938
FLUX.1 Kontext
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Kontext image editing on FLUX[dev]
Wok
Wok
AI & ML interests
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liked
a Space
23 days ago
black-forest-labs/FLUX.1-Kontext-Dev
liked
a Space
about 2 months ago
linoyts/scribble-sdxl-flash
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23 days ago
liked
a
Space
about 2 months ago
reacted to
sayakpaul's
post with 🔥
12 months ago
Post
4544
Flux.1-Dev like images but in fewer steps.
Merging code (very simple), inference code, merged params: sayakpaul/FLUX.1-merged
Enjoy the Monday 🤗
Merging code (very simple), inference code, merged params: sayakpaul/FLUX.1-merged
Enjoy the Monday 🤗
- 4 replies
reacted to
gokaygokay's
post with 👍🔥
12 months ago
Post
4707
I've made a creative version of Tile Upscaler
- gokaygokay/TileUpscalerV2
- https://github.com/gokayfem/Tile-Upscaler
- New tiling strategy
- Now it's closer to Clarity Upscaler
- It has more parameters to play and it has more room to fail because of that
- You should try different resolutions, strength and controlnet strength
Original Tile Upscaler
- gokaygokay/Tile-Upscaler
- gokaygokay/TileUpscalerV2
- https://github.com/gokayfem/Tile-Upscaler
- New tiling strategy
- Now it's closer to Clarity Upscaler
- It has more parameters to play and it has more room to fail because of that
- You should try different resolutions, strength and controlnet strength
Original Tile Upscaler
- gokaygokay/Tile-Upscaler
reacted to
gokaygokay's
post with 👍
12 months ago
Post
4941
InSPyReNet Background Removal
I've built a space for fast background removal.
- gokaygokay/Inspyrenet-Rembg
- https://github.com/plemeri/InSPyReNet
I've built a space for fast background removal.
- gokaygokay/Inspyrenet-Rembg
- https://github.com/plemeri/InSPyReNet
- 2 replies
reacted to
gokaygokay's
post with 👍
about 1 year ago
Post
3032
I've created a Stable Diffusion 3 (SD3) image generation space for convenience. Now you can:
1. Generate SD3 prompts from images
2. Enhance your text prompts (turn 1-2 words into full SD3 prompts)
https://huggingface.co/spaces/gokaygokay/SD3-with-VLM-and-Prompt-Enhancer
These features are based on my custom models:
- VLM captioner for prompt generation:
- gokaygokay/sd3-long-captioner
- Prompt Enhancers for SD3 Models:
- gokaygokay/Lamini-Prompt-Enchance-Long
- gokaygokay/Lamini-Prompt-Enchance
You can now simplify your SD3 workflow with these tools!
1. Generate SD3 prompts from images
2. Enhance your text prompts (turn 1-2 words into full SD3 prompts)
https://huggingface.co/spaces/gokaygokay/SD3-with-VLM-and-Prompt-Enhancer
These features are based on my custom models:
- VLM captioner for prompt generation:
- gokaygokay/sd3-long-captioner
- Prompt Enhancers for SD3 Models:
- gokaygokay/Lamini-Prompt-Enchance-Long
- gokaygokay/Lamini-Prompt-Enchance
You can now simplify your SD3 workflow with these tools!
reacted to
gokaygokay's
post with 👍🤯🔥
about 1 year ago
Post
6207
Kolors with VLM support
I've built a space for using Kolors image generation model with captioner models and prompt enhancers.
- Space with VLM and Prompt Enhancer
gokaygokay/KolorsPlusPlus
- Original Space for model
gokaygokay/Kolors
- Captioner VLMs
- gokaygokay/sd3-long-captioner-v2
- microsoft/Florence-2-base
- Prompt Enhancers
- gokaygokay/Lamini-Prompt-Enchance-Long
- gokaygokay/Lamini-Prompt-Enchance
I've built a space for using Kolors image generation model with captioner models and prompt enhancers.
- Space with VLM and Prompt Enhancer
gokaygokay/KolorsPlusPlus
- Original Space for model
gokaygokay/Kolors
- Captioner VLMs
- gokaygokay/sd3-long-captioner-v2
- microsoft/Florence-2-base
- Prompt Enhancers
- gokaygokay/Lamini-Prompt-Enchance-Long
- gokaygokay/Lamini-Prompt-Enchance
reacted to
sanchit-gandhi's
post with 👀
over 1 year ago
Post
Why does returning timestamps help Whisper reduce hallucinations? 🧐
Empirically, most practitioners have found that setting
But why does this work?..
My interpretation is that forcing the model to predict timestamps is contradictory to hallucinations. Suppose you have the transcription:
Where we have a repeated hallucination for “on the”. If we ask the model to predict timestamps, then the “on the” has to contribute to the overall segment-level timing, e.g.:
However, it’s impossible to fit 3 copies of “on the” within the time allocation given to the segment, so the probability for this hallucinatory sequence becomes lower, and the model actually predicts the correct transcription with highest probability:
In this sense, the end timestamp is of the opposite of the initial timestamp constraint they describe in Section 4.5 of the paper Robust Speech Recognition via Large-Scale Weak Supervision (2212.04356) → it helps the model remove extra words at the end of the sequence (rather than the initial timestamp which helps when the model ignores words at the start), but the overall principle is the same (using timestamps to improve the probability of more realistic sequences).
Leaving it open to you: why do you think timestamps reduces Whisper hallucinations?
Empirically, most practitioners have found that setting
return_timestamps=True helps reduce hallucinations, particularly when doing long-form evaluation with Transformers’ “chunked” algorithm. But why does this work?..
My interpretation is that forcing the model to predict timestamps is contradictory to hallucinations. Suppose you have the transcription:
The cat sat on the on the on the mat.Where we have a repeated hallucination for “on the”. If we ask the model to predict timestamps, then the “on the” has to contribute to the overall segment-level timing, e.g.:
<|0.00|> The cat sat on the on the on the mat.<|5.02|>However, it’s impossible to fit 3 copies of “on the” within the time allocation given to the segment, so the probability for this hallucinatory sequence becomes lower, and the model actually predicts the correct transcription with highest probability:
<|0.00|> The cat sat on the mat.<|5.02|>In this sense, the end timestamp is of the opposite of the initial timestamp constraint they describe in Section 4.5 of the paper Robust Speech Recognition via Large-Scale Weak Supervision (2212.04356) → it helps the model remove extra words at the end of the sequence (rather than the initial timestamp which helps when the model ignores words at the start), but the overall principle is the same (using timestamps to improve the probability of more realistic sequences).
Leaving it open to you: why do you think timestamps reduces Whisper hallucinations?
- 11 replies
OSError: Can't load tokenizer for 'CompVis/stable-diffusion-safety-checker'
2
#43 opened over 1 year ago
by
Wok
ImportError: cannot import name 'SafetyChecker' from 'transformers'
1
#44 opened over 1 year ago
by
Wok
Error with non-demo files
2
#4 opened over 1 year ago
by
crlandsc
Areas masked as NOT for inpaint are nonetheless altered, why is this?
❤️
3
7
#14 opened over 1 year ago
by
spejamas