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license: openrail++ |
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--- |
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This Repo contains a diffusers format version of the PixArt-Sigma Repos |
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PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers |
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PixArt-alpha/PixArt-Sigma-XL-2-1024-MS |
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with the models loaded and saved in fp16 and bf16 formats, roughly halfing their sizes. |
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It can be used where download bandwith, memory or diskspace are relatively low, a T4 Colab instance for example. |
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To use in a diffusers script you currently(15/04/2024) need to use a Source distribution of Diffusers |
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and an extra 'patch' from the PixArt0Alpha's teams Sigma Github repo |
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A simple Colab notebook can be found at https://github.com/Vargol/StableDiffusionColabs/blob/main/PixArt/PixArt_Sigma.ipynb |
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a Diffusers script looks like this. |
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```py |
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import random |
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import sys |
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import torch |
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from diffusers import Transformer2DModel |
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from scripts.diffusers_patches import pixart_sigma_init_patched_inputs, PixArtSigmaPipeline |
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assert getattr(Transformer2DModel, '_init_patched_inputs', False), "Need to Upgrade diffusers: pip install git+https://github.com/huggingface/diffusers" |
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setattr(Transformer2DModel, '_init_patched_inputs', pixart_sigma_init_patched_inputs) |
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device = 'mps' |
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weight_dtype = torch.bfloat16 |
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pipe = PixArtSigmaPipeline.from_pretrained( |
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"/Vargol/PixArt-Sigma_16bit", |
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torch_dtype=weight_dtype, |
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variant="fp16", |
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use_safetensors=True, |
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) |
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# Enable memory optimizations. |
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# pipe.enable_model_cpu_offload() |
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pipe.to(device) |
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prompt = "Cinematic science fiction film still.A cybernetic demon awaits her friend in a bar selling flaming oil drinks. The barman is a huge tree being, towering over the demon" |
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for i in range(4): |
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seed = random.randint(0, sys.maxsize) |
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generator = torch.Generator("mps").manual_seed(seed); |
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image = pipe(prompt, generator=generator, num_iferencenum_inference_steps=40).images[0] |
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image.save(f"pas_{seed}.png")a |
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``` |
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