Reddy V4

🚀 reddy-v4

reddy-v4 是一個基於 black-forest-labs/FLUX.1-dev 的標準 PEFT LoRA 模型，可用於文本到圖像的生成任務。

🚀 快速開始

推理示例

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Unmapped2895/reddy-v4'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "Realistic wide shot photo of woman posing in a luxurious satin lingerie set, featuring a plunging bra, delicate thong and a classic garter belt with black stockings. The satin lingerie shimmers softly in the light, and the cut emphasizes both sophistication and a hint of allure. The lingerie is detailed with fine lace edges, highlighting her alluring figure. She elegantly styled hair as if getting ready for a formal event. The photo has a cinematic quality with rays of light and dramatic play of shadow and light"


## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
    prompt=prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=832,
    height=1216,
    guidance_scale=3.5,
).images[0]

model_output.save("output.png", format="PNG")

✨ 主要特性

• 基於 black-forest-labs/FLUX.1-dev 基礎模型構建，屬於標準 PEFT LoRA 模型。
• 支持文本到圖像、圖像到圖像等多種生成任務。
• 訓練和推理過程中提供了詳細的參數設置。

📦 安裝指南

文檔未提及具體安裝步驟，暫不提供。

💻 使用示例

基礎用法

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Unmapped2895/reddy-v4'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "Realistic wide shot photo of woman posing in a luxurious satin lingerie set, featuring a plunging bra, delicate thong and a classic garter belt with black stockings. The satin lingerie shimmers softly in the light, and the cut emphasizes both sophistication and a hint of allure. The lingerie is detailed with fine lace edges, highlighting her alluring figure. She elegantly styled hair as if getting ready for a formal event. The photo has a cinematic quality with rays of light and dramatic play of shadow and light"

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
    prompt=prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=832,
    height=1216,
    guidance_scale=3.5,
).images[0]

model_output.save("output.png", format="PNG")

高級用法

文檔未提及高級用法示例，暫不提供。

📚 詳細文檔

驗證設置

• CFG：3.5
• CFG Rescale：0.0
• 步數：20
• 採樣器：FlowMatchEulerDiscreteScheduler
• 隨機種子：42
• 分辨率：832x1216
• 跳過層引導：無

注意：驗證設置不一定與訓練設置相同。

訓練設置

數據集

reddy-v2-512

• 重複次數：10
• 圖像總數：13
• 寬高比桶總數：1
• 分辨率：0.262144 兆像素
• 是否裁剪：否
• 裁剪風格：無
• 裁剪寬高比：無
• 是否用作正則化數據：否

reddy-v2-1024

• 重複次數：10
• 圖像總數：5
• 寬高比桶總數：1
• 分辨率：1.048576 兆像素
• 是否裁剪：否
• 裁剪風格：無
• 裁剪寬高比：無
• 是否用作正則化數據：否

🔧 技術細節

文本編碼器在訓練過程中未進行訓練，推理時可複用基礎模型的文本編碼器。

📄 許可證

本項目使用其他許可證。具體許可證信息請參考相關文件。