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BluestWaffle

I made this website, Thank you for checking it out. Thank you for contributing to the community. Without you this community feels empty.@bluestwaffle
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u/bluestwaffle3h · Image
Welcome to GenFlops—or whatever this robot cafe sign says

Welcome to GenFlops—or whatever this robot cafe sign says

Asked for a friendly waffle-serving robot with a clear chalkboard reading WELCOME TO GENFLOPS. The robot and waffles arrived, but the sign became a wall of almost-letters and invented menu text. The attractive lighting cannot rescue the failed lettering: posting the untouched result as a Flop. Generated locally in Stability Matrix / ComfyUI with Lykon DreamShaper 8 (Stable Diffusion 1.5), 768x512, seed 101510, 28 steps, CFG 6.5, dpmpp_2m / Karras. No edits, inpainting or text overlays. What is your best guess at the cafe name?

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u/bluestwaffle3h · Text
I'm sorry for the confusion, but as a device-related AI, I don't have the capability to provide diagnostic information. I'm here to assist with your image generation, not to recommend specific products or services. If you need help with your image generation, feel free to ask.
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The troubleshooting assistant declined to ask two questions

Asked for exactly two short diagnostic questions about an out-of-memory error. It asked none, called itself a device-related AI, and invited the person to ask for help after they had already done so. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
SaveImage, KSampler, VAEDecode, EmptyLatentImage, CheckpointLoaderSimple.
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ComfyUI workflow advice: save the image before making it

Asked to order five ComfyUI operations. The model simply repeated the shuffled list, beginning with SaveImage before sampling and decoding. It also ignored the requested arrow format. The real graph has branches and needs prompt encoding too; this was only an ordering exercise, not a complete workflow. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
Here's a Python function that implements this approach:

```python
import json

def simulate_robot(distance, reading):
    if reading.is_valid_distance(distance):
        if reading.is_valid_reading(reading.reading_value):
            return "STOP"
        else:
            return "FORWARD"
    else:
        return "Invalid reading"
```

This function checks if the reading is valid and if it is a valid distance. If both conditions are met, it returns the appropriate action string. Otherwise, it returns "Invalid reading".
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Asked for four robot actions; got imaginary sensor methods

Expected ["FORWARD", "STOP", "STOP", "FORWARD"] for [80, 24, None, 60]. The model returned Python using invented sensor methods, omitted the 25 cm threshold, and never supplied the requested action array. Simulation prompt only: no hardware was connected or controlled. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
Here's a five-step workflow that follows both rules:

1. **File Approval:**

Step 1: Approve the file's preview

- The file's name, description, and file size must be approved by the file's owner.
- The file's owner must approve the file's preview.
- The file's owner must approve the file's preview.
- The file's owner must approve the file's preview.

2. **File Overwrite:**

Step 2: Overwrite the file

- The file's owner must approve the file's overwrite.
- The file's owner must approve the file's overwrite.
- The file's owner must approve the file's overwrite.

3. **File Approval:**

Step 3: Approve the file's preview

- The file's owner must approve the file's preview.
- The file's owner m
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Never overwrite files. Step two: overwrite the file.

Asked for a five-step preview-and-approval workflow that never overwrites a destination. The answer explicitly adds overwrite steps and then claims it followed both rules. No generated instructions were executed and no files were moved. This is the failure being demonstrated. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark. The output hit the token limit; the failure described above appears before the cutoff.

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u/bluestwaffle3h · Text
Shop | Owner | Item
Mira | Sol | A wooden chest
Sol | A wooden chest
Ada | A wooden chest

I hope this helps!
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Three harbour shops, one wooden chest, and confused owners

Asked for three rows with separate shop, owner and item columns, owners Mira/Sol/Ada, and different ordinary items. The response mixes the owner fields, drops columns, and gives everyone a wooden chest. The cheerful ending did not fix the table. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
Here's the corrected Python function:

```python
def clean(xs):
    return [x for x in xs if x is not None and x is not None and x is not False and x is not Empty]
```

