Added prompt matrix to img2img

refactoring: separated duplicate code from img2img and txt2img into a single function

webui.py

@@ -97,16 +97,21 @@ class KDiffusionSampler:
         sigmas = self.model_wrap.get_sigmas(S)
         x = x_T * sigmas[0]
         model_wrap_cfg = CFGDenoiser(self.model_wrap)
+
         samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
 
         return samples_ddim, None
 
 
-def create_random_tensors(seed, shape, count, same_seed=False):
+def create_random_tensors(shape, seeds):
     xs = []
-    for i in range(count):
+    for seed in seeds:
-        current_seed = seed if same_seed else seed + i
+        torch.manual_seed(seed)
-        torch.manual_seed(current_seed)
+
+        # randn results depend on device; gpu and cpu get different results for same seed;
+        # the way I see it, it's better to do this on CPU, so that everyone gets same result;
+        # but the original script had it like this so i do not dare change it for now because
+        # it will break everyone's seeds.
         xs.append(torch.randn(shape, device=device))
     x = torch.stack(xs)
     return x
@@ -190,7 +195,7 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     color_inactive = (153, 153, 153)
 
     pad_top = height // 4
-    pad_left = width * 3 // 4
+    pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0
 
     cols = im.width // width
     rows = im.height // height
@@ -226,63 +231,53 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     return result
 
 
-def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
+def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN):
-    torch.cuda.empty_cache()
+    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
-    outpath = opt.outdir or "outputs/txt2img-samples"
+    assert prompt is not None
+    torch.cuda.empty_cache()
 
     if seed == -1:
         seed = random.randrange(4294967294)
-
     seed = int(seed)
-    keep_same_seed = False
-
-    if sampler_name == 'PLMS':
-        sampler = PLMSSampler(model)
-    elif sampler_name == 'DDIM':
-        sampler = DDIMSampler(model)
-    elif sampler_name == 'k-diffusion':
-        sampler = KDiffusionSampler(model)
-    else:
-        raise Exception("Unknown sampler: " + sampler_name)
 
     os.makedirs(outpath, exist_ok=True)
 
-    batch_size = n_samples
-
-    assert prompt is not None
-    prompts = batch_size * [prompt]
-
     sample_path = os.path.join(outpath, "samples")
     os.makedirs(sample_path, exist_ok=True)
     base_count = len(os.listdir(sample_path))
     grid_count = len(os.listdir(outpath)) - 1
 
-    prompt_matrix_prompts = []
     prompt_matrix_parts = []
     if prompt_matrix:
-        keep_same_seed = True
+        all_prompts = []
-
         prompt_matrix_parts = prompt.split("|")
-        combination_count = 2 ** (len(prompt_matrix_parts)-1)
+        combination_count = 2 ** (len(prompt_matrix_parts) - 1)
         for combination_num in range(combination_count):
             current = prompt_matrix_parts[0]
 
             for n, text in enumerate(prompt_matrix_parts[1:]):
-                if combination_num & (2**n) > 0:
+                if combination_num & (2 ** n) > 0:
                     current += ("" if text.strip().startswith(",") else ", ") + text
 
-            prompt_matrix_prompts.append(current)
+            all_prompts.append(current)
-        n_iter = math.ceil(len(prompt_matrix_prompts) / batch_size)
 
-        print(f"Prompt matrix will create {len(prompt_matrix_prompts)} images using a total of {n_iter} batches.")
+        n_iter = math.ceil(len(all_prompts) / batch_size)
+        all_seeds = len(all_prompts) * [seed]
+
+        print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.")
+    else:
+        all_prompts = batch_size * n_iter * [prompt]
+        all_seeds = [seed + x for x in range(len(all_prompts))]
 
     precision_scope = autocast if opt.precision == "autocast" else nullcontext
     output_images = []
     with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
+        init_data = func_init()
+
         for n in range(n_iter):
-            if prompt_matrix:
+            prompts = all_prompts[n * batch_size:(n + 1) * batch_size]
-                prompts = prompt_matrix_prompts[n*batch_size:(n+1)*batch_size]
+            seeds = all_seeds[n * batch_size:(n + 1) * batch_size]
 
             uc = None
             if cfg_scale != 1.0:
@@ -290,14 +285,11 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
             if isinstance(prompts, tuple):
                 prompts = list(prompts)
             c = model.get_learned_conditioning(prompts)
-            shape = [opt_C, height // opt_f, width // opt_f]
-
-            batch_seed = seed if keep_same_seed else seed + n * len(prompts)
 
