prunaai
/
flux-schneller
This is an optimised version of the flux schnell model from black forest labs with the pruna tool. We achieve a ~3x speedup over the original model with minimal quality loss.
- Public
- 152 runs
-
A100 (80GB)
- Weights
Prediction
prunaai/flux-schneller:13dd5411IDa7f8pnjb5nrj20ckqeksf2akr8StatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- seed
- 42
- prompt
- Three-quarters front view of a yellow 2017 Corvette coming around a curve in a mountain road and looking over a green valley on a cloudy day.
- start_step
- 0
- image_width
- 1024
- image_height
- 1024
- cache_interval
- 3
- guidance_scale
- 7.5
- num_inference_steps
- 4
{ "seed": 42, "prompt": "Three-quarters front view of a yellow 2017 Corvette coming around a curve in a mountain road and looking over a green valley on a cloudy day.", "start_step": 0, "image_width": 1024, "image_height": 1024, "cache_interval": 3, "guidance_scale": 7.5, "num_inference_steps": 4 }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 prunaai/flux-schneller using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prunaai/flux-schneller:13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", { input: { seed: 42, prompt: "Three-quarters front view of a yellow 2017 Corvette coming around a curve in a mountain road and looking over a green valley on a cloudy day.", image_width: 1024, image_height: 1024, guidance_scale: 7.5, num_inference_steps: 4 } } ); 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 prunaai/flux-schneller using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prunaai/flux-schneller:13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", input={ "seed": 42, "prompt": "Three-quarters front view of a yellow 2017 Corvette coming around a curve in a mountain road and looking over a green valley on a cloudy day.", "image_width": 1024, "image_height": 1024, "guidance_scale": 7.5, "num_inference_steps": 4 } ) 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 prunaai/flux-schneller 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": "13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", "input": { "seed": 42, "prompt": "Three-quarters front view of a yellow 2017 Corvette coming around a curve in a mountain road and looking over a green valley on a cloudy day.", "image_width": 1024, "image_height": 1024, "guidance_scale": 7.5, "num_inference_steps": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-12T16:15:24.758129Z", "created_at": "2024-12-12T16:15:23.437000Z", "data_removed": false, "error": null, "id": "a7f8pnjb5nrj20ckqeksf2akr8", "input": { "seed": 42, "prompt": "Three-quarters front view of a yellow 2017 Corvette coming around a curve in a mountain road and looking over a green valley on a cloudy day.", "start_step": 0, "image_width": 1024, "image_height": 1024, "cache_interval": 3, "guidance_scale": 7.5, "num_inference_steps": 4 }, "logs": "0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:01, 2.78it/s]\n100%|██████████| 4/4 [00:00<00:00, 9.55it/s]", "metrics": { "predict_time": 1.311223942, "total_time": 1.321129 }, "output": "https://replicate.delivery/yhqm/hfaevHmpmMvDJkzZGtk6vkQ0MefanAyEdbS3lirCqpAxhvoPB/output.png", "started_at": "2024-12-12T16:15:23.446905Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/qoxq-ri672dpwne2bson747t7jqujijy7duvlx32j45wm3tu6fh4ino3q", "get": "https://api.replicate.com/v1/predictions/a7f8pnjb5nrj20ckqeksf2akr8", "cancel": "https://api.replicate.com/v1/predictions/a7f8pnjb5nrj20ckqeksf2akr8/cancel" }, "version": "8783381a9459718f38e4bcb6e146231f3087a8377d5a88446d805df65cff65da" }Generated in0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:01, 2.78it/s] 100%|██████████| 4/4 [00:00<00:00, 9.55it/s]Prediction
prunaai/flux-schneller:13dd5411IDbaz87pa90nrj60ckqena3rc91gStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- seed
- 42
- prompt
- An anime illustration of Sydney Opera House sitting next to Eiffel tower, under a blue night sky of roiling energy, exploding yellow stars, and radiating swirls of blue.
