philz1337x/controlnet-deliberate 🔢🖼️📝❓ → 🖼️
About
Modify images with canny edge detection and Deliberate model twitter: @philz1337x

Example Output
Prompt:
"RAW photo, portrait photo of 30 y.o woman queen, pale skin, slim body, (high detailed skin:1.2), background ocean, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3"
Output


Performance Metrics
7.07s
Prediction Time
7.15s
Total Time
All Input Parameters
{ "seed": -67, "image": "https://replicate.delivery/pbxt/IW3cOsyRN9tZv2fRDZa8XmtQJ9ni2f1mm14dQyhKgHjGQfuN/Nofretete_Neues_Museum.jpg", "scale": 9, "prompt": "RAW photo, portrait photo of 30 y.o woman queen, pale skin, slim body, (high detailed skin:1.2), background ocean, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3", "weight": 0.6, "a_prompt": "best quality, extremely detailed", "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", "ddim_steps": 20, "num_samples": "1", "low_threshold": 100, "high_threshold": 200, "image_resolution": "512", "detect_resolution": 512 }
Input Parameters
- eta
- Controls the amount of noise that is added to the input data during the denoising diffusion process. Higher value -> more noise
- seed
- Seed
- image (required)
- Input image
- scale
- Scale for classifier-free guidance
- prompt (required)
- Prompt for the model
- weight
- Weight of ControlNet
- a_prompt
- Additional text to be appended to prompt
- n_prompt
- Negative Prompt
- ddim_steps
- Steps
- num_samples
- Number of samples (higher values may OOM)
- low_threshold
- Canny line detection low threshold
- high_threshold
- Canny line detection high threshold
- image_resolution
- Image resolution to be generated
- detect_resolution
- Resolution at which detection method will be applied)
Output Schema
Output
Example Execution Logs
this is the weight value: 0.6 this is the control value: tensor([[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], ..., [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], ..., [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], ..., [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], ..., [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], ..., [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], ..., [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], ..., [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]], device='cuda:0') Global seed set to 3536601535 Data shape for DDIM sampling is (1, 4, 96, 64), eta 0.0 Running DDIM Sampling with 20 timesteps DDIM Sampler: 0%| | 0/20 [00:00<?, ?it/s] DDIM Sampler: 5%|▌ | 1/20 [00:00<00:05, 3.37it/s] DDIM Sampler: 10%|█ | 2/20 [00:00<00:05, 3.57it/s] DDIM Sampler: 15%|█▌ | 3/20 [00:00<00:04, 3.65it/s] DDIM Sampler: 20%|██ | 4/20 [00:01<00:04, 3.68it/s] DDIM Sampler: 25%|██▌ | 5/20 [00:01<00:04, 3.71it/s] DDIM Sampler: 30%|███ | 6/20 [00:01<00:03, 3.71it/s] DDIM Sampler: 35%|███▌ | 7/20 [00:01<00:03, 3.72it/s] DDIM Sampler: 40%|████ | 8/20 [00:02<00:03, 3.73it/s] DDIM Sampler: 45%|████▌ | 9/20 [00:02<00:02, 3.73it/s] DDIM Sampler: 50%|█████ | 10/20 [00:02<00:02, 3.73it/s] DDIM Sampler: 55%|█████▌ | 11/20 [00:02<00:02, 3.73it/s] DDIM Sampler: 60%|██████ | 12/20 [00:03<00:02, 3.73it/s] DDIM Sampler: 65%|██████▌ | 13/20 [00:03<00:01, 3.74it/s] DDIM Sampler: 70%|███████ | 14/20 [00:03<00:01, 3.74it/s] DDIM Sampler: 75%|███████▌ | 15/20 [00:04<00:01, 3.74it/s] DDIM Sampler: 80%|████████ | 16/20 [00:04<00:01, 3.74it/s] DDIM Sampler: 85%|████████▌ | 17/20 [00:04<00:00, 3.74it/s] DDIM Sampler: 90%|█████████ | 18/20 [00:04<00:00, 3.74it/s] DDIM Sampler: 95%|█████████▌| 19/20 [00:05<00:00, 3.74it/s] DDIM Sampler: 100%|██████████| 20/20 [00:05<00:00, 3.75it/s] DDIM Sampler: 100%|██████████| 20/20 [00:05<00:00, 3.72it/s]
Version Details
- Version ID
57d86bd78018d138449fda45bfcafb8b10888379a600034cc2c7186faab98c66
- Version Created
- March 21, 2023