fewjative / ultimate-sd-upscale
Ultimate SD Upscale with ControlNet Tile
- Public
- 161.4K runs
-
A100 (80GB)
- GitHub
Prediction
fewjative/ultimate-sd-upscale:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7IDpw2lvnjbgnnjqx4yoillh2hqoaStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedby @fewjativeInput
- cfg
- 8
- image
- steps
- 20
- denoise
- 0.2
- upscaler
- 4x-UltraSharp
- mask_blur
- 8
- mode_type
- Linear
- scheduler
- normal
- tile_width
- 512
- upscale_by
- 2
- tile_height
- 512
- sampler_name
- euler
- tile_padding
- 32
- seam_fix_mode
- None
- seam_fix_width
- 64
- negative_prompt
- positive_prompt
- a dirty rust mech looking down at a kid wearing a backpack, in a jungle, low saturation, dirty, green foliage
- seam_fix_denoise
- 1
- seam_fix_padding
- 16
- seam_fix_mask_blur
- 8
- controlnet_strength
- 1
- force_uniform_tiles
- use_controlnet_tile
{ "cfg": 8, "image": "https://replicate.delivery/pbxt/JscFNQCfj4ohzBKyv1qkBs8Kw7gOsV8YOfImxF3v1A3HKmpC/ComfyUI_00476_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a dirty rust mech looking down at a kid wearing a backpack, in a jungle, low saturation, dirty, green foliage", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": true, "use_controlnet_tile": true }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 fewjative/ultimate-sd-upscale using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fewjative/ultimate-sd-upscale:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7", { input: { cfg: 8, image: "https://replicate.delivery/pbxt/JscFNQCfj4ohzBKyv1qkBs8Kw7gOsV8YOfImxF3v1A3HKmpC/ComfyUI_00476_.png", steps: 20, denoise: 0.2, upscaler: "4x-UltraSharp", mask_blur: 8, mode_type: "Linear", scheduler: "normal", tile_width: 512, upscale_by: 2, tile_height: 512, sampler_name: "euler", tile_padding: 32, seam_fix_mode: "None", seam_fix_width: 64, negative_prompt: "", positive_prompt: "a dirty rust mech looking down at a kid wearing a backpack, in a jungle, low saturation, dirty, green foliage", seam_fix_denoise: 1, seam_fix_padding: 16, seam_fix_mask_blur: 8, controlnet_strength: 1, force_uniform_tiles: true, use_controlnet_tile: true } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 fewjative/ultimate-sd-upscale using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fewjative/ultimate-sd-upscale:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7", input={ "cfg": 8, "image": "https://replicate.delivery/pbxt/JscFNQCfj4ohzBKyv1qkBs8Kw7gOsV8YOfImxF3v1A3HKmpC/ComfyUI_00476_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a dirty rust mech looking down at a kid wearing a backpack, in a jungle, low saturation, dirty, green foliage", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": True, "use_controlnet_tile": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fewjative/ultimate-sd-upscale 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": "fewjative/ultimate-sd-upscale:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7", "input": { "cfg": 8, "image": "https://replicate.delivery/pbxt/JscFNQCfj4ohzBKyv1qkBs8Kw7gOsV8YOfImxF3v1A3HKmpC/ComfyUI_00476_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a dirty rust mech looking down at a kid wearing a backpack, in a jungle, low saturation, dirty, green foliage", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": true, "use_controlnet_tile": true } }' \ 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/fewjative/ultimate-sd-upscale@sha256:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7 \ -i 'cfg=8' \ -i 'image="https://replicate.delivery/pbxt/JscFNQCfj4ohzBKyv1qkBs8Kw7gOsV8YOfImxF3v1A3HKmpC/ComfyUI_00476_.png"' \ -i 'steps=20' \ -i 'denoise=0.2' \ -i 'upscaler="4x-UltraSharp"' \ -i 'mask_blur=8' \ -i 'mode_type="Linear"' \ -i 'scheduler="normal"' \ -i 'tile_width=512' \ -i 'upscale_by=2' \ -i 'tile_height=512' \ -i 'sampler_name="euler"' \ -i 'tile_padding=32' \ -i 'seam_fix_mode="None"' \ -i 'seam_fix_width=64' \ -i 'negative_prompt=""' \ -i 'positive_prompt="a dirty rust mech looking down at a kid wearing a backpack, in a jungle, low saturation, dirty, green foliage"' \ -i 'seam_fix_denoise=1' \ -i 'seam_fix_padding=16' \ -i 'seam_fix_mask_blur=8' \ -i 'controlnet_strength=1' \ -i 'force_uniform_tiles=true' \ -i 'use_controlnet_tile=true'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/fewjative/ultimate-sd-upscale@sha256:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "cfg": 8, "image": "https://replicate.delivery/pbxt/JscFNQCfj4ohzBKyv1qkBs8Kw7gOsV8YOfImxF3v1A3HKmpC/ComfyUI_00476_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a dirty rust mech looking down at a kid wearing a backpack, in a jungle, low saturation, dirty, green foliage", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": true, "use_controlnet_tile": true } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/JscFNQCfj4ohzBKyv1qkBs8Kw7gOsV8YOfImxF3v1A3HKmpC/ComfyUI_00476_.png", "outut": "https://replicate.delivery/pbxt/bWfck1NLZb0e8UuMLnah8KEZfWHfL2L7xwOdARjP9Fo7vHiHB/out-16063994.png" }{ "completed_at": "2023-11-15T07:02:23.710758Z", "created_at": "2023-11-15T06:59:25.517047Z", "data_removed": false, "error": null, "id": "pw2lvnjbgnnjqx4yoillh2hqoa", "input": { "cfg": 8, "image": "https://replicate.delivery/pbxt/JscFNQCfj4ohzBKyv1qkBs8Kw7gOsV8YOfImxF3v1A3HKmpC/ComfyUI_00476_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a dirty rust mech looking down at a kid wearing a backpack, in a jungle, low saturation, dirty, green foliage", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": true, "use_controlnet_tile": true }, "logs": "Using seed: 16063994\nUsing ControlNet tile with Ultimate SD Upscale\ngot prompt\nmodel_type EPS\nadm 0\nUsing pytorch attention in VAE\nWorking with z of shape (1, 4, 32, 32) = 4096 dimensions.\nUsing pytorch attention in VAE\nmissing {'cond_stage_model.clip_l.logit_scale', 'cond_stage_model.clip_l.text_projection'}\nleft over keys: dict_keys(['cond_stage_model.clip_l.transformer.text_model.embeddings.position_ids'])\nRequested to load SD1ClipModel\nLoading 1 new model\n/root/.pyenv/versions/3.10.6/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\nreturn self.fget.__get__(instance, owner)()\n[] []\nCanva size: 1664x2432\nImage size: 832x1216\nScale factor: 2\nUpscaling iteration 1 with scale factor 2\nTile size: 512x512\nTiles amount: 20\nGrid: 5x4\nRedraw enabled: True\nSeams fix mode: NONE\nRequested to load BaseModel\nRequested to load ControlNet\nLoading 2 new models\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:07, 2.67it/s]\n 15%|█▌ | 3/20 [00:00<00:02, 6.96it/s]\n 25%|██▌ | 5/20 [00:00<00:01, 9.83it/s]\n 35%|███▌ | 7/20 [00:00<00:01, 11.36it/s]\n 45%|████▌ | 9/20 [00:00<00:00, 12.71it/s]\n 55%|█████▌ | 11/20 [00:01<00:00, 12.70it/s]\n 65%|██████▌ | 13/20 [00:01<00:00, 13.59it/s]\n 75%|███████▌ | 15/20 [00:01<00:00, 13.79it/s]\n 85%|████████▌ | 17/20 [00:01<00:00, 14.45it/s]\n 95%|█████████▌| 