alebeard
commented
1 month ago I'm not really sure how to make a new thread to ask this question so forgive me for asking it here, I have never really been able to get control in it working in PonyXL at all or well in XL, If someone could take a second to throw out a link to some processors and or preprocessors that work well for pony or just xl I would be grateful. I don't need any more help than that.
On Tue, Aug 13, 2024 at 12:47 PM danilomaiaweb @.***> wrote:
Stable Diffusion PATH: E:\ForgeFlux\webui Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)] Version: f2.0.1v1.10.1-previous-252-g00f1cd36 Commit hash: 00f1cd3 https://github.com/lllyasviel/stable-diffusion-webui-forge/commit/00f1cd36bd25431445eb67425ad39434c68c8665 Launching Web UI with arguments: --precision full --opt-split-attention --no-half --skip-torch-cuda-test --cuda-malloc Using cudaMallocAsync backend. Total VRAM 8191 MB, total RAM 32705 MB pytorch version: 2.3.1+cu121 WARNING:xformers:A matching Triton is not available, some optimizations will not be enabled Traceback (most recent call last): File "E:\ForgeFlux\system\python\lib\site-packages\xformersinit.py", line 57, in _is_triton_available import triton # noqa ModuleNotFoundError: No module named 'triton' xformers version: 0.0.27 Set vram state to: NORMAL_VRAM Device: cuda:0 NVIDIA GeForce RTX 3050 : cudaMallocAsync VAE dtype preferences: [torch.bfloat16, torch.float32] -> torch.bfloat16 CUDA Using Stream: False E:\ForgeFlux\system\python\lib\site-packages\transformers\utils\hub.py:127: FutureWarning: Using TRANSFORMERS_CACHE is deprecated and will be removed in v5 of Transformers. Use HF_HOME instead. warnings.warn( Using xformers cross attention Using xformers attention for VAE ControlNet preprocessor location: E:\ForgeFlux\webui\models\ControlNetPreprocessor 2024-08-13 16:43:31,069 - ControlNet - INFO - ControlNet UI callback registered. Model selected: {'checkpoint_info': {'filename': 'E:\ForgeFlux\webui\models\Stable-diffusion\flux1-dev-bnb-nf4.safetensors', 'hash': '0184473b'}, 'additional_modules': [], 'unet_storage_dtype': None} Running on local URL: http://127.0.0.1:7860
To create a public link, set share=True in launch(). Startup time: 14.8s (prepare environment: 1.0s, launcher: 2.4s, import torch: 4.6s, initialize shared: 0.1s, other imports: 1.0s, list SD models: 0.3s, load scripts: 2.2s, create ui: 2.1s, gradio launch: 1.3s). Environment vars changed: {'stream': False, 'inference_memory': 1024.0, 'pin_shared_memory': False} Loading Model: {'checkpoint_info': {'filename': 'E:\ForgeFlux\webui\models\Stable-diffusion\flux1-dev-bnb-nf4.safetensors', 'hash': '0184473b'}, 'additional_modules': [], 'unet_storage_dtype': None} StateDict Keys: {'transformer': 2350, 'vae': 244, 'text_encoder': 198, 'text_encoder_2': 220, 'ignore': 0} Using Detected T5 Data Type: torch.float8_e4m3fn Using Detected UNet Type: nf4 Using pre-quant state dict! Working with z of shape (1, 16, 32, 32) = 16384 dimensions. K-Model Created: {'storage_dtype': 'nf4', 'computation_dtype': torch.bfloat16} Model loaded in 0.7s (unload existing model: 0.2s, forge model load: 0.6s). Skipping unconditional conditioning when CFG = 1. Negative Prompts are ignored. To load target model JointTextEncoder Begin to load 1 model [Memory Management] Current Free GPU Memory: 7184.00 MB [Memory Management] Required Model Memory: 5154.62 MB [Memory Management] Required Inference Memory: 1024.00 MB [Memory Management] Estimated Remaining GPU Memory: 1005.38 MB Moving model(s) has taken 2.05 seconds Traceback (most recent call last): File "E:\ForgeFlux\webui\modules_forge\main_thread.py", line 37, in loop task.work() File "E:\ForgeFlux\webui\modules_forge\main_thread.py", line 26, in work self.result = self.func(*self.args, self.kwargs) File "E:\ForgeFlux\webui\modules\txt2img.py", line 110, in txt2img_function processed = processing.process_images(p) File "E:\ForgeFlux\webui\modules\processing.py", line 809, in process_images res = process_images_inner(p) File "E:\ForgeFlux\webui\modules\processing.py", line 922, in process_images_inner p.setup_conds() File "E:\ForgeFlux\webui\modules\processing.py", line 1507, in setup_conds super().setup_conds() File "E:\ForgeFlux\webui\modules\processing.py", line 494, in