Awesome-omni-skill flux-txt2img

Build Flux txt2img workflows — Flux.1 Dev (SRPO), Flux 2 Klein 9B, Turbo LoRAs, FluxGuidance, and DualCLIPLoader patterns

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/design/flux-txt2img" ~/.claude/skills/diegosouzapw-awesome-omni-skill-flux-txt2img && rm -rf "$T"

manifest: skills/design/flux-txt2img/SKILL.md

source content

Flux Text-to-Image Workflows

Overview

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:

Flux.1 Dev SRPO — Fine-tuned Flux.1 Dev with SRPO alignment. Uses DualCLIPLoader (T5XXL + CLIP-L). BF16 only.
Flux 2 Klein 9B — Distilled Flux 2 variant. Uses single CLIPLoader (Qwen3-8B) +
```
flux2-vae.safetensors
```
. Fast 4-step generation.
Flux 2 Turbo LoRA — Applied to Flux.1 Dev for 4-step generation.

Models

Flux.1 Dev SRPO

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/Z-Image VAE (320MB)

Flux 2 Klein 9B

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.

Flux 2 Turbo LoRA (applied to Flux.1 Dev)

Component	Node	Model	Notes
LoRA	`LoraLoaderModelOnly`	`flux2-turbo-lora.safetensors`	2.6GB, strength 1.0
Alt LoRA	`LoraLoaderModelOnly`	`Flux2TurboComfyv2.safetensors`	Community variant, same size

Conditioning

CLIPTextEncodeFlux (Recommended for Flux.1 Dev)

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.

FluxGuidance (Alternative)

If using standard

CLIPTextEncode

instead of

CLIPTextEncodeFlux

, apply guidance separately:

{
  "class_type": "FluxGuidance",
  "inputs": {
    "conditioning": ["<clip_text_encode>", 0],
    "guidance": 3.5
  }
}

Guidance Values

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

Negative Conditioning

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.

Sampler Settings

Flux.1 Dev SRPO

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.

Flux 2 Klein 9B (Distilled)

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

Flux 2 Klein 9B (Base/Undistilled)

Parameter	Value	Notes
steps	50	Full quality
cfg	5.0	Higher CFG for base model
sampler_name	euler
scheduler	simple

Flux.1 Dev + Turbo LoRA

Parameter	Value	Notes
steps	4	Turbo-distilled
cfg	1.0
sampler_name	euler
scheduler	simple
lora_strength	1.0

Resolutions

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.

Prompt Style

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"

Complete Workflow: Flux.1 Dev SRPO

{
  "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" }}
}

Complete Workflow: Flux 2 Klein 9B (Distilled, 4-Step)

{
  "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/

Complete Workflow: Flux.1 Dev + Turbo LoRA (4-Step)

{
  "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" }}
}

LoRA Support

Custom LoRAs (jellyfish, etc.)

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 LoRAs

Klein 9B LoRAs go in

loras/Flux.2 Klein 9B/

subfolder:

```
klein_slider_detail.safetensors
```
— Detail slider LoRA

VRAM Considerations

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

Always
clear_vram
before switching to Flux from another model family
T5XXL is the main VRAM consumer alongside the UNET — both stay loaded during sampling
CLIP-L is small (235MB) and negligible

Tips

KSampler CFG must always be 1.0 — all guidance is through
```
CLIPTextEncodeFlux
```
or
```
FluxGuidance
```
SRPO requires BF16 — the FP8 quantization is known to produce broken results with this fine-tune
For short prompts (1-2 sentences), increase guidance to 3.5–4.0. For long prompts (paragraph), decrease to 1.0–1.5
Flux generates excellent text in images — put text to render in quotes within your prompt
Klein 9B is the fastest option at 4 steps — use it for rapid iteration, then switch to SRPO for final quality