CFG (Guidance scale) seems to be ignored, So testing different samplers instead

Part 1 - Bookshop

Prompt: photorealistic girl in bookshop choosing the book in romantic stories shelf. smiling

Execution: Time: 1m 20.89s | total 103.47 pipeline 64.91 move 8.35 prompt 8.34 te 8.32 decode 7.62 offload 5.90 | GPU 32390 MB 25% | RAM 25.11 GB 20%

Prompt: photorealistic girl in bookshop choosing the book in romantic stories shelf. smiling

Execution: Time: 1m 56.07s | total 122.08 pipeline 107.91 decode 8.13 offload 5.99 | GPU 33324 MB 26% | RAM 25.16 GB 20%

	STEPS: 4	STEPS: 6	STEPS: 8	STEPS: 16	STEPS: 32
512px Euler FlowMatch
512px Heun FlowMatch
512px DPM2 FlowMatch
512px DPM2++ 2M FlowMatch
512px DPM2++ 2S FlowMatch
512px DPM2++ 3M SDE FlowMatch
768px DPM2 FlowMatch
768px DPM2++ 2M FlowMatch
768px DPM2++ 2S FlowMatch
768px DPM2++ 3M SDE FlowMatch

Part 2 - Face and hand

Prompt: Create a close-up photograph of a woman's face and hand, with her hand raised to her chin. She is wearing a white blazer and has a gold ring on her finger. Her nails are neatly manicured and her hair is pulled back into a low bun. She is smiling and has a radiant expression on her face. The background is a plain light gray color. The overall mood of the photo is elegant and sophisticated. The photo should have a soft, natural light and a slight warmth to it. The woman's hair is dark brown and pulled back into a low bun, with a few loose strands framing her face.

	12	16	20	24
CFG=1
CFG=2
CFG=3
CFG=5
CFG=8

Part 3 - Legs and ribbon

Prompt: Generate a photo of a woman's legs, with her feet crossed and wearing white high-heeled shoes with ribbons tied around her ankles. The shoes should have a pointed toe and a stiletto heel. The woman's legs should be smooth and tanned, with a slight sheen to them. The background should be a light gray color. The photo should be taken from a low angle, looking up at the woman's legs. The ribbons should be tied in a bow shape around the ankles. The shoes should have a red sole. The woman's legs should be slightly bent at the knee.

	12	16	20	24
CFG=1
CFG=2
CFG=3
CFG=5
CFG=8

System Info

app: sdnext.git updated: 2025-06-30 hash: 0d7c025a url: https://github.com/vladmandic/sdnext.git/tree/master
arch: x86_64 cpu: x86_64 system: Linux release: 6.11.0-29-generic
python: 3.12.3 Torch 2.7.1+xpu
device: Intel(R) Arc(TM) Graphics (1) ipex: 
ram: free:122.28 used:3.05 total:125.33
xformers: diffusers: 0.35.0.dev0 transformers: 4.53.0
active: xpu dtype: torch.bfloat16 vae: torch.bfloat16 unet: torch.bfloat16
base: Diffusers/shuttleai/shuttle-3.1-aesthetic [6e1883042a] refiner: none vae: none te: none unet: none

Model Data

Model: Diffusers/shuttleai/shuttle-3.1-aesthetic [6e1883042a]
Type: f1
Class: FluxPipeline
Size: 0 bytes
Modified: 2025-07-04 19:53:59

SD.Next dev 2025-06-30

Module	Class	Device	DType	Params	Modules	Config
vae	AutoencoderKL	cpu	torch.bfloat16	83819683	241	FrozenDict({'in_channels': 3, 'out_channels': 3, 'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D'], 'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'], 'block_out_channels': [128, 256, 512, 512], 'layers_per_block': 2, 'act_fn': 'silu', 'latent_channels': 16, 'norm_num_groups': 32, 'sample_size': 1024, 'scaling_factor': 0.3611, 'shift_factor': 0.1159, 'latents_mean': None, 'latents_std': None, 'force_upcast': True, 'use_quant_conv': False, 'use_post_quant_conv': False, 'mid_block_add_attention': True, '_class_name': 'AutoencoderKL', '_diffusers_version': '0.30.0.dev0', '_name_or_path': '/mnt/models/Diffusers/models--shuttleai--shuttle-3.1-aesthetic/snapshots/6e1883042a221853a4b8a89cd6b563bcfe784741/vae'})
text_encoder	CLIPTextModel	xpu:0	torch.bfloat16	123060480	152	CLIPTextConfig { "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 2, "hidden_act": "quick_gelu", "hidden_size": 768, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "projection_dim": 768, "torch_dtype": "bfloat16", "transformers_version": "4.53.0", "vocab_size": 49408 }
text_encoder_2	T5EncoderModel	xpu:0	torch.bfloat16	4762310656	463	T5Config { "architectures": [ "T5EncoderModel" ], "classifier_dropout": 0.0, "d_ff": 10240, "d_kv": 64, "d_model": 4096, "decoder_start_token_id": 0, "dense_act_fn": "gelu_new", "dropout_rate": 0.1, "eos_token_id": 1, "feed_forward_proj": "gated-gelu", "initializer_factor": 1.0, "is_encoder_decoder": true, "is_gated_act": true, "layer_norm_epsilon": 1e-06, "model_type": "t5", "num_decoder_layers": 24, "num_heads": 64, "num_layers": 24, "output_past": true, "pad_token_id": 0, "relative_attention_max_distance": 128, "relative_attention_num_buckets": 32, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.53.0", "use_cache": true, "vocab_size": 32128 }
tokenizer	CLIPTokenizer	None	None	0	0	None
tokenizer_2	T5TokenizerFast	None	None	0	0	None
transformer	FluxTransformer2DModel	xpu:0	torch.bfloat16	11891178560	1275	FrozenDict({'patch_size': 1, 'in_channels': 64, 'out_channels': None, 'num_layers': 19, 'num_single_layers': 38, 'attention_head_dim': 128, 'num_attention_heads': 24, 'joint_attention_dim': 4096, 'pooled_projection_dim': 768, 'guidance_embeds': False, 'axes_dims_rope': [16, 56, 56], '_use_default_values': ['out_channels'], '_class_name': 'FluxTransformer2DModel', '_diffusers_version': '0.30.3', '_name_or_path': '/mnt/models/Diffusers/models--shuttleai--shuttle-3.1-aesthetic/snapshots/6e1883042a221853a4b8a89cd6b563bcfe784741/transformer'})
scheduler	FlowMatchEulerDiscreteScheduler	None	None	0	0	FrozenDict({'num_train_timesteps': 1000, 'shift': 1.0, 'use_dynamic_shifting': False, 'base_shift': 0.5, 'max_shift': 1.15, 'base_image_seq_len': 256, 'max_image_seq_len': 4096, 'invert_sigmas': False, 'shift_terminal': None, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'time_shift_type': 'exponential', 'stochastic_sampling': False, '_use_default_values': ['time_shift_type', 'use_beta_sigmas', 'invert_sigmas', 'stochastic_sampling', 'shift_terminal', 'use_exponential_sigmas', 'use_karras_sigmas'], '_class_name': 'FlowMatchEulerDiscreteScheduler', '_diffusers_version': '0.30.3'})
image_encoder	NoneType	None	None	0	0	None
feature_extractor	NoneType	None	None	0	0	None
_name_or_path	str	None	None	0	0	None
_class_name	str	None	None	0	0	None
_diffusers_version	str	None	None	0	0	None
_name_or_path	str	None	None	0	0	None
_class_name	str	None	None	0	0	None
_diffusers_version	str	None	None	0	0	None