Spaces:
Running
on
Zero
Running
on
Zero
Commit
•
463b37d
1
Parent(s):
0c69724
Update app2.py
Browse files
app2.py
CHANGED
@@ -31,7 +31,7 @@ import sys
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sys.path.append("../")
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from models.depth_normal_pipeline_clip import DepthNormalEstimationPipeline
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#from models.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline
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from utils.seed_all import seed_all
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import matplotlib.pyplot as plt
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from utils.de_normalized import align_scale_shift
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@@ -56,13 +56,20 @@ image_encoder = CLIPVisionModelWithProjection.from_pretrained(sd_image_variation
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feature_extractor = CLIPImageProcessor.from_pretrained(sd_image_variations_diffusers_path, subfolder="feature_extractor")
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unet = UNet2DConditionModel.from_pretrained('./wocfg/unet_ema')
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pipe = DepthNormalEstimationPipeline(vae=vae,
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image_encoder=image_encoder,
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feature_extractor=feature_extractor,
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unet=unet,
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scheduler=scheduler)
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try:
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import xformers
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@@ -78,20 +85,34 @@ def depth_normal(img,
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denoising_steps,
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ensemble_size,
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processing_res,
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domain):
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#img = img.resize((processing_res, processing_res), Image.Resampling.LANCZOS)
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depth_colored = pipe_out.depth_colored
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normal_colored = pipe_out.normal_colored
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@@ -152,13 +173,13 @@ def run_demo():
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label="Data Type (Must Select One matches your image)",
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value="indoor",
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)
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denoising_steps = gr.Slider(
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label="Number of denoising steps (More stepes, better quality)",
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minimum=1,
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@@ -195,7 +216,7 @@ def run_demo():
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inputs=[input_image, denoising_steps,
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ensemble_size,
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processing_res,
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domain],
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outputs=[depth, normal]
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)
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sys.path.append("../")
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from models.depth_normal_pipeline_clip import DepthNormalEstimationPipeline
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#from models.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline
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from models.depth_normal_pipeline_clip_cfg_1 import DepthNormalEstimationPipeline as DepthNormalEstimationPipelineCFG
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from utils.seed_all import seed_all
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import matplotlib.pyplot as plt
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from utils.de_normalized import align_scale_shift
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feature_extractor = CLIPImageProcessor.from_pretrained(sd_image_variations_diffusers_path, subfolder="feature_extractor")
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unet = UNet2DConditionModel.from_pretrained('./wocfg/unet_ema')
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unet_cfg = UNet2DConditionModel.from_pretrained('./cfg/unet_ema')
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pipe = DepthNormalEstimationPipeline(vae=vae,
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image_encoder=image_encoder,
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feature_extractor=feature_extractor,
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unet=unet,
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scheduler=scheduler)
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pipe_cfg = DepthNormalEstimationPipelineCFG(vae=vae,
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image_encoder=image_encoder,
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feature_extractor=feature_extractor,
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unet=unet_cfg,
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scheduler=scheduler)
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try:
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import xformers
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denoising_steps,
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ensemble_size,
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processing_res,
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guidance_scale,
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domain):
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#img = img.resize((processing_res, processing_res), Image.Resampling.LANCZOS)
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if guidance_scale > 0:
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pipe_out = pipe_cfg(
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img,
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denoising_steps=denoising_steps,
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ensemble_size=ensemble_size,
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processing_res=processing_res,
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batch_size=0,
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guidance_scale=guidance_scale,
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domain=domain,
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show_progress_bar=True,
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)
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else:
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pipe_out = pipe(
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img,
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denoising_steps=denoising_steps,
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ensemble_size=ensemble_size,
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processing_res=processing_res,
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batch_size=0,
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#guidance_scale=guidance_scale,
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domain=domain,
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show_progress_bar=True,
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)
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depth_colored = pipe_out.depth_colored
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normal_colored = pipe_out.normal_colored
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label="Data Type (Must Select One matches your image)",
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value="indoor",
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)
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guidance_scale = gr.Slider(
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label="Classifier Free Guidance Scale, 0 for without guidance",
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minimum=0,
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maximum=5,
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step=1,
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value=0,
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)
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denoising_steps = gr.Slider(
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label="Number of denoising steps (More stepes, better quality)",
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minimum=1,
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inputs=[input_image, denoising_steps,
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ensemble_size,
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processing_res,
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guidance_scale,
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domain],
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outputs=[depth, normal]
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)
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