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RDNA 3 Updated 2025-07-16
qiskit/qft.py Updated 2025-07-16
This is an example of the
qiskit.circuit.library.QFT implementation of the Quantum Fourier transform function which is documented at: docs.quantum.ibm.com/api/qiskit/0.44/qiskit.circuit.library.QFTOutput:So this also serves as a more interesting example of quantum compilation, mapping the
init: [1, 0, 0, 0, 0, 0, 0, 0]
qc
┌──────────────────────────────┐┌──────┐
q_0: ┤0 ├┤0 ├
│ ││ │
q_1: ┤1 Initialize(1,0,0,0,0,0,0,0) ├┤1 QFT ├
│ ││ │
q_2: ┤2 ├┤2 ├
└──────────────────────────────┘└──────┘
transpiled qc
┌──────────────────────────────┐ ┌───┐
q_0: ┤0 ├────────────────────■────────■───────┤ H ├─X─
│ │ ┌───┐ │ │P(π/2) └───┘ │
q_1: ┤1 Initialize(1,0,0,0,0,0,0,0) ├──────■───────┤ H ├─┼────────■─────────────┼─
│ │┌───┐ │P(π/2) └───┘ │P(π/4) │
q_2: ┤2 ├┤ H ├─■─────────────■──────────────────────X─
└──────────────────────────────┘└───┘
Statevector([0.35355339+0.j, 0.35355339+0.j, 0.35355339+0.j,
0.35355339+0.j, 0.35355339+0.j, 0.35355339+0.j,
0.35355339+0.j, 0.35355339+0.j],
dims=(2, 2, 2))
init: [0.0, 0.35355339059327373, 0.5, 0.3535533905932738, 6.123233995736766e-17, -0.35355339059327373, -0.5, -0.35355339059327384]
Statevector([ 7.71600526e-17+5.22650714e-17j,
1.86749130e-16+7.07106781e-01j,
-6.10667421e-18+6.10667421e-18j,
1.13711443e-16-1.11022302e-16j,
2.16489014e-17-8.96726857e-18j,
-5.68557215e-17-1.11022302e-16j,
-6.10667421e-18-4.94044770e-17j,
-3.30200457e-16-7.07106781e-01j],
dims=(2, 2, 2))QFT gate to Qiskit Aer primitives.If we don't
transpile in this example, then running blows up with:qiskit_aer.aererror.AerError: 'unknown instruction: QFT'The second input is:and the output of that approximately:which can be defined simply as the normalized DFT of the input quantum state vector.
[0, 1j/sqrt(2), 0, 0, 0, 0, 0, 1j/sqrt(2)]From this we see that the Quantum Fourier transform is equivalent to a direct discrete Fourier transform on the quantum state vector, related: physics.stackexchange.com/questions/110073/how-to-derive-quantum-fourier-transform-from-discrete-fourier-transform-dft
qiskit/initialize.py Updated 2025-07-16
In this example we will initialize a quantum circuit with a single CNOT gate and see the output values.
By default, Qiskit initializes every qubit to 0 as shown in the qiskit/hello.py. But we can also initialize to arbitrary values as would be done when computing the output for various different inputs.
Output:which we should all be able to understand intuitively given our understanding of the CNOT gate and quantum state vectors.
┌──────────────────────┐
q_0: ┤0 ├──■──
│ Initialize(1,0,0,0) │┌─┴─┐
q_1: ┤1 ├┤ X ├
└──────────────────────┘└───┘
c: 2/═════════════════════════════
init: [1, 0, 0, 0]
probs: [1. 0. 0. 0.]
init: [0, 1, 0, 0]
probs: [0. 0. 0. 1.]
init: [0, 0, 1, 0]
probs: [0. 0. 1. 0.]
init: [0, 0, 0, 1]
probs: [0. 1. 0. 0.]
┌──────────────────────────────────┐
q_0: ┤0 ├──■──
│ Initialize(0.70711,0,0,0.70711) │┌─┴─┐
q_1: ┤1 ├┤ X ├
└──────────────────────────────────┘└───┘
c: 2/═════════════════════════════════════════
init: [0.7071067811865475, 0, 0, 0.7071067811865475]
probs: [0.5 0.5 0. 0. ]quantumcomputing.stackexchange.com/questions/13202/qiskit-initializing-n-qubits-with-binary-values-0s-and-1s describes how to initialize circuits qubits only with binary 0 or 1 to avoid dealing with the exponential number of elements of the quantum state vector.
qiskit.transpile() Updated 2025-07-16 City of Light: The Story of Fiber Optics Updated 2025-07-16
DeepFloyd IF Updated 2025-07-16
runwayml/stable-diffusion Updated 2025-07-16
Someone should package this better for end user "just works after Conda install" image generation, it is currently much more of a library setup.
