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Stable Diffusion Quantization

This repository demonstrates Quantization-aware Training (QAT) of Stable Diffusion Unet model wich is the most time-consuming element of the whole pipeline. The quantized model is exported to the OpenVINO IR.

The expected speedup from quantization is ~1.7x (for CPUs w/ Intel DL Boost) and can very depeding on the HW.

Knowledge distillation and EMA techniques can be used to improve the model accuracy.

Prerequisites

pipinstall-rrequirements.txt

Install NNCF from source:

pipinstallgit+https://github.com/openvinotoolkit/nncf.git

Running pre-optimized model

fromoptimum.intel.openvinoimportOVStableDiffusionPipelinepipe=OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt="Super cute fluffy cat warrior in armor, photorealistic, 4K, ultra detailed, vray rendering, unreal engine"output=pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")

HW Requirements for QAT

The minimal HW setup for the run is GPU with 24GB of memory.

NOTE: Potentially you can set the number of training steps to 0 and it will lead to Post-Training Quantization. CPU should be enough in this case but you may need to modify the scipt.

Run PTQ:

pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=0--model_id="runwayml/stable-diffusion-v1-5"

On a part of "laion/laion2B-en" dateset:

pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--model_id="stabilityai/stable-diffusion-2-1"--max_train_samples=800--opt_init_steps=800--dataloader_num_workers=6

NOTE: You may need to paly with seed or do one more try to get good results. The results can be better if to use the same dataset that was used to train the original model.

Run QAT:

  • The best results are achieved with Knowledge Distillation and EMA techniques when tuning on a laion dataset:
CUDA_VISIBLE_DEVICES=2pythonquantize.py--ema_device="cpu"--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--max_train_steps=4096--model_id="runwayml/stable-diffusion-v1-5"--max_train_samples=10000--dataloader_num_workers=8--gradient_checkpointing--output_dir=sd-1-5-quantied-laion
  • Tune model parameters on a target dataset with QAT:
pythonquantize.py--use_kd--ema_device="cpu"--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=15000

--ema_device="cpu" and --gradient_checkpointing are used to save GPU mememory.

  • Tune only quantization parameters for a short time. You can use smaller training steps and any relevant dataset:
pythonquantize.py--use_kd--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="laion/laion2B-en"--tune_quantizers_only--max_train_steps=256--max_train_samples=10000--opt_init_steps=800--opt_init_type="min_max"

