cloneofsimo
/
analog_diffusion_lora
- Public
- 5.1K runs
-
T4
Prediction
cloneofsimo/analog_diffusion_lora:cfda4034IDjgow3glngvexlpucx6zy6tunsaStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- analog style closeup portrait of <1> cowboy hat
- lora_urls
- https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.6
- num_outputs
- 1
- adapter_type
- sketch
- guidance_scale
- "6.5"
- negative_prompt
- blur, haze
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "analog style closeup portrait of <1> cowboy hat", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "sketch", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/analog_diffusion_lora:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", { input: { width: 512, height: 512, prompt: "analog style closeup portrait of <1> cowboy hat", lora_urls: "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.6", num_outputs: 1, adapter_type: "sketch", guidance_scale: "6.5", negative_prompt: "blur, haze", prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/analog_diffusion_lora:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", input={ "width": 512, "height": 512, "prompt": "analog style closeup portrait of <1> cowboy hat", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "sketch", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run cloneofsimo/analog_diffusion_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": "cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", "input": { "width": 512, "height": 512, "prompt": "analog style closeup portrait of <1> cowboy hat", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "sketch", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "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/cloneofsimo/analog_diffusion_lora@sha256:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6 \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="analog style closeup portrait of <1> cowboy hat"' \ -i 'lora_urls="https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors"' \ -i 'scheduler="DPMSolverMultistep"' \ -i 'lora_scales="0.6"' \ -i 'num_outputs=1' \ -i 'adapter_type="sketch"' \ -i 'guidance_scale="6.5"' \ -i 'negative_prompt="blur, haze"' \ -i 'prompt_strength=0.8' \ -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/cloneofsimo/analog_diffusion_lora@sha256:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "analog style closeup portrait of <1> cowboy hat", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "sketch", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-03-01T16:54:23.542761Z", "created_at": "2023-03-01T16:54:12.115799Z", "data_removed": false, "error": null, "id": "jgow3glngvexlpucx6zy6tunsa", "input": { "width": 512, "height": 512, "prompt": "analog style closeup portrait of <1> cowboy hat", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "sketch", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 16285\nGenerating image of 512 x 512 with prompt: analog style closeup portrait of <1> cowboy hat\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.94it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.41it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.65it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.78it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.85it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.86it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.88it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.90it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.92it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.93it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 4.93it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.94it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.93it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 4.94it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.95it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.95it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.94it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.93it/s]\n 38%|███▊ | 19/50 [00:03<00:06, 4.94it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.95it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.94it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.94it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.93it/s]\n 48%|████▊ | 24/50 [00:04<00:05, 4.94it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.93it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.93it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.93it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 4.92it/s]\n 58%|█████▊ | 29/50 [00:05<00:04, 4.92it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.92it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.92it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.91it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 4.91it/s]\n 68%|██████▊ | 34/50 [00:06<00:03, 4.89it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.88it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.86it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.88it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 4.89it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 