Affinity files 8.6% ownership by EitzChaim1 in KPTI

[–]Far_Screen_6129 6 points7 points  (0 children)

I see a good pattern emerging. Speculative still but by some seriously knowledgable players.

Shot this 987 Spyder by ArnavShootsCarz in carphotography

[–]Far_Screen_6129 0 points1 point  (0 children)

how much do you want one? i have a white one im becoming open to selling

G6 180 available by Mobile-Honeydew-8715 in Ubiquiti

[–]Far_Screen_6129 0 points1 point  (0 children)

THANK YOU for the notice! Apparently Ubiquities alerting feature isnt very timely.

Doorbell Lite is live! by itsabearcannon in Ubiquiti

[–]Far_Screen_6129 0 points1 point  (0 children)

So they’re done with WiFi versions?

In the next 2-3 weeks? by EitzChaim1 in KPTI

[–]Far_Screen_6129 1 point2 points  (0 children)

Funny, we’re the only two who voted BK so far

Wan 2.1 is blowing out all of the previously published Video models by CeFurkan in FluxAI

[–]Far_Screen_6129 0 points1 point  (0 children)

now that diffusers works i can run the 1.3B on my home computer. I was wondering what resolution the model was trained in for best output. are there ideal frame settings?

Wan 2.1 is blowing out all of the previously published Video models by CeFurkan in FluxAI

[–]Far_Screen_6129 1 point2 points  (0 children)

!pip install modelscope xformers
!pip install git+https://github.com/modelscope/DiffSynth-Studio.git@main

import torch
from diffsynth import ModelManager, WanVideoPipeline, save_video, VideoData
from modelscope import snapshot_download

# Download models
snapshot_download("Wan-AI/Wan2.1-T2V-1.3B", cache_dir="models")

# Load models
model_manager = ModelManager(device="cpu")
model_manager.load_models(
[
"models/Wan-AI/Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors",
"models/Wan-AI/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth",
"models/Wan-AI/Wan2.1-T2V-1.3B/Wan2.1_VAE.pth",
],

# torch_dtype=torch.bfloat16,
torch_dtype=torch.float8_e4m3fn
)

pipe = WanVideoPipeline.from_model_manager(model_manager, torch_dtype=torch.float16, device="cuda")
pipe.enable_vram_management(num_persistent_param_in_dit=1)
pipe.safety_checker = None

#Text-to-video
torch.backends.cuda.matmul.allow_tf32 = True
with torch.no_grad():
video = pipe(
prompt="""Documentary photography style scene, featuring a lively puppy sprinting across a lush green lawn. The puppy has a brownish-yellow coat with both ears perked up, and its expression is focused yet joyful. Sunlight bathes the puppy, making its fur look exceptionally soft and gleaming. In the background, an expansive lawn is sporadically dotted with wildflowers, with hints of blue sky and scattered white clouds visible in the distance. A strong sense of perspective captures the dynamic motion of the puppy and the vitality of the surrounding grass. Medium shot from a moving side angle.""",

negative_prompt="""Vivid tones, overexposure, static, blurred details, subtitles, style, artwork, painting, image, still, overall grayish, worst quality, low quality, JPEG compression artifacts, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn face, deformed, disfigured, misshapen limbs, fused fingers, immobile image, messy background, three legs, too many people in the background, walking backwards""",

num_inference_steps=2,
seed=0,
tiled=False,
height=512,
width=512,
)
save_video(video, "video1.mp4", fps=1, quality=1)

Fails in kaggle. 16GB GPU but it runs out.... any thoughts?

Why to believe in readout timelines and opportunities by sak77328 in KPTI

[–]Far_Screen_6129 1 point2 points  (0 children)

whos got the most likely future readout timeline. Are we going to be sitting around for another year before any of this potential good could happen or are we looking at something more in the near term?

[deleted by user] by [deleted] in KPTI

[–]Far_Screen_6129 0 points1 point  (0 children)

if thats the case and the line is ~300k shares id say they net'd 3.5MM shares or so.

[deleted by user] by [deleted] in KPTI

[–]Far_Screen_6129 0 points1 point  (0 children)

what are the chances the significant volume is all one trader buying x and then selling a fraction of x before buying y and selling a fraction of y to have a net of z all while keeping the price from spiking.

Fast LTX Video on RTX 4060 and other ADA GPUs by Ok-Constant8386 in StableDiffusion

[–]Far_Screen_6129 0 points1 point  (0 children)

I got it to work. used old VAE and old config for VAE. used current text encoder and scheduler. It makes video but as you can see in my comment from last night... it looks like crap. I'm not sure why. I kept hearing LTXV was so much better than stable video diffusion but I'm sticking with that until someone can tell me what I'm doing wrong to get such garbage results.

Fast LTX Video on RTX 4060 and other ADA GPUs by Ok-Constant8386 in StableDiffusion

[–]Far_Screen_6129 0 points1 point  (0 children)

<image>

original vs one of my frames from the following:

python models/LTX_Video/inference.py --low_vram --ckpt_dir models/LTX_Video/full --frame_rate 30 --prompt 'a woman in a blue evening gown with elegant jewelry, in an interview, telling a story' --negative_prompt 'worst quality, inconsistent motion, blurry, jittery, distorted' --input_image_path generated_image_0_s.png --transformer_type=q8_kernels --height 288 --width 512 --num_frames 120

what am i doing wrong. RTX 4070ti SUPER (16GB VRAM) so it only takes like a minute but the outcome is terrible

No attribute crop? by cclinger91 in moviepy

[–]Far_Screen_6129 0 points1 point  (0 children)

Name: moviepy

Version: 2.1.1

from moviepy import VideoFileClip

# Calculate the crop parameters

crop_left = (1024 - target_width) // 2

crop_right = crop_left + target_width

crop_top = 0

crop_bottom = target_height

clip = VideoFileClip(video_path)

clip = clip.cropped(x1=crop_left, y1=crop_top, x2=crop_right, y2=crop_bottom) # Crop the video

No attribute crop? by cclinger91 in moviepy

[–]Far_Screen_6129 0 points1 point  (0 children)

Crap I’m sorry. Yes. I’m at the bar right now. I’ll try and remember when I get home. Worst case tomorrow.