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
Stable Diffusion Quantization
This repository demonstrates Quantization-aware Training (QAT) of Stable Diffusion Unet model wich is the most time-consuming element of the whole pipeline. The quantized model is exported to the OpenVINO IR.
The expected speedup from quantization is ~1.7x (for CPUs w/ Intel DL Boost) and can very depeding on the HW.
Knowledge distillation and EMA techniques can be used to improve the model accuracy.
Prerequisites
pipinstall-rrequirements.txt
Install NNCF from source:
pipinstallgit+https://github.com/openvinotoolkit/nncf.git
Running pre-optimized model
fromoptimum.intel.openvinoimportOVStableDiffusionPipelinepipe=OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt="Super cute fluffy cat warrior in armor, photorealistic, 4K, ultra detailed, vray rendering, unreal engine"output=pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
HW Requirements for QAT
The minimal HW setup for the run is GPU with 24GB of memory.
NOTE: Potentially you can set the number of training steps to 0 and it will lead to Post-Training Quantization. CPU should be enough in this case but you may need to modify the scipt.
Run PTQ:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=0--model_id="runwayml/stable-diffusion-v1-5"
On a part of "laion/laion2B-en" dateset:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--model_id="stabilityai/stable-diffusion-2-1"--max_train_samples=800--opt_init_steps=800--dataloader_num_workers=6
NOTE: You may need to paly with seed or do one more try to get good results. The results can be better if to use the same dataset that was used to train the original model.
Run QAT:
  • The best results are achieved with Knowledge Distillation and EMA techniques when tuning on a laion dataset:
CUDA_VISIBLE_DEVICES=2pythonquantize.py--ema_device="cpu"--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--max_train_steps=4096--model_id="runwayml/stable-diffusion-v1-5"--max_train_samples=10000--dataloader_num_workers=8--gradient_checkpointing--output_dir=sd-1-5-quantied-laion
  • Tune model parameters on a target dataset with QAT:
pythonquantize.py--use_kd--ema_device="cpu"--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=15000
--ema_device="cpu" and --gradient_checkpointing are used to save GPU mememory.
  • Tune only quantization parameters for a short time. You can use smaller training steps and any relevant dataset:
pythonquantize.py--use_kd--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="laion/laion2B-en"--tune_quantizers_only--max_train_steps=256--max_train_samples=10000--opt_init_steps=800--opt_init_type="min_max"
About
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Stable Diffusion Quantization
This repository demonstrates Quantization-aware Training (QAT) of Stable Diffusion Unet model wich is the most time-consuming element of the whole pipeline. The quantized model is exported to the OpenVINO IR.
The expected speedup from quantization is ~1.7x (for CPUs w/ Intel DL Boost) and can very depeding on the HW.
Knowledge distillation and EMA techniques can be used to improve the model accuracy.
Prerequisites
pipinstall-rrequirements.txt
Install NNCF from source:
pipinstallgit+https://github.com/openvinotoolkit/nncf.git
Running pre-optimized model
fromoptimum.intel.openvinoimportOVStableDiffusionPipelinepipe=OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt="Super cute fluffy cat warrior in armor, photorealistic, 4K, ultra detailed, vray rendering, unreal engine"output=pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
HW Requirements for QAT
The minimal HW setup for the run is GPU with 24GB of memory.
NOTE: Potentially you can set the number of training steps to 0 and it will lead to Post-Training Quantization. CPU should be enough in this case but you may need to modify the scipt.
Run PTQ:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=0--model_id="runwayml/stable-diffusion-v1-5"
On a part of "laion/laion2B-en" dateset:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--model_id="stabilityai/stable-diffusion-2-1"--max_train_samples=800--opt_init_steps=800--dataloader_num_workers=6
NOTE: You may need to paly with seed or do one more try to get good results. The results can be better if to use the same dataset that was used to train the original model.
Run QAT:
  • The best results are achieved with Knowledge Distillation and EMA techniques when tuning on a laion dataset:
CUDA_VISIBLE_DEVICES=2pythonquantize.py--ema_device="cpu"--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--max_train_steps=4096--model_id="runwayml/stable-diffusion-v1-5"--max_train_samples=10000--dataloader_num_workers=8--gradient_checkpointing--output_dir=sd-1-5-quantied-laion
  • Tune model parameters on a target dataset with QAT:
pythonquantize.py--use_kd--ema_device="cpu"--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=15000
