fixed bug with images not resizing for img2img

added GFPGAN as an option for img2img
added GFPGAN as a tab
added autodetection for row counts for grids, enabled by default
removed Fixed Code sampling because no one can figure out what it does; maybe someone will be upset by removal and will tell me

webui.py

@@ -13,6 +13,7 @@ from torch import autocast
 from contextlib import contextmanager, nullcontext
 import mimetypes
 import random
+import math
 
 import k_diffusion as K
 from ldm.util import instantiate_from_config
@@ -31,7 +32,7 @@ parser = argparse.ArgumentParser()
 parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
 parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",)
 parser.add_argument("--skip_save", action='store_true', help="do not save indiviual samples. For speed measurements.",)
-parser.add_argument("--n_rows", type=int, default=0, help="rows in the grid (default: n_samples)",)
+parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",)
 parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
@@ -118,6 +119,7 @@ if os.path.exists(GFPGAN_dir):
         print("Error loading GFPGAN:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
 
+
 config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
 model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
 
@@ -125,18 +127,26 @@ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp
 model = model.half().to(device)
 
 
-def image_grid(imgs, rows):
+def image_grid(imgs, batch_size):
-    cols = len(imgs) // rows
+    if opt.n_rows > 0:
+        rows = opt.n_rows
+    elif opt.n_rows == 0:
+        rows = batch_size
+    else:
+        rows = round(math.sqrt(len(imgs)))
+
+    cols = math.ceil(len(imgs) / rows)
 
     w, h = imgs[0].size
-    grid = Image.new('RGB', size=(cols * w, rows * h))
+    grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
 
     for i, img in enumerate(imgs):
         grid.paste(img, box=(i % cols * w, i // cols * h))
 
     return grid
 
-def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
+
+def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
     torch.cuda.empty_cache()
 
     outpath = opt.outdir or "outputs/txt2img-samples"
@@ -165,7 +175,6 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use
     os.makedirs(outpath, exist_ok=True)
 
     batch_size = n_samples
-    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
 
     assert prompt is not None
     data = [batch_size * [prompt]]
@@ -175,15 +184,9 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use
     base_count = len(os.listdir(sample_path))
     grid_count = len(os.listdir(outpath)) - 1
 
-    start_code = None
-    if fixed_code:
-        start_code = torch.randn([n_samples, opt_C, height // opt_f, width // opt_f], device=device)
-
     precision_scope = autocast if opt.precision == "autocast" else nullcontext
     output_images = []
     with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
-        all_samples = []
-
         for n in range(n_iter):
             for batch_index, prompts in enumerate(data):
                 uc = None
@@ -204,7 +207,7 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use
                     samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}, disable=False)
 
                 elif sampler is not None:
-                    samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=start_code)
+                    samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=None)
 
                 x_samples_ddim = model.decode_first_stage(samples_ddim)
                 x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -224,12 +227,9 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use
                         output_images.append(image)
                         base_count += 1
 
-                        if not opt.skip_grid:
-                            all_samples.append(x_sample)
-
         if not opt.skip_grid:
             # additionally, save as grid
-            grid = image_grid(output_images, rows=n_rows)
+            grid = image_grid(output_images, batch_size)
             grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
             grid_count += 1
 
@@ -251,7 +251,6 @@ dream_interface = gr.Interface(
         gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
         gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
         gr.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"),
-        gr.Checkbox(label='Enable Fixed Code sampling', value=False),
         gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
         gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
         gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1),
@@ -272,7 +271,7 @@ dream_interface = gr.Interface(
 )
 
 
-def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
+def translation(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
     torch.cuda.empty_cache()
 
     outpath = opt.outdir or "outputs/img2img-samples"
@@ -280,14 +279,11 @@ def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter:
     if seed == -1:
         seed = random.randrange(4294967294)
 
