andreasjansson / flux-alphabet-book
- Public
- 3.6K runs
-
H100
Prediction
andreasjansson/flux-alphabet-book:2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2baIDb1why6std5rme0ckj9jsx48s2cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- The text "A is for ALPHABET". Drawing of a book, colorful background. In the style of ALPHBK.
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "The text \"A is for ALPHABET\". Drawing of a book, colorful background. In the style of ALPHBK.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run andreasjansson/flux-alphabet-book using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/flux-alphabet-book:2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2ba", { input: { model: "dev", prompt: "The text \"A is for ALPHABET\". Drawing of a book, colorful background. In the style of ALPHBK.", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run andreasjansson/flux-alphabet-book using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/flux-alphabet-book:2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2ba", input={ "model": "dev", "prompt": "The text \"A is for ALPHABET\". Drawing of a book, colorful background. In the style of ALPHBK.", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/flux-alphabet-book using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "andreasjansson/flux-alphabet-book:2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2ba", "input": { "model": "dev", "prompt": "The text \\"A is for ALPHABET\\". Drawing of a book, colorful background. In the style of ALPHBK.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-04T15:59:29.461756Z", "created_at": "2024-12-04T15:58:47.913000Z", "data_removed": false, "error": null, "id": "b1why6std5rme0ckj9jsx48s2c", "input": { "model": "dev", "prompt": "The text \"A is for ALPHABET\". Drawing of a book, colorful background. In the style of ALPHBK.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "2024-12-04 15:59:22.993 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys\n2024-12-04 15:59:22.993 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 94%|█████████▍| 287/304 [00:00<00:00, 2849.33it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2710.96it/s]\n2024-12-04 15:59:23.106 | SUCCESS | fp8.lora_loading:unload_loras:559 - LoRAs unloaded in 0.11s\n2024-12-04 15:59:23.107 | INFO | fp8.lora_loading:convert_lora_weights:493 - Loading LoRA weights for /src/weights-cache/79ec1e76ae440687\n2024-12-04 15:59:23.225 | INFO | fp8.lora_loading:convert_lora_weights:514 - LoRA weights loaded\n2024-12-04 15:59:23.225 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys\n2024-12-04 15:59:23.225 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 94%|█████████▍| 287/304 [00:00<00:00, 2849.40it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2710.54it/s]\n2024-12-04 15:59:23.338 | SUCCESS | fp8.lora_loading:load_lora:534 - LoRA applied in 0.23s\nUsing seed: 22389\n0it [00:00, ?it/s]\n1it [00:00, 8.38it/s]\n2it [00:00, 5.85it/s]\n3it [00:00, 5.34it/s]\n4it [00:00, 5.13it/s]\n5it [00:00, 4.98it/s]\n6it [00:01, 4.90it/s]\n7it [00:01, 4.87it/s]\n8it [00:01, 4.85it/s]\n9it [00:01, 4.85it/s]\n10it [00:01, 4.83it/s]\n11it [00:02, 4.82it/s]\n12it [00:02, 4.81it/s]\n13it [00:02, 4.81it/s]\n14it [00:02, 4.82it/s]\n15it [00:03, 4.81it/s]\n16it [00:03, 4.80it/s]\n17it [00:03, 4.79it/s]\n18it [00:03, 4.79it/s]\n19it [00:03, 4.79it/s]\n20it [00:04, 4.80it/s]\n21it [00:04, 4.80it/s]\n22it [00:04, 4.80it/s]\n23it [00:04, 4.80it/s]\n24it [00:04, 4.80it/s]\n25it [00:05, 4.80it/s]\n26it [00:05, 4.81it/s]\n27it [00:05, 4.80it/s]\n28it [00:05, 4.81it/s]\n28it [00:05, 4.88it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 6.467398149, "total_time": 41.548756 }, "output": [ "https://replicate.delivery/xezq/eJnoIQRDXP1gaad8WqOIb9HzzeehfeLzyMeOZZ1gXKf8wcx7JA/out-0.webp" ], "started_at": "2024-12-04T15:59:22.994358Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-3x2kparenncaibak6e2chkjn4xhnu6r2f4d5kzw75cfekorqeqaa", "get": "https://api.replicate.com/v1/predictions/b1why6std5rme0ckj9jsx48s2c", "cancel": "https://api.replicate.com/v1/predictions/b1why6std5rme0ckj9jsx48s2c/cancel" }, "version": "2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2ba" }
