Added prompt matrix to img2img
refactoring: separated duplicate code from img2img and txt2img into a single function
This commit is contained in:
AUTOMATIC
parent
cb118c4036
commit
aa67540eba
254
webui.py
254
webui.py
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@ -97,16 +97,21 @@ class KDiffusionSampler:
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sigmas = self.model_wrap.get_sigmas(S)
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x = x_T * sigmas[0]
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model_wrap_cfg = CFGDenoiser(self.model_wrap)
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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)
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return samples_ddim, None
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def create_random_tensors(seed, shape, count, same_seed=False):
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def create_random_tensors(shape, seeds):
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xs = []
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for i in range(count):
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current_seed = seed if same_seed else seed + i
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torch.manual_seed(current_seed)
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for seed in seeds:
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torch.manual_seed(seed)
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# randn results depend on device; gpu and cpu get different results for same seed;
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# the way I see it, it's better to do this on CPU, so that everyone gets same result;
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# but the original script had it like this so i do not dare change it for now because
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# it will break everyone's seeds.
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xs.append(torch.randn(shape, device=device))
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x = torch.stack(xs)
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return x
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@ -190,7 +195,7 @@ def draw_prompt_matrix(im, width, height, all_prompts):
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color_inactive = (153, 153, 153)
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pad_top = height // 4
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pad_left = width * 3 // 4
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pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0
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cols = im.width // width
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rows = im.height // height
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@ -226,63 +231,53 @@ def draw_prompt_matrix(im, width, height, all_prompts):
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return result
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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):
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torch.cuda.empty_cache()
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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):
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"""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"""
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outpath = opt.outdir or "outputs/txt2img-samples"
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assert prompt is not None
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torch.cuda.empty_cache()
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if seed == -1:
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seed = random.randrange(4294967294)
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seed = int(seed)
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keep_same_seed = False
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(model)
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elif sampler_name == 'DDIM':
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sampler = DDIMSampler(model)
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elif sampler_name == 'k-diffusion':
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sampler = KDiffusionSampler(model)
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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os.makedirs(outpath, exist_ok=True)
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batch_size = n_samples
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assert prompt is not None
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prompts = batch_size * [prompt]
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sample_path = os.path.join(outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(outpath)) - 1
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prompt_matrix_prompts = []
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prompt_matrix_parts = []
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if prompt_matrix:
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keep_same_seed = True
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all_prompts = []
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prompt_matrix_parts = prompt.split("|")
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combination_count = 2 ** (len(prompt_matrix_parts)-1)
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combination_count = 2 ** (len(prompt_matrix_parts) - 1)
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for combination_num in range(combination_count):
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current = prompt_matrix_parts[0]
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for n, text in enumerate(prompt_matrix_parts[1:]):
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if combination_num & (2**n) > 0:
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if combination_num & (2 ** n) > 0:
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current += ("" if text.strip().startswith(",") else ", ") + text
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prompt_matrix_prompts.append(current)
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n_iter = math.ceil(len(prompt_matrix_prompts) / batch_size)
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all_prompts.append(current)
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print(f"Prompt matrix will create {len(prompt_matrix_prompts)} images using a total of {n_iter} batches.")
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n_iter = math.ceil(len(all_prompts) / batch_size)
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all_seeds = len(all_prompts) * [seed]
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print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.")
