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Nike with HotArt 3 SPO
HotArt:3SPO
sparks
sparks
·
Jul 10
5
Input
prompt
Specify things to see in the output
Nike, Goddess of Victory: A statue of a woman with wings outstretched, her form sculpted from pure white marble, a laurel wreath clutched in her hand as she soars triumphantly above a battlefield bathed in the golden light of victory. (style: symbolic, triumph)
negative_prompt
Specify things to not see in the output
ugly, bad, wrong, boring, simple, plain, human skin
EnvyAwesomizeXL01
Envy Awesomize Xl01
2
detailer_xl
Detailer Xl
1
num_outputs
Number of output images
3
width
Output image width
768
height
Output image height
1024
enhance_face_with_adetailer
Enhance face with adetailer
true
enhance_hands_with_adetailer
Enhance hands with adetailer
false
adetailer_denoising_strength
1: completely redraw face or hands / 0: no effect on output images
0.4
detail
Enhance/diminish detail while keeping the overall style/character
0
brightness
Adjust brightness
0
contrast
Adjust contrast
0
seed
Same seed with the same prompt generates the same image. Set as -1 to randomize output.
4190250846
input_image
Base image that the output should be generated from. This is useful when you want to add some detail to input_image. For example, if prompt is "sunglasses" and input_image has a man, there is the man wearing sunglasses in the output.
input_image_redrawing_strength
How differ the output is from input_image. Used only when input_image is given.
0.55
reference_image
Image with which the output should share identity (e.g. face of a person or type of a dog)
reference_image_strength
Strength of applying reference_image. Used only when reference_image is given.
1
reference_pose_image
Image with a reference pose
reference_pose_strength
Strength of applying reference_pose_image. Used only when reference_pose_image is given.
1
reference_depth_image
Image with a reference depth
reference_depth_strength
Strength of applying reference_depth_image. Used only when reference_depth_image is given.
1
sampler
Sampler type
DPM++ 3M SDE
samping_steps
Number of denoising steps
40
cfg_scale
Scale for classifier-free guidance
4.5
clip_skip
The number of last layers of CLIP network to skip
2
vae
Select VAE
sdxl_vae.safetensors
lora_1
LoRA file. Apply by writing the following in prompt: <lora:FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE>
lora_2
LoRA file. Apply by writing the following in prompt: <lora:FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE>
lora_3
LoRA file. Apply by writing the following in prompt: <lora:FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE>
embedding_1
Embedding file (textural inversion). Apply by writing the following in prompt or negative prompt: (FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE)
embedding_2
Embedding file (textural inversion). Apply by writing the following in prompt or negative prompt: (FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE)
embedding_3
Embedding file (textural inversion). Apply by writing the following in prompt or negative prompt: (FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE)
disable_prompt_modification
Disable automatically adding suggested prompt modification. Built-in LoRAs and trigger words will remain.