Generated in2024-12-04 15:59:22.993 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys 2024-12-04 15:59:22.993 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 94%|█████████▍| 287/304 [00:00<00:00, 2849.33it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2710.96it/s] 2024-12-04 15:59:23.106 | SUCCESS | fp8.lora_loading:unload_loras:559 - LoRAs unloaded in 0.11s 2024-12-04 15:59:23.107 | INFO | fp8.lora_loading:convert_lora_weights:493 - Loading LoRA weights for /src/weights-cache/79ec1e76ae440687 2024-12-04 15:59:23.225 | INFO | fp8.lora_loading:convert_lora_weights:514 - LoRA weights loaded 2024-12-04 15:59:23.225 | DEBUG | fp8.lora_loading:apply_lora_to_model:569 - Extracting keys 2024-12-04 15:59:23.225 | DEBUG | fp8.lora_loading:apply_lora_to_model:576 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 94%|█████████▍| 287/304 [00:00<00:00, 2849.40it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2710.54it/s] 2024-12-04 15:59:23.338 | SUCCESS | fp8.lora_loading:load_lora:534 - LoRA applied in 0.23s Using seed: 22389 0it [00:00, ?it/s] 1it [00:00, 8.38it/s] 2it [00:00, 5.85it/s] 3it [00:00, 5.34it/s] 4it [00:00, 5.13it/s] 5it [00:00, 4.98it/s] 6it [00:01, 4.90it/s] 7it [00:01, 4.87it/s] 8it [00:01, 4.85it/s] 9it [00:01, 4.85it/s] 10it [00:01, 4.83it/s] 11it [00:02, 4.82it/s] 12it [00:02, 4.81it/s] 13it [00:02, 4.81it/s] 14it [00:02, 4.82it/s] 15it [00:03, 4.81it/s] 16it [00:03, 4.80it/s] 17it [00:03, 4.79it/s] 18it [00:03, 4.79it/s] 19it [00:03, 4.79it/s] 20it [00:04, 4.80it/s] 21it [00:04, 4.80it/s] 22it [00:04, 4.80it/s] 23it [00:04, 4.80it/s] 24it [00:04, 4.80it/s] 25it [00:05, 4.80it/s] 26it [00:05, 4.81it/s] 27it [00:05, 4.80it/s] 28it [00:05, 4.81it/s] 28it [00:05, 4.88it/s] Total safe images: 1 out of 1
Prediction
andreasjansson/flux-alphabet-book:2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2baID4mgg94yvtnrm80cm32ba8kag5gStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- The text "C IS FOR CAT". Drawing of a cat. Colorful background. In the style of ALPHBK.
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 50
{ "model": "dev", "prompt": "The text \"C IS FOR CAT\". Drawing of a cat. Colorful background. In the style of ALPHBK.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run andreasjansson/flux-alphabet-book using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "andreasjansson/flux-alphabet-book:2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2ba", { input: { model: "dev", prompt: "The text \"C IS FOR CAT\". Drawing of a cat. Colorful background. In the style of ALPHBK.", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run andreasjansson/flux-alphabet-book using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "andreasjansson/flux-alphabet-book:2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2ba", input={ "model": "dev", "prompt": "The text \"C IS FOR CAT\". Drawing of a cat. Colorful background. In the style of ALPHBK.", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 50 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run andreasjansson/flux-alphabet-book using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "andreasjansson/flux-alphabet-book:2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2ba", "input": { "model": "dev", "prompt": "The text \\"C IS FOR CAT\\". Drawing of a cat. Colorful background. In the style of ALPHBK.