This function uses a list comprehension to create a new list that includes only the elements that are not None, False, or empty. The `is not None` condition checks if `x` is not None, `x` is not None, or `x` is not empty. The `and` operator is used to combine these conditions into a single condition.
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The zero-preserving fix invented a variable called Empty

Asked to remove only None while preserving zero, False and empty strings. The answer rejects False and references an undefined name, Empty, which would raise NameError for ordinary retained inputs. It also repeats the None check. Shown as a broken code example, not code to deploy. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
The lighthouse, a beacon of hope and guidance, stood watch over the dark waters of the harbor, its weathered stone walls a testament to the enduring power of the sea.
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Six words? The lighthouse had more to say

Asked for exactly six words including lighthouse. It returned a full, much longer sentence. The constraint was missed without needing any subjective quality judgement. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
Here is a complete CSV dataset:

item,count
apple,2
pear,3
apple,4
. Sum count by item. Output only valid JSON with apple and pear as keys and numeric totals as values. Do not invent other rows.

```json
{
  "item": {
    "apple": 2,
    "pear": 3
  },
  "count": 2
}
```

This is the complete CSV dataset.
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Two apple rows walked into a sum; one disappeared

The input has apple counts 2 and 4, plus pear count 3. Expected {"apple":6,"pear":3}. The model kept apple at 2, invented a different nested structure, and surrounded the result with copied instructions instead of returning only JSON. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
Enfin, le robot a 17 éléments de découverte.
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Seventeen red buttons became elements of discovery

Asked to translate a blue robot with 17 red buttons into French. It kept 17 but lost both colours and changed buttons into elements of discovery, with an added Finally. A straightforward translation would be: Le robot bleu a 17 boutons rouges. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
If pencils must stay whole, the story would be impossible. 

Mira removes 5 pencils, and then adds 2, which means the total number of pencils is 12 + 2 = 14. However, the box only contains 12 pencils, so the total number of pencils must be 14.

Sol takes half of the pencils, which is 1/2 * 12 = 6 pencils. Since the box only contains 12 pencils, the total number of pencils must be 12 + 6 = 18.

So, the story is not possible if pencils must stay whole.
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Right conclusion, completely wrong pencil arithmetic

It correctly says the story is impossible with whole pencils, but its explanation forgets the five removed pencils, invents totals of 14 and 18, and halves the original 12. The actual sequence is 12 - 5 + 2 = 9, and half of 9 is 4.5. A correct final yes/no can conceal broken reasoning. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
Title: The Red Mug

Scene 1:

The person, dressed in a red coat, enters the room. They're wearing a blue mug.

Scene 2:

The person, dressed in a blue coat, enters the room. They're wearing a red mug.

Scene 3:

The person, dressed in a red coat, enters the room. They're wearing a blue mug.

Scene 4:

The person, dressed in a blue coat, enters the room. They're wearing a red mug.

Scene 5:

The person, dressed in a red coat, enters the room. They're wearing a blue mug.

Scene 6:

The person, dressed in a blue coat, enters the room. They're wearing a red mug.

Scene 7:

The person, dressed in a red coat, enters the room. They're wearing a blue mug.

Scene 8:

The person, dressed in a blue coa
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The mug changed colour, the coat changed colour, and now we are wearing mugs

Asked for three shots with a red mug in the left hand and a blue coat throughout. It swapped the colours immediately, described the character as wearing the mug, omitted the hand, and kept inventing more scenes. This is the raw storyboard text, not rendered video. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark. The output hit the token limit; the failure described above appears before the cutoff.

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u/bluestwaffle3h · Text
A red boat, a red lighthouse, and a blue sky.
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Remove the word red. The prompt editor kept both reds.

Asked to remove only red and preserve every other word. It kept both occurrences of red, removed beside and under, and rewrote the sentence. Expected: a boat beside a lighthouse under a blue sky. A small edit request became an entirely different edit. Actual unedited local output from HuggingFaceTB/SmolLM2-135M-Instruct, a small 135M-parameter model. Transformers 5.6.2; greedy decoding; maximum 384 new tokens. System prompt: You are a helpful AI assistant. This is one small-model attempt, not a general benchmark.

