openpilot v0.11.1 release
date: 2026-06-04T09:49:56 master commit: c0ab3550eca2e9daf197c46b7e4b24aa9637cf2e
This commit is contained in:
Vehicle Researcher
@@ -0,0 +1,237 @@
|
||||
import os
|
||||
from extra.export_model import compile_net, jit_model, dtype_to_js_type
|
||||
from extra.f16_decompress import u32_to_f16
|
||||
from examples.stable_diffusion import StableDiffusion
|
||||
from tinygrad.nn.state import get_state_dict, safe_save, safe_load_metadata, torch_load, load_state_dict
|
||||
from tinygrad.tensor import Tensor
|
||||
from tinygrad import dtypes
|
||||
from tinygrad.helpers import DEV, fetch
|
||||
from typing import NamedTuple, Any, List
|
||||
import requests
|
||||
import argparse
|
||||
import numpy as np
|
||||
|
||||
def convert_f32_to_f16(input_file, output_file):
|
||||
with open(input_file, 'rb') as f:
|
||||
metadata_length_bytes = f.read(8)
|
||||
metadata_length = int.from_bytes(metadata_length_bytes, byteorder='little', signed=False)
|
||||
metadata_json_bytes = f.read(metadata_length)
|
||||
float32_values = np.fromfile(f, dtype=np.float32)
|
||||
|
||||
first_text_model_offset = 3772703308
|
||||
num_elements = int((first_text_model_offset)/4)
|
||||
front_float16_values = float32_values[:num_elements].astype(np.float16)
|
||||
rest_float32_values = float32_values[num_elements:]
|
||||
|
||||
with open(output_file, 'wb') as f:
|
||||
f.write(metadata_length_bytes)
|
||||
f.write(metadata_json_bytes)
|
||||
front_float16_values.tofile(f)
|
||||
rest_float32_values.tofile(f)
|
||||
|
||||
def split_safetensor(fn):
|
||||
_, data_start, metadata = safe_load_metadata(fn)
|
||||
text_model_offset = 3772703308
|
||||
chunk_size = 536870912
|
||||
|
||||
for k in metadata:
|
||||
# safetensor is in fp16, except for text moel
|
||||
if (metadata[k]["data_offsets"][0] < text_model_offset):
|
||||
metadata[k]["data_offsets"][0] = int(metadata[k]["data_offsets"][0]/2)
|
||||
metadata[k]["data_offsets"][1] = int(metadata[k]["data_offsets"][1]/2)
|
||||
|
||||
last_offset = 0
|
||||
part_end_offsets = []
|
||||
|
||||
for k in metadata:
|
||||
offset = metadata[k]['data_offsets'][0]
|
||||
|
||||
if offset == text_model_offset:
|
||||
break
|
||||
|
||||
part_offset = offset - last_offset
|
||||
|
||||
if (part_offset >= chunk_size):
|
||||
part_end_offsets.append(data_start+offset)
|
||||
last_offset = offset
|
||||
|
||||
text_model_start = int(text_model_offset/2)
|
||||
net_bytes = bytes(open(fn, 'rb').read())
|
||||
part_end_offsets.append(text_model_start+data_start)
|
||||
cur_pos = 0
|
||||
|
||||
for i, end_pos in enumerate(part_end_offsets):
|
||||
with open(os.path.join(os.path.dirname(__file__), f'./net_part{i}.safetensors'), "wb+") as f:
|
||||
f.write(net_bytes[cur_pos:end_pos])
|
||||
cur_pos = end_pos
|
||||
|
||||
with open(os.path.join(os.path.dirname(__file__), f'./net_textmodel.safetensors'), "wb+") as f:
|
||||
f.write(net_bytes[text_model_start+data_start:])
|
||||
|
||||
return part_end_offsets
|
||||
|
||||
def fetch_dep(file, url):
|
||||
with open(file, "w", encoding="utf-8") as f:
|
||||
f.write(requests.get(url).text.replace("https://huggingface.co/wpmed/tinygrad-sd-f16/raw/main/bpe_simple_vocab_16e6.mjs", "./bpe_simple_vocab_16e6.mjs"))
|
||||
|
||||
if __name__ == "__main__":
|
||||
fetch_dep(os.path.join(os.path.dirname(__file__), "clip_tokenizer.js"), "https://huggingface.co/wpmed/tinygrad-sd-f16/raw/main/clip_tokenizer.js")
|
||||
fetch_dep(os.path.join(os.path.dirname(__file__), "bpe_simple_vocab_16e6.mjs"), "https://huggingface.co/wpmed/tinygrad-sd-f16/raw/main/bpe_simple_vocab_16e6.mjs")
|
||||
parser = argparse.ArgumentParser(description='Run Stable Diffusion', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--remoteweights', action='store_true', help="Use safetensors from Huggingface, or from local")
|
||||
args = parser.parse_args()
|
||||
DEV.value = "WEBGPU"
|
||||
|
||||
model = StableDiffusion()
|
||||
|
||||
# load in weights
|
||||
load_state_dict(model, torch_load(fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt'))['state_dict'], strict=False)