19/20 [00:01<00:00, 14.49it/s]\n100%|██████████| 20/20 [00:01<00:00, 12.18it/s]\nRequested to load ControlNet\nLoading 1 new 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14.67it/s]\n 90%|█████████ | 18/20 [00:01<00:00, 14.91it/s]\n100%|██████████| 20/20 [00:01<00:00, 15.21it/s]\n100%|██████████| 20/20 [00:01<00:00, 14.56it/s]\nRequested to load ControlNet\nLoading 1 new model\nunload clone 1\n 0%| | 0/20 [00:00<?, ?it/s]\n 10%|█ | 2/20 [00:00<00:01, 15.66it/s]\n 20%|██ | 4/20 [00:00<00:01, 14.24it/s]\n 30%|███ | 6/20 [00:00<00:00, 14.13it/s]\n 40%|████ | 8/20 [00:00<00:00, 14.03it/s]\n 50%|█████ | 10/20 [00:00<00:00, 13.92it/s]\n 60%|██████ | 12/20 [00:00<00:00, 13.38it/s]\n 70%|███████ | 14/20 [00:01<00:00, 13.39it/s]\n 80%|████████ | 16/20 [00:01<00:00, 13.52it/s]\n 90%|█████████ | 18/20 [00:01<00:00, 13.55it/s]\n100%|██████████| 20/20 [00:01<00:00, 13.50it/s]\n100%|██████████| 20/20 [00:01<00:00, 13.69it/s]\nRequested to load ControlNet\nLoading 1 new model\nunload clone 1\n 0%| | 0/20 [00:00<?, ?it/s]\n 10%|█ | 2/20 [00:00<00:01, 12.56it/s]\n 20%|██ | 4/20 [00:00<00:01, 13.31it/s]\n 30%|███ | 6/20 [00:00<00:01, 13.15it/s]\n 40%|████ | 8/20 [00:00<00:00, 13.82it/s]\n 50%|█████ | 10/20 [00:00<00:00, 14.37it/s]\n 60%|██████ | 12/20 [00:00<00:00, 14.16it/s]\n 70%|███████ | 14/20 [00:01<00:00, 14.20it/s]\n 80%|████████ | 16/20 [00:01<00:00, 13.48it/s]\n 90%|█████████ | 18/20 [00:01<00:00, 13.49it/s]\n100%|██████████| 20/20 [00:01<00:00, 12.94it/s]\n100%|██████████| 20/20 [00:01<00:00, 13.45it/s]\nPrompt executed in 54.65 seconds\nnode output: {'images': [{'filename': 'ComfyUI_00007_.png', 'subfolder': '', 'type': 'output'}]}\noutput", "metrics": { "predict_time": 58.918638, "total_time": 178.193711 }, "output": "https://replicate.delivery/pbxt/bWfck1NLZb0e8UuMLnah8KEZfWHfL2L7xwOdARjP9Fo7vHiHB/out-16063994.png", "started_at": "2023-11-15T07:01:24.792120Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pw2lvnjbgnnjqx4yoillh2hqoa", "cancel": "https://api.replicate.com/v1/predictions/pw2lvnjbgnnjqx4yoillh2hqoa/cancel" }, "version": "5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7" }Generated inUsing seed: 16063994 Using ControlNet tile with Ultimate SD Upscale got prompt model_type EPS adm 0 Using pytorch attention in VAE Working with z of shape (1, 4, 32, 32) = 4096 dimensions. Using pytorch attention in VAE missing {'cond_stage_model.clip_l.logit_scale', 'cond_stage_model.clip_l.text_projection'} left over keys: dict_keys(['cond_stage_model.clip_l.transformer.text_model.embeddings.position_ids']) Requested to load SD1ClipModel Loading 1 new model /root/.pyenv/versions/3.10.6/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() return self.fget.__get__(instance, owner)() [] [] Canva size: 1664x2432 Image size: 832x1216 Scale factor: 2 Upscaling iteration 1 with scale factor 2 Tile size: 512x512 Tiles amount: 20 Grid: 5x4 Redraw enabled: True Seams fix mode: NONE Requested to load BaseModel Requested to load ControlNet Loading 2 new models 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:07, 2.67it/s] 15%|█▌ | 3/20 [00:00<00:02, 6.96it/s] 25%|██▌ | 5/20 [00:00<00:01, 9.83it/s] 35%|███▌ | 7/20 [00:00<00:01, 11.36it/s] 45%|████▌ | 9/20 [00:00<00:00, 12.71it/s] 55%|█████▌ | 11/20 [00:01<00:00, 12.70it/s] 65%|██████▌ | 13/20 [00:01<00:00, 13.59it/s] 75%|███████▌ | 15/20 [00:01<00:00, 13.79it/s] 85%|████████▌ | 17/20 [00:01<00:00, 14.45it/s] 95%|█████████▌| 19/20 [00:01<00:00, 