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:

1. Flux.1 Dev SRPO — Fine-tuned Flux.1 Dev with SRPO alignment. Uses DualCLIPLoader (T5XXL + CLIP-L). BF16 only.
2. Flux 2 Klein 9B — Distilled Flux 2 variant. Uses single CLIPLoader (Qwen3-8B) + flux2-vae.safetensors. Fast 4-step generation.
3. 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

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