4.89it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.90it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.90it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.90it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 4.89it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 4.89it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.87it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.85it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.87it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.87it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.88it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.86it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.89it/s]", "metrics": { "predict_time": 11.342725, "total_time": 11.426962 }, "output": [ "https://replicate.delivery/pbxt/entxiWe9NlvZ9Ea8OayJ4vgBDjGEccav699HzP9hrlqepmGhA/out-0.png" ], "started_at": "2023-03-01T16:54:12.200036Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jgow3glngvexlpucx6zy6tunsa", "cancel": "https://api.replicate.com/v1/predictions/jgow3glngvexlpucx6zy6tunsa/cancel" }, "version": "b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87" }Generated inUsing seed: 16285 Generating image of 512 x 512 with prompt: analog style closeup portrait of <1> cowboy hat The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:12, 3.94it/s] 4%|▍ | 2/50 [00:00<00:10, 4.41it/s] 6%|▌ | 3/50 [00:00<00:10, 4.65it/s] 8%|▊ | 4/50 [00:00<00:09, 4.78it/s] 10%|█ | 5/50 [00:01<00:09, 4.85it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.86it/s] 14%|█▍ | 7/50 [00:01<00:08, 4.88it/s] 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cloneofsimo/analog_diffusion_lora:cfda4034IDnhbqzwzbrzd6xdyz343o57pkwaStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- "448"
- height
- "640"
- prompt
- analog style closeup portrait of <1> cowboy hat, sitting on a desk
- lora_urls
- https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.6
- num_outputs
- 1
- adapter_type
- depth
- guidance_scale
- "6.5"
- negative_prompt
- blur, haze
- prompt_strength
- 0.8
- num_inference_steps
- 50
- adapter_condition_image
{ "width": "448", "height": "640", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png" }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/analog_diffusion_lora:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", { input: { width: "448", height: "640", prompt: "analog style closeup portrait of <1> cowboy hat, sitting on a desk", lora_urls: "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.6", num_outputs: 1, adapter_type: "depth", guidance_scale: "6.5", negative_prompt: "blur, haze", prompt_strength: 0.8, num_inference_steps: 50, adapter_condition_image: "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/analog_diffusion_lora:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", input={ "width": "448", "height": "640", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run cloneofsimo/analog_diffusion_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": "cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", "input": { "width": "448", "height": "640", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png" } }' \ 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/cloneofsimo/analog_diffusion_lora@sha256:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6 \ -i 'width="448"' \ -i 'height="640"' \ -i 'prompt="analog style closeup portrait of <1> cowboy hat, sitting on a desk"' \ -i 'lora_urls="https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors"' \ -i 'scheduler="DPMSolverMultistep"' \ -i 'lora_scales="0.6"' \ -i 'num_outputs=1' \ -i 'adapter_type="depth"' \ -i 'guidance_scale="6.5"' \ -i 'negative_prompt="blur, haze"' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50' \ -i 'adapter_condition_image="https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png"'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/cloneofsimo/analog_diffusion_lora@sha256:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": "448", "height": "640", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-03-01T16:56:37.124433Z", "created_at": "2023-03-01T16:56:22.601102Z", "data_removed": false, "error": null, "id": "nhbqzwzbrzd6xdyz343o57pkwa", "input": { "width": "448", "height": "640", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png" }, "logs": "Using seed: 18066\nGenerating image of 448 x 640 with prompt: analog style closeup portrait of <1> cowboy hat, sitting on a desk\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 3.01it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.52it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.71it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.81it/s]\n 10%|█ | 5/50 [00:01<00:11, 3.86it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.89it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 3.93it/s]\n 16%|█▌ | 8/50 [00:02<00:10, 3.94it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 3.94it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.96it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 3.96it/s]\n 24%|██▍ | 12/50 [00:03<00:09, 3.96it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.94it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.96it/s]\n 30%|███ | 15/50 [00:03<00:08, 3.96it/s]\n 