|
||||
|
||||
class Step(NamedTuple):
|
||||
name: str = ""
|
||||
input: List[Tensor] = []
|
||||
forward: Any = None
|
||||
|
||||
sub_steps = [
|
||||
Step(name = "textModel", input = [Tensor.randint(1, 77, low=0, high=49408, dtype=dtypes.int32)], forward = model.cond_stage_model.transformer.text_model),
|
||||
Step(name = "diffusor", input = [Tensor.randn(1, 77, 768), Tensor.randn(1, 77, 768), Tensor.randn(1,4,64,64), Tensor.rand(1), Tensor.randn(1), Tensor.randn(1), Tensor.randn(1)], forward = model),
|
||||
Step(name = "decoder", input = [Tensor.randn(1,4,64,64)], forward = model.decode),
|
||||
Step(name = "f16tof32", input = [Tensor.randn(2097120, dtype=dtypes.uint32)], forward = u32_to_f16)
|
||||
]
|
||||
|
||||
prg = ""
|
||||
|
||||
def fixup_code(code, key):
|
||||
code = code.replace(key, 'main')\
|
||||
.replace("var<uniform> INFINITY : f32;\n", "fn inf(a: f32) -> f32 { return a/0.0; }\n")\
|
||||
.replace("@group(0) @binding(0)", "")\
|
||||
.replace("INFINITY", "inf(1.0)")
|
||||
|
||||
for i in range(1,9): code = code.replace(f"binding({i})", f"binding({i-1})")
|
||||
return code
|
||||
|
||||
def compile_step(model, step: Step):
|
||||
linear, output_bufs = jit_model(step, *step.input)
|
||||
functions, statements, bufs, _ = compile_net(linear, output_bufs)
|
||||
state = get_state_dict(model)
|
||||
weights = {(id(b), b.offset, b.size, b.dtype): name for name, x in state.items() if (b:=x.uop.base.realized) is not None}
|
||||
kernel_code = '\n\n'.join([f"const {key} = `{fixup_code(code, key)}`;" for key, code in functions.items()])
|
||||
kernel_names = ', '.join([name for (name, _, _, _) in statements])
|
||||
input_names = [f"input{i}" for i in range(len(step.input))]
|
||||
output_names = [f"output{i}" for i in range(len(output_bufs))]
|
||||
input_buf_types = [dtype_to_js_type(bufs[inp_name][1]) for inp_name in input_names]
|
||||
output_buf_types = [dtype_to_js_type(bufs[out_name][1]) for out_name in output_names]
|
||||
kernel_calls = '\n '.join([f"addComputePass(device, commandEncoder, piplines[{i}], [{', '.join(args)}], {global_size});" for i, (_name, args, global_size, _local_size) in enumerate(statements) ])
|
||||
exported_bufs = '\n '.join([f"const {name} = " + (f"createEmptyBuf(device, {size});" if _key not in weights else f"createWeightBuf(device, {size}, getTensorBuffer(safetensor, metadata['{weights[_key]}'], '{weights[_key]}'))") + ";" for name,(size,dtype,_key) in bufs.items()])
|
||||
gpu_write_bufs = '\n '.join([f"const gpuWriteBuffer{i} = device.createBuffer({{size:input{i}.size, usage: GPUBufferUsage.COPY_SRC | GPUBufferUsage.MAP_WRITE }});" for i in range(len(input_names))])
|
||||
input_writer = '\n '.join([f"await gpuWriteBuffer{i}.mapAsync(GPUMapMode.WRITE);\n new {input_buf_types[i]}(gpuWriteBuffer{i}.getMappedRange()).set(" + f'data{i});' + f"\n gpuWriteBuffer{i}.unmap();\ncommandEncoder.copyBufferToBuffer(gpuWriteBuffer{i}, 0, input{i}, 0, gpuWriteBuffer{i}.size);" for i,_ in enumerate(input_names)])
|
||||
return f"""\n var {step.name} = function() {{
|
||||
|
||||
{kernel_code}
|
||||
|
||||
return {{
|
||||
"setup": async (device, safetensor) => {{
|
||||
const metadata = safetensor ? getTensorMetadata(safetensor[0]) : null;
|
||||
|
||||
{exported_bufs}
|
||||
|
||||
{gpu_write_bufs}
|
||||
const gpuReadBuffer = device.createBuffer({{ size: output0.size, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ }});
|
||||
|
||||
const kernels = [{kernel_names}];
|
||||
const piplines = await Promise.all(kernels.map(name => device.createComputePipelineAsync({{layout: "auto", compute: {{ module: device.createShaderModule({{ code: name }}), entryPoint: "main" }}}})));
|
||||
|
||||
return async ({",".join([f'data{i}' for i in range(len(input_names))])}) => {{
|
||||
const commandEncoder = device.createCommandEncoder();
|
||||
|
||||
{input_writer}
|
||||
|
||||
{kernel_calls}
|
||||
commandEncoder.copyBufferToBuffer(output0, 0, gpuReadBuffer, 0, output0.size);
|
||||
const gpuCommands = commandEncoder.finish();
