galleri5/alien-green
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alien-green
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galleri5/alien-green
Trained on generations with this melting green aesthetic cultivated using Stable Diffusion 2.1 ( Realism Engine) with themes from Aliens, Bollywood, Surrealism among many. WIP. Refer to Examples for trigger words
galleri5/nammeh
SDXL LoRA. Trained on SDXL generations with a funky glitch aesthetic. No artists were referenced. Not Another Model Mimicking Exhausted Hues
galleri5/glitch
WIP - For as the neon nights beget dayglo days, Glitch doth glitch the glitched, a jumble-jam, a kerfuffle of kilobytes.
galleri5/icons
SDXL finetune to generate slick Icons and Flat Pop Constructivist Graphics with thick edges. Trained on Bing Generations
galleri5/symbols
SDXL finetune to generate curvy minimalist shapes and patterns. WIP . Trained on AI generated images that had South Indian alphabets as central motif in prompts to achieve satisfactory rotundness.
Trained on generations with this melting green aesthetic cultivated using Stable Diffusion 2.1 ( Realism Engine) with themes from Aliens, Bollywood, Surrealism among many. WIP. Refer to Examples for trigger words
Prediction
galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beIDhd4snldbvvwpx5dcsxee6g34jyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAP, a young woman with brightly colored hair
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAP, a young woman with brightly colored hair", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAP, a young woman with brightly colored hair", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAP, a young woman with brightly colored hair", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAP, a young woman with brightly colored hair", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAP, a young woman with brightly colored hair"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER_ANCESTRAL"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAP, a young woman with brightly colored hair", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-27T18:42:39.199288Z", "created_at": "2023-09-27T18:42:23.600462Z", "data_removed": false, "error": null, "id": "hd4snldbvvwpx5dcsxee6g34jy", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAP, a young woman with brightly colored hair", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 46816\nPrompt: In the style of GRINTHARAP, a young woman with brightly colored hair\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.70it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.71it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.71it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.71it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.71it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.71it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.71it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.70it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.70it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.69it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.69it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.69it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.69it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]", "metrics": { "predict_time": 15.636197, "total_time": 15.598826 }, "output": [ "https://pbxt.replicate.delivery/EKLpZFKASjqNLl0dnFxRF3eveBZmor3FCmcneSNZSpL9MFRjA/out-0.png" ], "started_at": "2023-09-27T18:42:23.563091Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hd4snldbvvwpx5dcsxee6g34jy", "cancel": "https://api.replicate.com/v1/predictions/hd4snldbvvwpx5dcsxee6g34jy/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 46816 Prompt: In the style of GRINTHARAP, a young woman with brightly colored hair txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.70it/s] 10%|█ | 5/50 [00:01<00:12, 3.71it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.71it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.71it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.71it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s] 20%|██ | 10/50 [00:02<00:10, 3.71it/s] 22%|██▏ | 11/50 [00:02<00:10, 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galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beID72jogpdbwikklt2fxg7axknyk4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAPE, reverse split cotton meat
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAPE, reverse split cotton meat", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAPE, reverse split cotton meat"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-27T18:54:39.791318Z", "created_at": "2023-09-27T18:54:24.149204Z", "data_removed": false, "error": null, "id": "72jogpdbwikklt2fxg7axknyk4", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 6546\nPrompt: In the style of GRINTHARAPE, reverse split cotton meat\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.70it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.70it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.70it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.70it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.70it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.70it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.69it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.69it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]", "metrics": { "predict_time": 15.635005, "total_time": 15.642114 }, "output": [ "https://pbxt.replicate.delivery/eyoIYgXIVmwSfU3TtOn5qGKZ3BZ74v7Ph7NnEE2vBI4uxioRA/out-0.png" ], "started_at": "2023-09-27T18:54:24.156313Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/72jogpdbwikklt2fxg7axknyk4", "cancel": "https://api.replicate.com/v1/predictions/72jogpdbwikklt2fxg7axknyk4/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 6546 Prompt: In the style of GRINTHARAPE, reverse split cotton meat txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:12, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.70it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.70it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s] 30%|███ | 15/50 [00:04<00:09, 3.70it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s] 40%|████ | 20/50 [00:05<00:08, 3.70it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s] 50%|█████ | 25/50 [00:06<00:06, 3.70it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s] 60%|██████ | 30/50 [00:08<00:05, 3.70it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s] 70%|███████ | 35/50 [00:09<00:04, 3.69it/s] 72%|███████▏ | 36/50 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galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beIDvzr4snlbtxz3nux5d5naaa2mzmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAPE, tree murder
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 1
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.73
- negative_prompt
- realistic, blurry