false
Output
https://files.tungsten.run/uploads/1f02dfe58a744cfdaac47db7c448054f/output-c408b8963c8a49e58f9280523e92ab6d-00000-4190250846.webp
https://files.tungsten.run/uploads/e80440c0d1454733b48b0a58c7c3c243/output-1a4478363db04a109c9cc2f7ac40ccbb-00001-4190250847.webp
https://files.tungsten.run/uploads/4a47865b6b96470e8763309cc98b84a2/output-59288e79b5c84153b85eabcc3c4d9aa1-00002-4190250848.webp
Finished in 114.0 seconds
Loading VAE weight: models/VAE/sdxl_vae.safetensors Full prompt: Nike, Goddess of Victory: A statue of a woman with wings outstretched, her form sculpted from pure white marble, a laurel wreath clutched in her hand as she soars triumphantly above a battlefield bathed in the golden light of victory. (style: symbolic, triumph), <lora:detailer-xl:1.0>, <lora:EnvyAwesomizeXL01:2.0> Full negative prompt: ugly, bad, wrong, boring, simple, plain, human skin 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:01<01:00, 1.54s/it] 5%|▌ | 2/40 [00:03<01:04, 1.70s/it] 8%|▊ | 3/40 [00:05<01:07, 1.83s/it] 10%|█ | 4/40 [00:07<01:11, 1.98s/it] 12%|█▎ | 5/40 [00:09<01:11, 2.04s/it] 15%|█▌ | 6/40 [00:11<01:08, 2.02s/it] 18%|█▊ | 7/40 [00:13<01:05, 1.97s/it] 20%|██ | 8/40 [00:15<01:04, 2.00s/it] 22%|██▎ | 9/40 [00:17<01:02, 2.01s/it] 25%|██▌ | 10/40 [00:19<01:02, 2.09s/it] 28%|██▊ | 11/40 [00:22<01:02, 2.15s/it] 30%|███ | 12/40 [00:24<01:00, 2.17s/it] 32%|███▎ | 13/40 [00:26<00:57, 2.13s/it] 35%|███▌ | 14/40 [00:28<00:55, 2.14s/it] 38%|███▊ | 15/40 [00:30<00:51, 2.08s/it] 40%|████ | 16/40 [00:32<00:49, 2.04s/it] 42%|████▎ | 17/40 [00:34<00:46, 2.00s/it] 45%|████▌ | 18/40 [00:36<00:44, 2.02s/it] 48%|████▊ | 19/40 [00:38<00:41, 1.97s/it] 50%|█████ | 20/40 [00:40<00:39, 1.96s/it] 52%|█████▎ | 21/40 [00:42<00:37, 1.96s/it] 55%|█████▌ | 22/40 [00:44<00:34, 1.93s/it] 57%|█████▊ | 23/40 [00:46<00:32, 1.93s/it] 60%|██████ | 24/40 [00:47<00:30, 1.91s/it] 62%|██████▎ | 25/40 [00:49<00:28, 1.89s/it] 65%|██████▌ | 26/40 [00:51<00:26, 1.86s/it] 68%|██████▊ | 27/40 [00:53<00:23, 1.81s/it] 70%|███████ | 28/40 [00:55<00:22, 1.85s/it] 72%|███████▎ | 29/40 [00:56<00:20, 1.83s/it] 75%|███████▌ | 30/40 [00:58<00:18, 1.82s/it] 78%|███████▊ | 31/40 [01:00<00:15, 1.75s/it] 80%|████████ | 32/40 [01:02<00:14, 1.76s/it] 82%|████████▎ | 33/40 [01:03<00:12, 1.77s/it] 85%|████████▌ | 34/40 [01:05<00:10, 1.83s/it] 88%|████████▊ | 35/40 [01:07<00:09, 1.80s/it] 90%|█████████ | 36/40 [01:09<00:07, 1.82s/it] 92%|█████████▎| 37/40 [01:11<00:05, 1.81s/it] 95%|█████████▌| 38/40 [01:13<00:03, 1.85s/it] 98%|█████████▊| 39/40 [01:14<00:01, 1.66s/it] 100%|██████████| 40/40 [01:15<00:00, 1.43s/it] 100%|██████████| 40/40 [01:15<00:00, 1.88s/it] Decoding latents in cuda:0... done