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","raw":"Found it inflexible? "},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Since the first dawn on earth, we have supported loading custom pipelines via a ","raw":"Since the first dawn on earth, we have supported loading custom pipelines via a "},{"type":"inline_code","code":"custom_pipeline","raw":"`custom_pipeline`"},{"type":"text","value":" argument 🌄","raw":" argument 🌄"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"These pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation. ","raw":"These pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation. "},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"We have many cool pipelines, implemented that way. They all share the same benefits available to a ","raw":"We have many cool pipelines, implemented that way. They all share the same benefits available to a "},{"type":"inline_code","code":"DiffusionPipeline","raw":"`DiffusionPipeline`"},{"type":"text","value":", no compromise there 🤗","raw":", no compromise there 🤗"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Check them here:","raw":"Check them here:"},{"type":"new_line","raw":"\n"},{"type":"link","href":"https://github.com/huggingface/diffusers/tree/main/examples/community","raw":"https://github.com/huggingface/diffusers/tree/main/examples/community"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Then we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible. ","raw":"Then we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible. "},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"All you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from.","raw":"All you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"SDXL Japanese was implemented like this 🔥","raw":"SDXL Japanese was implemented like this 🔥"},{"type":"new_line","raw":"\n"},{"type":"resource","resource":{"type":"model","id":"stabilityai/japanese-stable-diffusion-xl"},"url":"https://huggingface.co/stabilityai/japanese-stable-diffusion-xl","raw":"https://huggingface.co/stabilityai/japanese-stable-diffusion-xl"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Full guide is available here ⬇️","raw":"Full guide is available here ⬇️"},{"type":"new_line","raw":"\n"},{"type":"link","href":"https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview","raw":"https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"And, of course, these share all the benefits that come with ","raw":"And, of course, these share all the benefits that come with "},{"type":"inline_code","code":"DiffusionPipeline","raw":"`DiffusionPipeline`"},{"type":"text","value":". ","raw":". "}],"rawContent":"Custom pipelines and components in Diffusers 🎸\n\nWanted to use customized pipelines and other components (schedulers, unets, text encoders, etc.) in Diffusers?\n\nFound it inflexible? \n\nSince the first dawn on earth, we have supported loading custom pipelines via a `custom_pipeline` argument 🌄\n\nThese pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation. \n\nWe have many cool pipelines, implemented that way. They all share the same benefits available to a `DiffusionPipeline`, no compromise there 🤗\n\nCheck them here:\nhttps://github.com/huggingface/diffusers/tree/main/examples/community\n\nThen we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible. \n\nAll you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from.\n\nSDXL Japanese was implemented like this 🔥\nhttps://huggingface.co/stabilityai/japanese-stable-diffusion-xl\n\nFull guide is available here ⬇️\nhttps://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview\n\nAnd, of course, these share all the benefits that come with `DiffusionPipeline`. ","author":{"avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1649681653581-5f7fbd813e94f16a85448745.jpeg","fullname":"Sayak Paul","name":"sayakpaul","type":"user","isPro":false,"isHf":true,"isFollowing":false},"attachments":[],"mentions":[],"reactions":[{"reaction":"🔥","users":["radames"],"count":1}],"publishedAt":"2024-05-02T09:49:19.000Z","updatedAt":"2024-05-02T09:49:19.966Z","commentators":[],"url":"/posts/sayakpaul/759843110601726","totalUniqueImpressions":974,"numComments":0},{"slug":"155389581108273","content":[{"type":"text","value":"We're introducing experimental support for ","raw":"We're introducing experimental support for "},{"type":"inline_code","code":"device_map","raw":"`device_map`"},{"type":"text","value":" in Diffusers 🤗","raw":" in Diffusers 🤗"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"If you have multiple GPUs you want to use to distribute the pipeline models, you can do so. Additionally, this becomes more useful when you have multiple low-VRAM GPUs.","raw":"If you have multiple GPUs you want to use to distribute the pipeline models, you can do so. Additionally, this becomes more useful when you have multiple low-VRAM GPUs."},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"Documentation:","raw":"Documentation:"},{"type":"new_line","raw":"\n"},{"type":"link","href":"https://huggingface.co/docs/diffusers/main/en/training/distributed_inference#device-placement","raw":"https://huggingface.co/docs/diffusers/main/en/training/distributed_inference#device-placement"},{"type":"new_line","raw":"\n"},{"type":"new_line","raw":"\n"},{"type":"text","value":"🚨 Currently, only \"balanced\" device mapping strategy is supported. ","raw":"🚨 Currently, only \"balanced\" device mapping strategy is supported. "}],"rawContent":"We're introducing experimental support for `device_map` in Diffusers 🤗\n\nIf you have multiple GPUs you want to use to distribute the pipeline models, you can do so. Additionally, this becomes more useful when you have multiple low-VRAM GPUs.\n\nDocumentation:\nhttps://huggingface.co/docs/diffusers/main/en/training/distributed_inference#device-placement\n\n🚨 Currently, only \"balanced\" device mapping strategy is supported. 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Sayak Paul
sayakpaul
AI & ML interests
Diffusion models, representation learning
Articles
Organizations
Posts
5
Post
974
Custom pipelines and components in Diffusers 🎸
Wanted to use customized pipelines and other components (schedulers, unets, text encoders, etc.) in Diffusers?
Found it inflexible?
Since the first dawn on earth, we have supported loading custom pipelines via a
These pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation.
