Create README.md
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README.md
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---
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license: creativeml-openrail-m
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base_model: gsdf/Counterfeit-V2.5
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- text-to-image
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- diffusers
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- lora
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- civitai
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- a1111
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---
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This repository contains a LoRA model downloaded from [CivitAI](https://civitai.com/models/13239/light-and-shadow).
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You can load this checkpoint in 🧨 diffusers and perform like so:
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```python
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import torch
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from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
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pipeline = StableDiffusionPipeline.from_pretrained(
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"gsdf/Counterfeit-V2.5", torch_dtype=torch.float16, safety_checker=None
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).to("cuda")
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pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
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pipeline.scheduler.config, use_karras_sigmas=True
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)
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pipeline.load_lora_weights(
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"sayakpaul/civitai-light-shadow-lora", weight_name="light_and_shadow.safetensors"
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)
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prompt = "masterpiece, best quality, 1girl, at dusk"
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negative_prompt = ("(low quality, worst quality:1.4), (bad anatomy), (inaccurate limb:1.2), "
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"bad composition, inaccurate eyes, extra digit, fewer digits, (extra arms:1.2), large breasts")
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image = pipeline(prompt=prompt,
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negative_prompt=negative_prompt,
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width=512,
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height=768,
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num_inference_steps=15,
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# num_images_per_prompt=1,
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generator=torch.manual_seed(0)
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).images[0]
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image.save("image.png")
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```
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Below is a comparison between the LoRA and the non-LoRA results:
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<div align="center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lora_non_lora_comparison.png" width=650/>
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</div>
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