in 1.7s Move latents to cpu... done in 0.01s 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:11, 1.44it/s] 12%|█▏ | 2/17 [00:01<00:09, 1.51it/s] 18%|█▊ | 3/17 [00:01<00:09, 1.55it/s] 24%|██▎ | 4/17 [00:02<00:08, 1.61it/s] 29%|██▉ | 5/17 [00:03<00:07, 1.56it/s] 35%|███▌ | 6/17 [00:03<00:07, 1.56it/s] 41%|████ | 7/17 [00:04<00:06, 1.57it/s] 47%|████▋ | 8/17 [00:05<00:05, 1.63it/s] 53%|█████▎ | 9/17 [00:05<00:04, 1.61it/s] 59%|█████▉ | 10/17 [00:06<00:04, 1.61it/s] 65%|██████▍ | 11/17 [00:07<00:03, 1.55it/s] 71%|███████ | 12/17 [00:07<00:03, 1.58it/s] 76%|███████▋ | 13/17 [00:08<00:02, 1.56it/s] 82%|████████▏ | 14/17 [00:08<00:01, 1.56it/s] 88%|████████▊ | 15/17 [00:09<00:01, 1.52it/s] 94%|█████████▍| 16/17 [00:10<00:00, 1.69it/s] 100%|██████████| 17/17 [00:10<00:00, 1.95it/s] 100%|██████████| 17/17 [00:10<00:00, 1.64it/s] Decoding latents in cuda:0... done in 0.56s Move latents to cpu... done in 0.0s 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:11, 1.44it/s] 12%|█▏ | 2/17 [00:01<00:09, 1.53it/s] 18%|█▊ | 3/17 [00:01<00:08, 1.56it/s] 24%|██▎ | 4/17 [00:02<00:08, 1.62it/s] 29%|██▉ | 5/17 [00:03<00:07, 1.56it/s] 35%|███▌ | 6/17 [00:03<00:07, 1.56it/s] 41%|████ | 7/17 [00:04<00:06, 1.58it/s] 47%|████▋ | 8/17 [00:05<00:05, 1.64it/s] 53%|█████▎ | 9/17 [00:05<00:04, 1.62it/s] 59%|█████▉ | 10/17 [00:06<00:04, 1.61it/s] 65%|██████▍ | 11/17 [00:06<00:03, 1.56it/s] 71%|███████ | 12/17 [00:07<00:03, 1.59it/s] 76%|███████▋ | 13/17 [00:08<00:02, 1.58it/s] 82%|████████▏ | 14/17 [00:08<00:01, 1.58it/s] 88%|████████▊ | 15/17 [00:09<00:01, 1.55it/s] 94%|█████████▍| 16/17 [00:09<00:00, 1.71it/s] 100%|██████████| 17/17 [00:10<00:00, 1.96it/s] 100%|██████████| 17/17 [00:10<00:00, 1.65it/s] Decoding latents in cuda:0... done in 0.56s Move latents to cpu... done in 0.0s 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:11, 1.43it/s] 12%|█▏ | 2/17 [00:01<00:10, 1.49it/s] 18%|█▊ | 3/17 [00:01<00:09, 1.53it/s] 24%|██▎ | 4/17 [00:02<00:08, 1.59it/s] 29%|██▉ | 5/17 [00:03<00:07, 1.53it/s] 35%|███▌ | 6/17 [00:03<00:07, 1.55it/s] 41%|████ | 7/17 [00:04<00:06, 1.56it/s] 47%|████▋ | 8/17 [00:05<00:05, 1.62it/s] 53%|█████▎ | 9/17 [00:05<00:04, 1.60it/s] 59%|█████▉ | 10/17 [00:06<00:04, 1.59it/s] 65%|██████▍ | 11/17 [00:07<00:03, 1.55it/s] 71%|███████ | 12/17 [00:07<00:03, 1.57it/s] 76%|███████▋ | 13/17 [00:08<00:02, 1.55it/s] 82%|████████▏ | 14/17 [00:08<00:01, 1.57it/s] 88%|████████▊ | 15/17 [00:09<00:01, 1.53it/s] 94%|█████████▍| 16/17 [00:10<00:00, 1.70it/s] 100%|██████████| 17/17 [00:10<00:00, 1.94it/s] 100%|██████████| 17/17 [00:10<00:00, 1.63it/s] Decoding latents in cuda:0... done in 0.56s Move latents to cpu... done in 0.0s Uploading files.. Finished.