32%|███▏ | 16/50 [00:04<00:08, 3.96it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.94it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.96it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 3.94it/s]\n 40%|████ | 20/50 [00:05<00:07, 3.97it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.95it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.96it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 3.94it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.96it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.94it/s]\n 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[00:11<00:00, 3.92it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.93it/s]\n 98%|█████████▊| 49/50 [00:12<00:00, 3.92it/s]\n100%|██████████| 50/50 [00:12<00:00, 3.92it/s]\n100%|██████████| 50/50 [00:12<00:00, 3.92it/s]", "metrics": { "predict_time": 14.441374, "total_time": 14.523331 }, "output": [ "https://replicate.delivery/pbxt/yF7aZDG41n5YEFf9eDYgDS6HULdvT4l1XtUIi1AjetfScNNCB/out-0.png" ], "started_at": "2023-03-01T16:56:22.683059Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nhbqzwzbrzd6xdyz343o57pkwa", "cancel": "https://api.replicate.com/v1/predictions/nhbqzwzbrzd6xdyz343o57pkwa/cancel" }, "version": "b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87" }Generated inUsing seed: 18066 Generating image of 448 x 640 with prompt: analog style closeup portrait of <1> cowboy hat, sitting on a desk The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 3.01it/s] 4%|▍ | 2/50 [00:00<00:13, 3.52it/s] 6%|▌ | 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100%|██████████| 50/50 [00:12<00:00, 3.92it/s] 100%|██████████| 50/50 [00:12<00:00, 3.92it/s]Prediction
cloneofsimo/analog_diffusion_lora:cfda4034ID4hr4kzxhdrdnnp2thvj6566tmaStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- "512"
- height
- "512"
- prompt
- analog style closeup portrait of <1> cowboy hat, sitting on a desk
- lora_urls
- https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.6
- num_outputs
- 1
- adapter_type
- keypose
- guidance_scale
- "6.5"
- negative_prompt
- blur, haze
- prompt_strength
- 0.8
- num_inference_steps
- 50
- adapter_condition_image
{ "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "keypose", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpY59R7PD69SmGca7FB1999vQghLGDNBY1yT9nQs8IJXO7I/keypose_0%20copy%20-%20Copy.png" }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/analog_diffusion_lora:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", { input: { width: "512", height: "512", prompt: "analog style closeup portrait of <1> cowboy hat, sitting on a desk", lora_urls: "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.6", num_outputs: 1, adapter_type: "keypose", guidance_scale: "6.5", negative_prompt: "blur, haze", prompt_strength: 0.8, num_inference_steps: 50, adapter_condition_image: "https://replicate.delivery/pbxt/IOpY59R7PD69SmGca7FB1999vQghLGDNBY1yT9nQs8IJXO7I/keypose_0%20copy%20-%20Copy.png" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/analog_diffusion_lora:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", input={ "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "keypose", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpY59R7PD69SmGca7FB1999vQghLGDNBY1yT9nQs8IJXO7I/keypose_0%20copy%20-%20Copy.png" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run cloneofsimo/analog_diffusion_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": "cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", "input": { "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "keypose", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpY59R7PD69SmGca7FB1999vQghLGDNBY1yT9nQs8IJXO7I/keypose_0%20copy%20-%20Copy.png" } }' \ 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/cloneofsimo/analog_diffusion_lora@sha256:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6 \ -i 'width="512"' \ -i 'height="512"' \ -i 'prompt="analog style closeup portrait of <1> cowboy hat, sitting on a desk"' \ -i 'lora_urls="https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors"' \ -i 'scheduler="DPMSolverMultistep"' \ -i 'lora_scales="0.6"' \ -i 'num_outputs=1' \ -i 'adapter_type="keypose"' \ -i 'guidance_scale="6.5"' \ -i 'negative_prompt="blur, haze"' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50' \ -i 'adapter_condition_image="https://replicate.delivery/pbxt/IOpY59R7PD69SmGca7FB1999vQghLGDNBY1yT9nQs8IJXO7I/keypose_0%20copy%20-%20Copy.png"'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/cloneofsimo/analog_diffusion_lora@sha256:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "keypose", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpY59R7PD69SmGca7FB1999vQghLGDNBY1yT9nQs8IJXO7I/keypose_0%20copy%20-%20Copy.png" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-03-01T17:00:58.841080Z", "created_at": "2023-03-01T17:00:46.783148Z", "data_removed": false, "error": null, "id": "4hr4kzxhdrdnnp2thvj6566tma", "input": { "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "keypose", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpY59R7PD69SmGca7FB1999vQghLGDNBY1yT9nQs8IJXO7I/keypose_0%20copy%20-%20Copy.png" }, "logs": "Using seed: 36153\nGenerating image of 512 x 512 with prompt: analog style closeup