cloneofsimo / fad_v0_lora
LoRA, fp16 Foto-Assisted-Diffusion-FAD_V0
- Public
- 7.4K runs
-
A100 (80GB)
Prediction
cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01cIDo7xfr5yeejfjxl6zld3qa3vftyStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- "768"
- prompt
- close up of <1> centered, behind the ocean, film grain
- lora_urls
- https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.5
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "width": 512, "height": "768", "prompt": "close up of <1> centered, behind the ocean, film grain", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": 1, "guidance_scale": 7.5, "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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", { input: { width: 512, height: "768", prompt: "close up of <1> centered, behind the ocean, film grain", lora_urls: "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.5", num_outputs: 1, guidance_scale: 7.5, 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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", input={ "width": 512, "height": "768", "prompt": "close up of <1> centered, behind the ocean, film grain", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": 1, "guidance_scale": 7.5, "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 cloneofsimo/fad_v0_lora 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": "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", "input": { "width": 512, "height": "768", "prompt": "close up of <1> centered, behind the ocean, film grain", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-02-12T07:02:36.900032Z", "created_at": "2023-02-12T06:59:47.180957Z", "data_removed": false, "error": null, "id": "o7xfr5yeejfjxl6zld3qa3vfty", "input": { "width": 512, "height": "768", "prompt": "close up of <1> centered, behind the ocean, film grain", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 29305\nUsing disk cache...\nEmbedding <s1> replaced to <s0-0>\nEmbedding <s2> replaced to <s0-1>\n<s0-0>\n<s0-1>\nmerging time: 2.493532180786133\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<01:21, 1.65s/it]\n 4%|▍ | 2/50 [00:01<00:35, 1.34it/s]\n 6%|▌ | 3/50 [00:01<00:21, 2.18it/s]\n 8%|▊ | 4/50 [00:01<00:14, 3.09it/s]\n 10%|█ | 5/50 [00:02<00:11, 4.03it/s]\n 12%|█▏ | 6/50 [00:02<00:08, 4.92it/s]\n 14%|█▍ | 7/50 [00:02<00:07, 5.74it/s]\n 16%|█▌ | 8/50 [00:02<00:06, 6.36it/s]\n 18%|█▊ | 9/50 [00:02<00:05, 6.95it/s]\n 20%|██ | 10/50 [00:02<00:05, 7.43it/s]\n 22%|██▏ | 11/50 [00:02<00:05, 7.76it/s]\n 24%|██▍ | 12/50 [00:02<00:04, 8.06it/s]\n 26%|██▌ | 13/50 [00:03<00:04, 8.21it/s]\n 28%|██▊ | 14/50 [00:03<00:04, 8.39it/s]\n 30%|███ | 15/50 [00:03<00:04, 8.52it/s]\n 32%|███▏ | 16/50 [00:03<00:03, 8.61it/s]\n 34%|███▍ | 17/50 [00:03<00:03, 8.52it/s]\n 36%|███▌ | 18/50 [00:03<00:03, 8.62it/s]\n 38%|███▊ | 19/50 [00:03<00:03, 8.62it/s]\n 40%|████ | 20/50 [00:03<00:03, 8.68it/s]\n 42%|████▏ | 21/50 [00:03<00:03, 8.74it/s]\n 44%|████▍ | 22/50 [00:04<00:03, 8.71it/s]\n 46%|████▌ | 23/50 [00:04<00:03, 8.73it/s]\n 48%|████▊ | 24/50 [00:04<00:02, 8.80it/s]\n 50%|█████ | 25/50 [00:04<00:02, 8.88it/s]\n 52%|█████▏ | 26/50 [00:04<00:02, 8.95it/s]\n 54%|█████▍ | 27/50 [00:04<00:02, 8.89it/s]\n 56%|█████▌ | 28/50 [00:04<00:02, 8.83it/s]\n 58%|█████▊ | 29/50 [00:04<00:02, 8.87it/s]\n 60%|██████ | 30/50 [00:04<00:02, 8.91it/s]\n 62%|██████▏ | 31/50 [00:05<00:02, 8.93it/s]\n 64%|██████▍ | 32/50 [00:05<00:02, 9.00it/s]\n 66%|██████▌ | 33/50 [00:05<00:01, 9.15it/s]\n 68%|██████▊ | 34/50 [00:05<00:01, 9.36it/s]\n 70%|███████ | 35/50 [00:05<00:01, 9.48it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 9.57it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 9.63it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 9.66it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 9.69it/s]\n 80%|████████ | 40/50 [00:06<00:01, 9.68it/s]\n 82%|████████▏ | 41/50 [00:06<00:00, 9.72it/s]\n 84%|████████▍ | 42/50 [00:06<00:00, 9.71it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 9.69it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 9.71it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 9.69it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 9.71it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 9.68it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 9.52it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 