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--- |
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license: creativeml-openrail-m |
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tags: |
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- text-to-image |
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- stable-diffusion |
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- lora |
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- diffusers |
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base_model: stabilityai/stable-diffusion-xl-base-1.0 |
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instance_prompt: pixel art |
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widget: |
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- text: pixel art, a cute corgi, simple, flat colors |
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datasets: |
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- mlfoundations/dclm-baseline-1.0 |
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pipeline_tag: text-to-image |
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--- |
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# Pixel Art XL |
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## Consider supporting further research on [Patreon](https://www.patreon.com/user?u=29466374) or [Twitter](https://twitter.com/nerijs) |
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Downscale 8 times to get pixel perfect images (use Nearest Neighbors) |
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Use a fixed VAE to avoid artifacts (0.9 or fp16 fix) |
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### Need more performance? |
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Use it with a LCM Lora! |
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Use 8 steps and guidance scale of 1.5 |
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1.2 Lora strength for the Pixel Art XL works better |
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```python |
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from diffusers import DiffusionPipeline, LCMScheduler |
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import torch |
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model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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lcm_lora_id = "latent-consistency/lcm-lora-sdxl" |
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pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16") |
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
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pipe.load_lora_weights(lcm_lora_id, adapter_name="lora") |
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pipe.load_lora_weights("./pixel-art-xl.safetensors", adapter_name="pixel") |
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pipe.set_adapters(["lora", "pixel"], adapter_weights=[1.0, 1.2]) |
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pipe.to(device="cuda", dtype=torch.float16) |
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prompt = "pixel, a cute corgi" |
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negative_prompt = "3d render, realistic" |
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num_images = 9 |
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for i in range(num_images): |
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img = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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num_inference_steps=8, |
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guidance_scale=1.5, |
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).images[0] |
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img.save(f"lcm_lora_{i}.png") |
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``` |
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### Tips: |
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Don't use refiner |
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Works great with only 1 text encoder |
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No style prompt required |
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No trigger keyword require |
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Works great with isometric and non-isometric |
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Works with 0.9 and 1.0 |
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#### Changelog |
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v1: Initial release |