setup_conds self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data) File "E:\ForgeFlux\webui\modules\processing.py", line 463, in get_conds_with_caching cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling) File "E:\ForgeFlux\webui\modules\prompt_parser.py", line 262, in get_multicond_learned_conditioning learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling) File "E:\ForgeFlux\webui\modules\prompt_parser.py", line 189, in get_learned_conditioning conds = model.get_learned_conditioning(texts) File "E:\ForgeFlux\system\python\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context return func(*args, *kwargs) File "E:\ForgeFlux\webui\backend\diffusion_engine\flux.py", line 79, in get_learned_conditioning cond_t5 = self.text_processing_engine_t5(prompt) File "E:\ForgeFlux\webui\backend\text_processing\t5_engine.py", line 123, in call z = self.process_tokens([tokens], [multipliers])[0] File "E:\ForgeFlux\webui\backend\text_processing\t5_engine.py", line 134, in process_tokens z = self.encode_with_transformers(tokens) File "E:\ForgeFlux\webui\backend\text_processing\t5_engine.py", line 60, in encode_with_transformers z = self.text_encoder( File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(args, kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 205, in forward return self.encoder(x, *args, *kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(args, kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 186, in forward x, past_bias = l(x, mask, past_bias) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, *kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(args, kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 162, in forward x, past_bias = self.layer[0](x, mask, past_bias) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, *kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 149, in forward output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(args, kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 138, in forward out = attention_function(q, k * ((k.shape[-1] / self.num_heads) * 0.5), v, self.num_heads, mask) File "E:\ForgeFlux\webui\backend\attention.py", line 314, in attention_xformers mask_out[:, :, :mask.shape[-1]] = mask RuntimeError: The expanded size of the tensor (1) must match the existing size (64) at non-singleton dimension 0. Target sizes: [1, 256, 256]. Tensor sizes: [64, 256, 256] The expanded size of the tensor (1) must match the existing size (64) at non-singleton dimension 0. Target sizes: [1, 256, 256]. Tensor sizes: [64, 256, 256] Error completing request Arguments: ('task(s1hcu5w2p2fyufe)', <gradio.route_utils.Request object at 0x000001BB023C4340>, 'Cyberpunk sign that reads FLUX IN FORGE', '', [], 1, 1, 1, 3.5, 1152, 896, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', None, 0, 10, 'Euler', 'Simple', False, -1, False, -1, 0, 0, 0, ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=None, batch_mask_gallery=None, generated_image=None, mask_image=None, mask_image_fg=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, image_fg=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=None, batch_mask_gallery=None, generated_image=None, mask_image=None, mask_image_fg=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, image_fg=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=None, batch_mask_gallery=None, generated_image=None, mask_image=None, mask_image_fg=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, image_fg=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), False, 7, 1, 'Constant', 0, 'Constant', 0, 1, 'enable', 'MEAN', 'AD', 1, False, 1.01, 1.02, 0.99, 0.95, False, 0.5, 2, False, 3, False, 3, 2, 0, 0.35, True, 'bicubic', 'bicubic', False, 0, 'anisotropic', 0, 'reinhard', 100, 0, 'subtract', 0, 0, 'gaussian', 'add', 0, 100, 127, 0, 'hard_clamp', 5, 0, 'None', 'None', False, 'MultiDiffusion', 768, 768, 64, 4, False, False, False, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', '', 0, '', '', 0, '', '', True, False, False, False, False, False, False, 0, False) {} Traceback (most recent call last): File "E:\ForgeFlux\webui\modules\call_queue.py", line 74, in f res = list(func(args, kwargs)) TypeError: 'NoneType' object is not iterable
