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import torch, os, gc, random |
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import gradio as gr |
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from PIL import Image |
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from diffusers.utils import load_image |
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from accelerate import Accelerator |
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from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler |
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accelerator = Accelerator(cpu=True) |
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pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/lllyasviel/fav_models/fav/realisticStockPhoto_v10.safetensors", torch_dtype=torch.bfloat16, use_safetensors=True, variant=None, safety_checker=False) |
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pipe.load_lora_weights("JoPmt/Txt2Img_Rlstc_Stck_Pht_Xl_Fused", weight_name="SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors", repo_type="space")) |
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pipe.fuse_lora(fuse_unet=True,fuse_text_encoder=False) |
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pipe=accelerator.prepare(pipe.to("cpu")) |
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apol=[] |
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def plex(prompt,neg_prompt,stips,nut): |
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apol=[] |
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if nut == 0: |
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nm = random.randint(1, 2147483616) |
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while nm % 32 != 0: |
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nm = random.randint(1, 2147483616) |
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else: |
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nm=nut |
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lora_scale=0.8 |
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generator = torch.Generator(device="cpu").manual_seed(nm) |
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image = pipe(prompt=prompt, negative_prompt=neg_prompt, denoising_end=1.0,num_inference_steps=stips, output_type="pil",cross_attention_kwargs={"scale": lora_scale},generator=generator) |
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for i, imge in enumerate(image["images"]): |
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apol.append(imge) |
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return apol |
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iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt"),gr.Textbox(label="negative prompt",value="ugly, blurry, poor quality"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=10, value=8),gr.Slider(label="manual seed (leave 0 for random)", minimum=0,step=32,maximum=2147483616,value=0)], outputs=gr.Gallery(label="out", columns=1),description="Running on cpu, very slow! by JoPmt.") |
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iface.queue(max_size=1,api_open=False) |
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iface.launch(max_threads=1) |