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else:
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all_prompts = batch_size * n_iter * [prompt]
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all_seeds = [seed + x for x in range(len(all_prompts))]
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precision_scope = autocast if opt.precision == "autocast" else nullcontext
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output_images = []
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with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
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init_data = func_init()
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for n in range(n_iter):
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if prompt_matrix:
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prompts = prompt_matrix_prompts[n*batch_size:(n+1)*batch_size]
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prompts = all_prompts[n * batch_size:(n + 1) * batch_size]
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seeds = all_seeds[n * batch_size:(n + 1) * batch_size]
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uc = None
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if cfg_scale != 1.0:
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@ -290,14 +285,11 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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shape = [opt_C, height // opt_f, width // opt_f]
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batch_seed = seed if keep_same_seed else seed + n * len(prompts)
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# we manually generate all input noises because each one should have a specific seed
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x = create_random_tensors(batch_seed, shape, count=len(prompts), same_seed=keep_same_seed)
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x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=seeds)
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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)
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samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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@ -312,7 +304,7 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
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x_sample = restored_img
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image = Image.fromarray(x_sample)
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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"
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filename = f"{base_count:05}-{seeds[i]}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
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image.save(os.path.join(sample_path, filename))
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@ -323,21 +315,68 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, pro
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grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
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if prompt_matrix:
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grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
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try:
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grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
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except Exception:
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import traceback
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print("Error creating prompt_matrix text:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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output_images.insert(0, grid)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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del sampler
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info = f"""
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{prompt}
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Steps: {ddim_steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
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""".strip()
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Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
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""".strip()
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return output_images, seed, info
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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):
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outpath = opt.outdir or "outputs/txt2img-samples"
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(model)
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elif sampler_name == 'DDIM':
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sampler = DDIMSampler(model)
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elif sampler_name == 'k-diffusion':
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sampler = KDiffusionSampler(model)
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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def init():
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pass
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def sample(init_data, x, conditioning, unconditional_conditioning):
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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)
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return samples_ddim
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output_images, seed, info = process_images(
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outpath=outpath,
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func_init=init,
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name=sampler_name,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=prompt_matrix,
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use_GFPGAN=use_GFPGAN
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)
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del sampler
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return output_images, seed, info
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class Flagging(gr.FlaggingCallback):
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def setup(self, components, flagging_dir: str):
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@ -348,7 +387,7 @@ class Flagging(gr.FlaggingCallback):
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os.makedirs("log/images", exist_ok=True)
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# those must match the "dream" function
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# those must match the "txt2img" function
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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
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filenames = []
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@ -379,8 +418,8 @@ class Flagging(gr.FlaggingCallback):
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print("Logged:", filenames[0])
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dream_interface = gr.Interface(
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dream,
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txt2img_interface = gr.Interface(
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txt2img,
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inputs=[
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gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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@ -406,104 +445,70 @@ dream_interface = gr.Interface(
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)
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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):
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torch.cuda.empty_cache()
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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):
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outpath = opt.outdir or "outputs/img2img-samples"
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if seed == -1:
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seed = random.randrange(4294967294)
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sampler = KDiffusionSampler(model)
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model_wrap = K.external.CompVisDenoiser(model)
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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t_enc = int(denoising_strength * ddim_steps)
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os.makedirs(outpath, exist_ok=True)
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def init():
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image = init_img.convert("RGB")
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image = image.resize((width, height), resample=Image.Resampling.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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batch_size = n_samples
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assert prompt is not None
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sample_path = os.path.join(outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(outpath)) - 1
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image = init_img.convert("RGB")
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image = image.resize((width, height), resample=Image.Resampling.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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output_images = []
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precision_scope = autocast if opt.precision == "autocast" else nullcontext
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with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
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init_image = 2. * image - 1.
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init_image = init_image.to(device)
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init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
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init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
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x0 = init_latent
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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t_enc = int(denoising_strength * ddim_steps)
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return init_latent,
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for n in range(n_iter):
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prompts = batch_size * [prompt]
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def sample(init_data, x, conditioning, unconditional_conditioning):
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x0, = init_data
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uc = None
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if cfg_scale != 1.0:
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uc = model.get_learned_conditioning(batch_size * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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c = model.get_learned_conditioning(prompts)
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sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
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noise = x * sigmas[ddim_steps - t_enc - 1]
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batch_seed = seed + n * len(prompts)
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xi = x0 + noise
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sigma_sched = sigmas[ddim_steps - t_enc - 1:]
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model_wrap_cfg = CFGDenoiser(sampler.model_wrap)
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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)
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return samples_ddim
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sigmas = model_wrap.get_sigmas(ddim_steps)
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noise = create_random_tensors(batch_seed, x0.shape[1:], count=len(prompts))
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noise = noise * sigmas[ddim_steps - t_enc - 1]
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output_images, seed, info = process_images(
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outpath=outpath,
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func_init=init,
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name='k-diffusion',
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=prompt_matrix,
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use_GFPGAN=use_GFPGAN
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)
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xi = x0 + noise
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sigma_sched = sigmas[ddim_steps - t_enc - 1:]
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model_wrap_cfg = CFGDenoiser(model_wrap)
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extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}
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del sampler
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samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False)
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x_samples_ddim = model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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return output_images, seed, info
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if not opt.skip_save or not opt.skip_grid:
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for i, x_sample in enumerate(x_samples_ddim):
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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x_sample = x_sample.astype(np.uint8)
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if use_GFPGAN and GFPGAN is not None:
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
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x_sample = restored_img
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image = Image.fromarray(x_sample)
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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"))
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output_images.append(image)
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base_count += 1
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if not opt.skip_grid:
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# additionally, save as grid
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grid = image_grid(output_images, batch_size)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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return output_images, seed
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# prompt, init_img, ddim_steps, plms, ddim_eta, n_iter, n_samples, cfg_scale, denoising_strength, seed
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img2img_interface = gr.Interface(
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translation,
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img2img,
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inputs=[
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gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
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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"),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
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gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
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gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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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")
|
||||
]
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue