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cldm_v21.yaml

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+model:
+  target: cldm.cldm.ControlLDM
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    control_key: "hint"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+    only_mid_control: False
+
+    control_stage_config:
+      target: cldm.cldm.ControlNet
+      params:
+        use_checkpoint: True
+        image_size: 32 # unused
+        in_channels: 4
+        hint_channels: 3
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    unet_config:
+      target: cldm.cldm.ControlledUnetModel
+      params:
+        use_checkpoint: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

environment.yaml

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+name: control
+channels:
+  - pytorch
+  - defaults
+dependencies:
+  - python=3.8.5
+  - pip=20.3
+  - cudatoolkit=11.3
+  - pytorch=1.12.1
+  - torchvision=0.13.1
+  - numpy=1.23.1
+  - pip:
+      - gradio==3.16.2
+      - albumentations==1.3.0
+      - opencv-contrib-python==4.3.0.36
+      - imageio==2.9.0
+      - imageio-ffmpeg==0.4.2
+      - pytorch-lightning==1.5.0
+      - omegaconf==2.1.1
+      - test-tube>=0.7.5
+      - streamlit==1.12.1
+      - einops==0.3.0
+      - transformers==4.19.2
+      - webdataset==0.2.5
+      - kornia==0.6
+      - open_clip_torch==2.0.2
+      - invisible-watermark>=0.1.5
+      - streamlit-drawable-canvas==0.8.0
+      - torchmetrics==0.6.0
+      - timm==0.6.12
+      - addict==2.4.0
+      - yapf==0.32.0
+      - prettytable==3.6.0
+      - safetensors==0.2.7
+      - basicsr==1.4.2

gradio_seg2image.py

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+from share import *
+import config
+
+import cv2
+import einops
+import gradio as gr
+import numpy as np
+import torch
+import random
+
+from pytorch_lightning import seed_everything
+from annotator.util import resize_image, HWC3
+from annotator.uniformer import UniformerDetector
+from cldm.model import create_model, load_state_dict
+from cldm.ddim_hacked import DDIMSampler
+
+
+apply_uniformer = UniformerDetector()
+
+model = create_model('./models/cldm_v21.yaml').cpu()
+model.load_state_dict(load_state_dict('./lightning_logs/version_1/checkpoints/epoch=123-step=1476467.ckpt', location='cuda'))
+model = model.cuda()
+ddim_sampler = DDIMSampler(model)
+
+
+def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta):
+    with torch.no_grad():
+        input_image = HWC3(input_image)
+        detected_map = apply_uniformer(resize_image(input_image, detect_resolution))
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_NEAREST)
+
+        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
+        control = torch.stack([control for _ in range(num_samples)], dim=0)
+        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
+
+        if seed == -1:
+            seed = random.randint(0, 65535)
+        seed_everything(seed)
+
+        if config.save_memory:
+            model.low_vram_shift(is_diffusing=False)
+
+        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
+        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
+        shape = (4, H // 8, W // 8)
+
+        if config.save_memory:
+            model.low_vram_shift(is_diffusing=True)
+
+        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
+        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
+                                                     shape, cond, verbose=False, eta=eta,
+                                                     unconditional_guidance_scale=scale,
+                                                     unconditional_conditioning=un_cond)
+
+        if config.save_memory:
+            model.low_vram_shift(is_diffusing=False)
+
+        x_samples = model.decode_first_stage(samples)
+        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+        results = [x_samples[i] for i in range(num_samples)]
+    return [detected_map] + results
+
+
+block = gr.Blocks().queue()
+with block:
+    with gr.Row():
+        gr.Markdown("## Control Stable Diffusion with Segmentation Maps")
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(source='upload', type="numpy")
+            prompt = gr.Textbox(label="Prompt")
+            run_button = gr.Button(label="Run")
+            with gr.Accordion("Advanced options", open=False):
+                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
+                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
+                guess_mode = gr.Checkbox(label='Guess Mode', value=False)
+                detect_resolution = gr.Slider(label="Segmentation Resolution", minimum=64, maximum=1024, value=512, step=1)
+                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
+                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
+                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
+                eta = gr.Number(label="eta (DDIM)", value=0.0)
+                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
+                n_prompt = gr.Textbox(label="Negative Prompt",
+                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
+        with gr.Column():
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
+    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+
+
+block.launch(server_name='0.0.0.0')

tool_add_control_sd21.py

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+import sys
+import os
+
+assert len(sys.argv) == 3, 'Args are wrong.'
+
+input_path = sys.argv[1]
+output_path = sys.argv[2]
+
+assert os.path.exists(input_path), 'Input model does not exist.'
+assert not os.path.exists(output_path), 'Output filename already exists.'
+assert os.path.exists(os.path.dirname(output_path)), 'Output path is not valid.'
+
+import torch
+from share import *
+from cldm.model import create_model
+
+
+def get_node_name(name, parent_name):
+    if len(name) <= len(parent_name):
+        return False, ''
+    p = name[:len(parent_name)]
+    if p != parent_name:
+        return False, ''
+    return True, name[len(parent_name):]
+
+
+model = create_model(config_path='./models/cldm_v21.yaml')
+
+pretrained_weights = torch.load(input_path)
+if 'state_dict' in pretrained_weights:
+    pretrained_weights = pretrained_weights['state_dict']
+
+scratch_dict = model.state_dict()
+
+target_dict = {}
+for k in scratch_dict.keys():
+    is_control, name = get_node_name(k, 'control_')
+    if is_control:
+        copy_k = 'model.diffusion_' + name
+    else:
+        copy_k = k
+    if copy_k in pretrained_weights:
+        target_dict[k] = pretrained_weights[copy_k].clone()
+    else:
+        target_dict[k] = scratch_dict[k].clone()
+        print(f'These weights are newly added: {k}')
+
+model.load_state_dict(target_dict, strict=True)
+torch.save(model.state_dict(), output_path)
+print('Done.')

tutorial_train_sd21.py

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+from share import *
+
+import pytorch_lightning as pl
+from torch.utils.data import DataLoader
+from tutorial_dataset import MyDataset
+from cldm.logger import ImageLogger
+from cldm.model import create_model, load_state_dict
+
+
+# Configs
+resume_path = './models/control_sd21_ini.ckpt'
+batch_size = 4
+logger_freq = 300
+learning_rate = 1e-5
+sd_locked = True
+only_mid_control = False
+
+
+# First use cpu to load models. Pytorch Lightning will automatically move it to GPUs.
+model = create_model('./models/cldm_v21.yaml').cpu()
+model.load_state_dict(load_state_dict(resume_path, location='cpu'))
+model.learning_rate = learning_rate
+model.sd_locked = sd_locked
+model.only_mid_control = only_mid_control
+
+
+# Misc
+dataset = MyDataset()
+dataloader = DataLoader(dataset, num_workers=0, batch_size=batch_size, shuffle=True)
+logger = ImageLogger(batch_frequency=logger_freq)
+trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger])
+
+
+# Train!
+trainer.fit(model, dataloader)