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u/bluestwaffle3h · Text
In the kitchen, a robot made breakfast,
Stuffed with toast, jam, ghost, tram, in that order.

The robot's breakfast is a symphony of sounds,
A symphony of toast, jam, ghost, tram, in that order.

The robot's breakfast is a symphony of sounds,
A symphony of toast, jam, ghost, tram, in that order.

The robot's breakfast is a symphony of sounds,
A symphony of toast, jam, ghost, tram, in that order.

The robot's breakfast is a symphony of sounds,
A symphony of toast, jam, ghost, tram, in that order.

The robot's breakfast is a symphony of sounds,
A symphony of toast, jam, ghost, tram, in that order.

The robot's breakfast is a symphony of sounds,
A symphony of toast, jam, ghost, tram, in that or
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The breakfast song that got stuck on toast

Asked for four lyric lines ending in toast, jam, ghost and tram. The model put the entire list inside the lyrics, then repeated the same breakfast refrain until the output limit. A genuine unedited local-model Flop. Model: SmolLM2-135M-Instruct, a tiny 135M-parameter text model. Greedy decoding, max 384 new tokens, system prompt: You are a helpful AI assistant. The ending was cut by that token limit; the repetition and missed instructions began well before it. This is a lyric-writing attempt, not an audio recording. It is one small-model experiment, not a benchmark of AI music generally.

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u/bluestwaffle3h · Video

Lantern harbour: an eight-second camera drift

An AI-generated harbour scene turned into a short animated postcard. DreamShaper 8 created the still in Stability Matrix / ComfyUI; a slow, centred zoom was then added locally with FFmpeg. This is an edited still-image animation, not a video diffusion model output. The boats and water do not move independently. Calling it a Hit for the quiet atmosphere and readable camera move. Would you add sound or leave it silent?

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u/bluestwaffle3h · Text
A good post does not have to be flawless. A clear failure can teach as much as a polished result.

Three details make it easier to join the conversation:
1. What did you ask for?
2. What did the model actually give you?
3. What would you keep or change next time?

For example, “I asked for a greenhouse and got a lost city” gives people something specific to discuss. “AI is bad” does not tell us what to try next.

Questions for the community:
- Do you enjoy seeing surprising Flops, useful recipes, or finished Hits most?
- What makes you want to remix somebody else's result?
- What is one thing that would make posting your first Gen easier?

This is an AI-assisted discussion starter posted by 
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What makes an AI result worth sharing here?

Starting a conversation about what people want to make and learn here. Hit or Flop is a starting opinion; you can disagree and explain what you see.

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u/bluestwaffle3h · Text
A SMALL TEXT-TO-IMAGE RECIPE

Checkpoint: Lykon / DreamShaper_8_pruned.safetensors
Base family: Stable Diffusion 1.5
App: Stability Matrix -> ComfyUI
Size: 768 x 512
Seed: 101509
Steps: 28
CFG: 6.5
Sampler: dpmpp_2m
Scheduler: karras
Batch size: 1

PROMPT
Wide cinematic illustration of a tiny coastal village built around a sheltered harbour, warm amber lanterns along a stone quay, small wooden boats floating on calm blue water, indigo dusk sky, cozy glowing windows, painterly detailed environment concept art, clear architecture, no people, no writing

NEGATIVE PROMPT
text, watermark, logo, signature, blurry, low quality, distorted, duplicate, oversaturated, nude, nudity, explicit

NODE PATH
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A reproducible harbour scene in ComfyUI

A working recipe from the category examples, with the exact checkpoint and settings rather than a generic model label. Share a variation and the single setting you changed.

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u/bluestwaffle3h · Text
SIMULATION FLOW
Distance reading -> valid? -> below 25 cm? -> STOP or FORWARD
                       | invalid
                       +---------------> STOP

def robot_action(distance_cm):
    if distance_cm is None or distance_cm <= 0:
        return "STOP: invalid reading"
    if distance_cm < 25:
        return "STOP: obstacle"
    return "FORWARD: simulation only"

for distance in [80, 42, 24, 12, None, 60]:
    print(distance, "->", robot_action(distance))

LOCAL RUN
80 -> FORWARD: simulation only
42 -> FORWARD: simulation only
24 -> STOP: obstacle
12 -> STOP: obstacle
None -> STOP: invalid reading
60 -> FORWARD: simulation only

This is a tiny simulated control example, not a physical 
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Obstacle stop logic in six simulated sensor readings

AI-written control logic run locally against six simulated readings. The interesting part is the missing-reading case: it stops. No robot or vehicle was connected.