prompt
Specify things to see in the output
Nike, Goddess of Victory: A statue of a woman with wings outstretched, her form sculpted from pure white marble, a laurel wreath clutched in her hand as she soars triumphantly above a battlefield bathed in the golden light of victory. (style: symbolic, triumph)
negative_prompt
Specify things to not see in the output
ugly, bad, wrong, boring, simple, plain, human skin
EnvyAwesomizeXL01
Envy Awesomize Xl01
2
detailer_xl
Detailer Xl
1
num_outputs
Number of output images
3
width
Output image width
768
height
Output image height
1024
enhance_face_with_adetailer
Enhance face with adetailer
true
enhance_hands_with_adetailer
Enhance hands with adetailer
false
adetailer_denoising_strength
1: completely redraw face or hands / 0: no effect on output images
0.4
detail
Enhance/diminish detail while keeping the overall style/character
0
brightness
Adjust brightness
0
contrast
Adjust contrast
0
seed
Same seed with the same prompt generates the same image. Set as -1 to randomize output.
4190250846
input_image
Base image that the output should be generated from. This is useful when you want to add some detail to input_image. For example, if prompt is "sunglasses" and input_image has a man, there is the man wearing sunglasses in the output.
input_image_redrawing_strength
How differ the output is from input_image. Used only when input_image is given.
0.55
reference_image
Image with which the output should share identity (e.g. face of a person or type of a dog)
reference_image_strength
Strength of applying reference_image. Used only when reference_image is given.
1
reference_pose_image
Image with a reference pose
reference_pose_strength
Strength of applying reference_pose_image. Used only when reference_pose_image is given.
1
reference_depth_image
Image with a reference depth
reference_depth_strength
Strength of applying reference_depth_image. Used only when reference_depth_image is given.
1
sampler
Sampler type
DPM++ 3M SDE
samping_steps
Number of denoising steps
40
cfg_scale
Scale for classifier-free guidance
4.5
clip_skip
The number of last layers of CLIP network to skip
2
vae
Select VAE
sdxl_vae.safetensors
lora_1
LoRA file. Apply by writing the following in prompt: <lora:FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE>
lora_2
LoRA file. Apply by writing the following in prompt: <lora:FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE>
lora_3
LoRA file. Apply by writing the following in prompt: <lora:FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE>
embedding_1
Embedding file (textural inversion). Apply by writing the following in prompt or negative prompt: (FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE)
embedding_2
Embedding file (textural inversion). Apply by writing the following in prompt or negative prompt: (FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE)
embedding_3
Embedding file (textural inversion). Apply by writing the following in prompt or negative prompt: (FILE_NAME_WITHOUT_EXTENSION:MAGNITUDE)
disable_prompt_modification
Disable automatically adding suggested prompt modification. Built-in LoRAs and trigger words will remain.