--ema_device="cpu" and --gradient_checkpointing are used to save GPU mememory.
  • Tune only quantization parameters for a short time. You can use smaller training steps and any relevant dataset:
pythonquantize.py--use_kd--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="laion/laion2B-en"--tune_quantizers_only--max_train_steps=256--max_train_samples=10000--opt_init_steps=800--opt_init_type="min_max"
About
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Stable Diffusion Quantization
This repository demonstrates Quantization-aware Training (QAT) of Stable Diffusion Unet model wich is the most time-consuming element of the whole pipeline. The quantized model is exported to the OpenVINO IR.
The expected speedup from quantization is ~1.7x (for CPUs w/ Intel DL Boost) and can very depeding on the HW.
Knowledge distillation and EMA techniques can be used to improve the model accuracy.
Prerequisites
pipinstall-rrequirements.txt
Install NNCF from source:
pipinstallgit+https://github.com/openvinotoolkit/nncf.git
Running pre-optimized model
fromoptimum.intel.openvinoimportOVStableDiffusionPipelinepipe=OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt="Super cute fluffy cat warrior in armor, photorealistic, 4K, ultra detailed, vray rendering, unreal engine"output=pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
HW Requirements for QAT
The minimal HW setup for the run is GPU with 24GB of memory.
NOTE: Potentially you can set the number of training steps to 0 and it will lead to Post-Training Quantization. CPU should be enough in this case but you may need to modify the scipt.
Run PTQ:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=0--model_id="runwayml/stable-diffusion-v1-5"
On a part of "laion/laion2B-en" dateset:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--model_id="stabilityai/stable-diffusion-2-1"--max_train_samples=800--opt_init_steps=800--dataloader_num_workers=6
NOTE: You may need to paly with seed or do one more try to get good results. The results can be better if to use the same dataset that was used to train the original model.
Run QAT:
  • The best results are achieved with Knowledge Distillation and EMA techniques when tuning on a laion dataset:
CUDA_VISIBLE_DEVICES=2pythonquantize.py--ema_device="cpu"--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--max_train_steps=4096--model_id="runwayml/stable-diffusion-v1-5"--max_train_samples=10000--dataloader_num_workers=8--gradient_checkpointing--output_dir=sd-1-5-quantied-laion
  • Tune model parameters on a target dataset with QAT:
pythonquantize.py--use_kd--ema_device="cpu"--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=15000
--ema_device="cpu" and --gradient_checkpointing are used to save GPU mememory.
  • Tune only quantization parameters for a short time. You can use smaller training steps and any relevant dataset:
pythonquantize.py--use_kd--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="laion/laion2B-en"--tune_quantizers_only--max_train_steps=256--max_train_samples=10000--opt_init_steps=800--opt_init_type="min_max"
About
Quantization of Stable Diffusion POC
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Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
Stable Diffusion Quantization
This repository demonstrates Quantization-aware Training (QAT) of Stable Diffusion Unet model wich is the most time-consuming element of the whole pipeline. The quantized model is exported to the OpenVINO IR.
The expected speedup from quantization is ~1.7x (for CPUs w/ Intel DL Boost) and can very depeding on the HW.
Knowledge distillation and EMA techniques can be used to improve the model accuracy.
Prerequisites
pipinstall-rrequirements.txt
Install NNCF from source:
pipinstallgit+https://github.com/openvinotoolkit/nncf.git
Running pre-optimized model
fromoptimum.intel.openvinoimportOVStableDiffusionPipelinepipe=OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt="Super cute fluffy cat warrior in armor, photorealistic, 4K, ultra detailed, vray rendering, unreal engine"output=pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
HW Requirements for QAT
The minimal HW setup for the run is GPU with 24GB of memory.
NOTE: Potentially you can set the number of training steps to 0 and it will lead to Post-Training Quantization. CPU should be enough in this case but you may need to modify the scipt.
Run PTQ:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=0--model_id="runwayml/stable-diffusion-v1-5"
On a part of "laion/laion2B-en" dateset:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--model_id="stabilityai/stable-diffusion-2-1"--max_train_samples=800--opt_init_steps=800--dataloader_num_workers=6
NOTE: You may need to paly with seed or do one more try to get good results. The results can be better if to use the same dataset that was used to train the original model.
Run QAT:
