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gradio_demo.py

@@ -16,11 +16,9 @@ import time
 import spaces
 from huggingface_hub import hf_hub_download
 
-hf_hub_download(repo_id="laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", filename="open_clip_pytorch_model.bin", local_dir="laion_CLIP-ViT-bigG-14-laion2B-39B-b160k")
 hf_hub_download(repo_id="camenduru/SUPIR", filename="sd_xl_base_1.0_0.9vae.safetensors", local_dir="yushan777_SUPIR")
 hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0F.ckpt", local_dir="yushan777_SUPIR")
 hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0Q.ckpt", local_dir="yushan777_SUPIR")
-hf_hub_download(repo_id="RunDiffusion/Juggernaut-XL-Lightning", filename="Juggernaut_RunDiffusionPhoto2_Lightning_4Steps.safetensors", local_dir="RunDiffusion_Juggernaut-XL-Lightning")
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--opt", type=str, default='options/SUPIR_v0.yaml')
@@ -73,20 +71,19 @@ def stage1_process(input_image, gamma_correction):
         gr.Warning('Set this space to GPU config to make it work.')
         return None
     torch.cuda.set_device(SUPIR_device)
-    #LQ = HWC3(input_image)
-    #LQ = fix_resize(LQ, 512)
-    ## stage1
-    #LQ = np.array(LQ) / 255 * 2 - 1
-    #LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
-    #LQ = model.batchify_denoise(LQ, is_stage1=True)
-    #LQ = (LQ[0].permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().round().clip(0, 255).astype(np.uint8)
-    ## gamma correction
-    #LQ = LQ / 255.0
-    #LQ = np.power(LQ, gamma_correction)
-    #LQ *= 255.0
-    #LQ = LQ.round().clip(0, 255).astype(np.uint8)
-    #return LQ
-    return None
+    LQ = HWC3(input_image)
+    LQ = fix_resize(LQ, 512)
+    # stage1
+    LQ = np.array(LQ) / 255 * 2 - 1
+    LQ = torch.tensor(LQ, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
+    LQ = model.batchify_denoise(LQ, is_stage1=True)
+    LQ = (LQ[0].permute(1, 2, 0) * 127.5 + 127.5).cpu().numpy().round().clip(0, 255).astype(np.uint8)
+    # gamma correction
+    LQ = LQ / 255.0
+    LQ = np.power(LQ, gamma_correction)
+    LQ *= 255.0
+    LQ = LQ.round().clip(0, 255).astype(np.uint8)
+    return LQ
 
 @spaces.GPU(duration=120)
 def llave_process(input_image, temperature, top_p, qs=None):