Instructions to use codermert/model_malika with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codermert/model_malika with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("codermert/model_malika") prompt = "DHANUSH" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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by codermert - opened
README.md
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@@ -29,7 +29,7 @@ You should use `tugce` to trigger the image generation.
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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pipeline = AutoPipelineForText2Image.from_pretrained('
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pipeline.load_lora_weights('codermert/model_malika', weight_name='flux_train_replicate.safetensors')
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image = pipeline('your prompt').images[0]
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```
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-0.9', torch_dtype=torch.float16).to('cuda')
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pipeline.load_lora_weights('codermert/model_malika', weight_name='flux_train_replicate.safetensors')
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image = pipeline('your prompt').images[0]
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```
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