|
||||
device.queue.submit([gpuCommands]);
|
||||
|
||||
await gpuReadBuffer.mapAsync(GPUMapMode.READ);
|
||||
const resultBuffer = new {output_buf_types[0]}(gpuReadBuffer.size/{bufs[output_names[0]][1].itemsize});
|
||||
resultBuffer.set(new {output_buf_types[0]}(gpuReadBuffer.getMappedRange()));
|
||||
gpuReadBuffer.unmap();
|
||||
return resultBuffer;
|
||||
}}
|
||||
}}
|
||||
}}
|
||||
}}
|
||||
"""
|
||||
|
||||
for step in sub_steps:
|
||||
print(f'Executing step={step.name}')
|
||||
prg += compile_step(model, step)
|
||||
|
||||
if step.name == "diffusor":
|
||||
if args.remoteweights:
|
||||
base_url = "https://huggingface.co/wpmed/stable-diffusion-f16-new/resolve/main"
|
||||
else:
|
||||
state = get_state_dict(model)
|
||||
safe_save(state, os.path.join(os.path.dirname(__file__), "net.safetensors"))
|
||||
convert_f32_to_f16(os.path.join(os.path.dirname(__file__), "./net.safetensors"), os.path.join(os.path.dirname(__file__), "./net_conv.safetensors"))
|
||||
split_safetensor(os.path.join(os.path.dirname(__file__), "./net_conv.safetensors"))
|
||||
os.remove(os.path.join(os.path.dirname(__file__), "net.safetensors"))
|
||||
os.remove(os.path.join(os.path.dirname(__file__), "net_conv.safetensors"))
|
||||
base_url = "."
|
||||
|
||||
prekernel = f"""
|
||||
window.MODEL_BASE_URL= "{base_url}";
|
||||
const getTensorMetadata = (safetensorBuffer) => {{
|
||||
const metadataLength = Number(new DataView(safetensorBuffer.buffer).getBigUint64(0, true));
|
||||
const metadata = JSON.parse(new TextDecoder("utf8").decode(safetensorBuffer.subarray(8, 8 + metadataLength)));
|
||||
return Object.fromEntries(Object.entries(metadata).filter(([k, v]) => k !== "__metadata__").map(([k, v]) => [k, {{...v, data_offsets: v.data_offsets.map(x => 8 + metadataLength + x)}}]));
|
||||
}};
|
||||
|
||||
const getTensorBuffer = (safetensorParts, tensorMetadata, key) => {{
|
||||
let selectedPart = 0;
|
||||
let counter = 0;
|
||||
let partStartOffsets = [1131408336, 2227518416, 3308987856, 4265298864];
|
||||
let correctedOffsets = tensorMetadata.data_offsets;
|
||||
let prev_offset = 0;
|
||||
|
||||
for (let start of partStartOffsets) {{
|
||||
prev_offset = (counter == 0) ? 0 : partStartOffsets[counter-1];
|
||||
|
||||
if (tensorMetadata.data_offsets[0] < start) {{
|
||||
selectedPart = counter;
|
||||
correctedOffsets = [correctedOffsets[0]-prev_offset, correctedOffsets[1]-prev_offset];
|
||||
break;
|
||||
}}
|
||||
|
||||
counter++;
|
||||
}}
|
||||
|
||||
return safetensorParts[selectedPart].subarray(...correctedOffsets);
|
||||
}}
|
||||
|
||||
const getWeight = (safetensors, key) => {{
|
||||
let uint8Data = getTensorBuffer(safetensors, getTensorMetadata(safetensors[0])[key], key);
|
||||
return new Float32Array(uint8Data.buffer, uint8Data.byteOffset, uint8Data.byteLength / Float32Array.BYTES_PER_ELEMENT);
|
||||
}}
|
||||
|
||||
const createEmptyBuf = (device, size) => {{
|
||||
return device.createBuffer({{size, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST }});
|
||||
}};
|
||||
|
||||
const createWeightBuf = (device, size, data) => {{
|
||||
const buf = device.createBuffer({{ mappedAtCreation: true, size, usage: GPUBufferUsage.STORAGE }});
|
||||
new Uint8Array(buf.getMappedRange()).set(data);
|
||||
buf.unmap();
|
||||
return buf;
|
||||
}};
|
||||
|
||||
const addComputePass = (device, commandEncoder, pipeline, bufs, workgroup) => {{
|
||||
const bindGroup = device.createBindGroup({{layout: pipeline.getBindGroupLayout(0), entries: bufs.map((buffer, index) => ({{ binding: index, resource: {{ buffer }} }}))}});
|
||||
const passEncoder = commandEncoder.beginComputePass();
|
||||
passEncoder.setPipeline(pipeline);
|
||||
passEncoder.setBindGroup(0, bindGroup);
|
||||
passEncoder.dispatchWorkgroups(...workgroup);
|
||||
passEncoder.end();
|
||||
}};"""
|
||||
|
||||
with open(os.path.join(os.path.dirname(__file__), "net.js"), "w") as text_file:
|
||||
text_file.write(prekernel + prg)
|
||||
@@ -0,0 +1,632 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>tinygrad has WebGPU</title>