             # we manually generate all input noises because each one should have a specific seed
-            x = create_random_tensors(batch_seed, shape, count=len(prompts), same_seed=keep_same_seed)
+            x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=seeds)
 
-            samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=len(prompts), shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=x)
+            samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc)
 
             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)
@@ -312,7 +304,7 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
                         x_sample = restored_img
 
                     image = Image.fromarray(x_sample)
-                    filename = f"{base_count:05}-{seed if keep_same_seed else batch_seed + i}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
+                    filename = f"{base_count:05}-{seeds[i]}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
 
                     image.save(os.path.join(sample_path, filename))
 
@@ -323,21 +315,68 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
             grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
 
             if prompt_matrix:
-                grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
+
+                try:
+                    grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
+                except Exception:
+                    import traceback
+                    print("Error creating prompt_matrix text:", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+
                 output_images.insert(0, grid)
 
             grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
             grid_count += 1
 
-    del sampler
-
     info = f"""
 {prompt}
-Steps: {ddim_steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
+Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
-    """.strip()
+        """.strip()
 
     return output_images, seed, info
 
+
+def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int):
+    outpath = opt.outdir or "outputs/txt2img-samples"
+
+    if sampler_name == 'PLMS':
+        sampler = PLMSSampler(model)
+    elif sampler_name == 'DDIM':
+        sampler = DDIMSampler(model)
+    elif sampler_name == 'k-diffusion':
+        sampler = KDiffusionSampler(model)
+    else:
+        raise Exception("Unknown sampler: " + sampler_name)
+
+    def init():
+        pass
+
+    def sample(init_data, x, conditioning, unconditional_conditioning):
+        samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x)
+        return samples_ddim
+
+    output_images, seed, info = process_images(
+        outpath=outpath,
+        func_init=init,
+        func_sample=sample,
+        prompt=prompt,
+        seed=seed,
+        sampler_name=sampler_name,
+        batch_size=batch_size,
+        n_iter=n_iter,
+        steps=ddim_steps,
+        cfg_scale=cfg_scale,
+        width=width,
+        height=height,
+        prompt_matrix=prompt_matrix,
+        use_GFPGAN=use_GFPGAN
+    )
+
+    del sampler
+
+    return output_images, seed, info
+
+
 class Flagging(gr.FlaggingCallback):
 
     def setup(self, components, flagging_dir: str):
@@ -348,7 +387,7 @@ class Flagging(gr.FlaggingCallback):
 
         os.makedirs("log/images", exist_ok=True)
 
-        # those must match the "dream" function
+        # those must match the "txt2img" function
         prompt, ddim_steps, sampler_name, use_GFPGAN, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, images, seed, comment = flag_data
 
         filenames = []
@@ -379,8 +418,8 @@ class Flagging(gr.FlaggingCallback):
         print("Logged:", filenames[0])
 
 
-dream_interface = gr.Interface(
+txt2img_interface = gr.Interface(
-    dream,
+    txt2img,
     inputs=[
         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),
@@ -406,104 +445,70 @@ dream_interface = gr.Interface(
 )
 
 
-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):
+def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
-    torch.cuda.empty_cache()
-
     outpath = opt.outdir or "outputs/img2img-samples"
 
-    if seed == -1:
+    sampler = KDiffusionSampler(model)
-        seed = random.randrange(4294967294)
 
-    model_wrap = K.external.CompVisDenoiser(model)
+    assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
+    t_enc = int(denoising_strength * ddim_steps)
 
-    os.makedirs(outpath, exist_ok=True)
+    def init():
+        image = init_img.convert("RGB")
+        image = image.resize((width, height), resample=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)
 
-    batch_size = n_samples
-
-    assert prompt is not None
-
-    sample_path = os.path.join(outpath, "samples")
-    os.makedirs(sample_path, exist_ok=True)
-    base_count = len(os.listdir(sample_path))
-    grid_count = len(os.listdir(outpath)) - 1
-
-    image = init_img.convert("RGB")
-    image = image.resize((width, height), resample=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(), precision_scope("cuda"), model.ema_scope():
         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
 
-        assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
+        return init_latent,
-        t_enc = int(denoising_strength * ddim_steps)
 
-        for n in range(n_iter):
+    def sample(init_data, x, conditioning, unconditional_conditioning):
-            prompts = batch_size * [prompt]
+        x0, = init_data
 
-            uc = None
+        sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
-            if cfg_scale != 1.0:
+        noise = x * sigmas[ddim_steps - t_enc - 1]
-                uc = model.get_learned_conditioning(batch_size * [""])
-            if isinstance(prompts, tuple):
-                prompts = list(prompts)
-            c = model.get_learned_conditioning(prompts)
 
-            batch_seed = seed + n * len(prompts)
+        xi = x0 + noise
+        sigma_sched = sigmas[ddim_steps - t_enc - 1:]
+        model_wrap_cfg = CFGDenoiser(sampler.model_wrap)
+        samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
+        return samples_ddim
 
-            sigmas = model_wrap.get_sigmas(ddim_steps)
+    output_images, seed, info = process_images(
-            noise = create_random_tensors(batch_seed, x0.shape[1:], count=len(prompts))
+        outpath=outpath,
-            noise = noise * sigmas[ddim_steps - t_enc - 1]
+        func_init=init,
+        func_sample=sample,
+        prompt=prompt,
+        seed=seed,
+        sampler_name='k-diffusion',
+        batch_size=batch_size,
+        n_iter=n_iter,
+        steps=ddim_steps,
+        cfg_scale=cfg_scale,
+        width=width,
+        height=height,
+        prompt_matrix=prompt_matrix,
+        use_GFPGAN=use_GFPGAN
+    )
 
-            xi = x0 + noise
+    del sampler
-            sigma_sched = sigmas[ddim_steps - t_enc - 1:]
-            model_wrap_cfg = CFGDenoiser(model_wrap)
-            extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
 
-            samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False)
+    return output_images, seed, info
-            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 or not opt.skip_grid:
-                for i, x_sample in enumerate(x_samples_ddim):
-                    x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                    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}-{batch_seed+i}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"))
-
-                    output_images.append(image)
-                    base_count += 1
-
-        if not opt.skip_grid:
-            # additionally, save as grid
-            grid = image_grid(output_images, batch_size)
-            grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
-            grid_count += 1
-
-    return output_images, seed
-
-
-# prompt, init_img, ddim_steps, plms, ddim_eta, n_iter, n_samples, cfg_scale, denoising_strength, seed
 
 img2img_interface = gr.Interface(
-    translation,
+    img2img,
     inputs=[
         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.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
         gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
         gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
         gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
@@ -514,7 +519,8 @@ img2img_interface = gr.Interface(
     ],
     outputs=[
         gr.Gallery(),
-        gr.Number(label='Seed')
+        gr.Number(label='Seed'),
+        gr.Textbox(label="Copy-paste generation parameters"),
     ],
     title="Stable Diffusion Image-to-Image",
     description="Generate images from images with Stable Diffusion",
@@ -522,7 +528,7 @@ img2img_interface = gr.Interface(
 )
 
 interfaces = [
-    (dream_interface, "txt2img"),
+    (txt2img_interface, "txt2img"),
     (img2img_interface, "img2img")
 ]