9.56it/s]\n100%|██████████| 50/50 [00:07<00:00, 9.54it/s]\n100%|██████████| 50/50 [00:07<00:00, 7.09it/s]", "metrics": { "predict_time": 11.419015, "total_time": 169.719075 }, "output": [ "https://replicate.delivery/pbxt/sLXiqkwIYArNBRKKDninPTpqiq0urCoxr7GuzEajDhPDBZHE/out-0.png" ], "started_at": "2023-02-12T07:02:25.481017Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/o7xfr5yeejfjxl6zld3qa3vfty", "cancel": "https://api.replicate.com/v1/predictions/o7xfr5yeejfjxl6zld3qa3vfty/cancel" }, "version": "694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c" }Generated inUsing seed: 29305 Using disk cache... Embedding <s1> replaced to <s0-0> Embedding <s2> replaced to <s0-1> <s0-0> <s0-1> merging time: 2.493532180786133 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<01:21, 1.65s/it] 4%|▍ | 2/50 [00:01<00:35, 1.34it/s] 6%|▌ | 3/50 [00:01<00:21, 2.18it/s] 8%|▊ | 4/50 [00:01<00:14, 3.09it/s] 10%|█ | 5/50 [00:02<00:11, 4.03it/s] 12%|█▏ | 6/50 [00:02<00:08, 4.92it/s] 14%|█▍ | 7/50 [00:02<00:07, 5.74it/s] 16%|█▌ | 8/50 [00:02<00:06, 6.36it/s] 18%|█▊ | 9/50 [00:02<00:05, 6.95it/s] 20%|██ | 10/50 [00:02<00:05, 7.43it/s] 22%|██▏ | 11/50 [00:02<00:05, 7.76it/s] 24%|██▍ | 12/50 [00:02<00:04, 8.06it/s] 26%|██▌ | 13/50 [00:03<00:04, 8.21it/s] 28%|██▊ | 14/50 [00:03<00:04, 8.39it/s] 30%|███ | 15/50 [00:03<00:04, 8.52it/s] 32%|███▏ | 16/50 [00:03<00:03, 8.61it/s] 34%|███▍ | 17/50 [00:03<00:03, 8.52it/s] 36%|███▌ | 18/50 [00:03<00:03, 8.62it/s] 38%|███▊ | 19/50 [00:03<00:03, 8.62it/s] 40%|████ | 20/50 [00:03<00:03, 8.68it/s] 42%|████▏ | 21/50 [00:03<00:03, 8.74it/s] 44%|████▍ | 22/50 [00:04<00:03, 8.71it/s] 46%|████▌ | 23/50 [00:04<00:03, 8.73it/s] 48%|████▊ | 24/50 [00:04<00:02, 8.80it/s] 50%|█████ | 25/50 [00:04<00:02, 8.88it/s] 52%|█████▏ | 26/50 [00:04<00:02, 8.95it/s] 54%|█████▍ | 27/50 [00:04<00:02, 8.89it/s] 56%|█████▌ | 28/50 [00:04<00:02, 8.83it/s] 58%|█████▊ | 29/50 [00:04<00:02, 8.87it/s] 60%|██████ | 30/50 [00:04<00:02, 8.91it/s] 62%|██████▏ | 31/50 [00:05<00:02, 8.93it/s] 64%|██████▍ | 32/50 [00:05<00:02, 9.00it/s] 66%|██████▌ | 33/50 [00:05<00:01, 9.15it/s] 68%|██████▊ | 34/50 [00:05<00:01, 9.36it/s] 70%|███████ | 35/50 [00:05<00:01, 9.48it/s] 72%|███████▏ | 36/50 [00:05<00:01, 9.57it/s] 74%|███████▍ | 37/50 [00:05<00:01, 9.63it/s] 76%|███████▌ | 38/50 [00:05<00:01, 9.66it/s] 78%|███████▊ | 39/50 [00:05<00:01, 9.69it/s] 80%|████████ | 40/50 [00:06<00:01, 9.68it/s] 82%|████████▏ | 41/50 [00:06<00:00, 9.72it/s] 84%|████████▍ | 42/50 [00:06<00:00, 9.71it/s] 86%|████████▌ | 43/50 [00:06<00:00, 9.69it/s] 88%|████████▊ | 44/50 [00:06<00:00, 9.71it/s] 90%|█████████ | 45/50 [00:06<00:00, 9.69it/s] 92%|█████████▏| 46/50 [00:06<00:00, 9.71it/s] 94%|█████████▍| 47/50 [00:06<00:00, 9.68it/s] 96%|█████████▌| 48/50 [00:06<00:00, 9.52it/s] 98%|█████████▊| 49/50 [00:06<00:00, 9.56it/s] 100%|██████████| 50/50 [00:07<00:00, 9.54it/s] 100%|██████████| 50/50 [00:07<00:00, 7.09it/s]Prediction
cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01cIDbkjdfiqopbctxjvnvvx3gjuqvaStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- "768"
- prompt
- Portrait of <1>, photoreal, hyper detailed, steampunk, epic composition, high detailed skin, film grain, Fujifilm XT3
- lora_urls
- https://storage.googleapis.com/replicant-misc/lora/lora_krk.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.8
- num_outputs
- 1
- guidance_scale
- "4.0"
- negative_prompt
- 3d, game character
- num_inference_steps
- 50
{ "width": 512, "height": "768", "prompt": "Portrait of <1>, photoreal, hyper detailed, steampunk, epic composition, high detailed skin, film grain, Fujifilm XT3", "lora_urls": "https://storage.googleapis.com/replicant-misc/lora/lora_krk.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.8", "num_outputs": 1, "guidance_scale": "4.0", "negative_prompt": "3d, game character", "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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", { input: { width: 512, height: "768", prompt: "Portrait of <1>, photoreal, hyper detailed, steampunk, epic composition, high detailed skin, film grain, Fujifilm XT3", lora_urls: "https://storage.googleapis.com/replicant-misc/lora/lora_krk.