https://civitai.com/models/1157409/tempest-by-vlad
Flexible SDXL model with custom encoder and finetuned for larger landscape resolutions with high details and high contrast.
Recommended to use medium-low step count and guidance,
For example: steps=15-20 guidance=3-4
Model can generate consistent images up to 1920x1080.
Model is not censored, but its tuned for general content and not overly aggressive.
All example images are first roll-of-dice with text-to-image and no postprocessing.
Full information on components is embedded in model metadata.
Examples: Tempest-by-Vlad - v0.1 Showcase | Civitai
Prompt: photorealistic girl in bookshop choosing the book in romantic stories shelf. smiling
| CFG3.5, STEP18 | Seed: 1620085323 | Seed:1931701040 | Seed:4075624134 | Seed:2736029172 |
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| CFG3.5, STEP18 | Seed: 1620085323 | Seed:1931701040 | Seed:4075624134 | Seed:2736029172 |
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| bookshop girl | ||||
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| legs and shoes |
Prompt: photorealistic girl in bookshop choosing the book in romantic stories shelf. smiling
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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.
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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.
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Prompt: photo of a cute female teal robot, walking on water surface with rocks and mountains visible in background, during sunset, rich details
Parameters: Steps: 20| Size: 1024x1024| Seed: 159345170| CFG scale: 4| App: SD.Next| Version: 57fdc0a| Pipeline: StableDiffusionXLPipeline| Operations: txt2img| Model: tempestByVlad_baseV01| Model hash: 8bfad17222
Time: 1m 52.40s | total 128.89 pipeline 106.15 callback 8.31 preview 7.24 decode 6.20 prompt 0.68 gc 0.26 | GPU 9432 MB 8% | RAM 3.86 GB 3%
Sampler: Default Sampler: DPM2 FlowMatch Sampler: DPM2a FlowMatch Sampler: DPM2++ 2M FlowMatch Sampler: DPM2++ SDE FlowMatch Sampler: DPM2++ 2M SDE FlowMatch Sampler: DPM2++ 3M SDE FlowMatch Sampler: PeRFlow Sampler: Euler EDM |
Sampler: DPM++ Sampler: DPM++ 2M Sampler: DPM++ 3M |
Sampler: DPM++ SDE Sampler: DPM++ 2M SDE |
Sampler: DDPM |
Sampler: Euler |
Sampler: Euler a | ||
Sampler: DPM SDE |
Sampler: DPM++ 1S |
Sampler: DDIM | |
Sampler: UniPC |
Sampler: Heun |  Sampler: DEIS | |
Sampler: PNDM |
Sampler: DC Solver |
Sampler: SA Solver | Sampler: LMSD |
Sampler: LCM |
Sampler: TCD |
Sampler: TDD | |
Sampler: KDPM2 |
Sampler: KDPM2 a |
| CFG3.5 |
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| tempestByVlad_baseV01 [8bfad17222] | |
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Prompt: photo of a cute female teal robot, walking on water surface with rocks and mountains visible in background, during sunset, rich details Parameters: Steps: 20| Size: 1024x1024| Seed: 324| CFG scale: 3.4| App: SD.Next| Version: 57fdc0a| Pipeline: StableDiffusionXLPipeline| Operations: txt2img| Model: tempestByVlad_baseV01| Model hash: 8bfad17222 Time: 1m 52.22s | total 127.79 pipeline 106.10 callback 8.29 preview 6.33 decode 6.08 prompt 0.69 gc 0.26 | GPU 9434 MB 8% | RAM 4.52 GB 4% |
Prompt: photo of a cute female teal robot, walking on water surface with rocks and mountains visible in background, during sunset, rich details Parameters: Steps: 8| Size: 1024x1024| Seed: 324| CFG scale: 1.6| App: SD.Next| Version: 57fdc0a| Pipeline: StableDiffusionXLPipeline| Operations: txt2img| Model: tempestByVlad_hyperV01| Model hash: 4104fc6601 Time: 48.30s | total 54.56 pipeline 41.99 decode 6.27 callback 3.32 preview 2.67 gc 0.27 | GPU 9434 MB 8% | RAM 4.49 GB 4% |