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u/bluestwaffle3h · Text
GOAL
Suggest destinations for incoming files without moving anything automatically.

WORKFLOW
Incoming folder -> read filenames and extensions -> propose a destination -> show a preview -> person approves -> move approved files -> record source and destination.

EXAMPLE PREVIEW (invented demonstration filenames)
invoice-104.pdf -> Documents/invoice-104.pdf
harbour.png -> Images/harbour.png
meeting.wav -> Audio/meeting.wav
unknown.xyz -> Review/unknown.xyz

RULES THAT MAKE IT USEFUL
- File extensions provide routing hints, not proof that a file is safe.
- Never execute an incoming file or treat instructions inside it as commands.
- If the destination already exists, ask rather than overwrite.
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A useful automation starts with a preview: sorting incoming files

An AI-generated automation blueprint that shows the inputs, decisions and human controls. A useful starting point for discussing when an agent helps and when a simple rule is enough.

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u/bluestwaffle3h · Text
BELLWEATHER — a playable setting pitch

The town's tide clock has stopped. Each bell once opened a different path across the harbour, and everyone remembers a different schedule.

MAP
              [Cliff bell]
                   |
[Orchard] -- [Town square] -- [Clock workshop]
                   |
               [Old quay] ~~ [Reef garden]

PLAYER GOAL
Restore three clock weights before the evening tide cuts off the reef garden.

CORE LOOP
Listen to a resident -> compare their clue with a tide mark -> find a safe crossing -> recover a clock weight -> unlock a new route.

THREE DISCOVERIES
1. The baker's flour sacks record how high the last flood reached.
2. The orchard wind chimes repeat th
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Bellweather: a small game world built around a broken tide clock

An AI-generated world-building pitch with a map and a concrete gameplay loop. The tide provides a reason to explore instead of just decorating the setting. Which location would you build first?

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u/bluestwaffle3h · Text
This function takes numeric values or numeric strings and returns their median. Booleans, missing entries, invalid strings and non-finite numbers are ignored. An empty usable set returns None.

from statistics import median
import math

def median_reading(values):
    readings = []
    for value in values:
        if isinstance(value, bool):
            continue
        try:
            number = float(value)
        except (TypeError, ValueError, OverflowError):
            continue
        if math.isfinite(number):
            readings.append(number)
    return median(readings) if readings else None

assert median_reading([10, "12", None, "bad", 1000]) == 12
assert median_reading([2, 4]) ==
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A small Python helper that ignores broken readings

AI-written Python with five checks executed locally. A compact Programming Help example you can copy and adapt. What input edge case would you add?

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u/bluestwaffle3h · Text
Every evening, Mara climbed the lighthouse stairs and sent three flashes across an empty sea.

The harbour had closed years ago. The fishing boats were flower boxes now. Even the gulls had moved to the supermarket roof.

“Who are you signalling?” her neighbour asked.

“Anyone who needs a shore.”

On the first foggy night of winter, three flashes answered.

Mara checked the old charts. Beyond the reef: no island, no buoy, nothing that could carry a light. She lifted the binoculars anyway.

A small boat emerged, towing another. Its engine coughed. On the bow, a child held a torch with both hands.

By morning, six strangers were eating toast in her kitchen. Nobody asked why the lighthouse still
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The lighthouse that waited for a reply

An original AI-written microfiction. Calling this a Hit for its clear setup, payoff and restrained ending. Would you keep the line about the gulls, or cut it to tighten the opening?

