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README.md
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- flux
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- diffusers
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- lora
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base_model: "black-forest-labs/FLUX.1-dev"
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pipeline_tag: text-to-image
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#
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# - text: >-
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# prompt
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# output:
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# url: https://...
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instance_prompt: TOK
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---
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# Malika
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<Gallery />
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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#
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pipeline =
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"
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torch_dtype=torch.float16
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).to("cuda")
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#
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pipeline.load_lora_weights(
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"codermert/malikafinal",
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weight_name="lora.safetensors",
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adapter_name="
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cross_attention_scale=0.5 # LoRA etkisini hafiflet
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)
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#
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image = pipeline(
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prompt="portrait of TOK, <
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negative_prompt="blurry, deformed"
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).images[0]
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```
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For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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- flux
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- diffusers
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- lora
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- stable-diffusion
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- text-to-image
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base_model: "black-forest-labs/FLUX.1-dev"
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pipeline_tag: text-to-image
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inference: true # Bu satırı ekleyin
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---
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# Malika
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<Gallery />
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## Usage with 🧨 Diffusers
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```python
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from diffusers import DiffusionPipeline
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import torch
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# Load base model
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pipeline = DiffusionPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-dev",
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torch_dtype=torch.float16
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).to("cuda")
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# Load your LoRA
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pipeline.load_lora_weights(
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"codermert/malikafinal",
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weight_name="lora.safetensors",
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adapter_name="malika"
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)
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# Generate image
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image = pipeline(
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prompt="portrait of TOK, <malika>, photorealistic, 8K",
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negative_prompt="blurry, deformed"
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).images[0]
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