adding source

src/image_prep.py

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+import numpy as np
+from PIL import Image
+import cv2
+
+
+def canny_from_pil(image, low_threshold=100, high_threshold=200):
+    image = np.array(image)
+    image = cv2.Canny(image, low_threshold, high_threshold)
+    image = image[:, :, None]
+    image = np.concatenate([image, image, image], axis=2)
+    control_image = Image.fromarray(image)
+    return control_image

src/model.py

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+import os, sys, pdb
+
+import diffusers
+from transformers import AutoTokenizer, PretrainedConfig
+from diffusers import AutoencoderKL, UNet2DConditionModel, DDPMScheduler
+
+
+def make_1step_sched():
+    noise_scheduler = DDPMScheduler.from_pretrained("stabilityai/sd-turbo", subfolder="scheduler")
+    noise_scheduler_1step = DDPMScheduler.from_pretrained("stabilityai/sd-turbo", subfolder="scheduler")
+    noise_scheduler_1step.set_timesteps(1, device="cuda")
+    noise_scheduler_1step.alphas_cumprod = noise_scheduler_1step.alphas_cumprod.cuda()
+    return noise_scheduler_1step

src/pix2pix_turbo.py

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+import os, requests
+import pdb
+import copy
+from tqdm import tqdm
+import torch
+from transformers import AutoTokenizer, PretrainedConfig, CLIPTextModel
+from diffusers import AutoencoderKL, UNet2DConditionModel, DDPMScheduler
+from diffusers.utils.peft_utils import set_weights_and_activate_adapters
+from peft import LoraConfig
+from .model import make_1step_sched
+
+
+def my_vae_encoder_fwd(self, sample):
+    r"""The forward method of the `Encoder` class."""
+    sample = self.conv_in(sample)
+    l_blocks = []
+    # down
+    for down_block in self.down_blocks:
+        l_blocks.append(sample)
+        sample = down_block(sample)
+    # middle
+    sample = self.mid_block(sample)
+    sample = self.conv_norm_out(sample)
+    sample = self.conv_act(sample)
+    sample = self.conv_out(sample)
+    self.current_down_blocks = l_blocks
+    return sample
+
+
+def my_vae_decoder_fwd(self,sample, latent_embeds = None):
+    sample = self.conv_in(sample)
+    upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
+    # middle
+    sample = self.mid_block(sample, latent_embeds)
+    sample = sample.to(upscale_dtype)
+    if not self.ignore_skip:
+        skip_convs = [self.skip_conv_1, self.skip_conv_2, self.skip_conv_3, self.skip_conv_4]
+        # up
+        for idx, up_block in enumerate(self.up_blocks):
+            skip_in = skip_convs[idx](self.incoming_skip_acts[::-1][idx])
+            # add skip
+            sample = sample + skip_in
+            sample = up_block(sample, latent_embeds)
+    else:
+        for idx, up_block in enumerate(self.up_blocks):
+            sample = up_block(sample, latent_embeds)
+    # post-process
+    if latent_embeds is None:
+        sample = self.conv_norm_out(sample)
+    else:
+        sample = self.conv_norm_out(sample, latent_embeds)
+    sample = self.conv_act(sample)
+    sample = self.conv_out(sample)
+    return sample
+
+
+class TwinConv(torch.nn.Module):
+    def __init__(self, convin_pretrained, convin_curr):
+        super(TwinConv, self).__init__()
+        self.conv_in_pretrained = copy.deepcopy(convin_pretrained)
+        self.conv_in_curr = copy.deepcopy(convin_curr)
+        self.r = None
+    def forward(self, x):
+        x1 = self.conv_in_pretrained(x).detach()
+        x2 = self.conv_in_curr(x)
+        return x1*(1-self.r) + x2*(self.r)
+
+
+class Pix2Pix_Turbo(torch.nn.Module):
+    def __init__(self, name, ckpt_folder="checkpoints"):
+        super().__init__()
+        self.tokenizer = AutoTokenizer.from_pretrained("stabilityai/sd-turbo",subfolder="tokenizer")
+        self.text_encoder = CLIPTextModel.from_pretrained("stabilityai/sd-turbo", subfolder="text_encoder").cuda()
+        self.sched = make_1step_sched()
+
+        vae = AutoencoderKL.from_pretrained("stabilityai/sd-turbo", subfolder="vae")
+        unet = UNet2DConditionModel.from_pretrained("stabilityai/sd-turbo", subfolder="unet")
+
+        if name=="canny_to_image":
+            lora_rank = 8
+            P_UNET_SD="/home/gparmar/code/single_step_translation/output/paired/canny_canny_midjourney_512_512/sd21_turbo_direct_edge_withskip_opt_lora_8_proj/l2_lpips_gan_vagan_clip_224_patch_multilevel_sigmoid/lr_5e-5_l2_0.25_lpips_1_0.1_CLIPSIM_1.0/1node_8gpu_no_BS_1_GRAD_ACC_2/checkpoint-7501/unet_sd.pkl"
+            P_VAE_ENC_SD="/home/gparmar/code/single_step_translation/output/paired/canny_canny_midjourney_512_512/sd21_turbo_direct_edge_withskip_opt_lora_8_proj/l2_lpips_gan_vagan_clip_224_patch_multilevel_sigmoid/lr_5e-5_l2_0.25_lpips_1_0.1_CLIPSIM_1.0/1node_8gpu_no_BS_1_GRAD_ACC_2/checkpoint-7501/sd_vae_enc.pkl"
+            P_VAE_DEC_SD="/home/gparmar/code/single_step_translation/output/paired/canny_canny_midjourney_512_512/sd21_turbo_direct_edge_withskip_opt_lora_8_proj/l2_lpips_gan_vagan_clip_224_patch_multilevel_sigmoid/lr_5e-5_l2_0.25_lpips_1_0.1_CLIPSIM_1.0/1node_8gpu_no_BS_1_GRAD_ACC_2/checkpoint-7501/sd_vae_dec.pkl"