portrait of <1> cowboy hat, sitting on a desk\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 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4.78it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.79it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.79it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.77it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.79it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.82it/s]", "metrics": { "predict_time": 11.967978, "total_time": 12.057932 }, "output": [ "https://replicate.delivery/pbxt/6cLpm3KSwl6nAJ1f3jOlpLVl5n7hjOG3lnqfskRaZS5KbTjQA/out-0.png" ], "started_at": "2023-03-01T17:00:46.873102Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4hr4kzxhdrdnnp2thvj6566tma", "cancel": "https://api.replicate.com/v1/predictions/4hr4kzxhdrdnnp2thvj6566tma/cancel" }, "version": "b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87" }Generated inUsing seed: 36153 Generating image of 512 x 512 with prompt: analog style closeup portrait of <1> cowboy hat, sitting on a desk The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.15it/s] 4%|▍ | 2/50 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cloneofsimo/analog_diffusion_lora:cfda4034Input
- width
- "512"
- height
- "512"
- prompt
- analog style closeup portrait of <1> cowboy hat, sitting on a desk
- lora_urls
- https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.6
- num_outputs
- 1
- adapter_type
- depth
- guidance_scale
- "6.5"
- negative_prompt
- blur, haze
- prompt_strength
- 0.8
- num_inference_steps
- 50
- adapter_condition_image
{ "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpeCo6wM5SRds2Glzu9hTVKvA3ehO1RSjr3bvz6cNJ0z4Cy/cowboy.png" }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/analog_diffusion_lora:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", { input: { width: "512", height: "512", prompt: "analog style closeup portrait of <1> cowboy hat, sitting on a desk", lora_urls: "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.6", num_outputs: 1, adapter_type: "depth", guidance_scale: "6.5", negative_prompt: "blur, haze", prompt_strength: 0.8, num_inference_steps: 50, adapter_condition_image: "https://replicate.delivery/pbxt/IOpeCo6wM5SRds2Glzu9hTVKvA3ehO1RSjr3bvz6cNJ0z4Cy/cowboy.png" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/analog_diffusion_lora:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", input={ "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpeCo6wM5SRds2Glzu9hTVKvA3ehO1RSjr3bvz6cNJ0z4Cy/cowboy.png" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run cloneofsimo/analog_diffusion_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": "cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6", "input": { "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpeCo6wM5SRds2Glzu9hTVKvA3ehO1RSjr3bvz6cNJ0z4Cy/cowboy.png" } }' \ 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/cloneofsimo/analog_diffusion_lora@sha256:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6 \ -i 'width="512"' \ -i 'height="512"' \ -i 'prompt="analog style closeup portrait of <1> cowboy hat, sitting on a desk"' \ -i 'lora_urls="https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors"' \ -i 'scheduler="DPMSolverMultistep"' \ -i 'lora_scales="0.6"' \ -i 'num_outputs=1' \ -i 'adapter_type="depth"' \ -i 'guidance_scale="6.5"' \ -i 'negative_prompt="blur, haze"' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50' \ -i 'adapter_condition_image="https://replicate.delivery/pbxt/IOpeCo6wM5SRds2Glzu9hTVKvA3ehO1RSjr3bvz6cNJ0z4Cy/cowboy.png"'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/cloneofsimo/analog_diffusion_lora@sha256:cfda4034a453627da6dd002d2ad959acb4c585b4e7204fed1d270eb3641fbea6curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpeCo6wM5SRds2Glzu9hTVKvA3ehO1RSjr3bvz6cNJ0z4Cy/cowboy.png" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-03-01T17:07:26.379539Z", "created_at": "2023-03-01T17:07:13.493143Z", "data_removed": false, "error": null, "id": "6zytj67jvzcgvnbt3hze253pse", "input": { "width": "512", "height": "512", "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": "6.5", "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpeCo6wM5SRds2Glzu9hTVKvA3ehO1RSjr3bvz6cNJ0z4Cy/cowboy.png" }, "logs": "Using seed: 40868\nGenerating image of 512 x 512 with prompt: analog style closeup portrait of <1> cowboy hat, sitting on a desk\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.80it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.23it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.46it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.55it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.60it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.62it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.64it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.62it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.65it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.65it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.64it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.65it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.64it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.67it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.65it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.64it/s]\n 