callmejz-ai/doodle 🖼️🔢📝❓✓ → 🖼️
About
Doodles trained on black line drawings, fashion illustrations, and wire sculptures. Simple images for complex intellectuals, luxury brands, b2b marketing, saas..

Example Output
Prompt:
"flower"
Output

Performance Metrics
21.65s
Prediction Time
28.78s
Total Time
All Input Parameters
{ "width": 1024, "height": 1024, "prompt": "flower", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Input Parameters
- mask
- Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.
- seed
- Random seed. Leave blank to randomize the seed
- image
- Input image for img2img or inpaint mode
- width
- Width of output image
- height
- Height of output image
- prompt
- Input prompt
- refine
- Which refine style to use
- scheduler
- scheduler
- lora_scale
- LoRA additive scale. Only applicable on trained models.
- num_outputs
- Number of images to output.
- refine_steps
- For base_image_refiner, the number of steps to refine, defaults to num_inference_steps
- guidance_scale
- Scale for classifier-free guidance
- 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.
- high_noise_frac
- For expert_ensemble_refiner, the fraction of noise to use
- negative_prompt
- Input Negative Prompt
- prompt_strength
- Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image
- replicate_weights
- Replicate LoRA weights to use. Leave blank to use the default weights.
- num_inference_steps
- Number of denoising steps
- disable_safety_checker
- Disable safety checker for generated images. This feature is only available through the API. See [https://replicate.com/docs/how-does-replicate-work#safety](https://replicate.com/docs/how-does-replicate-work#safety)
Output Schema
Output
Example Execution Logs
Using seed: 7047 Ensuring enough disk space... Free disk space: 1439622897664 Downloading weights: https://replicate.delivery/pbxt/WZoBJcam9jK2OtB0Loes20TaJNuR8C87mhikU4gLnbTU7M1JA/trained_model.tar 2024-10-25T21:09:27Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/b9e40f01def7cc54 url=https://replicate.delivery/pbxt/WZoBJcam9jK2OtB0Loes20TaJNuR8C87mhikU4gLnbTU7M1JA/trained_model.tar 2024-10-25T21:09:32Z | INFO | [ Complete ] dest=/src/weights-cache/b9e40f01def7cc54 size="186 MB" total_elapsed=4.907s url=https://replicate.delivery/pbxt/WZoBJcam9jK2OtB0Loes20TaJNuR8C87mhikU4gLnbTU7M1JA/trained_model.tar b'' Downloaded weights in 5.0415003299713135 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: flower txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1946: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights` deprecate( 2%|▏ | 1/50 [00:00<00:11, 4.20it/s] 4%|▍ | 2/50 [00:00<00:11, 4.19it/s] 6%|▌ | 3/50 [00:00<00:11, 4.17it/s] 8%|▊ | 4/50 [00:00<00:11, 4.16it/s] 10%|█ | 5/50 [00:01<00:10, 4.16it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.16it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.16it/s] 16%|█▌ | 8/50 [00:01<00:10, 4.15it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.15it/s] 20%|██ | 10/50 [00:02<00:09, 4.15it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.15it/s] 24%|██▍ | 12/50 [00:02<00:09, 4.16it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.16it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.15it/s] 30%|███ | 15/50 [00:03<00:08, 4.15it/s] 32%|███▏ | 16/50 [00:03<00:08, 4.16it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.15it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.15it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.15it/s] 40%|████ | 20/50 [00:04<00:07, 4.15it/s] 42%|████▏ | 21/50 [00:05<00:06, 4.15it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.15it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.15it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.15it/s] 50%|█████ | 25/50 [00:06<00:06, 4.15it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.15it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.15it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.15it/s] 58%|█████▊ | 29/50 [00:06<00:05, 4.15it/s] 60%|██████ | 30/50 [00:07<00:04, 4.15it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.15it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.14it/s] 66%|██████▌ | 33/50 [00:07<00:04, 4.14it/s] 68%|██████▊ | 34/50 [00:08<00:03, 4.15it/s] 70%|███████ | 35/50 [00:08<00:03, 4.15it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.14it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.14it/s] 76%|███████▌ | 38/50 [00:09<00:02, 4.15it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.15it/s] 80%|████████ | 40/50 [00:09<00:02, 4.15it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.15it/s] 84%|████████▍ | 42/50 [00:10<00:01, 4.14it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.14it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.14it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.15it/s] 92%|█████████▏| 46/50 [00:11<00:00, 4.14it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.14it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.14it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.14it/s] 100%|██████████| 50/50 [00:12<00:00, 4.14it/s] 100%|██████████| 50/50 [00:12<00:00, 4.15it/s]
Version Details
- Version ID
b9e155a586824e58f5a5193d65b0992ae5b6e5ef7420c1a967638922c4e103a8
- Version Created
- October 25, 2024