|
||||
<style>
|
||||
/* General Reset */
|
||||
* {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
body {
|
||||
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
|
||||
background: #f4f7fb;
|
||||
color: #333;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
min-height: 100vh;
|
||||
padding: 20px;
|
||||
flex-direction: column;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
h1 {
|
||||
font-size: 2.2rem;
|
||||
margin-bottom: 20px;
|
||||
color: #4A90E2;
|
||||
}
|
||||
|
||||
#wgpuError {
|
||||
color: red;
|
||||
font-size: 1.2rem;
|
||||
margin-top: 20px;
|
||||
display: none;
|
||||
}
|
||||
|
||||
#sdTitle {
|
||||
font-size: 1.5rem;
|
||||
margin-bottom: 30px;
|
||||
}
|
||||
|
||||
#mybox {
|
||||
background: #ffffff;
|
||||
padding: 15px;
|
||||
border-radius: 10px;
|
||||
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
|
||||
width: 120%;
|
||||
max-width: 550px;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
input[type="text"] {
|
||||
width: 100%;
|
||||
padding: 12px;
|
||||
margin-bottom: 20px;
|
||||
font-size: 1rem;
|
||||
border: 1px solid #ccc;
|
||||
border-radius: 8px;
|
||||
outline: none;
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
input[type="text"]:focus {
|
||||
border-color: #4A90E2;
|
||||
}
|
||||
|
||||
label {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
font-size: 1rem;
|
||||
margin-bottom: 15px;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
input[type="range"] {
|
||||
width: 100%;
|
||||
margin-left: 10px;
|
||||
-webkit-appearance: none;
|
||||
appearance: none;
|
||||
height: 8px;
|
||||
border-radius: 4px;
|
||||
background: #ddd;
|
||||
outline: none;
|
||||
transition: background 0.3s ease;
|
||||
}
|
||||
|
||||
input[type="range"]:focus {
|
||||
background: #4A90E2;
|
||||
}
|
||||
|
||||
#stepRange,
|
||||
#guidanceRange {
|
||||
width: 80%;
|
||||
}
|
||||
|
||||
span {
|
||||
font-size: 1.1rem;
|
||||
font-weight: 600;
|
||||
color: #333;
|
||||
}
|
||||
|
||||
input[type="button"] {
|
||||
padding: 12px 25px;
|
||||
background-color: #4A90E2;
|
||||
color: #fff;
|
||||
font-size: 1.2rem;
|
||||
border: none;
|
||||
border-radius: 8px;
|
||||
cursor: pointer;
|
||||
transition: background-color 0.3s ease;
|
||||
width: 100%;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
input[type="button"]:disabled {
|
||||
background-color: #ccc;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
|
||||
input[type="button"]:hover {
|
||||
background-color: #357ABD;
|
||||
}
|
||||
|
||||
#divModelDl, #divStepProgress {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
#modelDlProgressBar,
|
||||
#progressBar {
|
||||
width: 80%;
|
||||
height: 12px;
|
||||
border-radius: 6px;
|
||||
background-color: #e0e0e0;
|
||||
}
|
||||
|
||||
#modelDlProgressBar::-webkit-progress-bar,
|
||||
#progressBar::-webkit-progress-bar {
|
||||
border-radius: 6px;
|
||||
}
|
||||
|
||||
#modelDlProgressValue, #progressFraction {
|
||||
font-size: 1rem;
|
||||
font-weight: 600;
|
||||
color: #333;
|
||||
}
|
||||
|
||||
canvas {
|
||||
max-width: 100%;
|
||||
max-height: 450px;
|
||||
margin-top: 10px;
|
||||
border-radius: 8px;
|
||||
border: 1px solid #ddd;
|
||||
}
|
||||
</style>
|
||||
|
||||
<script type="module">
|
||||
import ClipTokenizer from './clip_tokenizer.js';
|
||||
window.clipTokenizer = new ClipTokenizer();
|
||||
</script>
|
||||
<script src="./net.js"></script>
|
||||
</head>
|
||||
<body>
|
||||
<h1 id="wgpuError" style="display: none;">WebGPU is not supported in this browser</h1>
|
||||
<h1 id="sdTitle">StableDiffusion powered by <a href="https://github.com/tinygrad/tinygrad" target="_blank" style="color: #4A90E2;">tinygrad</a></h1>
|
||||
<a href="https://github.com/tinygrad/tinygrad" target="_blank" style="position: absolute; top: 20px; right: 20px;">
|
||||
<img src="https://upload.wikimedia.org/wikipedia/commons/9/91/Octicons-mark-github.svg"
|
||||
alt="GitHub Logo"
|
||||
style="width: 32px; height: 32px;">
|
||||
</a>
|
||||
|
||||
<div id="mybox">
|
||||
<form id="promptForm">