false
https://files.tungsten.run/uploads/1f02dfe58a744cfdaac47db7c448054f/output-c408b8963c8a49e58f9280523e92ab6d-00000-4190250846.webp
https://files.tungsten.run/uploads/e80440c0d1454733b48b0a58c7c3c243/output-1a4478363db04a109c9cc2f7ac40ccbb-00001-4190250847.webp
https://files.tungsten.run/uploads/4a47865b6b96470e8763309cc98b84a2/output-59288e79b5c84153b85eabcc3c4d9aa1-00002-4190250848.webp
Finished in 114.0 seconds
Loading VAE weight: models/VAE/sdxl_vae.safetensors Full prompt: Nike, Goddess of Victory: A statue of a woman with wings outstretched, her form sculpted from pure white marble, a laurel wreath clutched in her hand as she soars triumphantly above a battlefield bathed in the golden light of victory. (style: symbolic, triumph), <lora:detailer-xl:1.0>, <lora:EnvyAwesomizeXL01:2.0> Full negative prompt: ugly, bad, wrong, boring, simple, plain, human skin 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:01<01:00, 1.54s/it] 5%|▌ | 2/40 [00:03<01:04, 1.70s/it] 8%|▊ | 3/40 [00:05<01:07, 1.83s/it] 10%|█ | 4/40 [00:07<01:11, 1.98s/it] 12%|█▎ | 5/40 [00:09<01:11, 2.04s/it] 15%|█▌ | 6/40 [00:11<01:08, 2.02s/it] 18%|█▊ | 7/40 [00:13<01:05, 1.97s/it] 20%|██ | 8/40 [00:15<01:04, 2.00s/it] 22%|██▎ | 9/40 [00:17<01:02, 2.01s/it] 25%|██▌ | 10/40 [00:19<01:02, 2.09s/it] 28%|██▊ | 11/40 [00:22<01:02, 2.15s/it] 30%|███ | 12/40 [00:24<01:00, 2.17s/it] 32%|███▎ | 13/40 [00:26<00:57, 2.13s/it] 35%|███▌ | 14/40 [00:28<00:55, 2.14s/it] 38%|███▊ | 15/40 [00:30<00:51, 2.08s/it] 40%|████ | 16/40 [00:32<00:49, 2.04s/it] 42%|████▎ | 17/40 [00:34<00:46, 2.00s/it] 45%|████▌ | 18/40 [00:36<00:44, 2.02s/it] 48%|████▊ | 19/40 [00:38<00:41, 1.97s/it] 50%|█████ | 20/40 [00:40<00:39, 1.96s/it] 52%|█████▎ | 21/40 [00:42<00:37, 1.96s/it] 55%|█████▌ | 22/40 [00:44<00:34, 1.93s/it] 57%|█████▊ | 23/40 [00:46<00:32, 1.93s/it] 60%|██████ | 24/40 [00:47<00:30, 1.91s/it] 62%|██████▎ | 25/40 [00:49<00:28, 1.89s/it] 65%|██████▌ | 26/40 [00:51<00:26, 1.86s/it] 68%|██████▊ | 27/40 [00:53<00:23, 1.81s/it] 70%|███████ | 28/40 [00:55<00:22, 1.85s/it] 72%|███████▎ | 29/40 [00:56<00:20, 1.83s/it] 75%|███████▌ | 30/40 [00:58<00:18, 1.82s/it] 78%|███████▊ | 31/40 [01:00<00:15, 1.75s/it] 80%|████████ | 32/40 [01:02<00:14, 1.76s/it] 82%|████████▎ | 33/40 [01:03<00:12, 1.77s/it] 85%|████████▌ | 34/40 [01:05<00:10, 1.83s/it] 88%|████████▊ | 35/40 [01:07<00:09, 1.80s/it] 90%|█████████ | 36/40 [01:09<00:07, 1.82s/it] 92%|█████████▎| 37/40 [01:11<00:05, 1.81s/it] 95%|█████████▌| 38/40 [01:13<00:03, 1.85s/it] 98%|█████████▊| 39/40 [01:14<00:01, 1.66s/it] 100%|██████████| 40/40 [01:15<00:00, 1.43s/it] 100%|██████████| 40/40 [01:15<00:00, 1.88s/it] Decoding latents in cuda:0... done in 