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.8", num_outputs: 1, guidance_scale: "4.0", negative_prompt: "3d, game character", 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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", input={ "width": 512, "height": "768", "prompt": "Portrait of <1>, photoreal, hyper detailed, steampunk, epic composition, high detailed skin, film grain, Fujifilm XT3", "lora_urls": "https://storage.googleapis.com/replicant-misc/lora/lora_krk.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.8", "num_outputs": 1, "guidance_scale": "4.0", "negative_prompt": "3d, game character", "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 cloneofsimo/fad_v0_lora 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": "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", "input": { "width": 512, "height": "768", "prompt": "Portrait of <1>, photoreal, hyper detailed, steampunk, epic composition, high detailed skin, film grain, Fujifilm XT3", "lora_urls": "https://storage.googleapis.com/replicant-misc/lora/lora_krk.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.8", "num_outputs": 1, "guidance_scale": "4.0", "negative_prompt": "3d, game character", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-02-12T07:07:16.871688Z", "created_at": "2023-02-12T07:07:10.504532Z", "data_removed": false, "error": null, "id": "bkjdfiqopbctxjvnvvx3gjuqva", "input": { "width": 512, "height": "768", "prompt": "Portrait of <1>, photoreal, hyper detailed, steampunk, epic composition, high detailed skin, film grain, Fujifilm XT3", "lora_urls": "https://storage.googleapis.com/replicant-misc/lora/lora_krk.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.8", "num_outputs": 1, "guidance_scale": "4.0", "negative_prompt": "3d, game character", "num_inference_steps": 50 }, "logs": "Using seed: 52648\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 9.14it/s]\n 4%|▍ | 2/50 [00:00<00:05, 9.26it/s]\n 6%|▌ | 3/50 [00:00<00:04, 9.46it/s]\n 8%|▊ | 4/50 [00:00<00:04, 9.51it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.50it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.56it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.58it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.59it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.62it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.52it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.49it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 9.53it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 9.58it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.59it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.57it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.58it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.62it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.62it/s]\n 38%|███▊ | 19/50 [00:01<00:03, 9.53it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.58it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 9.46it/s]\n 44%|████▍ | 22/50 [00:02<00:02, 9.56it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.63it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.66it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.66it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.60it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.63it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 9.65it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.56it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.56it/s]\n 62%|██████▏ | 31/50 [00:03<00:01, 9.55it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.58it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.59it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.60it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.50it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.58it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 9.59it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 9.59it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.58it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.37it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.44it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.50it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.50it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.50it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.47it/s]\n 92%|█████████▏| 46/50 [00:04<00:00, 9.47it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 