multimodalart HF staff commited on
Commit
00f6a78
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1 Parent(s): 4cdfd9c

Update base model, better vae

Browse files
Files changed (1) hide show
  1. app.py +10 -5
app.py CHANGED
@@ -4,6 +4,7 @@ import gradio as gr
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  from PIL import Image
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  from diffusers import (
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  DiffusionPipeline,
 
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  StableDiffusionControlNetPipeline,
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  ControlNetModel,
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  StableDiffusionLatentUpscalePipeline,
@@ -11,18 +12,22 @@ from diffusers import (
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  EulerDiscreteScheduler # <-- Added import
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  )
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  # Initialize both pipelines
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- init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V2.0", torch_dtype=torch.float16).to("cuda")
 
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  controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)
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  main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
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- "SG161222/Realistic_Vision_V2.0",
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  controlnet=controlnet,
 
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  safety_checker=None,
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  torch_dtype=torch.float16,
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  ).to("cuda")
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- model_id = "stabilityai/sd-x2-latent-upscaler"
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- upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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- upscaler.to("cuda")
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  # Sampler map
 
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  from PIL import Image
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  from diffusers import (
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  DiffusionPipeline,
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+ AutoencoderKL,
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  StableDiffusionControlNetPipeline,
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  ControlNetModel,
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  StableDiffusionLatentUpscalePipeline,
 
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  EulerDiscreteScheduler # <-- Added import
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  )
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+ BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
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+
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  # Initialize both pipelines
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+ vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
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+ #init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", torch_dtype=torch.float16)
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  controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)
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  main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
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+ BASE_MODEL,
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  controlnet=controlnet,
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+ vae=vae,
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  safety_checker=None,
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  torch_dtype=torch.float16,
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  ).to("cuda")
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+ #model_id = "stabilityai/sd-x2-latent-upscaler"
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+ #upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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+ #upscaler.to("cuda")
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  # Sampler map