- start_step
- 0
- image_width
- 1024
- image_height
- 1024
- cache_interval
- 3
- guidance_scale
- 7.5
- num_inference_steps
- 4
{ "seed": 42, "prompt": "An anime illustration of Sydney Opera House sitting next to Eiffel tower, under a blue night sky of roiling energy, exploding yellow stars, and radiating swirls of blue.", "start_step": 0, "image_width": 1024, "image_height": 1024, "cache_interval": 3, "guidance_scale": 7.5, "num_inference_steps": 4 }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 prunaai/flux-schneller using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prunaai/flux-schneller:13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", { input: { seed: 42, prompt: "An anime illustration of Sydney Opera House sitting next to Eiffel tower, under a blue night sky of roiling energy, exploding yellow stars, and radiating swirls of blue.", image_width: 1024, image_height: 1024, guidance_scale: 7.5, num_inference_steps: 4 } } ); 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 prunaai/flux-schneller using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prunaai/flux-schneller:13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", input={ "seed": 42, "prompt": "An anime illustration of Sydney Opera House sitting next to Eiffel tower, under a blue night sky of roiling energy, exploding yellow stars, and radiating swirls of blue.", "image_width": 1024, "image_height": 1024, "guidance_scale": 7.5, "num_inference_steps": 4 } ) 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 prunaai/flux-schneller 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": "13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", "input": { "seed": 42, "prompt": "An anime illustration of Sydney Opera House sitting next to Eiffel tower, under a blue night sky of roiling energy, exploding yellow stars, and radiating swirls of blue.", "image_width": 1024, "image_height": 1024, "guidance_scale": 7.5, "num_inference_steps": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-12T16:18:40.823244Z", "created_at": "2024-12-12T16:18:39.493000Z", "data_removed": false, "error": null, "id": "baz87pa90nrj60ckqena3rc91g", "input": { "seed": 42, "prompt": "An anime illustration of Sydney Opera House sitting next to Eiffel tower, under a blue night sky of roiling energy, exploding yellow stars, and radiating swirls of blue.", "start_step": 0, "image_width": 1024, "image_height": 1024, "cache_interval": 3, "guidance_scale": 7.5, "num_inference_steps": 4 }, "logs": "0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:01, 2.78it/s]\n100%|██████████| 4/4 [00:00<00:00, 9.56it/s]", "metrics": { "predict_time": 1.320266082, "total_time": 1.330244 }, "output": "https://replicate.delivery/yhqm/g8sDoVzFobotBJFHyhwTczcDcTfB0ARjqLc1podHCVbw9F9JA/output.png", "started_at": "2024-12-12T16:18:39.502978Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/qoxq-vpfvlxwme6hols3xrwzfrdg424p6mvzjpyngzndzfzb7tkvlaemq", "get": "https://api.replicate.com/v1/predictions/baz87pa90nrj60ckqena3rc91g", "cancel": "https://api.replicate.com/v1/predictions/baz87pa90nrj60ckqena3rc91g/cancel" }, "version": "8783381a9459718f38e4bcb6e146231f3087a8377d5a88446d805df65cff65da" }Generated in0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:01, 2.78it/s] 100%|██████████| 4/4 [00:00<00:00, 9.56it/s]Prediction
prunaai/flux-schneller:13dd5411ID8b75m8ha4nrj40cnc7ssaps5zwStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- seed
- 42
- prompt
- A cat holding a sign saying "Pruna Open Source 20 March"
- start_step
- 0
- image_width
- 1024
- image_height
- 1024
- cache_interval
- 3
- guidance_scale
- 7.5
- num_inference_steps
- 4
{ "seed": 42, "prompt": "A cat holding a sign saying \"Pruna Open Source 20 March\"", "start_step": 0, "image_width": 1024, "image_height": 1024, "cache_interval": 3, "guidance_scale": 7.5, "num_inference_steps": 4 }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 prunaai/flux-schneller using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "prunaai/flux-schneller:13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", { input: { seed: 42, prompt: "A cat holding a sign saying \"Pruna Open Source 20 March\"", image_width: 1024, image_height: 1024, guidance_scale: 7.5, num_inference_steps: 4 } } ); 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 prunaai/flux-schneller using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "prunaai/flux-schneller:13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", input={ "seed": 42, "prompt": "A cat holding a sign saying \"Pruna Open Source 20 March\"", "image_width": 1024, "image_height": 1024, "guidance_scale": 7.5, "num_inference_steps": 4 } ) 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 prunaai/flux-schneller 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": "13dd5411fda4cc9bf19734204e6d4751df0e80258c2097f03e9c0538f9890baa", "input": { "seed": 42, "prompt": "A cat holding a sign saying \\"Pruna Open Source 20 March\\"", "image_width": 1024, "image_height": 1024, "guidance_scale": 7.5, "num_inference_steps": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-03-04T16:23:27.317057Z", "created_at": "2025-03-04T16:18:00.869000Z", "data_removed": false, "error": null, "id": "8b75m8ha4nrj40cnc7ssaps5zw", "input": { "seed": 42, "prompt": "A cat holding a sign saying \"Pruna Open Source 20 March\"", "start_step": 0, "image_width": 1024, "image_height": 1024, "cache_interval": 3, "guidance_scale": 7.5, "num_inference_steps": 4 }, "logs": "0%| | 0/4 [00:00<?, ?it/s]W0304 16:20:51.864000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/0] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:20:56.828000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/1] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:20:59.326000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/2] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:02.262000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/3] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:04.745000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/4] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:07.254000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/5] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:09.736000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/6] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:12.221000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/7] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] torch._dynamo hit config.cache_size_limit (8)\nW0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] function: 'forward' (/root/.pyenv/versions/3.11.11/lib/python3.11/site-packages/diffusers/models/transformers/transformer_flux.py:165)\nW0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] last reason: ___check_obj_id(L['self'].ff, 128477178711952)\nW0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] To log all recompilation reasons, use TORCH_LOGS=\"recompiles\".\nW0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html.