|
||||
<input id="promptText" type="text" placeholder="Enter your prompt here" value="a human standing on the surface of mars">
|
||||
|
||||
<label>
|
||||
Steps: <span id="stepValue">9</span>
|
||||
<input id="stepRange" type="range" min="5" max="20" value="9" step="1">
|
||||
</label>
|
||||
|
||||
<label>
|
||||
Guidance: <span id="guidanceValue">8.0</span>
|
||||
<input id="guidanceRange" type="range" min="3" max="15" value="8.0" step="0.1">
|
||||
</label>
|
||||
|
||||
<input id="btnRunNet" type="button" value="Run" disabled>
|
||||
</form>
|
||||
|
||||
<div id="divModelDl" style="display: flex; align-items: center; width: 100%; gap: 10px;">
|
||||
<span id="modelDlTitle">Downloading model</span>
|
||||
<progress id="modelDlProgressBar" value="0" max="100" style="flex-grow: 1;"></progress>
|
||||
<span id="modelDlProgressValue"></span>
|
||||
</div>
|
||||
|
||||
<div id="divStepProgress" style="display: none; align-items: center; width: 100%; gap: 10px;">
|
||||
<progress id="progressBar" value="0" max="100" style="flex-grow: 1;"></progress>
|
||||
<span id="progressFraction"></span>
|
||||
</div>
|
||||
|
||||
<div id="divStepTime" style="display: none; align-items: center; width: 100%; gap: 10px;">
|
||||
<span id="stepTimeValue">0 ms</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<canvas id="canvas" width="512" height="512"></canvas>
|
||||
|
||||
<script>
|
||||
let f16decomp = null;
|
||||
|
||||
function initDb() {
|
||||
return new Promise((resolve, reject) => {
|
||||
let db;
|
||||
const request = indexedDB.open('tinydb', 1);
|
||||
request.onerror = (event) => {
|
||||
console.error('Database error:', event.target.error);
|
||||
resolve(null);
|
||||
};
|
||||
|
||||
request.onsuccess = (event) => {
|
||||
db = event.target.result;
|
||||
console.log("Db initialized.");
|
||||
resolve(db);
|
||||
};
|
||||
|
||||
request.onupgradeneeded = (event) => {
|
||||
db = event.target.result;
|
||||
if (!db.objectStoreNames.contains('tensors')) {
|
||||
db.createObjectStore('tensors', { keyPath: 'id' });
|
||||
}
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
function saveTensorToDb(db, id, tensor) {
|
||||
return readTensorFromDb(db, id).then((result) => {
|
||||
if (!result) {
|
||||
new Promise((resolve, reject) => {
|
||||
if (db == null) {
|
||||
resolve(null);
|
||||
}
|
||||
|
||||
const transaction = db.transaction(['tensors'], 'readwrite');
|
||||
const store = transaction.objectStore('tensors');
|
||||
const request = store.put({ id: id, content: tensor });
|
||||
|
||||
transaction.onabort = (event) => {
|
||||
console.log("Transaction error while saving tensor: " + event.target.error);
|
||||
resolve(null);
|
||||
};
|
||||
|
||||
request.onsuccess = () => {
|
||||
console.log('Tensor saved successfully.');
|
||||
resolve();
|
||||
};
|
||||
|
||||
request.onerror = (event) => {
|
||||
console.error('Tensor save failed:', event.target.error);
|
||||
resolve(null);
|
||||
};
|
||||
});
|
||||
} else {
|
||||
return null;
|
||||
}
|
||||
}).catch(()=> null);
|
||||
}
|
||||
|
||||
function readTensorFromDb(db, id) {
|
||||
return new Promise((resolve, reject) => {
|
||||
if (db == null) {
|
||||
resolve(null);
|
||||
}
|
||||
|
||||
const transaction = db.transaction(['tensors'], 'readonly');
|
||||
const store = transaction.objectStore('tensors');
|
||||
const request = store.get(id);
|
||||
|
||||
transaction.onabort = (event) => {
|
||||
console.log("Transaction error while reading tensor: " + event.target.error);
|
||||
resolve(null);
|
||||
};
|
||||
|
||||
request.onsuccess = (event) => {
|
||||
const result = event.target.result;
|
||||
if (result) {
|
||||
resolve(result);
|
||||
} else {
|
||||
resolve(null);
|
||||
}
|
||||
};
|
||||
|
||||
request.onerror = (event) => {
|
||||
console.error('Tensor retrieve failed: ', event.target.error);
|
||||
resolve(null);
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
window.addEventListener('load', async function() {
|
||||
if (!navigator.gpu) {
|
||||
document.getElementById("wgpuError").style.display = "block";
