adirik/t2i-adapter-sdxl-openpose 🖼️📝❓🔢 → 🖼️

▶️ 61.8K runs 📅 Sep 2023 ⚙️ Cog 0.8.3 🔗 GitHub 📄 Paper ⚖️ License
image-editing image-to-image openpose

About

Modify images using human pose

Example Output

Prompt:

"A couple, 4k photo, highly detailed"

Output

Example outputExample output

Performance Metrics

12.43s Prediction Time
115.54s Total Time
All Input Parameters 
{
  "image": "https://replicate.delivery/pbxt/JbnAELoOIkMhteHqHJnRfB0ATKgRdZqLjdIgcZB34WlRNCNF/people.jpg",
  "prompt": "A couple, 4k photo, highly detailed",
  "scheduler": "K_EULER_ANCESTRAL",
  "num_samples": 1,
  "guidance_scale": 7.5,
  "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
  "num_inference_steps": 30,
  "adapter_conditioning_scale": 0.9,
  "adapter_conditioning_factor": 0.9
}
Input Parameters
image (required) Type: string
Input image
prompt Type: stringDefault: A couple, 4k photo, highly detailed
Input prompt
scheduler Default: K_EULER_ANCESTRAL
Which scheduler to use
num_samples Type: integerDefault: 1Range: 1 - 4
Number of outputs to generate
random_seed Type: integer
Random seed for reproducibility, leave blank to randomize output
guidance_scale Type: numberDefault: 7.5Range: 0 - 10
Guidance scale to match the prompt
negative_prompt Type: stringDefault: anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured
Specify things to not see in the output
num_inference_steps Type: integerDefault: 30Range: 0 - 100
Number of diffusion steps
adapter_conditioning_scale Type: numberDefault: 1Range: 0 - 5
Conditioning scale
adapter_conditioning_factor Type: numberDefault: 1Range: 0 - 1
Factor to scale image by
Output Schema

Output

Type: array • Items Type: string • Items Format: uri

Example Execution Logs 
0%|          | 0/30 [00:00<?, ?it/s]
  3%|▎         | 1/30 [00:00<00:04,  6.05it/s]
  7%|▋         | 2/30 [00:00<00:06,  4.50it/s]
 10%|█         | 3/30 [00:00<00:06,  4.12it/s]
 13%|█▎        | 4/30 [00:00<00:06,  3.97it/s]
 17%|█▋        | 5/30 [00:01<00:06,  3.89it/s]
 20%|██        | 6/30 [00:01<00:06,  3.83it/s]
 23%|██▎       | 7/30 [00:01<00:06,  3.80it/s]
 27%|██▋       | 8/30 [00:02<00:05,  3.78it/s]
 30%|███       | 9/30 [00:02<00:05,  3.77it/s]
 33%|███▎      | 10/30 [00:02<00:05,  3.76it/s]
 37%|███▋      | 11/30 [00:02<00:05,  3.74it/s]
 40%|████      | 12/30 [00:03<00:04,  3.74it/s]
 43%|████▎     | 13/30 [00:03<00:04,  3.74it/s]
 47%|████▋     | 14/30 [00:03<00:04,  3.73it/s]
 50%|█████     | 15/30 [00:03<00:04,  3.73it/s]
 53%|█████▎    | 16/30 [00:04<00:03,  3.73it/s]
 57%|█████▋    | 17/30 [00:04<00:03,  3.73it/s]
 60%|██████    | 18/30 [00:04<00:03,  3.72it/s]
 63%|██████▎   | 19/30 [00:04<00:02,  3.73it/s]
 67%|██████▋   | 20/30 [00:05<00:02,  3.72it/s]
 70%|███████   | 21/30 [00:05<00:02,  3.72it/s]
 73%|███████▎  | 22/30 [00:05<00:02,  3.72it/s]
 77%|███████▋  | 23/30 [00:06<00:01,  3.73it/s]
 80%|████████  | 24/30 [00:06<00:01,  3.72it/s]
 83%|████████▎ | 25/30 [00:06<00:01,  3.72it/s]
 87%|████████▋ | 26/30 [00:06<00:01,  3.72it/s]
 90%|█████████ | 27/30 [00:07<00:00,  3.71it/s]
 93%|█████████▎| 28/30 [00:07<00:00,  3.72it/s]
 97%|█████████▋| 29/30 [00:07<00:00,  3.72it/s]
100%|██████████| 30/30 [00:07<00:00,  3.72it/s]
100%|██████████| 30/30 [00:07<00:00,  3.78it/s]
Version Details
Version ID
ff250494f7328552c64ee50ae3ed9b61e09ca18c7aa51f77ed187a3fb9ec9093
Version Created
October 30, 2023
Run on Replicate →