remodela-ai/style-transfer-ii 🔢🖼️📝❓✓ → 🖼️
About
Example Output
Prompt:
"modern interior living room"
Output
Performance Metrics
8.81s
Prediction Time
129.30s
Total Time
All Input Parameters
{
"image": "https://replicate.delivery/pbxt/KbUKCMMihGkSc5MXYYYkjEI6hlf1GNmGgwugu6JfSQATqiCN/3d.jpeg",
"prompt": "modern interior living room",
"ip_image": "https://replicate.delivery/pbxt/KbUKD3uoQjyTzdWbeAqYrypl0RWVJc0gI5UHHa3yYWNaqIoc/DALL%C2%B7E%202024-03-20%2017.57.33%20-%20Create%20a%20professional%20photograph%20of%20a%20modern%20interior%20design.%20This%20image%20should%20capture%20a%20living%20room%20space%20that%20combines%20contemporary%20elegance%20with%20c.webp",
"ip_scale": 1,
"strength": 1,
"scheduler": "K_EULER",
"lora_scale": 0.9,
"num_outputs": 1,
"guidance_scale": 4,
"resizing_scale": 1,
"apply_watermark": true,
"negative_prompt": "",
"background_color": "#A2A2A2",
"num_inference_steps": 30,
"condition_canny_scale": 0.5,
"condition_depth_scale": 0.5
}
Input Parameters
- seed
- Random seed. Leave blank to randomize the seed
- image
- Input image
- prompt
- Input prompt
- ip_image
- Input image for img2img or inpaint mode
- ip_scale
- IP Adapter strength.
- strength
- When img2img is active, the denoising strength. 1 means total destruction of the input image.
- scheduler
- scheduler
- lora_scale
- LoRA additive scale. Only applicable on trained models.
- num_outputs
- Number of images to output
- lora_weights
- Replicate LoRA weights to use. Leave blank to use the default weights.
- guidance_scale
- Scale for classifier-free guidance
- resizing_scale
- If you want the image to have a solid margin. Scale of the solid margin. 1.0 means no resizing.
- apply_watermark
- Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.
- negative_prompt
- Input Negative Prompt
- background_color
- When passing an image with alpha channel, it will be replaced with this color
- num_inference_steps
- Number of denoising steps
- condition_canny_scale
- The bigger this number is, the more ControlNet interferes
- condition_depth_scale
- The bigger this number is, the more ControlNet interferes
Output Schema
Output
Example Execution Logs
INFO:root:Using seed: 50543 INFO:root:Prompt: modern interior living room INFO:root:Original width:1024, height:1024 INFO:root:Aspect Ratio: 1.00 INFO:root:new_width:1024, new_height:1024 You have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts. 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:09, 3.05it/s] 7%|▋ | 2/30 [00:00<00:06, 4.16it/s] 10%|█ | 3/30 [00:00<00:06, 4.30it/s] 13%|█▎ | 4/30 [00:00<00:05, 4.37it/s] 17%|█▋ | 5/30 [00:01<00:05, 4.42it/s] 20%|██ | 6/30 [00:01<00:05, 4.44it/s] 23%|██▎ | 7/30 [00:01<00:05, 4.45it/s] 27%|██▋ | 8/30 [00:01<00:04, 4.46it/s] 30%|███ | 9/30 [00:02<00:04, 4.47it/s] 33%|███▎ | 10/30 [00:02<00:04, 4.47it/s] 37%|███▋ | 11/30 [00:02<00:04, 4.47it/s] 40%|████ | 12/30 [00:02<00:04, 4.47it/s] 43%|████▎ | 13/30 [00:02<00:03, 4.47it/s] 47%|████▋ | 14/30 [00:03<00:03, 4.47it/s] 50%|█████ | 15/30 [00:03<00:03, 4.47it/s] 53%|█████▎ | 16/30 [00:03<00:03, 4.47it/s] 57%|█████▋ | 17/30 [00:03<00:02, 4.47it/s] 60%|██████ | 18/30 [00:04<00:02, 4.48it/s] 63%|██████▎ | 19/30 [00:04<00:02, 4.48it/s] 67%|██████▋ | 20/30 [00:04<00:02, 4.47it/s] 70%|███████ | 21/30 [00:04<00:02, 4.47it/s] 73%|███████▎ | 22/30 [00:04<00:01, 4.47it/s] 77%|███████▋ | 23/30 [00:05<00:01, 4.47it/s] 80%|████████ | 24/30 [00:05<00:01, 4.47it/s] 83%|████████▎ | 25/30 [00:05<00:01, 4.47it/s] 87%|████████▋ | 26/30 [00:05<00:00, 4.47it/s] 90%|█████████ | 27/30 [00:06<00:00, 4.47it/s] 93%|█████████▎| 28/30 [00:06<00:00, 4.47it/s] 97%|█████████▋| 29/30 [00:06<00:00, 4.47it/s] 100%|██████████| 30/30 [00:06<00:00, 4.47it/s] 100%|██████████| 30/30 [00:06<00:00, 4.44it/s]
Version Details
- Version ID
dd55311b5b866a46c5473e8fb71bcc38d58d651682c51940f45c1b78525cab30- Version Created
- September 12, 2024