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A plant in a livingroom
hello-objects:v1.2c-base-v1.3
wetdreams
wetdreams
·
10/13/2023
365
Input
prompt
Input prompt
midcentury modern living room dimly lit with dark rainy evening outside, (foggy rainy evening:1.2), pacific northwest, (dim lighting:1.4), (moody lighting:1.2), plants, large plants, rainy, monstera, many plants, (foggy windows:1.2), masterpiece, best quality, twilight hour, (nighttime:1.4), rainy evening, after sunset,
negative_prompt
Specify things to not see in the output
reference_image
Image that the output should be similar to
reference_pose_image
Image with a reference pose
reference_depth_image
Image with a reference depth
image_dimensions
Pixel dimensions of output image (width x height)
512x768
num_outputs
Number of output images
1
seed
Random seed. Set as -1 to randomize the seed
-1
sampler
Sampler type
DPM++ SDE Karras
samping_steps
Number of denoising steps
30
cfg_scale
Scale for classifier-free guidance
7
clip_skip
Whether to ignore the last layer of CLIP network or not
true
lora
LoRA file. You can apply and adjust the magnitude by putting the following to the prompt: <lora:[FILE_NAME]:[MAGNITUDE]>
Output
https://files.tungsten.run/uploads/ea991d4685a44d2fb79d8c8342f8ff9d/tmp73img_vc.png
Finished in 26.0 seconds
Setting up the model... Processing... Using seed 2536829940 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:01<00:39, 1.36s/it] 7%|▋ | 2/30 [00:02<00:30, 1.08s/it] 10%|█ | 3/30 [00:03<00:26, 1.03it/s] 13%|█▎ | 4/30 [00:03<00:23, 1.09it/s] 17%|█▋ | 5/30 [00:04<00:22, 1.12it/s] 20%|██ | 6/30 [00:05<00:21, 1.14it/s] 23%|██▎ | 7/30 [00:06<00:19, 1.15it/s] 27%|██▋ | 8/30 [00:07<00:18, 1.18it/s] 30%|███ | 9/30 [00:08<00:17, 1.18it/s] 33%|███▎ | 10/30 [00:08<00:16, 1.19it/s] 37%|███▋ | 11/30 [00:09<00:15, 1.20it/s] 40%|████ | 12/30 [00:10<00:14, 1.21it/s] 43%|████▎ | 13/30 [00:11<00:14, 1.21it/s] 47%|████▋ | 14/30 [00:12<00:12, 1.27it/s] 50%|█████ | 15/30 [00:12<00:11, 1.32it/s] 53%|█████▎ | 16/30 [00:13<00:10, 1.34it/s] 57%|█████▋ | 17/30 [00:14<00:09, 1.37it/s] 60%|██████ | 18/30 [00:14<00:08, 1.34it/s] 63%|██████▎ | 19/30 [00:15<00:08, 1.32it/s] 67%|██████▋ | 20/30 [00:16<00:07, 1.34it/s] 70%|███████ | 21/30 [00:17<00:06, 1.31it/s] 73%|███████▎ | 22/30 [00:18<00:06, 1.31it/s] 77%|███████▋ | 23/30 [00:18<00:05, 1.36it/s] 80%|████████ | 24/30 [00:19<00:04, 1.40it/s] 83%|████████▎ | 25/30 [00:20<00:03, 1.36it/s] 87%|████████▋ | 26/30 [00:20<00:02, 1.35it/s] 90%|█████████ | 27/30 [00:21<00:02, 1.37it/s] 93%|█████████▎| 28/30 [00:22<00:01, 1.40it/s] 97%|█████████▋| 29/30 [00:22<00:00, 1.48it/s] 100%|██████████| 30/30 [00:23<00:00, 1.83it/s] 100%|██████████| 30/30 [00:23<00:00, 1.30it/s] Decoding latents in cuda:0...
prompt
Input prompt
midcentury modern living room dimly lit with dark rainy evening outside, (foggy rainy evening:1.2), pacific northwest, (dim lighting:1.4), (moody lighting:1.2), plants, large plants, rainy, monstera, many plants, (foggy windows:1.2), masterpiece, best quality, twilight hour, (nighttime:1.4), rainy evening, after sunset,
negative_prompt
Specify things to not see in the output
reference_image
Image that the output should be similar to
reference_pose_image
Image with a reference pose
reference_depth_image
Image with a reference depth
image_dimensions
Pixel dimensions of output image (width x height)
512x768
num_outputs
Number of output images
1
seed
Random seed. Set as -1 to randomize the seed
-1
sampler
Sampler type
DPM++ SDE Karras
samping_steps
Number of denoising steps
30
cfg_scale
Scale for classifier-free guidance
7
clip_skip
Whether to ignore the last layer of CLIP network or not
true
lora
LoRA file. You can apply and adjust the magnitude by putting the following to the prompt: <lora:[FILE_NAME]:[MAGNITUDE]>
https://files.tungsten.run/uploads/ea991d4685a44d2fb79d8c8342f8ff9d/tmp73img_vc.png
Finished in 26.0 seconds
Setting up the model... Processing... Using seed 2536829940 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:01<00:39, 1.36s/it] 7%|▋ | 2/30 [00:02<00:30, 1.08s/it] 10%|█ | 3/30 [00:03<00:26, 1.03it/s] 13%|█▎ | 4/30 [00:03<00:23, 1.09it/s] 17%|█▋ | 5/30 [00:04<00:22, 1.12it/s] 20%|██ | 6/30 [00:05<00:21, 1.14it/s] 23%|██▎ | 7/30 [00:06<00:19, 1.15it/s] 27%|██▋ | 8/30 [00:07<00:18, 1.18it/s] 30%|███ | 9/30 [00:08<00:17, 1.18it/s] 33%|███▎ | 10/30 [00:08<00:16, 1.19it/s] 37%|███▋ | 11/30 [00:09<00:15, 1.20it/s] 40%|████ | 12/30 [00:10<00:14, 1.21it/s] 43%|████▎ | 13/30 [00:11<00:14, 1.21it/s] 47%|████▋ | 14/30 [00:12<00:12, 1.27it/s] 50%|█████ | 15/30 [00:12<00:11, 1.32it/s] 53%|█████▎ | 16/30 [00:13<00:10, 1.34it/s] 57%|█████▋ | 17/30 [00:14<00:09, 1.37it/s] 60%|██████ | 18/30 [00:14<00:08, 1.34it/s] 63%|██████▎ | 19/30 [00:15<00:08, 1.32it/s] 67%|██████▋ | 20/30 [00:16<00:07, 1.34it/s] 70%|███████ | 21/30 [00:17<00:06, 1.31it/s] 73%|███████▎ | 22/30 [00:18<00:06, 1.31it/s] 77%|███████▋ | 23/30 [00:18<00:05, 1.36it/s] 80%|████████ | 24/30 [00:19<00:04, 1.40it/s] 83%|████████▎ | 25/30 [00:20<00:03, 1.36it/s] 87%|████████▋ | 26/30 [00:20<00:02, 1.35it/s] 90%|█████████ | 27/30 [00:21<00:02, 1.37it/s] 93%|█████████▎| 28/30 [00:22<00:01, 1.40it/s] 97%|█████████▋| 29/30 [00:22<00:00, 1.48it/s] 100%|██████████| 30/30 [00:23<00:00, 1.83it/s] 100%|██████████| 30/30 [00:23<00:00, 1.30it/s] Decoding latents in cuda:0...