  • The best results are achieved with Knowledge Distillation and EMA techniques when tuning on a laion dataset:
CUDA_VISIBLE_DEVICES=2pythonquantize.py--ema_device="cpu"--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--max_train_steps=4096--model_id="runwayml/stable-diffusion-v1-5"--max_train_samples=10000--dataloader_num_workers=8--gradient_checkpointing--output_dir=sd-1-5-quantied-laion
  • Tune model parameters on a target dataset with QAT:
pythonquantize.py--use_kd--ema_device="cpu"--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=15000
--ema_device="cpu" and --gradient_checkpointing are used to save GPU mememory.
  • Tune only quantization parameters for a short time. You can use smaller training steps and any relevant dataset:
pythonquantize.py--use_kd--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="laion/laion2B-en"--tune_quantizers_only--max_train_steps=256--max_train_samples=10000--opt_init_steps=800--opt_init_type="min_max"
About
Quantization of Stable Diffusion POC
Resources
Readme
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Activity
Stars
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Watchers
1 watching
Forks
1 fork
Report repository
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injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Stable Diffusion Quantization
This repository demonstrates Quantization-aware Training (QAT) of Stable Diffusion Unet model wich is the most time-consuming element of the whole pipeline. The quantized model is exported to the OpenVINO IR.
The expected speedup from quantization is ~1.7x (for CPUs w/ Intel DL Boost) and can very depeding on the HW.
Knowledge distillation and EMA techniques can be used to improve the model accuracy.
Prerequisites
pipinstall-rrequirements.txt
Install NNCF from source:
pipinstallgit+https://github.com/openvinotoolkit/nncf.git
Running pre-optimized model
fromoptimum.intel.openvinoimportOVStableDiffusionPipelinepipe=OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt="Super cute fluffy cat warrior in armor, photorealistic, 4K, ultra detailed, vray rendering, unreal engine"output=pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
HW Requirements for QAT
The minimal HW setup for the run is GPU with 24GB of memory.
NOTE: Potentially you can set the number of training steps to 0 and it will lead to Post-Training Quantization. CPU should be enough in this case but you may need to modify the scipt.
Run PTQ:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=0--model_id="runwayml/stable-diffusion-v1-5"
On a part of "laion/laion2B-en" dateset:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--model_id="stabilityai/stable-diffusion-2-1"--max_train_samples=800--opt_init_steps=800--dataloader_num_workers=6
NOTE: You may need to paly with seed or do one more try to get good results. The results can be better if to use the same dataset that was used to train the original model.
Run QAT:
  • The best results are achieved with Knowledge Distillation and EMA techniques when tuning on a laion dataset:
CUDA_VISIBLE_DEVICES=2pythonquantize.py--ema_device="cpu"--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--max_train_steps=4096--model_id="runwayml/stable-diffusion-v1-5"--max_train_samples=10000--dataloader_num_workers=8--gradient_checkpointing--output_dir=sd-1-5-quantied-laion
  • Tune model parameters on a target dataset with QAT:
pythonquantize.py--use_kd--ema_device="cpu"--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=15000
--ema_device="cpu" and --gradient_checkpointing are used to save GPU mememory.
  • Tune only quantization parameters for a short time. You can use smaller training steps and any relevant dataset:
pythonquantize.py--use_kd--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="laion/laion2B-en"--tune_quantizers_only--max_train_steps=256--max_train_samples=10000--opt_init_steps=800--opt_init_type="min_max"
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Stable Diffusion Quantization
This repository demonstrates Quantization-aware Training (QAT) of Stable Diffusion Unet model wich is the most time-consuming element of the whole pipeline. The quantized model is exported to the OpenVINO IR.
The expected speedup from quantization is ~1.7x (for CPUs w/ Intel DL Boost) and can very depeding on the HW.
Knowledge distillation and EMA techniques can be used to improve the model accuracy.
Prerequisites
pipinstall-rrequirements.txt
Install NNCF from source:
pipinstallgit+https://github.com/openvinotoolkit/nncf.git
Running pre-optimized model
fromoptimum.intel.openvinoimportOVStableDiffusionPipelinepipe=OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt="Super cute fluffy cat warrior in armor, photorealistic, 4K, ultra detailed, vray rendering, unreal engine"output=pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
HW Requirements for QAT
The minimal HW setup for the run is GPU with 24GB of memory.