|
||||
document.getElementById("sdTitle").style.display = "none";
|
||||
return;
|
||||
}
|
||||
|
||||
let db = await initDb();
|
||||
|
||||
const ctx = document.getElementById("canvas").getContext("2d", { willReadFrequently: true });
|
||||
let labels, nets, safetensorParts;
|
||||
|
||||
const getDevice = async () => {
|
||||
const adapter = await navigator.gpu.requestAdapter();
|
||||
const requiredLimits = {};
|
||||
const maxBufferSizeInSDModel = 1073741824;
|
||||
requiredLimits.maxStorageBufferBindingSize = maxBufferSizeInSDModel;
|
||||
requiredLimits.maxBufferSize = maxBufferSizeInSDModel;
|
||||
|
||||
return await adapter.requestDevice({
|
||||
requiredLimits,
|
||||
requiredFeatures: ["shader-f16"],
|
||||
powerPreference: "high-performance"
|
||||
});
|
||||
};
|
||||
|
||||
const timer = async (func, label = "") => {
|
||||
const start = performance.now();
|
||||
const out = await func();
|
||||
const delta = (performance.now() - start).toFixed(1)
|
||||
console.log(`${delta} ms ${label}`);
|
||||
return out;
|
||||
}
|
||||
|
||||
const getProgressDlForPart = async (part, progressCallback) => {
|
||||
const response = await fetch(part);
|
||||
const contentLength = response.headers.get('content-length');
|
||||
const total = parseInt(contentLength, 10);
|
||||
|
||||
const res = new Response(new ReadableStream({
|
||||
async start(controller) {
|
||||
const reader = response.body.getReader();
|
||||
for (;;) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
progressCallback(part, value.byteLength, total);
|
||||
controller.enqueue(value);
|
||||
}
|
||||
|
||||
controller.close();
|
||||
},
|
||||
}));
|
||||
|
||||
return res.arrayBuffer();
|
||||
};
|
||||
|
||||
const getAndDecompressF16Safetensors = async (device, progress) => {
|
||||
let totalLoaded = 0;
|
||||
let totalSize = 0;
|
||||
let partSize = {};
|
||||
|
||||
const getPart = async(key) => {
|
||||
let part = await readTensorFromDb(db, key);
|
||||
|
||||
if (part) {
|
||||
console.log(`Cache hit: ${key}`);
|
||||
return Promise.resolve(part.content);
|
||||
} else {
|
||||
console.log(`Cache miss: ${key}`);
|
||||
return getProgressDlForPart(`${window.MODEL_BASE_URL}/${key}.safetensors`, progressCallback);
|
||||
}
|
||||
}
|
||||
|
||||
const progressCallback = (part, loaded, total) => {
|
||||
totalLoaded += loaded;
|
||||
|
||||
if (!partSize[part]) {
|
||||
totalSize += total;
|
||||
partSize[part] = true;
|
||||
}
|
||||
|
||||
progress(totalLoaded, totalSize);
|
||||
};
|
||||
|
||||
let netKeys = ["net_part0", "net_part1", "net_part2", "net_part3", "net_textmodel"];
|
||||
let buffers = await Promise.all(netKeys.map(key => getPart(key)));
|
||||
|
||||
// Combine everything except for text model, since that's already f32
|
||||
const totalLength = buffers.reduce((acc, buffer, index, array) => {
|
||||
if (index < 4) {
|
||||
return acc + buffer.byteLength;
|
||||
} else {
|
||||
return acc;
|
||||
}
|
||||
}, 0
|
||||
);
|
||||
|
||||
combinedBuffer = new Uint8Array(totalLength);
|
||||
let offset = 0;
|
||||
buffers.forEach((buffer, index) => {
|
||||
saveTensorToDb(db, netKeys[index], new Uint8Array(buffer));
|
||||
if (index < 4) {
|
||||
combinedBuffer.set(new Uint8Array(buffer), offset);
|
||||
offset += buffer.byteLength;
|
||||
buffer = null;
|
||||
}
|
||||
});
|
||||
|
||||
let textModelU8 = new Uint8Array(buffers[4]);
|
||||
document.getElementById("modelDlTitle").innerHTML = "Decompressing model";
|
||||
|
||||
const textModelOffset = 3772703308;
|
||||
const metadataLength = Number(new DataView(combinedBuffer.buffer).getBigUint64(0, true));
|
||||
const metadata = JSON.parse(new TextDecoder("utf8").decode(combinedBuffer.subarray(8, 8 + metadataLength)));
|
||||
|
||||
const allToDecomp = combinedBuffer.byteLength - (8 + metadataLength);
|
||||
const decodeChunkSize = 8388480;
|
||||
const numChunks = Math.ceil(allToDecomp/decodeChunkSize);
|
||||
|
||||
console.log(allToDecomp + " bytes to decompress");
|
||||
console.log("Will be decompressed in " + numChunks+ " chunks");