34%|███▍ | 17/50 [00:03<00:07, 4.64it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.63it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.65it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.66it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.63it/s]\n 44%|████▍ | 22/50 [00:04<00:06, 4.66it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.66it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.67it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.67it/s]\n 52%|█████▏ | 26/50 [00:05<00:05, 4.66it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.64it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.64it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.63it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.63it/s]\n 62%|██████▏ | 31/50 [00:06<00:04, 4.64it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.64it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.64it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.63it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.59it/s]\n 72%|███████▏ | 36/50 [00:07<00:03, 4.60it/s]\n 74%|███████▍ | 37/50 [00:08<00:02, 4.60it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.61it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.60it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.61it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.59it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.59it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.59it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.58it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.58it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.61it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.60it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.61it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.60it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.59it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.61it/s]", "metrics": { "predict_time": 12.687465, "total_time": 12.886396 }, "output": [ "https://replicate.delivery/pbxt/bsChzJUe410kUiqod6SfTSjP0jOFWTR4tf6GN5DS8mcbCnGhA/out-0.png" ], "started_at": "2023-03-01T17:07:13.692074Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6zytj67jvzcgvnbt3hze253pse", "cancel": "https://api.replicate.com/v1/predictions/6zytj67jvzcgvnbt3hze253pse/cancel" }, "version": "b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87" }Generated inUsing seed: 40868 Generating image of 512 x 512 with prompt: analog style closeup portrait of <1> cowboy hat, sitting on a desk The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:12, 3.80it/s] 4%|▍ | 2/50 [00:00<00:11, 4.23it/s] 6%|▌ | 3/50 [00:00<00:10, 4.46it/s] 8%|▊ | 4/50 [00:00<00:10, 4.55it/s] 10%|█ | 5/50 [00:01<00:09, 4.60it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.62it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.64it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.62it/s] 18%|█▊ | 9/50 [00:01<00:08, 4.65it/s] 20%|██ | 10/50 [00:02<00:08, 4.65it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.64it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.65it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.64it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.67it/s] 30%|███ | 15/50 [00:03<00:07, 4.65it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.64it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.64it/s] 36%|███▌ | 18/50 [00:03<00:06, 4.63it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.65it/s] 40%|████ | 20/50 [00:04<00:06, 4.66it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.63it/s] 44%|████▍ | 22/50 [00:04<00:06, 4.66it/s] 46%|████▌ | 23/50 [00:04<00:05, 4.66it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.67it/s] 50%|█████ | 25/50 [00:05<00:05, 4.67it/s] 52%|█████▏ | 26/50 [00:05<00:05, 4.66it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.64it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.64it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.63it/s] 60%|██████ | 30/50 [00:06<00:04, 4.63it/s] 62%|██████▏ | 31/50 [00:06<00:04, 4.64it/s] 64%|██████▍ | 32/50 [00:06<00:03, 4.64it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.64it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.63it/s] 70%|███████ | 35/50 [00:07<00:03, 4.59it/s] 72%|███████▏ | 36/50 [00:07<00:03, 4.60it/s] 74%|███████▍ | 37/50 [00:08<00:02, 4.60it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.61it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.60it/s] 80%|████████ | 40/50 [00:08<00:02, 4.61it/s] 82%|████████▏ | 41/50 [00:08<00:01, 4.59it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.59it/s] 86%|████████▌ | 43/50 [00:09<00:01, 4.59it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.58it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.58it/s] 92%|█████████▏| 46/50 [00:09<00:00, 4.61it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.60it/s] 96%|█████████▌| 48/50 [00:10<00:00, 4.61it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.60it/s] 100%|██████████| 50/50 [00:10<00:00, 4.59it/s] 100%|██████████| 50/50 [00:10<00:00, 4.61it/s]
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