Merge pull request #7481 from Klace/master (127bfb6c) · Commits · github_fork / Stable Diffusion Webui

modules/img2img.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -76,7 +76,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
		processed_image.save(os.path.join(output_dir, filename))


		def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
		def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
		override_settings = create_override_settings_dict(override_settings_texts)

		is_batch = mode == 5
		@@ -142,6 +142,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
		inpainting_fill=inpainting_fill,
		resize_mode=resize_mode,
		denoising_strength=denoising_strength,
		image_cfg_scale=image_cfg_scale,
		inpaint_full_res=inpaint_full_res,
		inpaint_full_res_padding=inpaint_full_res_padding,
		inpainting_mask_invert=inpainting_mask_invert,

modules/processing.py

+5 −2

Original line number	Diff line number	Diff line
		@@ -186,7 +186,7 @@ class StableDiffusionProcessing:
		return conditioning

		def edit_image_conditioning(self, source_image):
		conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
		conditioning_image = self.sd_model.encode_first_stage(source_image).mode()

		return conditioning_image

		@@ -268,6 +268,7 @@ class Processed:
		self.height = p.height
		self.sampler_name = p.sampler_name
		self.cfg_scale = p.cfg_scale
		self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
		self.steps = p.steps
		self.batch_size = p.batch_size
		self.restore_faces = p.restore_faces
		@@ -445,6 +446,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
		"Steps": p.steps,
		"Sampler": p.sampler_name,
		"CFG scale": p.cfg_scale,
		"Image CFG scale": getattr(p, 'image_cfg_scale', None),
		"Seed": all_seeds[index],
		"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
		"Size": f"{p.width}x{p.height}",
		@@ -901,12 +903,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		sampler = None

		def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
		def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
		super().__init__(**kwargs)

		self.init_images = init_images
		self.resize_mode: int = resize_mode
		self.denoising_strength: float = denoising_strength
		self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
		self.init_latent = None
		self.image_mask = mask
		self.latent_mask = None

modules/sd_samplers_kdiffusion.py

+97 −5

Original line number	Diff line number	Diff line
		from collections import deque
		import torch
		import inspect
		import einops
		import k_diffusion.sampling
		from modules import prompt_parser, devices, sd_samplers_common

		@@ -40,6 +41,90 @@ sampler_extra_params = {
		'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
		}

		class CFGDenoiserEdit(torch.nn.Module):
		"""
		Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
		that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
		instead of one. Originally, the second prompt is just an empty string, but we use non-empty
		negative prompt.
		"""

		def __init__(self, model):
		super().__init__()
		self.inner_model = model
		self.mask = None
		self.nmask = None
		self.init_latent = None
		self.step = 0

		def combine_denoised(self, x_out, conds_list, uncond, cond_scale, image_cfg_scale):
		denoised_uncond = x_out[-uncond.shape[0]:]
		denoised = torch.clone(denoised_uncond)

		for i, conds in enumerate(conds_list):
		for cond_index, weight in conds:
		out_cond, out_img_cond, out_uncond = x_out.chunk(3)
		denoised[i] = out_uncond[cond_index] + cond_scale * (out_cond[cond_index] - out_img_cond[cond_index]) + image_cfg_scale * (out_img_cond[cond_index] - out_uncond[cond_index])

		return denoised

		def forward(self, x, sigma, uncond, cond, cond_scale, image_cond, image_cfg_scale):
		if state.interrupted or state.skipped:
		raise sd_samplers_common.InterruptedException

		conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
		uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)

		batch_size = len(conds_list)
		repeats = [len(conds_list[i]) for i in range(batch_size)]

		x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
		sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
		image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])

		denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
		cfg_denoiser_callback(denoiser_params)
		x_in = denoiser_params.x
		image_cond_in = denoiser_params.image_cond
		sigma_in = denoiser_params.sigma

		if tensor.shape[1] == uncond.shape[1]:
		cond_in = torch.cat([tensor, uncond, uncond])

		if shared.batch_cond_uncond:
		x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
		else:
		x_out = torch.zeros_like(x_in)
		for batch_offset in range(0, x_out.shape[0], batch_size):
		a = batch_offset
		b = a + batch_size
		x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
		else:
		x_out = torch.zeros_like(x_in)
		batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
		for batch_offset in range(0, tensor.shape[0], batch_size):
		a = batch_offset
		b = min(a + batch_size, tensor.shape[0])
		x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": torch.cat([tensor[a:b]], uncond) , "c_concat": [image_cond_in[a:b]]})

		x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})

		devices.test_for_nans(x_out, "unet")

		if opts.live_preview_content == "Prompt":
		sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
		elif opts.live_preview_content == "Negative prompt":
		sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])

		denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale, image_cfg_scale)

		if self.mask is not None:
		denoised = self.init_latent * self.mask + self.nmask * denoised

		self.step += 1

		return denoised


		class CFGDenoiser(torch.nn.Module):
		"""
		@@ -78,8 +163,8 @@ class CFGDenoiser(torch.nn.Module):
		repeats = [len(conds_list[i]) for i in range(batch_size)]

		x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
		image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
		sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
		image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])

		denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
		cfg_denoiser_callback(denoiser_params)
		@@ -195,6 +280,9 @@ class KDiffusionSampler:
		return p.steps

		def initialize(self, p):
		if shared.sd_model.cond_stage_key == "edit" and getattr(p, 'image_cfg_scale', None) != 1:
		self.model_wrap_cfg = CFGDenoiserEdit(self.model_wrap)

		self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
		self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
		self.model_wrap_cfg.step = 0
		@@ -260,13 +348,17 @@ class KDiffusionSampler:

		self.model_wrap_cfg.init_latent = x
		self.last_latent = x

		samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
		extra_args={
		'cond': conditioning,
		'image_cond': image_conditioning,
		'uncond': unconditional_conditioning,
		'cond_scale': p.cfg_scale
		}, disable=False, callback=self.callback_state, **extra_params_kwargs))
		'cond_scale': p.cfg_scale,
		}

		if hasattr(p, 'image_cfg_scale') and p.image_cfg_scale != 1 and p.image_cfg_scale != None:
		extra_args['image_cfg_scale'] = p.image_cfg_scale

		samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

		return samples

modules/ui.py

+3 −0

Original line number	Diff line number	Diff line
		@@ -766,6 +766,7 @@ def create_ui():
		elif category == "cfg":
		with FormGroup():
		cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
		image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale (for instruct-pix2pix models only)', value=1.5, elem_id="img2img_image_cfg_scale")
		denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")

		elif category == "seed":
		@@ -861,6 +862,7 @@ def create_ui():
		batch_count,
		batch_size,
		cfg_scale,
		image_cfg_scale,
		denoising_strength,
		seed,
		subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
		@@ -947,6 +949,7 @@ def create_ui():
		(sampler_index, "Sampler"),
		(restore_faces, "Face restoration"),
		(cfg_scale, "CFG scale"),
		(image_cfg_scale, "Image CFG scale"),
		(seed, "Seed"),
		(width, "Size-1"),
		(height, "Size-2"),

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