— Reply to this email directly, view it on GitHub https://github.com/lllyasviel/stable-diffusion-webui-forge/issues/1062, or unsubscribe https://github.com/notifications/unsubscribe-auth/AHN7U7ZYLBX3EZXKMWWD5VTZRJPEZAVCNFSM6AAAAABMO56V2WVHI2DSMVQWIX3LMV43ASLTON2WKOZSGQ3DIMBZG4YDEOA . You are receiving this because you are subscribed to this thread.Message ID: @.***>
nvm01
DivinesImmortus
danilomaiaweb
Stable Diffusion PATH: E:\ForgeFlux\webui Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)] Version: f2.0.1v1.10.1-previous-252-g00f1cd36 Commit hash: 00f1cd36bd25431445eb67425ad39434c68c8665 Launching Web UI with arguments: --precision full --opt-split-attention --no-half --skip-torch-cuda-test --cuda-malloc Using cudaMallocAsync backend. Total VRAM 8191 MB, total RAM 32705 MB pytorch version: 2.3.1+cu121 WARNING:xformers:A matching Triton is not available, some optimizations will not be enabled Traceback (most recent call last): File "E:\ForgeFlux\system\python\lib\site-packages\xformers__init__.py", line 57, in _is_triton_available import triton # noqa ModuleNotFoundError: No module named 'triton' xformers version: 0.0.27 Set vram state to: NORMAL_VRAM Device: cuda:0 NVIDIA GeForce RTX 3050 : cudaMallocAsync VAE dtype preferences: [torch.bfloat16, torch.float32] -> torch.bfloat16 CUDA Using Stream: False E:\ForgeFlux\system\python\lib\site-packages\transformers\utils\hub.py:127: FutureWarning: Using
TRANSFORMERS_CACHEis deprecated and will be removed in v5 of Transformers. UseHF_HOMEinstead. warnings.warn( Using xformers cross attention Using xformers attention for VAE ControlNet preprocessor location: E:\ForgeFlux\webui\models\ControlNetPreprocessor 2024-08-13 16:43:31,069 - ControlNet - INFO - ControlNet UI callback registered. Model selected: {'checkpoint_info': {'filename': 'E:\ForgeFlux\webui\models\Stable-diffusion\flux1-dev-bnb-nf4.safetensors', 'hash': '0184473b'}, 'additional_modules': [], 'unet_storage_dtype': None} Running on local URL: http://127.0.0.1:7860To create a public link, set
share=Trueinlaunch(). Startup time: 14.8s (prepare environment: 1.0s, launcher: 2.4s, import torch: 4.6s, initialize shared: 0.1s, other imports: 1.0s, list SD models: 0.3s, load scripts: 2.2s, create ui: 2.1s, gradio launch: 1.3s). Environment vars changed: {'stream': False, 'inference_memory': 1024.0, 'pin_shared_memory': False} Loading Model: {'checkpoint_info': {'filename': 'E:\ForgeFlux\webui\models\Stable-diffusion\flux1-dev-bnb-nf4.safetensors', 'hash': '0184473b'}, 'additional_modules': [], 'unet_storage_dtype': None} StateDict Keys: {'transformer': 2350, 'vae': 244, 'text_encoder': 198, 'text_encoder_2': 220, 'ignore': 0} Using Detected T5 Data Type: torch.float8_e4m3fn Using Detected UNet Type: nf4 Using pre-quant state dict! Working with z of shape (1, 16, 32, 32) = 16384 dimensions. K-Model Created: {'storage_dtype': 'nf4', 'computation_dtype': torch.bfloat16} Model loaded in 0.7s (unload existing model: 0.2s, forge model load: 0.6s). Skipping unconditional conditioning when CFG = 1. Negative Prompts are ignored. To load target model JointTextEncoder Begin to load 1 model [Memory Management] Current Free GPU Memory: 7184.00 MB [Memory Management] Required Model Memory: 5154.62 MB [Memory Management] Required Inference Memory: 1024.00 MB [Memory Management] Estimated Remaining GPU Memory: 1005.38 MB Moving model(s) has taken 2.05 seconds Traceback (most recent call last): File "E:\ForgeFlux\webui\modules_forge\main_thread.py", line 37, in loop task.work() File "E:\ForgeFlux\webui\modules_forge\main_thread.py", line 26, in work self.result = self.func(*self.args, self.kwargs) File "E:\ForgeFlux\webui\modules\txt2img.py", line 110, in txt2img_function processed = processing.process_images(p) File "E:\ForgeFlux\webui\modules\processing.py", line 809, in process_images res = process_images_inner(p) File "E:\ForgeFlux\webui\modules\processing.py", line 922, in process_images_inner p.setup_conds() File "E:\ForgeFlux\webui\modules\processing.py", line 1507, in setup_conds super().setup_conds() File "E:\ForgeFlux\webui\modules\processing.py", line 494, in setup_conds self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data) File "E:\ForgeFlux\webui\modules\processing.py", line 463, in get_conds_with_caching cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling) File "E:\ForgeFlux\webui\modules\prompt_parser.py", line 262, in get_multicond_learned_conditioning learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling) File "E:\ForgeFlux\webui\modules\prompt_parser.py", line 189, in get_learned_conditioning conds = model.get_learned_conditioning(texts) File "E:\ForgeFlux\system\python\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context return func(*args, *kwargs) File "E:\ForgeFlux\webui\backend\diffusion_engine\flux.py", line 79, in get_learned_conditioning cond_t5 = self.text_processing_engine_t5(prompt) File "E:\ForgeFlux\webui\backend\text_processing\t5_engine.py", line 123, in call z = self.process_tokens([tokens], [multipliers])[0] File "E:\ForgeFlux\webui\backend\text_processing\t5_engine.py", line 134, in process_tokens z = self.encode_with_transformers(tokens) File "E:\ForgeFlux\webui\backend\text_processing\t5_engine.py", line 60, in encode_with_transformers z = self.text_encoder( File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(args, kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 205, in forward return self.encoder(x, *args, *kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(args, kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 186, in forward x, past_bias = l(x, mask, past_bias) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, *kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(args, kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 162, in forward x, past_bias = self.layer[0](x, mask, past_bias) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, *kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 149, in forward output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl return self._call_impl(args, kwargs) File "E:\ForgeFlux\system\python\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl return forward_call(*args, kwargs) File "E:\ForgeFlux\webui\backend\nn\t5.py", line 138, in forward out = attention_function(q, k * ((k.shape[-1] / self.num_heads) * 0.5), v, self.num_heads, mask) File "E:\ForgeFlux\webui\backend\attention.py", line 314, in attention_xformers mask_out[:, :, :mask.shape[-1]] = mask RuntimeError: The expanded size of the tensor (1) must match the existing size (64) at non-singleton dimension 0. Target sizes: [1, 256, 256]. Tensor sizes: [64, 256, 256] The expanded size of the tensor (1) must match the existing size (64) at non-singleton dimension 0. Target sizes: [1, 256, 256]. Tensor sizes: [64, 256, 256] Error completing request Arguments: ('task(s1hcu5w2p2fyufe)', <gradio.route_utils.Request object at 0x000001BB023C4340>, 'Cyberpunk sign that reads FLUX IN FORGE', '', [], 1, 1, 1, 3.5, 1152, 896, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', None, 0, 10, 'Euler', 'Simple', False, -1, False, -1, 0, 0, 0, ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=None, batch_mask_gallery=None, generated_image=None, mask_image=None, mask_image_fg=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, image_fg=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=None, batch_mask_gallery=None, generated_image=None, mask_image=None, mask_image_fg=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, image_fg=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=None, batch_mask_gallery=None, generated_image=None, mask_image=None, mask_image_fg=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, image_fg=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0.0, guidance_end=1.0, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), False, 7, 1, 'Constant', 0, 'Constant', 0, 1, 'enable', 'MEAN', 'AD', 1, False, 1.01, 1.02, 0.99, 0.95, False, 0.5, 2, False, 3, False, 3, 2, 0, 0.35, True, 'bicubic', 'bicubic', False, 0, 'anisotropic', 0, 'reinhard', 100, 0, 'subtract', 0, 0, 'gaussian', 'add', 0, 100, 127, 0, 'hard_clamp', 5, 0, 'None', 'None', False, 'MultiDiffusion', 768, 768, 64, 4, False, False, False, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', '', 0, '', '', 0, '', '', True, False, False, False, False, False, False, 0, False) {} Traceback (most recent call last): File "E:\ForgeFlux\webui\modules\call_queue.py", line 74, in f res = list(func(args, kwargs)) TypeError: 'NoneType' object is not iterable