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Reverted txt2vid to use the StableDiffusionPipeline instead of StableDiffusionWalkPipeline when loading the model. (#1630)
This commit is contained in:
Alejandro Gil
GitHub
commit
e13132c78c
@ -654,40 +654,6 @@ def layout():
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
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#if 'latestImages' in st.session_state:
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#for i in output_images:
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##push the new image to the list of latest images and remove the oldest one
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##remove the last index from the list\
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#st.session_state['latestImages'].pop()
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##add the new image to the start of the list
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#st.session_state['latestImages'].insert(0, i)
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#PlaceHolder.empty()
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#with PlaceHolder.container():
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#col1, col2, col3 = st.columns(3)
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#col1_cont = st.container()
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#col2_cont = st.container()
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#col3_cont = st.container()
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#images = st.session_state['latestImages']
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#with col1_cont:
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#with col1:
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#[st.image(images[index]) for index in [0, 3, 6] if index < len(images)]
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#with col2_cont:
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#with col2:
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#[st.image(images[index]) for index in [1, 4, 7] if index < len(images)]
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#with col3_cont:
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#with col3:
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#[st.image(images[index]) for index in [2, 5, 8] if index < len(images)]
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#historyGallery = st.empty()
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## check if output_images length is the same as seeds length
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#with gallery_tab:
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#st.markdown(createHTMLGallery(output_images,seeds), unsafe_allow_html=True)
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#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
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with gallery_tab:
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logger.info(seeds)
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st.session_state["gallery"].text = ""
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@ -377,10 +377,9 @@ class StableDiffusionWalkPipeline(DiffusionPipeline):
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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print(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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print("The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
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text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
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else:
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@ -613,7 +612,7 @@ class StableDiffusionWalkPipeline(DiffusionPipeline):
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def walk(
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self,
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prompts: Optional[List[str]] = None,
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prompt: Optional[List[str]] = None,
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seeds: Optional[List[int]] = None,
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num_interpolation_steps: Optional[Union[int, List[int]]] = 5, # int or list of int
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output_dir: Optional[str] = "./dreams",
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@ -1108,7 +1107,7 @@ def load_diffusers_model(weights_path,torch_device):
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model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-5")
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if not os.path.exists(model_path + "/model_index.json"):
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server_state["pipe"] = StableDiffusionWalkPipeline.from_pretrained(
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server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
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weights_path,
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use_local_file=True,
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use_auth_token=st.session_state["defaults"].general.huggingface_token,
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@ -1116,11 +1115,12 @@ def load_diffusers_model(weights_path,torch_device):
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revision="fp16" if not st.session_state['defaults'].general.no_half else None,
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safety_checker=None, # Very important for videos...lots of false positives while interpolating
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#custom_pipeline="interpolate_stable_diffusion",
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)
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StableDiffusionWalkPipeline.save_pretrained(server_state["pipe"], model_path)
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StableDiffusionPipeline.save_pretrained(server_state["pipe"], model_path)
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else:
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server_state["pipe"] = StableDiffusionWalkPipeline.from_pretrained(
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server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
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model_path,
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use_local_file=True,
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torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
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@ -1166,9 +1166,11 @@ def load_diffusers_model(weights_path,torch_device):
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if "huggingface_token" not in st.session_state or st.session_state["defaults"].general.huggingface_token == "None":
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if "progress_bar_text" in st.session_state:
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st.session_state["progress_bar_text"].error(
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"You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token."
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)
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raise OSError("You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token.")
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"You need a huggingface token in order to use the Text to Video tab. Use the Settings page to add your token under the Huggingface section. "
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"Make sure you save your settings after adding it."
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)
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raise OSError("You need a huggingface token in order to use the Text to Video tab. Use the Settings page to add your token under the Huggingface section. "
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"Make sure you save your settings after adding it.")
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else:
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if "progress_bar_text" in st.session_state:
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st.session_state["progress_bar_text"].error(e)
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@ -1434,29 +1436,30 @@ def txt2vid(
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# works correctly generating all frames but do not show the preview image
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# we also do not have control over the generation and cant stop it until the end of it.