+            unet_lora_config = LoraConfig(r=lora_rank, init_lora_weights="gaussian", target_modules=[
+                "to_k", "to_q", "to_v", "to_out.0", "conv", "conv1", "conv2", "conv_shortcut", "conv_out",
+                "proj_in", "proj_out", "ff.net.2", "ff.net.0.proj"]
+            )
+
+        if name=="sketch_to_image_stochastic":
+            # download from url
+            url = "https://www.cs.cmu.edu/~clean-fid/tmp/img2img_turbo/ckpt/sketch_to_image_stochastic.pkl"
+            os.makedirs(ckpt_folder, exist_ok=True)
+            outf = os.path.join(ckpt_folder, "sketch_to_image_stochastic.pkl")
+            if not os.path.exists(outf):
+                print(f"Downloading checkpoint to {outf}")
+                response = requests.get(url, stream=True)
+                total_size_in_bytes= int(response.headers.get('content-length', 0))
+                block_size = 1024  # 1 Kibibyte
+                progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
+                with open(outf, 'wb') as file:
+                    for data in response.iter_content(block_size):
+                        progress_bar.update(len(data))
+                        file.write(data)
+                progress_bar.close()
+                if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
+                    print("ERROR, something went wrong")
+                print(f"Downloaded successfully to {outf}")
+            # p_ckpt = "/home/gparmar/code/img2img-turbo/single_step_translation/notebooks/DEMO/sketch_to_image_stochastic.pkl"
+            p_ckpt = outf
+            sd = torch.load(p_ckpt, map_location="cpu")
+            unet_lora_config = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["unet_lora_target_modules"])
+            convin_pretrained = copy.deepcopy(unet.conv_in)
+            unet.conv_in = TwinConv(convin_pretrained, unet.conv_in)
+        
+        vae.encoder.forward = my_vae_encoder_fwd.__get__(vae.encoder, vae.encoder.__class__)
+        vae.decoder.forward = my_vae_decoder_fwd.__get__(vae.decoder, vae.decoder.__class__)
+        # add the skip connection convs
+        vae.decoder.skip_conv_1 = torch.nn.Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False).cuda()
+        vae.decoder.skip_conv_2 = torch.nn.Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False).cuda()
+        vae.decoder.skip_conv_3 = torch.nn.Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False).cuda()
+        vae.decoder.skip_conv_4 = torch.nn.Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False).cuda()
+        vae_lora_config = LoraConfig(r=sd["rank_vae"], init_lora_weights="gaussian", target_modules=sd["vae_lora_target_modules"])
+        vae.decoder.ignore_skip = False
+        vae.add_adapter(vae_lora_config, adapter_name="vae_skip")
+        unet.add_adapter(unet_lora_config)
+        unet.load_state_dict(sd["state_dict_unet"])
+        unet.enable_xformers_memory_efficient_attention()
+
+        vae.load_state_dict(sd["state_dict_vae"])
+        unet.to("cuda")
+        vae.to("cuda")
+        unet.eval()
+        vae.eval()
+        
+        self.unet, self.vae = unet, vae
+        self.timesteps = torch.tensor([999], device="cuda").long()
+
+
+    def forward(self, c_t, prompt, deterministic=True, r=1.0, noise_map=None):
+        # encode the text prompt
+        caption_tokens = self.tokenizer(prompt, max_length=self.tokenizer.model_max_length,
+                padding="max_length", truncation=True, return_tensors="pt").input_ids.cuda()
+        caption_enc = self.text_encoder(caption_tokens)[0]
+
+        if deterministic:
+            encoded_control = self.vae.encode(c_t).latent_dist.sample()*self.vae.config.scaling_factor
+            model_pred = self.unet(encoded_control, self.timesteps, encoder_hidden_states=caption_enc,).sample
+            x_denoised = self.sched.step(model_pred, self.timesteps, encoded_control, return_dict=True).prev_sample
+            self.vae.decoder.incoming_skip_acts = self.vae.encoder.current_down_blocks
+            output_image = (self.vae.decode(x_denoised / self.vae.config.scaling_factor ).sample).clamp(-1,1)
+        else:
+            # scale the lora weights based on the r value
+            self.unet.set_adapters(["default"], weights=[r])
+            set_weights_and_activate_adapters(self.vae, ["vae_skip"], [r])
+            encoded_control = self.vae.encode(c_t).latent_dist.sample()*self.vae.config.scaling_factor
+            # combine the input and noise
+            unet_input = encoded_control*r + noise_map*(1-r)
+            self.unet.conv_in.r = r
+            unet_output = self.unet(unet_input, self.timesteps, encoder_hidden_states=caption_enc,).sample
+            self.unet.conv_in.r = None
+            x_denoised = self.sched.step(unet_output, self.timesteps, unet_input, return_dict=True).prev_sample
+            self.vae.decoder.incoming_skip_acts = self.vae.encoder.current_down_blocks
+            output_image = (self.vae.decode(x_denoised / self.vae.config.scaling_factor ).sample).clamp(-1,1)
+
+        return output_image
+
+
+