Mon Sep 29 13:03:03 2025 app: sdnext.git updated: 2025-09-27 hash: 57fdc0ad url: https://github.com/vladmandic/sdnext.git/tree/dev arch: x86_64 cpu: x86_64 system: Linux release: 6.14.0-29-generic python: 3.12.3 Torch: 2.7.1+xpu device: Intel(R) Arc(TM) Graphics (1) ipex: 2.7.10+xpu ram: free:121.5 used:3.83 total:125.33 gpu: free:106.91 used:10.46 total:117.37 gpu-active: current:6.71 peak:8.0 gpu-allocated: current:6.71 peak:8.0 gpu-reserved: current:10.46 peak:10.46 gpu-inactive: current:0.5 peak:0.83 events: retries:0 oom:0 utilization: 0 xformers: diffusers: 0.36.0.dev0 transformers: 4.56.2 active: xpu dtype: torch.bfloat16 vae: torch.bfloat16 unet: torch.bfloat16 base: tempestByVlad_baseV01 [8bfad17222] refiner: none vae: none te: none unet: none Backend: ipex Cross-attention: Scaled-Dot-Product |
{
"theme_type": "Standard",
"diffusers_version": "1448b035859dd57bbb565239dcdd79a025a85422",
"diffusers_offload_mode": "none",
"ui_request_timeout": 300000,
"huggingface_token": "hf_xxx",
"samples_filename_pattern": "[date]-[seq]-[model_name]-[height]x[width]-Seed[seed]-CFG[cfg]-AG[pag]-STEP[steps]",
"sd_model_checkpoint": "tempestByVlad_baseV01 [8bfad17222]",
"sd_checkpoint_hash": "8bfad1722243955b3f94103c69079c280d348b14729251e86824972c1063b616",
"hf_transfer_mode": "xet",
"extra_networks_sort": "sort: no cards",
"schedulers_solver_order": 1,
"cuda_compile_backend": "none",
"diffusers_to_gpu": true,
"device_map": "gpu"
} |
{
modelspec.usage_hint: "Flexible SDXL model with custom encoder and finetuned for larger landscape resolutions with high details and high contrast. Recommended to use medium-low step count and guidance.",
modelspec.implementation: "diffusers",
modelspec.license: "CC-BY-SA-4.0",
modelspec.date: "2025-01-17T16:03",
modelspec.title: "tempest-by-vlad",
modelspec.dtype: "float16",
recipe: {
base: "TempestV0.1-Artistic.safetensors",
unet: "default",
vae: "sdxl-vae-fp16-fix.safetensors",
te1: "ViT-L-14-TEXT-detail-improved-hiT-GmP-TE-only-HF.safetensors",
te2: "default",
scheduler: "UniPCMultistepScheduler",
lora: [
0: "offset-example-1.0.safetensors:0.25",
1: "hyper-sdxl-8step.safetensors:0.25",
2: "add-detail-xl.safetensors:2.0"
]
},
modelspec.prediction_type: "epsilon",
modelspec.thumbnail: "data",
modelspec.sai_model_spec: "1.0.0",
modelspec.version: "0.1",
modelspec.hash_sha256: "ce49361cbf77bc591552ca3efa3b29ea10539aa4ba7741cf966f6b9ea7be7c1f",
modelspec.author: "vladmandic",
modelspec.description: "Tempest by VladMandic",
modelspec.architecture: "stable-diffusion-xl-v1-base"
} |
| Module | Class | Device | Dtype | Quant | Params | Modules | Config |
|---|---|---|---|---|---|---|---|
| vae | AutoencoderKL | xpu:0 | torch.bfloat16 | None | 83653863 | 243 | 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': 4, 'norm_num_groups': 32, 'sample_size': 1024, 'scaling_factor': 0.13025, 'shift_factor': None, 'latents_mean': None, 'latents_std': None, 'force_upcast': False, 'use_quant_conv': True, 'use_post_quant_conv': True, 'mid_block_add_attention': True, '_use_default_values': ['mid_block_add_attention', 'latents_std', 'shift_factor', 'use_post_quant_conv', 'latents_mean', 'use_quant_conv'], '_class_name': 'AutoencoderKL', '_diffusers_version': '0.20.0.dev0', '_name_or_path': '../sdxl-vae/'}) |
| text_encoder | CLIPTextModel | xpu:0 | torch.bfloat16 | None | 123060480 | 152 | CLIPTextConfig { "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "dtype": "float16", "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, "transformers_version": "4.56.2", "vocab_size": 49408 } |