9.51it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 9.55it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.56it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.46it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.54it/s]", "metrics": { "predict_time": 6.309238, "total_time": 6.367156 }, "output": [ "https://replicate.delivery/pbxt/8ICUi4hTWVqCDR3gJxvqa1McJX2Z9aZcWHO58FfnRuXSEyOIA/out-0.png" ], "started_at": "2023-02-12T07:07:10.562450Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bkjdfiqopbctxjvnvvx3gjuqva", "cancel": "https://api.replicate.com/v1/predictions/bkjdfiqopbctxjvnvvx3gjuqva/cancel" }, "version": "694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c" }Generated inUsing seed: 52648 The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:05, 9.14it/s] 4%|▍ | 2/50 [00:00<00:05, 9.26it/s] 6%|▌ | 3/50 [00:00<00:04, 9.46it/s] 8%|▊ | 4/50 [00:00<00:04, 9.51it/s] 10%|█ | 5/50 [00:00<00:04, 9.50it/s] 12%|█▏ | 6/50 [00:00<00:04, 9.56it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.58it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.59it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.62it/s] 20%|██ | 10/50 [00:01<00:04, 9.52it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.49it/s] 24%|██▍ | 12/50 [00:01<00:03, 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cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01cInput
- width
- 512
- height
- "768"
- prompt
- a man riding his bike down the city streets of japan, magic hour
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- "4.0"
- num_inference_steps
- 50
{ "width": 512, "height": "768", "prompt": "a man riding his bike down the city streets of japan, magic hour", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": "4.0", "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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", { input: { width: 512, height: "768", prompt: "a man riding his bike down the city streets of japan, magic hour", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: "4.0", 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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", input={ "width": 512, "height": "768", "prompt": "a man riding his bike down the city streets of japan, magic hour", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": "4.0", "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 cloneofsimo/fad_v0_lora 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": "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", "input": { "width": 512, "height": "768", "prompt": "a man riding his bike down the city streets of japan, magic hour", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": "4.0", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-02-12T07:08:51.143538Z", "created_at": "2023-02-12T07:08:44.719268Z", "data_removed": false, "error": null, "id": "bpqodotkorgtbf5qvmimpjfnzi", "input": { "width": 512, "height": "768", "prompt": "a man riding his bike down the city streets of japan, magic hour", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": "4.0", "num_inference_steps": 50 }, "logs": "Using seed: 39270\nNo LoRA models provided, using default model...\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.46it/s]\n 8%|▊ | 4/50 [00:00<00:03, 13.06it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 13.45it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 13.64it/s]\n 20%|██ | 10/50 [00:00<00:02, 13.69it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 13.71it/s]\n 28%|██▊ | 14/50 [00:01<00:02, 13.74it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 13.82it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 13.77it/s]\n 40%|████ | 20/50 [00:01<00:02, 13.78it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 13.79it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 13.86it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 13.87it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 13.86it/s]\n 60%|██████ | 30/50 [00:02<00:01, 13.82it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 13.77it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 13.67it/s]\n 72%|███████▏ | 36/50 [00:02<00:01, 13.65it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 13.74it/s]\n 80%|████████ | 40/50 [00:02<00:00, 13.76it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 