cjwbw/blipdiffusion-controlnet 🔢📝🖼️❓ → 🖼️
About
Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing with ControlNet

Example Output
Prompt:
"on a marble table"
Output

Performance Metrics
3.04s
Prediction Time
3.11s
Total Time
All Input Parameters
{ "seed": 10, "prompt": "on a marble table", "style_image": "https://replicate.delivery/pbxt/KNc7eIC5UqBH5GxAUu3i5WWgQooBWg1WQmKlZyFczvaftVw5/flower.jpg", "guidance_scale": 7.5, "controlnet_type": "canny", "negative_prompt": "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, ugly, bad anatomy, bad proportions, deformed, blurry", "condtioning_image": "https://replicate.delivery/pbxt/KNc7dtQmRYksrDn4eC1qGrtlBJIkHHmk4S7OSMT8UAYMHRul/kettle.jpg", "num_inference_steps": 25, "style_subject_category": "flower", "target_subject_category": "teapot" }
Input Parameters
- seed
- Random seed. Leave blank to randomize the seed
- prompt
- The prompt to guide the image generation.
- style_image (required)
- The reference style image to condition the generation on.
- guidance_scale
- Scale for classifier-free guidance.
- controlnet_type
- Choose a control net
- negative_prompt
- The prompt or prompts not to guide the image generation.
- condtioning_image (required)
- The conditioning canny edge image to condition the generation on.
- num_inference_steps
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
- style_subject_category
- The source subject category (subject that defines the style).
- target_subject_category
- The target subject category (subject to geenrate).
Output Schema
Output
Example Execution Logs
Using seed: 10 0%| | 0/26 [00:00<?, ?it/s] 8%|▊ | 2/26 [00:00<00:01, 19.88it/s] 15%|█▌ | 4/26 [00:00<00:01, 18.13it/s] 23%|██▎ | 6/26 [00:00<00:01, 17.66it/s] 31%|███ | 8/26 [00:00<00:01, 17.46it/s] 38%|███▊ | 10/26 [00:00<00:00, 17.34it/s] 46%|████▌ | 12/26 [00:00<00:00, 17.29it/s] 54%|█████▍ | 14/26 [00:00<00:00, 17.27it/s] 62%|██████▏ | 16/26 [00:00<00:00, 17.23it/s] 69%|██████▉ | 18/26 [00:01<00:00, 17.18it/s] 77%|███████▋ | 20/26 [00:01<00:00, 17.16it/s] 85%|████████▍ | 22/26 [00:01<00:00, 17.17it/s] 92%|█████████▏| 24/26 [00:01<00:00, 17.16it/s] 100%|██████████| 26/26 [00:01<00:00, 17.16it/s] 100%|██████████| 26/26 [00:01<00:00, 17.33it/s]
Version Details
- Version ID
0072c227cdc7eea6c41411b54655b874faac1a7b8288386dc2a3c677c6faa403
- Version Created
- February 10, 2024