astelvida
/
genmoji-gen
- Public
- 486 runs
-
H100
Prediction
astelvida/genmoji-gen:a72b26a6IDxf8saw6n0xrma0cmbp5b62yj5cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- GENMOJI , emoji of a girl eating a pumpkin
- 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": "GENMOJI , emoji of a girl eating a pumpkin", "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 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run astelvida/genmoji-gen using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "astelvida/genmoji-gen:a72b26a6ff81284aca4f889cf47f8dd6ca6aafc46901c95afb38141db9f9183c", { input: { model: "dev", prompt: "GENMOJI , emoji of a girl eating a pumpkin", 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 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run astelvida/genmoji-gen using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "astelvida/genmoji-gen:a72b26a6ff81284aca4f889cf47f8dd6ca6aafc46901c95afb38141db9f9183c", input={ "model": "dev", "prompt": "GENMOJI , emoji of a girl eating a pumpkin", "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run astelvida/genmoji-gen 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": "a72b26a6ff81284aca4f889cf47f8dd6ca6aafc46901c95afb38141db9f9183c", "input": { "model": "dev", "prompt": "GENMOJI , emoji of a girl eating a pumpkin", "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.
Install Cogbrew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run astelvida/genmoji-gen using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/astelvida/genmoji-gen@sha256:a72b26a6ff81284aca4f889cf47f8dd6ca6aafc46901c95afb38141db9f9183c \ -i 'model="dev"' \ -i 'prompt="GENMOJI , emoji of a girl eating a pumpkin"' \ -i 'go_fast=false' \ -i 'lora_scale=1' \ -i 'megapixels="1"' \ -i 'num_outputs=1' \ -i 'aspect_ratio="1:1"' \ -i 'output_format="webp"' \ -i 'guidance_scale=3' \ -i 'output_quality=80' \ -i 'prompt_strength=0.8' \ -i 'extra_lora_scale=1' \ -i 'num_inference_steps=28'
To learn more, take a look at the Cog documentation.
Pull and run astelvida/genmoji-gen using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/astelvida/genmoji-gen@sha256:a72b26a6ff81284aca4f889cf47f8dd6ca6aafc46901c95afb38141db9f9183c
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "GENMOJI , emoji of a girl eating a pumpkin", "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 } }' \ http://localhost:5000/predictions
Output
{ "completed_at": "2025-01-13T02:42:57.594624Z", "created_at": "2025-01-13T02:42:47.175000Z", "data_removed": false, "error": null, "id": "xf8saw6n0xrma0cmbp5b62yj5c", "input": { "model": "dev", "prompt": "GENMOJI , emoji of a girl eating a pumpkin", "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": "2025-01-13 02:42:49.255 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-13 02:42:49.256 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2803.01it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2678.28it/s]\n2025-01-13 02:42:49.370 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s\nfree=29227847204864\nDownloading weights\n2025-01-13T02:42:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpoxnwxjau/weights url=https://replicate.delivery/xezq/QWqxm2x5YgK0PZKGfzvVL8wCvHTyaqB1Ue25pgl94qxV4iEUA/trained_model.tar\n2025-01-13T02:42:51Z | INFO | [ Complete ] dest=/tmp/tmpoxnwxjau/weights size=\"172 MB\" total_elapsed=1.928s url=https://replicate.delivery/xezq/QWqxm2x5YgK0PZKGfzvVL8wCvHTyaqB1Ue25pgl94qxV4iEUA/trained_model.tar\nDownloaded weights in 1.95s\n2025-01-13 02:42:51.324 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/a47eb634a5b0e427\n2025-01-13 02:42:51.395 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-01-13 