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, tree murder", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.73, "negative_prompt": "realistic, blurry", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAPE, tree murder", refine: "no_refiner", scheduler: "DDIM", lora_scale: 1, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.73, negative_prompt: "realistic, blurry", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, tree murder", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.73, "negative_prompt": "realistic, blurry", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, tree murder", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.73, "negative_prompt": "realistic, blurry", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAPE, tree murder"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.73' \ -i 'negative_prompt="realistic, blurry"' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, tree murder", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.73, "negative_prompt": "realistic, blurry", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-27T19:05:28.784450Z", "created_at": "2023-09-27T19:05:12.753182Z", "data_removed": false, "error": null, "id": "vzr4snlbtxz3nux5d5naaa2mzm", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, tree murder", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.73, "negative_prompt": "realistic, blurry", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 55240\nPrompt: In the style of GRINTHARAPE, tree murder\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.70it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.70it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.975525, "total_time": 16.031268 }, "output": [ "https://pbxt.replicate.delivery/neQ1gCu43mUUZ6VHZm9T9UswtykqqpKy8zTfyNeUwrvv3FRjA/out-0.png" ], "started_at": "2023-09-27T19:05:12.808925Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vzr4snlbtxz3nux5d5naaa2mzm", "cancel": "https://api.replicate.com/v1/predictions/vzr4snlbtxz3nux5d5naaa2mzm/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 55240 Prompt: In the style of GRINTHARAPE, tree murder txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.70it/s] 10%|█ | 5/50 [00:01<00:12, 3.70it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beIDvd6agllbio3f7pxdsmxckohbyeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAPE, reverse split cotton meat juice
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat juice", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAPE, reverse split cotton meat juice", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat juice", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat juice", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAPE, reverse split cotton meat juice"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat juice", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-27T19:52:07.405490Z", "created_at": "2023-09-27T19:51:37.890070Z", "data_removed": false, "error": null, "id": "vd6agllbio3f7pxdsmxckohbye", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton meat juice", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 40908\nPrompt: In the style of GRINTHARAPE, reverse split cotton meat juice\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.71it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.70it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.70it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.70it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.70it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.70it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.70it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.69it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.69it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.69it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.69it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]", "metrics": { "predict_time": 15.958814, "total_time": 29.51542 }, "output": [ "https://pbxt.replicate.delivery/4PT7ExR8RvpEKpJNBQvbkCF8F67gCFVewzDTWrwjBqAzzR0IA/out-0.png" ], "started_at": "2023-09-27T19:51:51.446676Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vd6agllbio3f7pxdsmxckohbye", "cancel": "https://api.replicate.com/v1/predictions/vd6agllbio3f7pxdsmxckohbye/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 40908 Prompt: In the style of GRINTHARAPE, reverse split cotton meat juice txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.71it/s] 8%|▊ | 4/50 [00:01<00:12, 3.70it/s] 10%|█ | 5/50 [00:01<00:12, 3.70it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.70it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.70it/s] 20%|██ | 10/50 [00:02<00:10, 3.70it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.70it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s] 30%|███ | 15/50 [00:04<00:09, 3.70it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s] 40%|████ | 20/50 [00:05<00:08, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s] 50%|█████ | 25/50 [00:06<00:06, 3.69it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s] 60%|██████ | 30/50 [00:08<00:05, 3.69it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.69it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s]Prediction
galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beIDtbapo5dbsib527k7qzkjtd3lrqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAPE, onion crow hybrid
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, onion crow hybrid", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAPE, onion crow hybrid", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, onion crow hybrid", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, onion crow hybrid", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAPE, onion crow hybrid"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, onion crow hybrid", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-27T20:03:53.760959Z", "created_at": "2023-09-27T20:03:37.789117Z", "data_removed": false, "error": null, "id": "tbapo5dbsib527k7qzkjtd3lrq", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, onion crow hybrid", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 37279\nPrompt: In the style of GRINTHARAPE, onion crow hybrid\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s]\n 20%|██ | 10/50 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"https://pbxt.replicate.delivery/vaKUfqd6ven5QEe5k6kDAOogwp43hkhEpsYLrdr2ciIQlHRjA/out-0.png" ], "started_at": "2023-09-27T20:03:37.787376Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tbapo5dbsib527k7qzkjtd3lrq", "cancel": "https://api.replicate.com/v1/predictions/tbapo5dbsib527k7qzkjtd3lrq/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 37279 Prompt: In the style of GRINTHARAPE, onion crow hybrid txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 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galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beIDxwh2bmtbso6xmyzs3pz3nvsbwmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARA, onion crow bubblegum flower juice