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#with torch.autocast("cuda"):
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#print (prompts)
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#video_path = server_state["pipe"].walk(
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#prompts=prompts,
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#seeds=seeds,
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#num_interpolation_steps=num_steps,
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#height=height, # use multiples of 64 if > 512. Multiples of 8 if < 512.
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#width=width, # use multiples of 64 if > 512. Multiples of 8 if < 512.
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#batch_size=4,
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#fps=30,
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#image_file_ext = ".png",
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#eta = 0.0,
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#output_dir=full_path, # Where images/videos will be saved
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##name='test', # Subdirectory of output_dir where images/videos will be saved
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#guidance_scale=cfg_scale, # Higher adheres to prompt more, lower lets model take the wheel
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#num_inference_steps=num_inference_steps, # Number of diffusion steps per image generated. 50 is good default
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#upsample = False,
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##do_loop=do_loop, # Change to True if you want last prompt to loop back to first prompt
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#resume = False,
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#audio_filepath = None,
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#audio_start_sec = None,
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#margin = 1.0,
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#smooth = 0.0,
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#callback=txt2vid_generation_callback, # our callback function will be called with the arguments callback(step, timestep, latents)
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#callback_steps=1 # our callback function will be called once this many steps are processed in a single frame
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#prompt=prompts,
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#seeds=seeds,
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#num_interpolation_steps=num_steps,
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#height=height, # use multiples of 64 if > 512. Multiples of 8 if < 512.
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#width=width, # use multiples of 64 if > 512. Multiples of 8 if < 512.
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#batch_size=4,
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#fps=30,
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#image_file_ext = ".png",
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#eta = 0.0,
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#output_dir=full_path, # Where images/videos will be saved
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##name='test', # Subdirectory of output_dir where images/videos will be saved
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#guidance_scale=cfg_scale, # Higher adheres to prompt more, lower lets model take the wheel
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#num_inference_steps=num_inference_steps, # Number of diffusion steps per image generated. 50 is good default
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#upsample = False,
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##do_loop=do_loop, # Change to True if you want last prompt to loop back to first prompt
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#resume = False,
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#audio_filepath = None,
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#audio_start_sec = None,
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#margin = 1.0,
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#smooth = 0.0,
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#callback=txt2vid_generation_callback, # our callback function will be called with the arguments callback(step, timestep, latents)
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#callback_steps=1 # our callback function will be called once this many steps are processed in a single frame
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#)
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# old code
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@ -123,7 +123,10 @@ def layout():
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# specify the primary menu definition
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menu_data = [
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{'id': 'Stable Diffusion', 'label': 'Stable Diffusion', 'icon': 'bi bi-grid-1x2-fill'},
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{'id': 'Textual Inversion', 'label': 'Textual Inversion', 'icon': 'bi bi-lightbulb-fill'},
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{'id': 'Train','label':"Train", 'icon': "bi bi-lightbulb-fill", 'submenu':[
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{'id': 'Textual Inversion', 'label': 'Textual Inversion', 'icon': 'bi bi-lightbulb-fill'},
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{'id': 'Fine Tunning', 'label': 'Fine Tunning', 'icon': 'bi bi-lightbulb-fill'},
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]},
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{'id': 'Model Manager', 'label': 'Model Manager', 'icon': 'bi bi-cloud-arrow-down-fill'},
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{'id': 'Tools','label':"Tools", 'icon': "bi bi-tools", 'submenu':[
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{'id': 'API Server', 'label': 'API Server', 'icon': 'bi bi-server'},
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@ -188,8 +191,9 @@ def layout():
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st.experimental_rerun()
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txt2img_tab, img2img_tab, txt2vid_tab, img2txt_tab, concept_library_tab = st.tabs(["Text-to-Image", "Image-to-Image",
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#"Inpainting",
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"Text-to-Video", "Image-To-Text",
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"Concept Library"])
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"Concept Library"])
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#with home_tab:
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#from home import layout
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#layout()
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@ -229,6 +233,11 @@ def layout():
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from textual_inversion import layout
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layout()
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elif menu_id == 'Fine Tunning':
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#from textual_inversion import layout
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#layout()
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st.info("Under Construction. :construction_worker:")
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elif menu_id == 'API Server':
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set_page_title("API Server - Stable Diffusion Playground")
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from APIServer import layout
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