|
||||
|
||||
let partOffsets = [{start: 0, end: 1131408336}, {start: 1131408336, end: 2227518416}, {start: 2227518416, end: 3308987856}, {start: 3308987856, end: 4265298864}];
|
||||
let parts = [];
|
||||
|
||||
for (let offsets of partOffsets) {
|
||||
parts.push(new Uint8Array(offsets.end-offsets.start));
|
||||
}
|
||||
parts[0].set(new Uint8Array(new BigUint64Array([BigInt(metadataLength)]).buffer), 0);
|
||||
parts[0].set(combinedBuffer.subarray(8, 8 + metadataLength), 8);
|
||||
parts[3].set(textModelU8, textModelOffset+8+metadataLength - partOffsets[3].start);
|
||||
|
||||
let start = Date.now();
|
||||
let cursor = 0;
|
||||
|
||||
for (let i = 0; i < numChunks; i++) {
|
||||
progress(i, numChunks);
|
||||
let chunkStartF16 = 8 + metadataLength + (decodeChunkSize * i);
|
||||
let chunkEndF16 = chunkStartF16 + decodeChunkSize;
|
||||
let chunk = combinedBuffer.subarray(chunkStartF16, chunkEndF16);
|
||||
let uint32Chunk = new Uint32Array(chunk.buffer, chunk.byteOffset, chunk.byteLength / 4);
|
||||
let result = await f16decomp(uint32Chunk);
|
||||
let resultUint8 = new Uint8Array(result.buffer);
|
||||
let chunkStartF32 = 8 + metadataLength + (decodeChunkSize * i * 2);
|
||||
let chunkEndF32 = chunkStartF32 + resultUint8.byteLength;
|
||||
let offsetInPart = chunkStartF32 - partOffsets[cursor].start;
|
||||
|
||||
if (chunkEndF32 < partOffsets[cursor].end || cursor === parts.length - 1) {
|
||||
parts[cursor].set(resultUint8, offsetInPart);
|
||||
} else {
|
||||
let spaceLeftInCurrentPart = partOffsets[cursor].end - chunkStartF32;
|
||||
parts[cursor].set(resultUint8.subarray(0, spaceLeftInCurrentPart), offsetInPart);
|
||||
|
||||
cursor++;
|
||||
|
||||
if (cursor < parts.length) {
|
||||
let nextPartOffset = spaceLeftInCurrentPart;
|
||||
let nextPartLength = resultUint8.length - nextPartOffset;
|
||||
parts[cursor].set(resultUint8.subarray(nextPartOffset, nextPartOffset + nextPartLength), 0);
|
||||
}
|
||||
}
|
||||
|
||||
resultUint8 = null;
|
||||
result = null;
|
||||
}
|
||||
|
||||
combinedBuffer = null;
|
||||
|
||||
let end = Date.now();
|
||||
console.log("Decoding took: " + ((end - start) / 1000) + " s");
|
||||
console.log("Avarage " + ((end - start) / numChunks) + " ms per chunk");
|
||||
|
||||
return parts;
|
||||
};
|
||||
|
||||
const loadNet = async () => {
|
||||
const modelDlTitle = document.getElementById("modelDlTitle");
|
||||
|
||||
const progress = (loaded, total) => {
|
||||
document.getElementById("modelDlProgressBar").value = (loaded/total) * 100
|
||||
document.getElementById("modelDlProgressValue").innerHTML = Math.trunc((loaded/total) * 100) + "%"
|
||||
}
|
||||
|
||||
const device = await getDevice();
|
||||
f16decomp = await f16tof32().setup(device, safetensorParts),
|
||||
safetensorParts = await getAndDecompressF16Safetensors(device, progress);
|
||||
|
||||
modelDlTitle.innerHTML = "Compiling model"
|
||||
|
||||
let models = ["textModel", "diffusor", "decoder"];
|
||||
|
||||
nets = await timer(() => Promise.all([
|
||||
textModel().setup(device, safetensorParts),
|
||||
diffusor().setup(device, safetensorParts),
|
||||
decoder().setup(device, safetensorParts)
|
||||
]).then((loadedModels) => loadedModels.reduce((acc, model, index) => { acc[models[index]] = model; return acc; }, {})), "(compilation)")
|
||||
|
||||
progress(1, 1);
|
||||
|
||||
modelDlTitle.innerHTML = "Model ready"
|
||||
setTimeout(() => {
|
||||
document.getElementById("modelDlProgressBar").style.display = "none";
|
||||
document.getElementById("modelDlProgressValue").style.display = "none";
|
||||
document.getElementById("divStepProgress").style.display = "flex";
|
||||
}, 1000);
|
||||
document.getElementById("btnRunNet").disabled = false;
|
||||
}
|
||||
|
||||
function runStableDiffusion(prompt, steps, guidance, showStep) {
|
||||
return new Promise(async (resolve, reject) => {
|
||||
let context = await timer(() => nets["textModel"](clipTokenizer.encodeForCLIP(prompt)));
|
||||
let unconditional_context = await timer(() => nets["textModel"](clipTokenizer.encodeForCLIP("")));