13.75it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 13.72it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 13.67it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 13.71it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.65it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.70it/s]", "metrics": { "predict_time": 6.365065, "total_time": 6.42427 }, "output": [ "https://replicate.delivery/pbxt/fSaTPTVgwDwGDie7QFVjjlHQ1ryOu5T8uUuwGqSZ6FPCKkdQA/out-0.png" ], "started_at": "2023-02-12T07:08:44.778473Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bpqodotkorgtbf5qvmimpjfnzi", "cancel": "https://api.replicate.com/v1/predictions/bpqodotkorgtbf5qvmimpjfnzi/cancel" }, "version": "694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c" }Generated inUsing seed: 39270 No LoRA models provided, using default model... 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.46it/s] 8%|▊ | 4/50 [00:00<00:03, 13.06it/s] 12%|█▏ | 6/50 [00:00<00:03, 13.45it/s] 16%|█▌ | 8/50 [00:00<00:03, 13.64it/s] 20%|██ | 10/50 [00:00<00:02, 13.69it/s] 24%|██▍ | 12/50 [00:00<00:02, 13.71it/s] 28%|██▊ | 14/50 [00:01<00:02, 13.74it/s] 32%|███▏ | 16/50 [00:01<00:02, 13.82it/s] 36%|███▌ | 18/50 [00:01<00:02, 13.77it/s] 40%|████ | 20/50 [00:01<00:02, 13.78it/s] 44%|████▍ | 22/50 [00:01<00:02, 13.79it/s] 48%|████▊ | 24/50 [00:01<00:01, 13.86it/s] 52%|█████▏ | 26/50 [00:01<00:01, 13.87it/s] 56%|█████▌ | 28/50 [00:02<00:01, 13.86it/s] 60%|██████ | 30/50 [00:02<00:01, 13.82it/s] 64%|██████▍ | 32/50 [00:02<00:01, 13.77it/s] 68%|██████▊ | 34/50 [00:02<00:01, 13.67it/s] 72%|███████▏ | 36/50 [00:02<00:01, 13.65it/s] 76%|███████▌ | 38/50 [00:02<00:00, 13.74it/s] 80%|████████ | 40/50 [00:02<00:00, 13.76it/s] 84%|████████▍ | 42/50 [00:03<00:00, 13.75it/s] 88%|████████▊ | 44/50 [00:03<00:00, 13.72it/s] 92%|█████████▏| 46/50 [00:03<00:00, 13.67it/s] 96%|█████████▌| 48/50 [00:03<00:00, 13.71it/s] 100%|██████████| 50/50 [00:03<00:00, 13.65it/s] 100%|██████████| 50/50 [00:03<00:00, 13.70it/s]Prediction
cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01cIDajcnke2msfgrjohhkxzgxr2ghuStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- "512"
- prompt
- a cat walking by the ocean, blue sky, vast clouds, cute tail
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.5
- num_outputs
- 1
- guidance_scale
- "4.0"
- num_inference_steps
- 50
{ "width": 512, "height": "512", "prompt": "a cat walking by the ocean, blue sky, vast clouds, cute tail", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": 1, "guidance_scale": "4.0", "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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", { input: { width: 512, height: "512", prompt: "a cat walking by the ocean, blue sky, vast clouds, cute tail", scheduler: "DPMSolverMultistep", lora_scales: "0.5", num_outputs: 1, guidance_scale: "4.0", 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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", input={ "width": 512, "height": "512", "prompt": "a cat walking by the ocean, blue sky, vast clouds, cute tail", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": 1, "guidance_scale": "4.0", "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 cloneofsimo/fad_v0_lora 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": "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", "input": { "width": 512, "height": "512", "prompt": "a cat walking by the ocean, blue sky, vast clouds, cute tail", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": 1, "guidance_scale": "4.0", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-02-12T07:12:21.443986Z", "created_at": "2023-02-12T07:12:17.322138Z", "data_removed": false, "error": null, "id": "ajcnke2msfgrjohhkxzgxr2ghu", "input": { "width": 512, "height": "512", "prompt": "a cat walking by the ocean, blue sky, vast clouds, cute tail", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": 1, "guidance_scale": "4.0", "num_inference_steps": 50 }, "logs": "Using seed: 10746\nNo LoRA models provided, using default model...