We have many cool pipelines, implemented that way. They all share the same benefits available to a
Check them here:
https://github.com/huggingface/diffusers/tree/main/examples/community
Then we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible.
All you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from.
SDXL Japanese was implemented like this 🔥
stabilityai/japanese-stable-diffusion-xl
Full guide is available here ⬇️
https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview
And, of course, these share all the benefits that come with
Wanted to use customized pipelines and other components (schedulers, unets, text encoders, etc.) in Diffusers?
Found it inflexible?
Since the first dawn on earth, we have supported loading custom pipelines via a
custom_pipeline
argument 🌄These pipelines are inference-only, i.e., the assumption is that we're leveraging an existing checkpoint (e.g., runwayml/stable-diffusion-v1-5) and ONLY modifying the pipeline implementation.
We have many cool pipelines, implemented that way. They all share the same benefits available to a
DiffusionPipeline
, no compromise there 🤗Check them here:
https://github.com/huggingface/diffusers/tree/main/examples/community
Then we might have a requirement of everything customized i.e., custom components along with a custom pipeline. Sure, that's all possible.
All you have to do is keep the implementations of those custom components on the Hub repository you're loading your pipeline checkpoint from.
SDXL Japanese was implemented like this 🔥
stabilityai/japanese-stable-diffusion-xl
Full guide is available here ⬇️
https://huggingface.co/docs/diffusers/main/en/using-diffusers/custom_pipeline_overview
And, of course, these share all the benefits that come with
DiffusionPipeline
.
Post
2563
We're introducing experimental support for
If you have multiple GPUs you want to use to distribute the pipeline models, you can do so. Additionally, this becomes more useful when you have multiple low-VRAM GPUs.
Documentation:
https://huggingface.co/docs/diffusers/main/en/training/distributed_inference#device-placement
🚨 Currently, only "balanced" device mapping strategy is supported.
device_map
in Diffusers 🤗If you have multiple GPUs you want to use to distribute the pipeline models, you can do so. Additionally, this becomes more useful when you have multiple low-VRAM GPUs.
Documentation:
https://huggingface.co/docs/diffusers/main/en/training/distributed_inference#device-placement
🚨 Currently, only "balanced" device mapping strategy is supported.
Collections
2
This collection contains a list of my most favorite text-to-image diffusion models.
Provides a list of papers focusing on optimizing T2I diffusion models, targeting fewer timesteps, architecture optimization, and more.
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Progressive Distillation for Fast Sampling of Diffusion Models
Paper • 2202.00512 • Published • 1 -
On Distillation of Guided Diffusion Models
Paper • 2210.03142 • Published -
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Paper • 2309.06380 • Published • 31 -
Consistency Models
Paper • 2303.01469 • Published • 5
spaces
19
Running
12
📈
Demo Docker Gradio
Sleeping
🧨
Diffusers Docs QA Chatbot
Ask questions to the Diffusers documentation.
Runtime error
7
🧨
Convert Kerascv SD to Diffusers
Runtime error
🚀
Inpainting Tool
Build error
13
🐶
Generate Custom Pokemons with Stable Diffusion
Sleeping
8
⏰
Evaluate StableDiffusionPipeline with Different Schedulers
models
65
sayakpaul/sdxl-orpo-large-beta_orpo-0.01-beta_inner-500-lr-1e-6
Updated
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7
sayakpaul/sdxl-orpo-large-beta_orpo-0.01-beta_inner-100-lr-1e-6
Updated
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2
sayakpaul/sdxl-orpo-large-beta_orpo-0.005-beta_inner-500-lr-1e-6
Updated
sayakpaul/sdxl-orpo-large-beta_orpo-0.005-beta_inner-500-lr-5e-6
Updated
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8
sayakpaul/sdxl-orpo-large-beta_orpo-0.005-beta_inner-500-lr-5e-7-steps-5000-no-warmup
Updated
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12
sayakpaul/sdxl-orpo-large-beta_orpo-0.005-beta_inner-500-lr-5e-7-scaled-steps-5000-no-warmup
Updated
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12
sayakpaul/sdxl-orpo-large-beta_orpo-0.005-beta_inner-500-lr-5e-7-no-decay-steps-5000
Updated
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10
sayakpaul/sdxl-orpo-large-beta_orpo-0.005-beta_inner-500-lr-5e-7-scaled-steps-5000
Updated
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14
sayakpaul/sdxl-orpo-large-beta_orpo-0.05-beta_inner-500-lr-5e-7
Updated
sayakpaul/sdxl-orpo-large-beta_orpo-0.05-beta_inner-500-lr-5e-7-no-decay
Updated
datasets
30
sayakpaul/sample-datasets
Viewer
•
Updated
sayakpaul/pickapic_v2_webdataset
Viewer
•
Updated
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4
sayakpaul/generated-gemini-responses
Viewer
•
Updated
sayakpaul/no_robots_only_coding
Viewer
•
Updated
•
325
sayakpaul/temp-dataset-autotrain-553
Viewer
•
Updated
sayakpaul/diffusers-qa-chatbot-artifacts
Viewer
•
Updated
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1
sayakpaul/mgie-results
Viewer
•
Updated
sayakpaul/coco-30-val-2014
Viewer
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Updated
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126
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2
sayakpaul/drawbench-sdxl-refiner
Viewer
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Updated
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1
sayakpaul/drawbench-kandinsky-v22
Viewer
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Updated