| text_encoder_2 | CLIPTextModelWithProjection | xpu:0 | torch.bfloat16 | None | 694659840 | 393 | CLIPTextConfig { "architectures": [ "CLIPTextModelWithProjection" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "dtype": "float16", "eos_token_id": 2, "hidden_act": "gelu", "hidden_size": 1280, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 5120, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 20, "num_hidden_layers": 32, "pad_token_id": 1, "projection_dim": 1280, "transformers_version": "4.56.2", "vocab_size": 49408 } |
| tokenizer | CLIPTokenizer | None | None | None | 0 | 0 | None |
| tokenizer_2 | CLIPTokenizer | None | None | None | 0 | 0 | None |
| unet | UNet2DConditionModel | xpu:0 | torch.bfloat16 | None | 2567463684 | 1930 | FrozenDict({'sample_size': 128, 'in_channels': 4, 'out_channels': 4, 'center_input_sample': False, 'flip_sin_to_cos': True, 'freq_shift': 0, 'down_block_types': ['DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D'], 'mid_block_type': 'UNetMidBlock2DCrossAttn', 'up_block_types': ['CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D'], 'only_cross_attention': False, 'block_out_channels': [320, 640, 1280], 'layers_per_block': 2, 'downsample_padding': 1, 'mid_block_scale_factor': 1, 'dropout': 0.0, 'act_fn': 'silu', 'norm_num_groups': 32, 'norm_eps': 1e-05, 'cross_attention_dim': 2048, 'transformer_layers_per_block': [1, 2, 10], 'reverse_transformer_layers_per_block': None, 'encoder_hid_dim': None, 'encoder_hid_dim_type': None, 'attention_head_dim': [5, 10, 20], 'num_attention_heads': None, 'dual_cross_attention': False, 'use_linear_projection': True, 'class_embed_type': None, 'addition_embed_type': 'text_time', 'addition_time_embed_dim': 256, 'num_class_embeds': None, 'upcast_attention': None, 'resnet_time_scale_shift': 'default', 'resnet_skip_time_act': False, 'resnet_out_scale_factor': 1.0, 'time_embedding_type': 'positional', 'time_embedding_dim': None, 'time_embedding_act_fn': None, 'timestep_post_act': None, 'time_cond_proj_dim': None, 'conv_in_kernel': 3, 'conv_out_kernel': 3, 'projection_class_embeddings_input_dim': 2816, 'attention_type': 'default', 'class_embeddings_concat': False, 'mid_block_only_cross_attention': None, 'cross_attention_norm': None, 'addition_embed_type_num_heads': 64, '_use_default_values': ['dropout', 'reverse_transformer_layers_per_block', 'attention_type'], '_class_name': 'UNet2DConditionModel', '_diffusers_version': '0.19.0.dev0'}) |
| scheduler | DPMSolverMultistepScheduler | None | None | None | 0 | 0 | FrozenDict({'num_train_timesteps': 1000, 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear', 'trained_betas': None, 'solver_order': 1, 'prediction_type': 'epsilon', 'thresholding': False, 'dynamic_thresholding_ratio': 0.995, 'sample_max_value': 1.0, 'algorithm_type': 'dpmsolver++', 'solver_type': 'midpoint', 'lower_order_final': True, 'euler_at_final': False, 'use_karras_sigmas': False, 'use_exponential_sigmas': False, 'use_beta_sigmas': False, 'use_lu_lambdas': False, 'use_flow_sigmas': False, 'flow_shift': 1.0, 'final_sigmas_type': 'zero', 'lambda_min_clipped': -inf, 'variance_type': None, 'timestep_spacing': 'linspace', 'steps_offset': 0, 'rescale_betas_zero_snr': False, 'use_dynamic_shifting': False, 'time_shift_type': 'exponential', '_use_default_values': ['trained_betas', 'lambda_min_clipped', 'variance_type', 'steps_offset', 'dynamic_thresholding_ratio', 'euler_at_final', 'time_shift_type', 'rescale_betas_zero_snr', 'use_dynamic_shifting', 'flow_shift']}) |
| image_encoder | NoneType | None | None | None | 0 | 0 | None |
| feature_extractor | NoneType | None | None | None | 0 | 0 | None |
| force_zeros_for_empty_prompt | bool | None | None | None | 0 | 0 | None |