\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 15.24it/s]\n 8%|▊ | 4/50 [00:00<00:02, 15.65it/s]\n 12%|█▏ | 6/50 [00:00<00:02, 15.66it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 15.74it/s]\n 20%|██ | 10/50 [00:00<00:02, 15.63it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 15.84it/s]\n 28%|██▊ | 14/50 [00:00<00:02, 15.98it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 16.12it/s]\n 36%|███▌ | 18/50 [00:01<00:01, 16.07it/s]\n 40%|████ | 20/50 [00:01<00:01, 15.84it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 15.75it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 15.82it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 15.79it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 15.82it/s]\n 60%|██████ | 30/50 [00:01<00:01, 15.92it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 16.03it/s]\n 68%|██████▊ | 34/50 [00:02<00:00, 16.07it/s]\n 72%|███████▏ | 36/50 [00:02<00:00, 15.79it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 15.69it/s]\n 80%|████████ | 40/50 [00:02<00:00, 15.72it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 15.70it/s]\n 88%|████████▊ | 44/50 [00:02<00:00, 15.86it/s]\n 92%|█████████▏| 46/50 [00:02<00:00, 15.74it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 15.88it/s]\n100%|██████████| 50/50 [00:03<00:00, 16.02it/s]\n100%|██████████| 50/50 [00:03<00:00, 15.85it/s]", "metrics": { "predict_time": 4.061794, "total_time": 4.121848 }, "output": [ "https://replicate.delivery/pbxt/fB9PeStaI1oOJEF5j7JXf5XlGKZL5nV4432JRbq3RqJpaI7gA/out-0.png" ], "started_at": "2023-02-12T07:12:17.382192Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ajcnke2msfgrjohhkxzgxr2ghu", "cancel": "https://api.replicate.com/v1/predictions/ajcnke2msfgrjohhkxzgxr2ghu/cancel" }, "version": "694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c" }Generated inUsing seed: 10746 No LoRA models provided, using default model... 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 15.24it/s] 8%|▊ | 4/50 [00:00<00:02, 15.65it/s] 12%|█▏ | 6/50 [00:00<00:02, 15.66it/s] 16%|█▌ | 8/50 [00:00<00:02, 15.74it/s] 20%|██ | 10/50 [00:00<00:02, 15.63it/s] 24%|██▍ | 12/50 [00:00<00:02, 15.84it/s] 28%|██▊ | 14/50 [00:00<00:02, 15.98it/s] 32%|███▏ | 16/50 [00:01<00:02, 16.12it/s] 36%|███▌ | 18/50 [00:01<00:01, 16.07it/s] 40%|████ | 20/50 [00:01<00:01, 15.84it/s] 44%|████▍ | 22/50 [00:01<00:01, 15.75it/s] 48%|████▊ | 24/50 [00:01<00:01, 15.82it/s] 52%|█████▏ | 26/50 [00:01<00:01, 15.79it/s] 56%|█████▌ | 28/50 [00:01<00:01, 15.82it/s] 60%|██████ | 30/50 [00:01<00:01, 15.92it/s] 64%|██████▍ | 32/50 [00:02<00:01, 16.03it/s] 68%|██████▊ | 34/50 [00:02<00:00, 16.07it/s] 72%|███████▏ | 36/50 [00:02<00:00, 15.79it/s] 76%|███████▌ | 38/50 [00:02<00:00, 15.69it/s] 80%|████████ | 40/50 [00:02<00:00, 15.72it/s] 84%|████████▍ | 42/50 [00:02<00:00, 15.70it/s] 88%|████████▊ | 44/50 [00:02<00:00, 15.86it/s] 92%|█████████▏| 46/50 [00:02<00:00, 15.74it/s] 96%|█████████▌| 48/50 [00:03<00:00, 15.88it/s] 100%|██████████| 50/50 [00:03<00:00, 16.02it/s] 100%|██████████| 50/50 [00:03<00:00, 15.85it/s]Prediction
cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01cIDfyznqyv7drderifxhxefyg6lfeStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- "768"
- prompt
- close up portrait of <1>, photo 8k uhd, dslr
- lora_urls
- https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.8
- num_outputs
- 1
- guidance_scale
- "4.0"
- num_inference_steps
- 50
{ "width": 512, "height": "768", "prompt": "close up portrait of <1>, photo 8k uhd, dslr", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.8", "num_outputs": 1, "guidance_scale": "4.0", "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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", { input: { width: 512, height: "768", prompt: "close up portrait of <1>, photo 8k uhd, dslr", lora_urls: "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.8", num_outputs: 1, guidance_scale: "4.0", 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 cloneofsimo/fad_v0_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", input={ "width": 512, "height": "768", "prompt": "close up portrait of <1>, photo 8k uhd, dslr", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.8", "num_outputs": 1, "guidance_scale": "4.0", "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 cloneofsimo/fad_v0_lora 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": "cloneofsimo/fad_v0_lora:694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c", "input": { "width": 512, "height": "768", "prompt": "close up portrait of <1>, photo 8k uhd, dslr", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.8", "num_outputs": 