14.49it/s] 100%|██████████| 20/20 [00:01<00:00, 12.18it/s] Requested to load ControlNet Loading 1 new model unload clone 1 0%| | 0/20 [00:00<?, 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[00:01<00:00, 13.39it/s] 80%|████████ | 16/20 [00:01<00:00, 13.52it/s] 90%|█████████ | 18/20 [00:01<00:00, 13.55it/s] 100%|██████████| 20/20 [00:01<00:00, 13.50it/s] 100%|██████████| 20/20 [00:01<00:00, 13.69it/s] Requested to load ControlNet Loading 1 new model unload clone 1 0%| | 0/20 [00:00<?, ?it/s] 10%|█ | 2/20 [00:00<00:01, 12.56it/s] 20%|██ | 4/20 [00:00<00:01, 13.31it/s] 30%|███ | 6/20 [00:00<00:01, 13.15it/s] 40%|████ | 8/20 [00:00<00:00, 13.82it/s] 50%|█████ | 10/20 [00:00<00:00, 14.37it/s] 60%|██████ | 12/20 [00:00<00:00, 14.16it/s] 70%|███████ | 14/20 [00:01<00:00, 14.20it/s] 80%|████████ | 16/20 [00:01<00:00, 13.48it/s] 90%|█████████ | 18/20 [00:01<00:00, 13.49it/s] 100%|██████████| 20/20 [00:01<00:00, 12.94it/s] 100%|██████████| 20/20 [00:01<00:00, 13.45it/s] Prompt executed in 54.65 seconds node output: {'images': [{'filename': 'ComfyUI_00007_.png', 'subfolder': '', 'type': 'output'}]} outputPrediction
fewjative/ultimate-sd-upscale:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7IDsj2el3bbp4gd2wmthsxusm6wliStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- cfg
- 8
- image
- steps
- 20
- denoise
- 0.2
- upscaler
- 4x-UltraSharp
- mask_blur
- 8
- mode_type
- Linear
- scheduler
- normal
- tile_width
- 512
- upscale_by
- 2
- tile_height
- 512
- sampler_name
- euler
- tile_padding
- 32
- seam_fix_mode
- None
- seam_fix_width
- 64
- negative_prompt
- positive_prompt
- a car in a tokyo village, anime art style, cozy palette, with cherry blossom trees off to the sides
- seam_fix_denoise
- 1
- seam_fix_padding
- 16
- seam_fix_mask_blur
- 8
- controlnet_strength
- 1
- force_uniform_tiles
- use_controlnet_tile
{ "cfg": 8, "image": "https://replicate.delivery/pbxt/JscKum1DOYSwz1GathRwgIeur2iOWkRLwEpuC9JUbCLiIoSp/ComfyUI_02029_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a car in a tokyo village, anime art style, cozy palette, with cherry blossom trees off to the sides", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": true, "use_controlnet_tile": true }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 fewjative/ultimate-sd-upscale using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fewjative/ultimate-sd-upscale:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7", { input: { cfg: 8, image: "https://replicate.delivery/pbxt/JscKum1DOYSwz1GathRwgIeur2iOWkRLwEpuC9JUbCLiIoSp/ComfyUI_02029_.png", steps: 20, denoise: 0.2, upscaler: "4x-UltraSharp", mask_blur: 8, mode_type: "Linear", scheduler: "normal", tile_width: 512, upscale_by: 2, tile_height: 512, sampler_name: "euler", tile_padding: 32, seam_fix_mode: "None", seam_fix_width: 64, negative_prompt: "", positive_prompt: "a car in a tokyo village, anime art style, cozy palette, with cherry blossom trees off to the sides", seam_fix_denoise: 1, seam_fix_padding: 16, seam_fix_mask_blur: 8, controlnet_strength: 1, force_uniform_tiles: true, use_controlnet_tile: true } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);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 fewjative/ultimate-sd-upscale using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fewjative/ultimate-sd-upscale:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7", input={ "cfg": 