callmejz-ai/doodle 🖼️🔢📝❓✓ → 🖼️
Performance
21.6sTypical run time
693Total runs
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`
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Version Details
- Version ID
b9e155a586824e58f5a5193d65b0992ae5b6e5ef7420c1a967638922c4e103a8- Version Created
- October 25, 2024