1, "guidance_scale": "4.0", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-02-12T07:14:36.385874Z", "created_at": "2023-02-12T07:14:30.028043Z", "data_removed": false, "error": null, "id": "fyznqyv7drderifxhxefyg6lfe", "input": { "width": 512, "height": "768", "prompt": "close up portrait of <1>, photo 8k uhd, dslr", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.8", "num_outputs": 1, "guidance_scale": "4.0", "num_inference_steps": 50 }, "logs": "Using seed: 3832\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 8.88it/s]\n 4%|▍ | 2/50 [00:00<00:05, 9.14it/s]\n 6%|▌ | 3/50 [00:00<00:05, 9.34it/s]\n 8%|▊ | 4/50 [00:00<00:04, 9.47it/s]\n 10%|█ | 5/50 [00:00<00:04, 9.51it/s]\n 12%|█▏ | 6/50 [00:00<00:04, 9.55it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 9.60it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 9.60it/s]\n 18%|█▊ | 9/50 [00:00<00:04, 9.28it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.36it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.41it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 9.39it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 9.45it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.53it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.57it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.51it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.60it/s]\n 36%|███▌ | 18/50 [00:01<00:03, 9.65it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 9.65it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.66it/s]\n 42%|████▏ | 21/50 [00:02<00:02, 9.67it/s]\n 44%|████▍ | 22/50 [00:02<00:02, 9.67it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.66it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.67it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.63it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.64it/s]\n 54%|█████▍ | 27/50 [00:02<00:02, 9.66it/s]\n 56%|█████▌ | 28/50 [00:02<00:02, 9.68it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.69it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.69it/s]\n 62%|██████▏ | 31/50 [00:03<00:01, 9.61it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.54it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.60it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.64it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.52it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 9.57it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 9.62it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 9.65it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.67it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.66it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.70it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.69it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.66it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.62it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 9.58it/s]\n 92%|█████████▏| 46/50 [00:04<00:00, 9.62it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 9.63it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 9.65it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.67it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.66it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.58it/s]", "metrics": { "predict_time": 6.297665, "total_time": 6.357831 }, "output": [ "https://replicate.delivery/pbxt/QyPSDSWF96YjHJCLxlEnebb6UtegopRo1jVhKSBRe7I2eQ2BB/out-0.png" ], "started_at": "2023-02-12T07:14:30.088209Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fyznqyv7drderifxhxefyg6lfe", "cancel": "https://api.replicate.com/v1/predictions/fyznqyv7drderifxhxefyg6lfe/cancel" }, "version": "694fa248ecf0f4de0c138b6e2267f2a15c51185463e84734962f7620502bb01c" }Generated inUsing seed: 3832 The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:05, 8.88it/s] 4%|▍ | 2/50 [00:00<00:05, 9.14it/s] 6%|▌ | 3/50 [00:00<00:05, 9.34it/s] 8%|▊ | 4/50 [00:00<00:04, 9.47it/s] 10%|█ | 5/50 [00:00<00:04, 9.51it/s] 12%|█▏ | 6/50 [00:00<00:04, 9.55it/s] 14%|█▍ | 7/50 [00:00<00:04, 9.60it/s] 16%|█▌ | 8/50 [00:00<00:04, 9.60it/s] 18%|█▊ | 9/50 [00:00<00:04, 9.28it/s] 20%|██ | 10/50 [00:01<00:04, 9.36it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.41it/s] 24%|██▍ | 12/50 [00:01<00:04, 9.39it/s] 26%|██▌ | 13/50 [00:01<00:03, 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