\nW0304 16:21:15.494000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/0] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:20.573000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/1] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:25.371000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/2] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:29.277000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/3] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:33.184000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/4] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:37.645000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/5] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:41.572000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/6] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:45.445000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/7] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:49.313000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/8] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:53.851000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/9] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:21:57.794000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/10] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] torch._dynamo hit config.cache_size_limit (8)\nW0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] function: 'forward' (/root/.pyenv/versions/3.11.11/lib/python3.11/site-packages/diffusers/models/transformers/transformer_flux.py:86)\nW0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] last reason: ___check_obj_id(L['self'].attn, 128477184171472)\nW0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] To log all recompilation reasons, use TORCH_LOGS=\"recompiles\".\nW0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html.\n 25%|██▌ | 1/4 [01:58<05:55, 118.59s/it]W0304 16:22:50.431000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/41] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:22:54.145000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/42] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:22:57.816000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/43] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:23:01.460000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/44] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:23:05.213000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/45] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:23:09.803000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/46] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:23:13.505000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/47] xindex is not in var_ranges, defaulting to unknown range.\nW0304 16:23:17.263000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/48] xindex is not in var_ranges, defaulting to unknown range.\n100%|██████████| 4/4 [02:37<00:00, 32.97s/it] \n100%|██████████| 4/4 [02:37<00:00, 39.39s/it]", "metrics": { "predict_time": 196.528349457, "total_time": 326.448057 }, "output": "https://replicate.delivery/yhqm/ObW99JQg8DKHHRgRQug4ijy8KCqAYCbsuSUwNd63HY6f1mKKA/output.png", "started_at": "2025-03-04T16:20:10.788707Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/yswh-phy5pbcujsrfx23wua3fjur2twbify7knhvtuk22vqahvzsow7xq", "get": "https://api.replicate.com/v1/predictions/8b75m8ha4nrj40cnc7ssaps5zw", "cancel": "https://api.replicate.com/v1/predictions/8b75m8ha4nrj40cnc7ssaps5zw/cancel" }, "version": "5c6ad500d7b56378fd18789d0ab17ab845e9043cd86d7e635ff47bc31d22e143" }Generated in0%| | 0/4 [00:00<?, ?it/s]W0304 16:20:51.864000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/0] xindex is not in var_ranges, defaulting to unknown range. W0304 16:20:56.828000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/1] xindex is not in var_ranges, defaulting to unknown range. W0304 16:20:59.326000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/2] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:02.262000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/3] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:04.745000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/4] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:07.254000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/5] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:09.736000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/6] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:12.221000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [2/7] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] torch._dynamo hit config.cache_size_limit (8) W0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] function: 'forward' (/root/.pyenv/versions/3.11.11/lib/python3.11/site-packages/diffusers/models/transformers/transformer_flux.py:165) W0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] last reason: ___check_obj_id(L['self'].ff, 128477178711952) W0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". W0304 16:21:12.479000 128482012704256 torch/_dynamo/convert_frame.py:762] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. W0304 16:21:15.494000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/0] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:20.573000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/1] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:25.371000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/2] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:29.277000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/3] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:33.184000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/4] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:37.645000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/5] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:41.572000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/6] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:45.445000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/7] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:49.313000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/8] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:53.851000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/9] xindex is not in var_ranges, defaulting to unknown range. W0304 16:21:57.794000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/10] xindex is not in var_ranges, defaulting to unknown range. W0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] torch._dynamo hit config.cache_size_limit (8) W0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] function: 'forward' (/root/.pyenv/versions/3.11.11/lib/python3.11/site-packages/diffusers/models/transformers/transformer_flux.py:86) W0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] last reason: ___check_obj_id(L['self'].attn, 128477184171472) W0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". W0304 16:22:13.795000 128482012704256 torch/_dynamo/convert_frame.py:762] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 25%|██▌ | 1/4 [01:58<05:55, 118.59s/it]W0304 16:22:50.431000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/41] xindex is not in var_ranges, defaulting to unknown range. W0304 16:22:54.145000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/42] xindex is not in var_ranges, defaulting to unknown range. W0304 16:22:57.816000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/43] xindex is not in var_ranges, defaulting to unknown range. W0304 16:23:01.460000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/44] xindex is not in var_ranges, defaulting to unknown range. W0304 16:23:05.213000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/45] xindex is not in var_ranges, defaulting to unknown range. W0304 16:23:09.803000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/46] xindex is not in var_ranges, defaulting to unknown range. W0304 16:23:13.505000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/47] xindex is not in var_ranges, defaulting to unknown range. W0304 16:23:17.263000 128482012704256 torch/fx/experimental/symbolic_shapes.py:4449] [4/48] xindex is not in var_ranges, defaulting to unknown range. 100%|██████████| 4/4 [02:37<00:00, 32.97s/it] 100%|██████████| 4/4 [02:37<00:00, 39.39s/it]
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