02:42:51.396 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-13 02:42:51.396 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA: 0%| | 0/304 [00:00<?, ?it/s]\nApplying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2804.62it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2679.59it/s]\n2025-01-13 02:42:51.510 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s\nUsing seed: 48331\n0it [00:00, ?it/s]\n1it [00:00, 8.34it/s]\n2it [00:00, 5.85it/s]\n3it [00:00, 5.33it/s]\n4it [00:00, 5.12it/s]\n5it [00:00, 5.01it/s]\n6it [00:01, 4.93it/s]\n7it [00:01, 4.89it/s]\n8it [00:01, 4.87it/s]\n9it [00:01, 4.86it/s]\n10it [00:01, 4.84it/s]\n11it [00:02, 4.83it/s]\n12it [00:02, 4.83it/s]\n13it [00:02, 4.82it/s]\n14it [00:02, 4.82it/s]\n15it [00:03, 4.82it/s]\n16it [00:03, 4.82it/s]\n17it [00:03, 4.82it/s]\n18it [00:03, 4.82it/s]\n19it [00:03, 4.81it/s]\n20it [00:04, 4.82it/s]\n21it [00:04, 4.82it/s]\n22it [00:04, 4.82it/s]\n23it [00:04, 4.82it/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, 4.82it/s]\n28it [00:05, 4.89it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 8.337822605, "total_time": 10.419624 }, "output": [ "https://replicate.delivery/xezq/XK6TngG04aqjPte1q7pfTgf2bo0w1803JKQRVRnVbc2i9FJoA/out-0.webp" ], "started_at": "2025-01-13T02:42:49.256802Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-oqfan2vmpak4kqkyssvilszcyl6okc3wf56mba2fly4gqm4p56ha", "get": "https://api.replicate.com/v1/predictions/xf8saw6n0xrma0cmbp5b62yj5c", "cancel": "https://api.replicate.com/v1/predictions/xf8saw6n0xrma0cmbp5b62yj5c/cancel" }, "version": "a72b26a6ff81284aca4f889cf47f8dd6ca6aafc46901c95afb38141db9f9183c" }
Generated in2025-01-13 02:42:49.255 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-13 02:42:49.256 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2803.01it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2678.28it/s] 2025-01-13 02:42:49.370 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.11s free=29227847204864 Downloading weights 2025-01-13T02:42:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpoxnwxjau/weights url=https://replicate.delivery/xezq/QWqxm2x5YgK0PZKGfzvVL8wCvHTyaqB1Ue25pgl94qxV4iEUA/trained_model.tar 2025-01-13T02:42:51Z | INFO | [ Complete ] dest=/tmp/tmpoxnwxjau/weights size="172 MB" total_elapsed=1.928s url=https://replicate.delivery/xezq/QWqxm2x5YgK0PZKGfzvVL8wCvHTyaqB1Ue25pgl94qxV4iEUA/trained_model.tar Downloaded weights in 1.95s 2025-01-13 02:42:51.324 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/a47eb634a5b0e427 2025-01-13 02:42:51.395 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-01-13 02:42:51.396 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-13 02:42:51.396 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 93%|█████████▎| 282/304 [00:00<00:00, 2804.62it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2679.59it/s] 2025-01-13 02:42:51.510 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.19s Using seed: 48331 0it [00:00, ?it/s] 1it [00:00, 8.34it/s] 2it [00:00, 5.85it/s] 3it [00:00, 5.33it/s] 4it [00:00, 5.12it/s] 5it [00:00, 5.01it/s] 6it [00:01, 4.93it/s] 7it [00:01, 4.89it/s] 8it [00:01, 4.87it/s] 9it [00:01, 4.86it/s] 10it [00:01, 4.84it/s] 11it [00:02, 4.83it/s] 12it [00:02, 4.83it/s] 13it [00:02, 4.82it/s] 14it [00:02, 4.82it/s] 15it [00:03, 4.82it/s] 16it [00:03, 4.82it/s] 17it [00:03, 4.82it/s] 18it [00:03, 4.82it/s] 19it [00:03, 4.81it/s] 20it [00:04, 4.82it/s] 21it [00:04, 4.82it/s] 22it [00:04, 4.82it/s] 23it [00:04, 4.82it/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, 4.82it/s] 28it [00:05, 4.89it/s] Total safe images: 1 out of 1
Want to make some of these yourself?
Run this model