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, onion crow bubblegum flower juice", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARA, onion crow bubblegum flower juice", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, onion crow bubblegum flower juice", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, onion crow bubblegum flower juice", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARA, onion crow bubblegum flower juice"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER_ANCESTRAL"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, onion crow bubblegum flower juice", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-27T20:15:27.608356Z", "created_at": "2023-09-27T20:15:11.709369Z", "data_removed": false, "error": null, "id": "xwh2bmtbso6xmyzs3pz3nvsbwm", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, onion crow bubblegum flower juice", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 18872\nPrompt: In the style of GRINTHARA, onion crow bubblegum flower juice\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.70it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.70it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.70it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.70it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.70it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.70it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.70it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.70it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.70it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.70it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]", "metrics": { "predict_time": 15.908707, "total_time": 15.898987 }, "output": [ "https://pbxt.replicate.delivery/XMDtBvHqQIZtO1VC5BaHX6A77cV1QaFePJSuinZ9FzDvejoRA/out-0.png" ], "started_at": "2023-09-27T20:15:11.699649Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xwh2bmtbso6xmyzs3pz3nvsbwm", "cancel": "https://api.replicate.com/v1/predictions/xwh2bmtbso6xmyzs3pz3nvsbwm/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 18872 Prompt: In the style of GRINTHARA, onion crow bubblegum flower juice txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.70it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s] 30%|███ | 15/50 [00:04<00:09, 3.70it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s] 40%|████ | 20/50 [00:05<00:08, 3.70it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s] 50%|█████ | 25/50 [00:06<00:06, 3.70it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s] 60%|██████ | 30/50 [00:08<00:05, 3.70it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s] 70%|███████ | 35/50 [00:09<00:04, 3.70it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.70it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s] 80%|████████ | 40/50 [00:10<00:02, 3.70it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.70it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.70it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.70it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s]Prediction
galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beIDoswqettbcf6dxmh5ynmekxtrxeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARA, velvet barbie flower slime
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, velvet barbie flower slime", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARA, velvet barbie flower slime", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, velvet barbie flower slime", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, velvet barbie flower slime", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARA, velvet barbie flower slime"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, velvet barbie flower slime", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-27T20:21:26.692943Z", "created_at": "2023-09-27T20:21:10.791407Z", "data_removed": false, "error": null, "id": "oswqettbcf6dxmh5ynmekxtrxe", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARA, velvet barbie flower slime", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 38791\nPrompt: In the style of GRINTHARA, velvet barbie flower slime\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.69it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.70it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.70it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.70it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.69it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.69it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.69it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]", "metrics": { "predict_time": 15.903637, "total_time": 15.901536 }, "output": [ "https://pbxt.replicate.delivery/0bviowG8ELJ4NZfyaC3RwWxEfnXZSsfV8tAcd4Y6cKTLGIRjA/out-0.png" ], "started_at": "2023-09-27T20:21:10.789306Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/oswqettbcf6dxmh5ynmekxtrxe", "cancel": "https://api.replicate.com/v1/predictions/oswqettbcf6dxmh5ynmekxtrxe/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 38791 Prompt: In the style of GRINTHARA, velvet barbie flower slime txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s] 40%|████ | 20/50 [00:05<00:08, 3.70it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.70it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s] 50%|█████ | 25/50 [00:06<00:06, 3.70it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s] 60%|██████ | 30/50 [00:08<00:05, 3.70it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.69it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s] 70%|███████ | 35/50 [00:09<00:04, 3.69it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.70it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s] 80%|████████ | 40/50 [00:10<00:02, 3.69it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s]Prediction
galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beIDglgquptbnmu3j6fvgmz3jsgmjiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAPE, reverse split cotton hair teeth fish
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAPE, reverse split cotton hair teeth fish"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-28T18:22:06.150059Z", "created_at": "2023-09-28T18:21:50.319103Z", "data_removed": false, "error": null, "id": "glgquptbnmu3j6fvgmz3jsgmji", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 53242\nPrompt: In the style of GRINTHARAPE, reverse split cotton hair teeth fish\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 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"output": [ "https://pbxt.replicate.delivery/gm2D7wcyD7Y9Al3HVlmrqw2D5LL0ZYiceKeYaHChUz9NZ3oRA/out-0.png" ], "started_at": "2023-09-28T18:21:50.310359Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/glgquptbnmu3j6fvgmz3jsgmji", "cancel": "https://api.replicate.com/v1/predictions/glgquptbnmu3j6fvgmz3jsgmji/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 53242 Prompt: In the style of GRINTHARAPE, reverse split cotton hair teeth fish txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 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galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beIDqterk2tbpcpz2if6tzdbgr4ikqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAPE, reverse split cotton hair teeth fish