8, "image": "https://replicate.delivery/pbxt/JscKum1DOYSwz1GathRwgIeur2iOWkRLwEpuC9JUbCLiIoSp/ComfyUI_02029_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a car in a tokyo village, anime art style, cozy palette, with cherry blossom trees off to the sides", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": True, "use_controlnet_tile": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fewjative/ultimate-sd-upscale 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": "fewjative/ultimate-sd-upscale:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7", "input": { "cfg": 8, "image": "https://replicate.delivery/pbxt/JscKum1DOYSwz1GathRwgIeur2iOWkRLwEpuC9JUbCLiIoSp/ComfyUI_02029_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a car in a tokyo village, anime art style, cozy palette, with cherry blossom trees off to the sides", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": true, "use_controlnet_tile": true } }' \ 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/fewjative/ultimate-sd-upscale@sha256:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7 \ -i 'cfg=8' \ -i 'image="https://replicate.delivery/pbxt/JscKum1DOYSwz1GathRwgIeur2iOWkRLwEpuC9JUbCLiIoSp/ComfyUI_02029_.png"' \ -i 'steps=20' \ -i 'denoise=0.2' \ -i 'upscaler="4x-UltraSharp"' \ -i 'mask_blur=8' \ -i 'mode_type="Linear"' \ -i 'scheduler="normal"' \ -i 'tile_width=512' \ -i 'upscale_by=2' \ -i 'tile_height=512' \ -i 'sampler_name="euler"' \ -i 'tile_padding=32' \ -i 'seam_fix_mode="None"' \ -i 'seam_fix_width=64' \ -i 'negative_prompt=""' \ -i 'positive_prompt="a car in a tokyo village, anime art style, cozy palette, with cherry blossom trees off to the sides"' \ -i 'seam_fix_denoise=1' \ -i 'seam_fix_padding=16' \ -i 'seam_fix_mask_blur=8' \ -i 'controlnet_strength=1' \ -i 'force_uniform_tiles=true' \ -i 'use_controlnet_tile=true'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/fewjative/ultimate-sd-upscale@sha256:5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "cfg": 8, "image": "https://replicate.delivery/pbxt/JscKum1DOYSwz1GathRwgIeur2iOWkRLwEpuC9JUbCLiIoSp/ComfyUI_02029_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a car in a tokyo village, anime art style, cozy palette, with cherry blossom trees off to the sides", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": true, "use_controlnet_tile": true } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/JscKum1DOYSwz1GathRwgIeur2iOWkRLwEpuC9JUbCLiIoSp/ComfyUI_02029_.png", "outut": "https://replicate.delivery/pbxt/0tljb75ASRokFRO6zPFk2Pi4ydmo0vf1QbUG4y9Js1Fvfh4RA/out-16196863.png" }{ "completed_at": "2023-11-15T07:06:07.005080Z", "created_at": "2023-11-15T07:05:15.037954Z", "data_removed": false, "error": null, "id": "sj2el3bbp4gd2wmthsxusm6wli", "input": { "cfg": 8, "image": "https://replicate.delivery/pbxt/JscKum1DOYSwz1GathRwgIeur2iOWkRLwEpuC9JUbCLiIoSp/ComfyUI_02029_.png", "steps": 20, "denoise": 0.2, "upscaler": "4x-UltraSharp", "mask_blur": 8, "mode_type": "Linear", "scheduler": "normal", "tile_width": 512, "upscale_by": 2, "tile_height": 512, "sampler_name": "euler", "tile_padding": 32, "seam_fix_mode": "None", "seam_fix_width": 64, "negative_prompt": "", "positive_prompt": "a car in a tokyo village, anime art style, cozy palette, with cherry blossom trees off to the sides", "seam_fix_denoise": 1, "seam_fix_padding": 16, "seam_fix_mask_blur": 8, "controlnet_strength": 1, "force_uniform_tiles": true, "use_controlnet_tile": true }, "logs": "Using seed: 16196863\nUsing ControlNet