mirror of https://github.com/vladmandic/automatic
34 lines
1.7 KiB
Python
34 lines
1.7 KiB
Python
import sys
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sys.path.append("./")
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# import torch
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# from torchvision import transforms
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from meissonic.transformer import Transformer2DModel as TransformerMeissonic
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from meissonic.pipeline import MeissonicPipeline
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from meissonic.scheduler import Scheduler as MeissonicScheduler
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from transformers import CLIPTextModelWithProjection, CLIPTokenizer
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from diffusers import VQModel
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device = 'cuda'
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model_path = 'MeissonFlow/Meissonic'
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cache_dir = '/mnt/models/Diffusers'
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# diffusers_load_config['variant'] = fp16
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model = TransformerMeissonic.from_pretrained(model_path, subfolder="transformer", cache_dir=cache_dir)
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vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", cache_dir=cache_dir)
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# text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path,subfolder="text_encoder",)
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text_encoder = CLIPTextModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K", cache_dir=cache_dir)
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tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
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scheduler = MeissonicScheduler.from_pretrained(model_path, subfolder="scheduler")
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pipe = MeissonicPipeline(vq_model, tokenizer=tokenizer, text_encoder=text_encoder, transformer=model, scheduler=scheduler)
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pipe = pipe.to(device)
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steps = 64
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guidance_scale = 9
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resolution = 1024
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negative = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
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prompt = "Beautiful young woman posing on a lake with snow covered mountains in the background"
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image = pipe(prompt=prompt, negative_prompt=negative, height=resolution, width=resolution, guidance_scale=guidance_scale, num_inference_steps=steps).images[0]
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image.save('/tmp/meissonic.png')
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