diff --git a/README.md b/README.md index d83b4bdac..97677921a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ ComfyUI ======= -A powerful and modular stable diffusion GUI and backend. +The most powerful and modular stable diffusion GUI and backend. ----------- ![ComfyUI Screenshot](comfyui_screenshot.png) @@ -94,8 +94,8 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins ```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.4.2``` -This is the command to install the nightly with ROCm 5.6 that supports the 7000 series and might have some performance improvements: -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.6``` +This is the command to install the nightly with ROCm 5.7 that supports the 7000 series and might have some performance improvements: +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.7``` ### NVIDIA diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py index 7eaf6ff62..7e88bb9fa 100644 --- a/comfy/extra_samplers/uni_pc.py +++ b/comfy/extra_samplers/uni_pc.py @@ -688,7 +688,7 @@ class UniPC: x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) else: x_t_ = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dimss) * x + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 ) if x_t is None: diff --git a/comfy/samplers.py b/comfy/samplers.py index a7c240f40..e43f7a6fe 100644 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -598,7 +598,7 @@ KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral" "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm"] -def ksampler(sampler_name): +def ksampler(sampler_name, extra_options={}): class KSAMPLER(Sampler): def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): extra_args["denoise_mask"] = denoise_mask @@ -627,7 +627,7 @@ def ksampler(sampler_name): elif sampler_name == "dpm_adaptive": samples = k_diffusion_sampling.sample_dpm_adaptive(model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar) else: - samples = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar) + samples = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **extra_options) return samples return KSAMPLER diff --git a/comfy/sd.py b/comfy/sd.py index 9bdb2ad64..f186273ea 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -152,7 +152,9 @@ class VAE: sd = comfy.utils.load_torch_file(ckpt_path) if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) - self.first_stage_model.load_state_dict(sd, strict=False) + m, u = self.first_stage_model.load_state_dict(sd, strict=False) + if len(m) > 0: + print("Missing VAE keys", m) if device is None: device = model_management.vae_device() @@ -181,7 +183,7 @@ class VAE: steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = comfy.utils.ProgressBar(steps) - encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float() + encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).sample().float() samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) @@ -200,7 +202,7 @@ class VAE: pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu") for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) - pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu().float() + pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).cpu().float() + 1.0) / 2.0, min=0.0, max=1.0) except model_management.OOM_EXCEPTION as e: print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") pixel_samples = self.decode_tiled_(samples_in) diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index efe03ad24..42a1fd6ba 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -43,6 +43,62 @@ class KarrasScheduler: sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) return (sigmas, ) +class ExponentialScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "_for_testing/custom_sampling" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min): + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max) + return (sigmas, ) + +class PolyexponentialScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "_for_testing/custom_sampling" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, rho): + sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) + return (sigmas, ) + +class VPScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), #TODO: fix default values + "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "_for_testing/custom_sampling" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, beta_d, beta_min, eps_s): + sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s) + return (sigmas, ) + class SplitSigmas: @classmethod def INPUT_TYPES(s): @@ -58,7 +114,7 @@ class SplitSigmas: def get_sigmas(self, sigmas, step): sigmas1 = sigmas[:step + 1] - sigmas2 = sigmas[step + 1:] + sigmas2 = sigmas[step:] return (sigmas1, sigmas2) class KSamplerSelect: @@ -77,6 +133,53 @@ class KSamplerSelect: sampler = comfy.samplers.sampler_class(sampler_name)() return (sampler, ) +class SamplerDPMPP_2M_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"solver_type": (['midpoint', 'heun'], ), + "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "_for_testing/custom_sampling" + + FUNCTION = "get_sampler" + + def get_sampler(self, solver_type, eta, s_noise, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_2m_sde" + else: + sampler_name = "dpmpp_2m_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})() + return (sampler, ) + + +class SamplerDPMPP_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "_for_testing/custom_sampling" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise, r, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_sde" + else: + sampler_name = "dpmpp_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})() + return (sampler, ) + class SamplerCustom: @classmethod def INPUT_TYPES(s): @@ -131,7 +234,12 @@ class SamplerCustom: NODE_CLASS_MAPPINGS = { "SamplerCustom": SamplerCustom, "KarrasScheduler": KarrasScheduler, + "ExponentialScheduler": ExponentialScheduler, + "PolyexponentialScheduler": PolyexponentialScheduler, + "VPScheduler": VPScheduler, "KSamplerSelect": KSamplerSelect, + "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, + "SamplerDPMPP_SDE": SamplerDPMPP_SDE, "BasicScheduler": BasicScheduler, "SplitSigmas": SplitSigmas, } diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index 80f738a69..df8ae56b9 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -331,15 +331,14 @@ class GrowMask: out = [] for m in mask: output = m.numpy() - while expand < 0: - output = scipy.ndimage.grey_erosion(output, footprint=kernel) - expand += 1 - while expand > 0: - output = scipy.ndimage.grey_dilation(output, footprint=kernel) - expand -= 1 + for _ in range(abs(expand)): + if expand < 0: + output = scipy.ndimage.grey_erosion(output, footprint=kernel) + else: + output = scipy.ndimage.grey_dilation(output, footprint=kernel) output = torch.from_numpy(output) out.append(output) - return (torch.cat(out, dim=0),) + return (torch.stack(out, dim=0),) diff --git a/nodes.py b/nodes.py index 1232373be..919aac89e 100644 --- a/nodes.py +++ b/nodes.py @@ -1781,16 +1781,23 @@ def load_custom_nodes(): print() def init_custom_nodes(): - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_latent.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_hypernetwork.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_rebatch.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_model_merging.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_tomesd.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_clip_sdxl.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_canny.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_freelunch.py")) - load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_custom_sampler.py")) + extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") + extras_files = [ + "nodes_latent.py", + "nodes_hypernetwork.py", + "nodes_upscale_model.py", + "nodes_post_processing.py", + "nodes_mask.py", + "nodes_rebatch.py", + "nodes_model_merging.py", + "nodes_tomesd.py", + "nodes_clip_sdxl.py", + "nodes_canny.py", + "nodes_freelunch.py", + "nodes_custom_sampler.py" + ] + + for node_file in extras_files: + load_custom_node(os.path.join(extras_dir, node_file)) + load_custom_nodes() diff --git a/server.py b/server.py index b2e16716b..63f337a87 100644 --- a/server.py +++ b/server.py @@ -413,7 +413,11 @@ class PromptServer(): async def get_object_info(request): out = {} for x in nodes.NODE_CLASS_MAPPINGS: - out[x] = node_info(x) + try: + out[x] = node_info(x) + except Exception as e: + print(f"[ERROR] An error occurred while retrieving information for the '{x}' node.", file=sys.stderr) + traceback.print_exc() return web.json_response(out) @routes.get("/object_info/{node_class}")