NOTE: Potentially you can set the number of training steps to 0 and it will lead to Post-Training Quantization. CPU should be enough in this case but you may need to modify the scipt.
Run PTQ:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=0--model_id="runwayml/stable-diffusion-v1-5"
On a part of "laion/laion2B-en" dateset:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--model_id="stabilityai/stable-diffusion-2-1"--max_train_samples=800--opt_init_steps=800--dataloader_num_workers=6
NOTE: You may need to paly with seed or do one more try to get good results. The results can be better if to use the same dataset that was used to train the original model.
Run QAT:
  • The best results are achieved with Knowledge Distillation and EMA techniques when tuning on a laion dataset:
CUDA_VISIBLE_DEVICES=2pythonquantize.py--ema_device="cpu"--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--max_train_steps=4096--model_id="runwayml/stable-diffusion-v1-5"--max_train_samples=10000--dataloader_num_workers=8--gradient_checkpointing--output_dir=sd-1-5-quantied-laion
  • Tune model parameters on a target dataset with QAT:
pythonquantize.py--use_kd--ema_device="cpu"--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=15000
--ema_device="cpu" and --gradient_checkpointing are used to save GPU mememory.
  • Tune only quantization parameters for a short time. You can use smaller training steps and any relevant dataset:
pythonquantize.py--use_kd--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="laion/laion2B-en"--tune_quantizers_only--max_train_steps=256--max_train_samples=10000--opt_init_steps=800--opt_init_type="min_max"
About
Quantization of Stable Diffusion POC
Resources
Readme
Apache-2.0 license
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Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Repository files navigation
Stable Diffusion Quantization
This repository demonstrates Quantization-aware Training (QAT) of Stable Diffusion Unet model wich is the most time-consuming element of the whole pipeline. The quantized model is exported to the OpenVINO IR.
The expected speedup from quantization is ~1.7x (for CPUs w/ Intel DL Boost) and can very depeding on the HW.
Knowledge distillation and EMA techniques can be used to improve the model accuracy.
Prerequisites
pipinstall-rrequirements.txt
Install NNCF from source:
pipinstallgit+https://github.com/openvinotoolkit/nncf.git
Running pre-optimized model
fromoptimum.intel.openvinoimportOVStableDiffusionPipelinepipe=OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-quantized", compile=False)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt="Super cute fluffy cat warrior in armor, photorealistic, 4K, ultra detailed, vray rendering, unreal engine"output=pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
HW Requirements for QAT
The minimal HW setup for the run is GPU with 24GB of memory.
NOTE: Potentially you can set the number of training steps to 0 and it will lead to Post-Training Quantization. CPU should be enough in this case but you may need to modify the scipt.
Run PTQ:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=0--model_id="runwayml/stable-diffusion-v1-5"
On a part of "laion/laion2B-en" dateset:
pythonquantize.py--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--model_id="stabilityai/stable-diffusion-2-1"--max_train_samples=800--opt_init_steps=800--dataloader_num_workers=6
NOTE: You may need to paly with seed or do one more try to get good results. The results can be better if to use the same dataset that was used to train the original model.
Run QAT:
  • The best results are achieved with Knowledge Distillation and EMA techniques when tuning on a laion dataset:
CUDA_VISIBLE_DEVICES=2pythonquantize.py--ema_device="cpu"--use_kd--center_crop--random_flip--dataset_name="laion/laion2B-en"--max_train_steps=4096--model_id="runwayml/stable-diffusion-v1-5"--max_train_samples=10000--dataloader_num_workers=8--gradient_checkpointing--output_dir=sd-1-5-quantied-laion
  • Tune model parameters on a target dataset with QAT:
pythonquantize.py--use_kd--ema_device="cpu"--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="lambdalabs/pokemon-blip-captions"--max_train_steps=15000
--ema_device="cpu" and --gradient_checkpointing are used to save GPU mememory.
  • Tune only quantization parameters for a short time. You can use smaller training steps and any relevant dataset:
pythonquantize.py--use_kd--model_id="runwayml/stable-diffusion-v1-5"--center_crop--random_flip--gradient_checkpointing--dataloader_num_workers=8--dataset_name="laion/laion2B-en"--tune_quantizers_only--max_train_steps=256--max_train_samples=10000--opt_init_steps=800--opt_init_type="min_max"
About
Quantization of Stable Diffusion POC
Resources
Readme
Apache-2.0 license
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
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