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Term invented by Ciro Santilli, it refers to Richard Feynman, after helping to build the atomic bomb:
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Sponsor Ciro Santilli's work on OurBigBook.com Financial crime preventive measures Updated 2025-07-16
Crazy shady crypto people seem to like Ciro Santilli, so this is in order.
Giving to Ciro Santilli is the worst possible way to launder your money, as donations amounts are clearly publicly disclosed (though not donor identities if they with to remain anonymous), and clear records kept of every donation made (including private note of donor identities if known). Also suspicious donations are promptly reported to the authorities.
Donation refunds upon donor's requests are only made at our discretion, and may be declined, unless required by law of course. This is to reduce the risks of us unknowingly serving as money mules or aiding money laundering.
Ciro Santilli believes that he is not require to report large donations to either:But note that Ciro will preventively report if there are any further suspicious aspects to any donations received.
- charities have to report anonymous donations of £25,000 or more as "serious incident", but Ciro Santilli does not have a registered charity: www.gov.uk/guidance/how-to-report-a-serious-incident-in-your-charity, related: docs.ourbigbook.com/project-governance
- dealers selling goods over 10,000 euros for cash must make a Suspicious Activity Report (SAR) www.gov.uk/guidance/money-laundering-regulations-high-value-dealer-registration. However Ciro Santilli does not sell any goods, only provides services, and services are excluded from the report requirements
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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"))
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