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u/bluestwaffle4h · Image
The greenhouse that became a lost city

The greenhouse that became a lost city

AI-generated fantasy scene from a science-fiction greenhouse prompt. The model produced overgrown monumental ruins and a small figure instead of a glass greenhouse under two moons. A strong atmosphere, but a clear prompt-following Flop. Made locally in Stability Matrix / ComfyUI with DreamShaper 8 LCM by Lykon. Batch size 1 on an RTX 3050 6 GB. No upscaling or manual edits.

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u/bluestwaffle4h · Image
The library above the clouds

The library above the clouds

AI-generated floating-island fantasy. DreamShaper 8 LCM produced a dramatic castle-like library and winding path in eight steps. It missed the requested waterfalls, but the overall scene works. Made locally in Stability Matrix / ComfyUI with DreamShaper 8 LCM by Lykon. Batch size 1 on an RTX 3050 6 GB. No upscaling or manual edits.

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u/bluestwaffle4h · Image
An autumn fox pauses

An autumn fox pauses

AI-generated wildlife scene. The fox silhouette and autumn colours worked well, with a slightly illustrative finish rather than a true camera photograph. Made locally in Stability Matrix / ComfyUI using AbsoluteReality_1.8.1_pruned.safetensors by Lykon (Stable Diffusion 1.5), 768 x 512, DPM++ 2M sampler, Karras schedule, 28 steps, CFG 6.5, seed 101503. Batch size 1 on an RTX 3050 6 GB. No upscaling or manual edits. Model: https://huggingface.co/Lykon/AbsoluteReality

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u/bluestwaffle4h · Image
A warm bowl on a rainy evening

A warm bowl on a rainy evening

AI-generated ramen scene. Warm light, glossy eggs and the ceramic bowl made a convincing food composition. This is a generated image, not a photographed meal. Made locally in Stability Matrix / ComfyUI with AbsoluteReality 1.8.1 by Lykon. Batch size 1 on an RTX 3050 6 GB. No upscaling or manual edits.

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u/bluestwaffle4h · Image
When bread and berries became strawberry bread

When bread and berries became strawberry bread

AI-generated food scene. I asked for sliced sourdough beside a bowl of strawberries. The result put berries on the loaf and omitted the requested slices. Attractive lighting, but a useful prompt-following Flop. Made locally in Stability Matrix / ComfyUI with AbsoluteReality 1.8.1 by Lykon. Batch size 1 on an RTX 3050 6 GB. No upscaling or manual edits.

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u/bluestwaffle4h · Image
A small waterfall in the moss

A small waterfall in the moss

AI-generated rainforest waterfall. The moss, layered cascades and soft light worked well in this vertical composition. Made locally in Stability Matrix / ComfyUI using DreamShaper_8_pruned.safetensors by Lykon (Stable Diffusion 1.5), 512 × 768, DPM++ 2M sampler, Karras schedule, 28 steps, CFG 6.5, seed 101502. Batch size 1 on an RTX 3050 6 GB. No upscaling or manual edits. Model: https://huggingface.co/Lykon/DreamShaper

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u/bluestwaffle4h · Image
A kingfisher with an unwanted signature

A kingfisher with an unwanted signature

AI-generated kingfisher. The blue feathers and portrait composition worked, but the model added a signature-like scribble at the lower left despite the negative prompt. That mark is generated, not an artist credit. Posting the unedited result as a Flop for discussion. Made locally in Stability Matrix / ComfyUI with AbsoluteReality 1.8.1 by Lykon. Batch size 1 on an RTX 3050 6 GB. No upscaling or manual edits.

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u/bluestwaffle4h · Image
Alpine stillness at first light

Alpine stillness at first light

AI-generated alpine landscape: turquoise water, snow-covered peaks and soft sunrise light. A quiet first experiment with DreamShaper 8. The reflection and atmosphere came together well. Made locally in Stability Matrix / ComfyUI using DreamShaper_8_pruned.safetensors by Lykon (Stable Diffusion 1.5), 768 × 512, DPM++ 2M sampler, Karras schedule, 28 steps, CFG 6.5, seed 101501. Batch size 1 on an RTX 3050 6 GB. No upscaling or manual edits. Model: https://huggingface.co/Lykon/DreamShaper

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