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-30T17:22:18.095378Z", "created_at": "2024-12-30T17:22:07.445000Z", "data_removed": false, "error": null, "id": "4mgg94yvtnrm80cm32ba8kag5g", "input": { "model": "dev", "prompt": "The text \"C IS FOR CAT\". Drawing of a cat. Colorful background. In the style of ALPHBK.", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 50 }, "logs": "Lora https://replicate.delivery/xezq/zTFwz1SWt6JXOhRFhuhh1ruxSgJotuAN2aeQQ2iCkSQeipyTA/trained_model.tar already loaded\nUsing seed: 54052\n0it [00:00, ?it/s]\n1it [00:00, 8.41it/s]\n2it [00:00, 5.87it/s]\n3it [00:00, 5.37it/s]\n4it [00:00, 5.15it/s]\n5it [00:00, 5.01it/s]\n6it [00:01, 4.91it/s]\n7it [00:01, 4.88it/s]\n8it [00:01, 4.88it/s]\n9it [00:01, 4.87it/s]\n10it [00:01, 4.84it/s]\n11it [00:02, 4.82it/s]\n12it [00:02, 4.81it/s]\n13it [00:02, 4.81it/s]\n14it [00:02, 4.83it/s]\n15it [00:03, 4.82it/s]\n16it [00:03, 4.81it/s]\n17it [00:03, 4.81it/s]\n18it [00:03, 4.80it/s]\n19it [00:03, 4.81it/s]\n20it [00:04, 4.81it/s]\n21it [00:04, 4.80it/s]\n22it [00:04, 4.81it/s]\n23it [00:04, 4.81it/s]\n24it [00:04, 4.81it/s]\n25it [00:05, 4.81it/s]\n26it [00:05, 4.81it/s]\n27it [00:05, 4.81it/s]\n28it [00:05, 4.81it/s]\n29it [00:05, 4.80it/s]\n30it [00:06, 4.80it/s]\n31it [00:06, 4.81it/s]\n32it [00:06, 4.81it/s]\n33it [00:06, 4.81it/s]\n34it [00:06, 4.80it/s]\n35it [00:07, 4.81it/s]\n36it [00:07, 4.80it/s]\n37it [00:07, 4.81it/s]\n38it [00:07, 4.81it/s]\n39it [00:08, 4.81it/s]\n40it [00:08, 4.81it/s]\n41it [00:08, 4.81it/s]\n42it [00:08, 4.81it/s]\n43it [00:08, 4.80it/s]\n44it [00:09, 4.81it/s]\n45it [00:09, 4.81it/s]\n46it [00:09, 4.81it/s]\n47it [00:09, 4.80it/s]\n48it [00:09, 4.79it/s]\n49it [00:10, 4.79it/s]\n50it [00:10, 4.80it/s]\n50it [00:10, 4.85it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 10.64264838, "total_time": 10.650378 }, "output": [ "https://replicate.delivery/xezq/tFRp9JWzPbprJtW6nisSeO9nCMBh5aRtt85eucJCC6nKjIAUA/out-0.webp" ], "started_at": "2024-12-30T17:22:07.452730Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-elhwhtoljak3skfjw4xybczk3hwogz5vgmgph3sbs64oojguniwa", "get": "https://api.replicate.com/v1/predictions/4mgg94yvtnrm80cm32ba8kag5g", "cancel": "https://api.replicate.com/v1/predictions/4mgg94yvtnrm80cm32ba8kag5g/cancel" }, "version": "2c5bd682a971b4594003cff5e0086b4e60d235ffb3435a167a2fe79b3a24c2ba" }
Generated inLora https://replicate.delivery/xezq/zTFwz1SWt6JXOhRFhuhh1ruxSgJotuAN2aeQQ2iCkSQeipyTA/trained_model.tar already loaded Using seed: 54052 0it [00:00, ?it/s] 1it [00:00, 8.41it/s] 2it [00:00, 5.87it/s] 3it [00:00, 5.37it/s] 4it [00:00, 5.15it/s] 5it [00:00, 5.01it/s] 6it [00:01, 4.91it/s] 7it [00:01, 4.88it/s] 8it [00:01, 4.88it/s] 9it [00:01, 4.87it/s] 10it [00:01, 4.84it/s] 11it [00:02, 4.82it/s] 12it [00:02, 4.81it/s] 13it [00:02, 4.81it/s] 14it [00:02, 4.83it/s] 15it [00:03, 4.82it/s] 16it [00:03, 4.81it/s] 17it [00:03, 4.81it/s] 18it [00:03, 4.80it/s] 19it [00:03, 4.81it/s] 20it [00:04, 4.81it/s] 21it [00:04, 4.80it/s] 22it [00:04, 4.81it/s] 23it [00:04, 4.81it/s] 24it [00:04, 4.81it/s] 25it [00:05, 4.81it/s] 26it [00:05, 4.81it/s] 27it [00:05, 4.81it/s] 28it [00:05, 4.81it/s] 29it [00:05, 4.80it/s] 30it [00:06, 4.80it/s] 31it [00:06, 4.81it/s] 32it [00:06, 4.81it/s] 33it [00:06, 4.81it/s] 34it [00:06, 4.80it/s] 35it [00:07, 4.81it/s] 36it [00:07, 4.80it/s] 37it [00:07, 4.81it/s] 38it [00:07, 4.81it/s] 39it [00:08, 4.81it/s] 40it [00:08, 4.81it/s] 41it [00:08, 4.81it/s] 42it [00:08, 4.81it/s] 43it [00:08, 4.80it/s] 44it [00:09, 4.81it/s] 45it [00:09, 4.81it/s] 46it [00:09, 4.81it/s] 47it [00:09, 4.80it/s] 48it [00:09, 4.79it/s] 49it [00:10, 4.79it/s] 50it [00:10, 4.80it/s] 50it [00:10, 4.85it/s] Total safe images: 1 out of 1
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