELoader", "inputs": { "vae_name": "ae.safetensors" }},
"4": { "class_type": "CLIPTextEncodeFlux", "inputs": {
"clip": ["2", 0],
"clip_l": "<short prompt>",
"t5xxl": "<detailed prompt>",
"guidance": 3.5
}},
"5": { "class_type": "ConditioningZeroOut", "inputs": { "conditioning": ["4", 0] }},
"6": { "class_type": "EmptyLatentImage", "inputs": { "width": 896, "height": 1152, "batch_size": 1 }},
"7": { "class_type": "KSampler", "inputs": {
"model": ["1", 0],
"positive": ["4", 0],
"negative": ["5", 0],
"latent_image": ["6", 0],
"seed": 42, "steps": 20, "cfg": 1, "sampler_name": "ipndm", "scheduler": "beta", "denoise": 1
}},
"8": { "class_type": "VAEDecode", "inputs": { "samples": ["7", 0], "vae": ["3", 0] }},
"9": { "class_type": "SaveImage", "inputs": { "images": ["8", 0], "filename_prefix": "flux_srpo" }}
}
{
"1": { "class_type": "UNETLoader", "inputs": { "unet_name": "bigLove_klein1.safetensors", "weight_dtype": "default" }},
"2": { "class_type": "CLIPLoader", "inputs": { "clip_name": "qwen_3_8b_fp8mixed.safetensors", "type": "flux" }},
"3": { "class_type": "VAELoader", "inputs": { "vae_name": "flux2-vae.safetensors" }},
"4": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["2", 0], "text": "<prompt>" }},
"5": { "class_type": "ConditioningZeroOut", "inputs": { "conditioning": ["4", 0] }},
"6": { "class_type": "EmptyLatentImage", "inputs": { "width": 1024, "height": 1024, "batch_size": 1 }},
"7": { "class_type": "KSampler", "inputs": {
"model": ["1", 0],
"positive": ["4", 0],
"negative": ["5", 0],
"latent_image": ["6", 0],
"seed": 42, "steps": 4, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1
}},
"8": { "class_type": "VAEDecode", "inputs": { "samples": ["7", 0], "vae": ["3", 0] }},
"9": { "class_type": "SaveImage", "inputs": { "images": ["8", 0], "filename_prefix": "flux_klein" }}
}
Klein note: Uses single CLIPLoader (not DualCLIPLoader) with type: "flux" and the Qwen3-8B text encoder from text_encoders/. The CLIP loader path resolves from models/text_encoders/.
{
"1": { "class_type": "UNETLoader", "inputs": { "unet_name": "flux.1-dev-SRPO-BFL-bf16.safetensors", "weight_dtype": "default" }},
"2": { "class_type": "LoraLoaderModelOnly", "inputs": { "model": ["1", 0], "lora_name": "flux2-turbo-lora.safetensors", "strength_model": 1.0 }},
"3": { "class_type": "DualCLIPLoader", "inputs": { "clip_name1": "t5xxl_fp8_e4m3fn.safetensors", "clip_name2": "clip_l.safetensors", "type": "flux" }},
"4": { "class_type": "VAELoader", "inputs": { "vae_name": "ae.safetensors" }},
"5": { "class_type": "CLIPTextEncodeFlux", "inputs": {
"clip": ["3", 0],
"clip_l": "<short prompt>",
"t5xxl": "<detailed prompt>",
"guidance": 3.5
}},
"6": { "class_type": "ConditioningZeroOut", "inputs": { "conditioning": ["5", 0] }},
"7": { "class_type": "EmptyLatentImage", "inputs": { "width": 1024, "height": 1024, "batch_size": 1 }},
"8": { "class_type": "KSampler", "inputs": {
"model": ["2", 0],
"positive": ["5", 0],
"negative": ["6", 0],
"latent_image": ["7", 0],
"seed": 42, "steps": 4, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1
}},
"9": { "class_type": "VAEDecode", "inputs": { "samples": ["8", 0], "vae": ["4", 0] }},
"10": { "class_type": "SaveImage", "inputs": { "images": ["9", 0], "filename_prefix": "flux_turbo" }}
}
Apply Flux LoRAs with LoraLoaderModelOnly between UNET and KSampler:
{
"class_type": "LoraLoaderModelOnly",
"inputs": {
"model": ["<unet_or_previous_lora>", 0],
"lora_name": "<lora_file>.safetensors",
"strength_model": 1.0
}
}
Klein 9B LoRAs go in loras/Flux.2 Klein 9B/ subfolder:
klein_slider_detail.safetensors — Detail slider LoRA| Model | VRAM | Notes |
|---|---|---|
| SRPO BF16 + DualCLIP | ~24GB | Fills RTX 4090 exactly. Must use BF16 — FP8 is broken for SRPO |
| Klein 9B FP8 + Qwen3-8B | ~20GB | Fits comfortably on 4090 |
| SRPO + Turbo LoRA | ~24GB | Same as SRPO base |
clear_vram before switching to Flux from another model familyCLIPTextEncodeFlux or FluxGuidance