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAPE, reverse split cotton hair teeth fish"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-28T18:28:59.944258Z", "created_at": "2023-09-28T18:28:44.158236Z", "data_removed": false, "error": null, "id": "qterk2tbpcpz2if6tzdbgr4ikq", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth fish", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 37482\nPrompt: In the style of GRINTHARAPE, reverse split cotton hair teeth fish\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.62it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 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"output": [ "https://pbxt.replicate.delivery/VnFTjhR05GJKM9iM8RNOaJBL9vQLsqSK9CAUCRc5eiH1vb0IA/out-0.png" ], "started_at": "2023-09-28T18:28:44.129267Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qterk2tbpcpz2if6tzdbgr4ikq", "cancel": "https://api.replicate.com/v1/predictions/qterk2tbpcpz2if6tzdbgr4ikq/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 37482 Prompt: In the style of GRINTHARAPE, reverse split cotton hair teeth fish txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 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galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beID4pckvblbibof7gz4euwg32skwyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-28T19:10:07.219503Z", "created_at": "2023-09-28T19:09:51.517472Z", "data_removed": false, "error": null, "id": "4pckvblbibof7gz4euwg32skwy", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 41800\nPrompt: In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.71it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.740864, "total_time": 15.702031 }, "output": [ "https://pbxt.replicate.delivery/hZM5qda2dSbsKhWO0vLWq3HKfvBb2NdHgPujcpOxwEGHDc0IA/out-0.png" ], "started_at": "2023-09-28T19:09:51.478639Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4pckvblbibof7gz4euwg32skwy", "cancel": "https://api.replicate.com/v1/predictions/4pckvblbibof7gz4euwg32skwy/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 41800 Prompt: In the style of GRINTHARAPE, reverse split cotton hair teeth eel bulb txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.71it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 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3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05beID5xe657lbk4ljqqelnsaaq5t5myStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of GRINTHARAPE, reverse split cotton hair nose shrimp
- refine
- no_refiner
- scheduler
- DDIM
- lora_scale
- 0.88
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.6
- negative_prompt
- prompt_strength
- 1
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair nose shrimp", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", { input: { width: 1024, height: 1024, prompt: "In the style of GRINTHARAPE, reverse split cotton hair nose shrimp", refine: "no_refiner", scheduler: "DDIM", lora_scale: 0.88, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.6, negative_prompt: "", prompt_strength: 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 replicateImport the client:import replicateRun galleri5/alien-green using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", input={ "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair nose shrimp", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run galleri5/alien-green 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": "galleri5/alien-green:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair nose shrimp", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="In the style of GRINTHARAPE, reverse split cotton hair nose shrimp"' \ -i 'refine="no_refiner"' \ -i 'scheduler="DDIM"' \ -i 'lora_scale=0.88' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.6' \ -i 'negative_prompt=""' \ -i 'prompt_strength=1' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/galleri5/alien-green@sha256:3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05becurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair nose shrimp", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-28T20:37:38.408224Z", "created_at": "2023-09-28T20:37:22.812736Z", "data_removed": false, "error": null, "id": "5xe657lbk4ljqqelnsaaq5t5my", "input": { "width": 1024, "height": 1024, "prompt": "In the style of GRINTHARAPE, reverse split cotton hair nose shrimp", "refine": "no_refiner", "scheduler": "DDIM", "lora_scale": 0.88, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.6, "negative_prompt": "", "prompt_strength": 1, "num_inference_steps": 50 }, "logs": "Using seed: 4899\nPrompt: In the style of GRINTHARAPE, reverse split cotton hair nose shrimp\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.72it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.72it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.72it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.72it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.71it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.71it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.71it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.70it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.70it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.70it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.69it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.70it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.70it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.69it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.69it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.69it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.69it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.69it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.69it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.69it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.69it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.69it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]", "metrics": { "predict_time": 15.614544, "total_time": 15.595488 }, "output": [ "https://pbxt.replicate.delivery/kqXp5aeY51SOf0cw222pQX13myuT2aRQhsYU7p3L7LfiwyRjA/out-0.png" ], "started_at": "2023-09-28T20:37:22.793680Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5xe657lbk4ljqqelnsaaq5t5my", "cancel": "https://api.replicate.com/v1/predictions/5xe657lbk4ljqqelnsaaq5t5my/cancel" }, "version": "3be20de19393753415d9191c508481561c3ce8bdc8b4742a3dd6f20be7ed05be" }Generated inUsing seed: 4899 Prompt: In the style of GRINTHARAPE, reverse split cotton hair nose shrimp txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.72it/s] 6%|▌ | 3/50 [00:00<00:12, 3.72it/s] 8%|▊ | 4/50 [00:01<00:12, 3.72it/s] 10%|█ | 5/50 [00:01<00:12, 3.72it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.71it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.71it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.71it/s] 20%|██ | 10/50 [00:02<00:10, 3.71it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s] 24%|██▍ | 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