-    sampler = DDIMSampler(model)
-
     model_wrap = K.external.CompVisDenoiser(model)
 
     os.makedirs(outpath, exist_ok=True)
 
     batch_size = n_samples
-    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
 
     assert prompt is not None
     data = [batch_size * [prompt]]
@@ -299,28 +295,23 @@ def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter:
     seedit = 0
 
     image = init_img.convert("RGB")
-    w, h = image.size
+    image = image.resize((width, height), resample=PIL.Image.Resampling.LANCZOS)
     image = np.array(image).astype(np.float32) / 255.0
     image = image[None].transpose(0, 3, 1, 2)
     image = torch.from_numpy(image)
 
     output_images = []
     precision_scope = autocast if opt.precision == "autocast" else nullcontext
-    with torch.no_grad():
+    with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
-        with precision_scope("cuda"):
         init_image = 2. * image - 1.
         init_image = init_image.to(device)
         init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
         init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))  # move to latent space
         x0 = init_latent
 
-            sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta, verbose=False)
-
         assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
         t_enc = int(denoising_strength * ddim_steps)
-            print(f"target t_enc is {t_enc} steps")
+
-            with model.ema_scope():
-                all_samples = list()
         for n in range(n_iter):
             for batch_index, prompts in enumerate(data):
                 uc = None
@@ -338,7 +329,6 @@ def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter:
                 noise = torch.randn_like(x0) * sigmas[ddim_steps - t_enc - 1]  # for GPU draw
                 xi = x0 + noise
                 sigma_sched = sigmas[ddim_steps - t_enc - 1:]
-                        # x = torch.randn([n_samples, *shape]).to(device) * sigmas[0] # for CPU draw
                 model_wrap_cfg = CFGDenoiser(model_wrap)
                 extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
 
@@ -346,31 +336,27 @@ def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter:
                 x_samples_ddim = model.decode_first_stage(samples_ddim)
                 x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-                        if not opt.skip_save:
+                if not opt.skip_save or not opt.skip_grid:
                     for x_sample in x_samples_ddim:
                         x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                                image = Image.fromarray(x_sample.astype(np.uint8))
+                        x_sample = x_sample.astype(np.uint8)
+
+                        if use_GFPGAN and GFPGAN is not None:
+                            cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
+                            x_sample = restored_img
+
+                        image = Image.fromarray(x_sample)
+
                         image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
                         output_images.append(image)
                         base_count += 1
-                                seedit += 1
-
-                        if not opt.skip_grid:
-                            all_samples.append(x_samples_ddim)
 
         if not opt.skip_grid:
             # additionally, save as grid
-                    grid = torch.stack(all_samples, 0)
+            grid = image_grid(output_images, batch_size)
-                    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+            grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
-                    grid = make_grid(grid, nrow=n_rows)
-
-                    # to image
-                    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
-                    Image.fromarray(grid.astype(np.uint8))
             grid_count += 1
 
-    del sampler
     return output_images, seed
 
 
@@ -382,9 +368,10 @@ img2img_interface = gr.Interface(
         gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
         gr.Image(value="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg", source="upload", interactive=True, type="pil"),
         gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
+        gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
         gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
-        gr.Slider(minimum=1, maximum=50, step=1, label='Sampling iterations', value=2),
+        gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1),
-        gr.Slider(minimum=1, maximum=8, step=1, label='Samples per iteration', value=2),
+        gr.Slider(minimum=1, maximum=4, step=1, label='Samples per iteration', value=1),
         gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),
         gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
         gr.Number(label='Seed', value=-1),
@@ -399,6 +386,37 @@ img2img_interface = gr.Interface(
     description="Generate images from images with Stable Diffusion",
 )
 
-demo = gr.TabbedInterface(interface_list=[dream_interface, img2img_interface], tab_names=["Dream", "Image Translation"])
+interfaces = [
+    (dream_interface, "Dream"),
+    (img2img_interface, "Image Translation")
+]
+
+def run_GFPGAN(image, strength):
+    image = image.convert("RGB")
+
+    cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
+    res = Image.fromarray(restored_img)
+
+    if strength < 1.0:
+        res = PIL.Image.blend(image, res, strength)
+
+    return res
+
+
+if GFPGAN is not None:
+    interfaces.append((gr.Interface(
+        run_GFPGAN,
+        inputs=[
+            gr.Image(label="Source", source="upload", interactive=True, type="pil"),
+            gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100),
+        ],
+        outputs=[
+            gr.Image(label="Result"),
+        ],
+        title="GFPGAN",
+        description="Fix faces on images",
+    ), "GFPGAN"))
+
+demo = gr.TabbedInterface(interface_list=[x[0] for x in interfaces], tab_names=[x[1] for x in interfaces])
 
 demo.launch()