Tested on Amazon EC2 on a g5.xlarge machine, which has an Nvidia A10G, using the AWS Deep Learning Base GPU AMI (Ubuntu 20.04) image.
First install Conda as per Section "Install Conda on Ubuntu", and then just follow the instructions from the README, notably the Reference sampling script section.This took about 2 minutes and generated 6 images under
git clone https://github.com/runwayml/stable-diffusion
cd stable-diffusion/
git checkout 08ab4d326c96854026c4eb3454cd3b02109ee982
conda env create -f environment.yaml
conda activate ldm
mkdir -p models/ldm/stable-diffusion-v1/
wget -O models/ldm/stable-diffusion-v1/model.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plmsoutputs/txt2img-samples/samples, includining an image outputs/txt2img-samples/grid-0000.png which is a grid montage containing all the six images in one:
A quick attempt at removing their useless safety features (watermark and NSFW text filter) is:but that produced 4 black images and only two unfiltered ones. Also likely the lack of sexual training data makes its porn suck, and not in the good way.
diff --git a/scripts/txt2img.py b/scripts/txt2img.py
index 59c16a1..0b8ef25 100644
--- a/scripts/txt2img.py
+++ b/scripts/txt2img.py
@@ -87,10 +87,10 @@ def load_replacement(x):
def check_safety(x_image):
safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
- assert x_checked_image.shape[0] == len(has_nsfw_concept)
- for i in range(len(has_nsfw_concept)):
- if has_nsfw_concept[i]:
- x_checked_image[i] = load_replacement(x_checked_image[i])
+ #assert x_checked_image.shape[0] == len(has_nsfw_concept)
+ #for i in range(len(has_nsfw_concept)):
+ # if has_nsfw_concept[i]:
+ # x_checked_image[i] = load_replacement(x_checked_image[i])
return x_checked_image, has_nsfw_concept
@@ -314,7 +314,7 @@ def main():
for x_sample in x_checked_image_torch:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
img = Image.fromarray(x_sample.astype(np.uint8))
- img = put_watermark(img, wm_encoder)
+ # img = put_watermark(img, wm_encoder)
img.save(os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1 ludicrains/deep-gaze Updated 2025-07-16
This just works, but it is also so incredibly slow that it is useless (or at least the quality it reaches in the time we have patience to wait from), at least on any setup we've managed to try, including e.g. on an Nvidia A10G on a g5.xlarge. Running:would likely take hours to complete.
time imagine "a house in the forest" OpenNMT Updated 2025-07-16
Open source LLM Updated 2025-07-19
GitHub repo Updated 2025-07-16
bsub get job stdout and stderr Updated 2025-07-16
By default, LSF only sends you an email with the stdout and stderr included in it, and does not show or store anything locally.
One option to store things locally is to use:as documented at:
bsub -oo stdout.log -eo stderr.log 'echo myout; echo myerr 1>&2'Or to use files with the job id in them:
bsub -oo %J.out -eo %J.err 'echo myout; echo myerr 1>&2'By default as mentioned at:
bsub -oo:To get just the stdout to the file, use
bsub -N -oo which:- stores only stdout on the file
- re-enables the completion email
Another option is to run with the bsub This immediately prints stdout and stderr to the terminal.
-I option:bsub -I 'echo a;sleep 1;echo b;sleep 1;echo c' AMDGPU Updated 2025-07-16
Bibliography:
EC2 instance type Updated 2025-07-16
Amazon's informtion about their own intances is so bad and non-public that this was created: instances.vantage.sh/
vCPU Updated 2025-07-16
EC2 instance store volume Updated 2025-07-16
Large but ephemeral storage for EC2 instances. Predetermined by the EC2 instance type. Stays in the local server disk. Not automatically mounted.
- docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html (archive) notably highlights what it persists, which is basically nothing
- serverfault.com/questions/433703/how-to-use-instance-store-volumes-storage-in-amazon-ec2 mentions that you have to mount it
There are unlisted articles, also show them or only show them.