tile with Ultimate SD Upscale\ngot prompt\nCanva size: 1536x2688\nImage size: 768x1344\nScale factor: 2\nUpscaling iteration 1 with scale factor 2\nTile size: 512x512\nTiles amount: 18\nGrid: 6x3\nRedraw enabled: True\nSeams fix mode: NONE\nRequested to load ControlNet\nLoading 1 new model\n 0%| | 0/20 [00:00<?, ?it/s]\n 10%|█ | 2/20 [00:00<00:01, 14.34it/s]\n 20%|██ | 4/20 [00:00<00:01, 14.85it/s]\n 30%|███ | 6/20 [00:00<00:00, 14.38it/s]\n 40%|████ | 8/20 [00:00<00:00, 14.37it/s]\n 50%|█████ | 10/20 [00:00<00:00, 14.16it/s]\n 60%|██████ | 12/20 [00:00<00:00, 14.66it/s]\n 70%|███████ | 14/20 [00:00<00:00, 14.62it/s]\n 80%|████████ | 16/20 [00:01<00:00, 14.58it/s]\n 90%|█████████ | 18/20 [00:01<00:00, 14.52it/s]\n100%|██████████| 20/20 [00:01<00:00, 14.52it/s]\n100%|██████████| 20/20 [00:01<00:00, 14.50it/s]\nRequested to load 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model\nunload clone 1\n 0%| | 0/20 [00:00<?, ?it/s]\n 10%|█ | 2/20 [00:00<00:01, 10.73it/s]\n 20%|██ | 4/20 [00:00<00:01, 11.40it/s]\n 30%|███ | 6/20 [00:00<00:01, 12.77it/s]\n 40%|████ | 8/20 [00:00<00:00, 13.76it/s]\n 50%|█████ | 10/20 [00:00<00:00, 14.27it/s]\n 60%|██████ | 12/20 [00:00<00:00, 13.97it/s]\n 70%|███████ | 14/20 [00:01<00:00, 14.26it/s]\n 80%|████████ | 16/20 [00:01<00:00, 14.16it/s]\n 90%|█████████ | 18/20 [00:01<00:00, 14.05it/s]\n100%|██████████| 20/20 [00:01<00:00, 13.68it/s]\n100%|██████████| 20/20 [00:01<00:00, 13.57it/s]\nRequested to load ControlNet\nLoading 1 new model\nunload clone 1\n 0%| | 0/20 [00:00<?, ?it/s]\n 10%|█ | 2/20 [00:00<00:01, 15.48it/s]\n 20%|██ | 4/20 [00:00<00:01, 15.35it/s]\n 30%|███ | 6/20 [00:00<00:00, 14.15it/s]\n 40%|████ | 8/20 [00:00<00:00, 14.00it/s]\n 50%|█████ | 10/20 [00:00<00:00, 13.50it/s]\n 60%|██████ | 12/20 [00:00<00:00, 14.11it/s]\n 70%|███████ | 14/20 [00:01<00:00, 13.47it/s]\n 80%|████████ | 16/20 [00:01<00:00, 13.26it/s]\n 90%|█████████ | 18/20 [00:01<00:00, 13.40it/s]\n100%|██████████| 20/20 [00:01<00:00, 12.90it/s]\n100%|██████████| 20/20 [00:01<00:00, 13.52it/s]\nPrompt executed in 47.49 seconds\nnode output: {'images': [{'filename': 'ComfyUI_00008_.png', 'subfolder': '', 'type': 'output'}]}\noutput", "metrics": { "predict_time": 51.949829, "total_time": 51.967126 }, "output": "https://replicate.delivery/pbxt/0tljb75ASRokFRO6zPFk2Pi4ydmo0vf1QbUG4y9Js1Fvfh4RA/out-16196863.png", "started_at": "2023-11-15T07:05:15.055251Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sj2el3bbp4gd2wmthsxusm6wli", "cancel": "https://api.replicate.com/v1/predictions/sj2el3bbp4gd2wmthsxusm6wli/cancel" }, "version": "5daf1012d946160622cd1bd45ed8f12d9675d24659276ccfe24804035f3b3ad7" }Generated inUsing seed: 16196863 Using ControlNet tile with Ultimate SD Upscale got prompt Canva size: 1536x2688 Image size: 768x1344 Scale factor: 2 Upscaling iteration 1 with scale factor 2 Tile 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40%|████ | 8/20 [00:00<00:00, 14.00it/s] 50%|█████ | 10/20 [00:00<00:00, 13.50it/s] 60%|██████ | 12/20 [00:00<00:00, 14.11it/s] 70%|███████ | 14/20 [00:01<00:00, 13.47it/s] 80%|████████ | 16/20 [00:01<00:00, 13.26it/s] 90%|█████████ | 18/20 [00:01<00:00, 13.40it/s] 100%|██████████| 20/20 [00:01<00:00, 12.90it/s] 100%|██████████| 20/20 [00:01<00:00, 13.52it/s] Prompt executed in 47.49 seconds node output: {'images': [{'filename': 'ComfyUI_00008_.png', 'subfolder': '', 'type': 'output'}]} output
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