|
||||
|
||||
let timesteps = [];
|
||||
|
||||
for (let i = 1; i < 1000; i += (1000/steps)) {
|
||||
timesteps.push(i);
|
||||
}
|
||||
|
||||
console.log("Timesteps: " + timesteps);
|
||||
|
||||
let alphasCumprod = getWeight(safetensorParts,"alphas_cumprod");
|
||||
let alphas = [];
|
||||
|
||||
for (t of timesteps) {
|
||||
alphas.push(alphasCumprod[Math.floor(t)]);
|
||||
}
|
||||
|
||||
alphas_prev = [1.0];
|
||||
|
||||
for (let i = 0; i < alphas.length-1; i++) {
|
||||
alphas_prev.push(alphas[i]);
|
||||
}
|
||||
|
||||
let inpSize = 4*64*64;
|
||||
latent = new Float32Array(inpSize);
|
||||
|
||||
for (let i = 0; i < inpSize; i++) {
|
||||
latent[i] = Math.sqrt(-2.0 * Math.log(Math.random())) * Math.cos(2.0 * Math.PI * Math.random());
|
||||
}
|
||||
|
||||
for (let i = timesteps.length - 1; i >= 0; i--) {
|
||||
let timestep = new Float32Array([timesteps[i]]);
|
||||
let start = performance.now()
|
||||
let x_prev = await nets["diffusor"](unconditional_context, context, latent, timestep, new Float32Array([alphas[i]]), new Float32Array([alphas_prev[i]]), new Float32Array([guidance]));
|
||||
document.getElementById("divStepTime").style.display = "block";
|
||||
document.getElementById("stepTimeValue").innerText = `${(performance.now() - start).toFixed(1)} ms / step`
|
||||
latent = x_prev;
|
||||
|
||||
if (showStep != null) {
|
||||
showStep(await nets["decoder"](latent));
|
||||
}
|
||||
|
||||
document.getElementById("progressBar").value = ((steps - i) / steps) * 100
|
||||
document.getElementById("progressFraction").innerHTML = (steps - i) + "/" + steps
|
||||
}
|
||||
|
||||
resolve(await timer(() => nets["decoder"](latent)));
|
||||
});
|
||||
}
|
||||
|
||||
function renderImage(image) {
|
||||
let pixels = []
|
||||
let pixelCounter = 0
|
||||
|
||||
for (let j = 0; j < 512; j++) {
|
||||
for (let k = 0; k < 512; k++) {
|
||||
pixels.push(image[pixelCounter])
|
||||
pixels.push(image[pixelCounter+1])
|
||||
pixels.push(image[pixelCounter+2])
|
||||
pixels.push(255)
|
||||
pixelCounter += 3
|
||||
}
|
||||
}
|
||||
|
||||
ctx.putImageData(new ImageData(new Uint8ClampedArray(pixels), 512, 512), 0, 0);
|
||||
}
|
||||
|
||||
const handleRunNetAndRenderResult = () => {
|
||||
document.getElementById("btnRunNet").disabled = true;
|
||||
const canvas = document.getElementById("canvas");
|
||||
ctx.clearRect(0, 0, canvas.width, canvas.height);
|
||||
|
||||
const prevTitleValue = document.getElementById("modelDlTitle").innerHTML;
|
||||
document.getElementById("modelDlTitle").innerHTML = "Running model";
|
||||
|
||||
runStableDiffusion(
|
||||
document.getElementById("promptText").value,
|
||||
document.getElementById("stepRange").value,
|
||||
document.getElementById("guidanceRange").value,
|
||||
// Decode at each step
|
||||
null
|
||||
).then((image) => {
|
||||
renderImage(image);
|
||||
}).finally(() => {
|
||||
document.getElementById("modelDlTitle").innerHTML = prevTitleValue;
|
||||
document.getElementById("btnRunNet").disabled = false;
|
||||
});
|
||||
};
|
||||
|
||||
document.getElementById("btnRunNet").addEventListener("click", handleRunNetAndRenderResult, false);
|
||||
|
||||
document.getElementById("promptForm").addEventListener("submit", function (event) {
|
||||
event.preventDefault();
|
||||
if (document.getElementById("btnRunNet").disabled) return;
|
||||
|
||||
handleRunNetAndRenderResult();
|
||||
})
|
||||
|
||||
const stepSlider = document.getElementById('stepRange');
|
||||
const stepValue = document.getElementById('stepValue');
|
||||
|
||||
stepSlider.addEventListener('input', function() {
|
||||
stepValue.textContent = stepSlider.value;
|
||||
});
|
||||
|
||||
const guidanceSlider = document.getElementById('guidanceRange');
|
||||
const guidanceValue = document.getElementById('guidanceValue');
|
||||
|
||||
guidanceSlider.addEventListener('input', function() {
|
||||
guidanceValue.textContent = guidanceSlider.value;
|
||||
});
|
||||
|
||||
loadNet();
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user