1.7s Move latents to cpu... done in 0.01s 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:11, 1.44it/s] 12%|█▏ | 2/17 [00:01<00:09, 1.51it/s] 18%|█▊ | 3/17 [00:01<00:09, 1.55it/s] 24%|██▎ | 4/17 [00:02<00:08, 1.61it/s] 29%|██▉ | 5/17 [00:03<00:07, 1.56it/s] 35%|███▌ | 6/17 [00:03<00:07, 1.56it/s] 41%|████ | 7/17 [00:04<00:06, 1.57it/s] 47%|████▋ | 8/17 [00:05<00:05, 1.63it/s] 53%|█████▎ | 9/17 [00:05<00:04, 1.61it/s] 59%|█████▉ | 10/17 [00:06<00:04, 1.61it/s] 65%|██████▍ | 11/17 [00:07<00:03, 1.55it/s] 71%|███████ | 12/17 [00:07<00:03, 1.58it/s] 76%|███████▋ | 13/17 [00:08<00:02, 1.56it/s] 82%|████████▏ | 14/17 [00:08<00:01, 1.56it/s] 88%|████████▊ | 15/17 [00:09<00:01, 1.52it/s] 94%|█████████▍| 16/17 [00:10<00:00, 1.69it/s] 100%|██████████| 17/17 [00:10<00:00, 1.95it/s] 100%|██████████| 17/17 [00:10<00:00, 1.64it/s] Decoding latents in cuda:0... done in 0.56s Move latents to cpu... done in 0.0s 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:11, 1.44it/s] 12%|█▏ | 2/17 [00:01<00:09, 1.53it/s] 18%|█▊ | 3/17 [00:01<00:08, 1.56it/s] 24%|██▎ | 4/17 [00:02<00:08, 1.62it/s] 29%|██▉ | 5/17 [00:03<00:07, 1.56it/s] 35%|███▌ | 6/17 [00:03<00:07, 1.56it/s] 41%|████ | 7/17 [00:04<00:06, 1.58it/s] 47%|████▋ | 8/17 [00:05<00:05, 1.64it/s] 53%|█████▎ | 9/17 [00:05<00:04, 1.62it/s] 59%|█████▉ | 10/17 [00:06<00:04, 1.61it/s] 65%|██████▍ | 11/17 [00:06<00:03, 1.56it/s] 71%|███████ | 12/17 [00:07<00:03, 1.59it/s] 76%|███████▋ | 13/17 [00:08<00:02, 1.58it/s] 82%|████████▏ | 14/17 [00:08<00:01, 1.58it/s] 88%|████████▊ | 15/17 [00:09<00:01, 1.55it/s] 94%|█████████▍| 16/17 [00:09<00:00, 1.71it/s] 100%|██████████| 17/17 [00:10<00:00, 1.96it/s] 100%|██████████| 17/17 [00:10<00:00, 1.65it/s] Decoding latents in cuda:0... done in 0.56s Move latents to cpu... done in 0.0s 0%| | 0/17 [00:00<?, ?it/s] 6%|▌ | 1/17 [00:00<00:11, 1.43it/s] 12%|█▏ | 2/17 [00:01<00:10, 1.49it/s] 18%|█▊ | 3/17 [00:01<00:09, 1.53it/s] 24%|██▎ | 4/17 [00:02<00:08, 1.59it/s] 29%|██▉ | 5/17 [00:03<00:07, 1.53it/s] 35%|███▌ | 6/17 [00:03<00:07, 1.55it/s] 41%|████ | 7/17 [00:04<00:06, 1.56it/s] 47%|████▋ | 8/17 [00:05<00:05, 1.62it/s] 53%|█████▎ | 9/17 [00:05<00:04, 1.60it/s] 59%|█████▉ | 10/17 [00:06<00:04, 1.59it/s] 65%|██████▍ | 11/17 [00:07<00:03, 1.55it/s] 71%|███████ | 12/17 [00:07<00:03, 1.57it/s] 76%|███████▋ | 13/17 [00:08<00:02, 1.55it/s] 82%|████████▏ | 14/17 [00:08<00:01, 1.57it/s] 88%|████████▊ | 15/17 [00:09<00:01, 1.53it/s] 94%|█████████▍| 16/17 [00:10<00:00, 1.70it/s] 100%|██████████| 17/17 [00:10<00:00, 1.94it/s] 100%|██████████| 17/17 [00:10<00:00, 1.63it/s] Decoding latents in cuda:0... done in 0.56s Move latents to cpu... done in 0.0s Uploading files.. Finished.