sync

requirements.txt

@@ -48,7 +48,7 @@ barfi==0.7.0
 # Environment Dependencies for WebUI (flet)
 
 # txt2vid
-diffusers==0.6.0
+diffusers==0.7.2
 librosa==0.9.2
 
 # img2img inpainting

scripts/convert_original_stable_diffusion_to_diffusers.py

@@ -30,7 +30,7 @@
 from diffusers import (
     AutoencoderKL,
     DDIMScheduler,
-    DPMSolverMultistepScheduler,
+    #DPMSolverMultistepScheduler,
     EulerAncestralDiscreteScheduler,
     EulerDiscreteScheduler,
     LDMTextToImagePipeline,
@@ -627,8 +627,11 @@ def convert_ldm_clip_checkpoint(checkpoint):
     for key in keys:
         if key.startswith("cond_stage_model.transformer"):
             text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
-
-    text_model.load_state_dict(text_model_dict)
+
+    try:
+        text_model.load_state_dict(text_model_dict)
+    except RuntimeError:
+        pass
 
     return text_model
 
@@ -748,4 +751,4 @@ def convert_ldm_clip_checkpoint(checkpoint):
         tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
         pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
 
-    pipe.save_pretrained(args.dump_path)
+    pipe.save_pretrained(args.dump_path)

scripts/txt2vid.py

@@ -54,7 +54,7 @@
 #from stable_diffusion_videos import StableDiffusionWalkPipeline
 
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, \
-     PNDMScheduler
+     PNDMScheduler, DDPMScheduler
 
 from diffusers.configuration_utils import FrozenDict
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
@@ -1125,28 +1125,30 @@ def load_diffusers_model(weights_path,torch_device):
 
                 if weights_path == "runwayml/stable-diffusion-v1-5":
                     model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-5")
+                else:
+                    model_path = weights_path
 
                 if not os.path.exists(model_path + "/model_index.json"):
                     server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
                         weights_path,
-                                            use_local_file=True,
-                                                use_auth_token=st.session_state["defaults"].general.huggingface_token,
-                                                torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
-                                                revision="fp16" if not st.session_state['defaults'].general.no_half else None,
-                                                safety_checker=None,  # Very important for videos...lots of false positives while interpolating
-                                                #custom_pipeline="interpolate_stable_diffusion",
+                        use_local_file=True,
+                        use_auth_token=st.session_state["defaults"].general.huggingface_token,
+                        torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
+                        revision="fp16" if not st.session_state['defaults'].general.no_half else None,
+                        safety_checker=None,  # Very important for videos...lots of false positives while interpolating
+                        #custom_pipeline="interpolate_stable_diffusion",
 
                     )
 
                     StableDiffusionPipeline.save_pretrained(server_state["pipe"], model_path)
                 else:
                     server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
                         model_path,
-                                            use_local_file=True,
-                                                torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
-                                                revision="fp16" if not st.session_state['defaults'].general.no_half else None,
-                                                safety_checker=None,  # Very important for videos...lots of false positives while interpolating
-                                                #custom_pipeline="interpolate_stable_diffusion",
+                        use_local_file=True,
+                        torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
+                        revision="fp16" if not st.session_state['defaults'].general.no_half else None,
+                        safety_checker=None,  # Very important for videos...lots of false positives while interpolating
+                        #custom_pipeline="interpolate_stable_diffusion",
                     )
 
                 server_state["pipe"].unet.to(torch_device)
@@ -1358,8 +1360,30 @@ def txt2vid(
     klms_scheduler = LMSDiscreteScheduler(
         beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
     )
+
+    #flaxddims_scheduler = FlaxDDIMScheduler(
+        #beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
+    #)
+
+    #flaxddpms_scheduler = FlaxDDPMScheduler(
+        #beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
+    #)
+
+    #flaxpndms_scheduler = FlaxPNDMScheduler(
+        #beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
+    #)
+
+    ddpms_scheduler = DDPMScheduler(
+        beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
+    )    
 
-    SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler, klms=klms_scheduler)
+    SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler,
+                      klms=klms_scheduler,
+                      ddpms=ddpms_scheduler,
+                      #flaxddims=flaxddims_scheduler,
+                      #flaxddpms=flaxddpms_scheduler,
+                      #flaxpndms=flaxpndms_scheduler,
+                      )
 
     with st.session_state["progress_bar_text"].container():
         with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
@@ -1721,7 +1745,9 @@ def layout():
         #sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a",  "k_heun", "PLMS", "DDIM"]
         #sampler_name = st.selectbox("Sampling method", sampler_name_list,
                         #index=sampler_name_list.index(st.session_state['defaults'].txt2vid.default_sampler), help="Sampling method to use. Default: k_euler")
-        scheduler_name_list = ["klms", "ddim"]
+        scheduler_name_list = ["klms", "ddim", "ddpms", 
+                               #"flaxddims", "flaxddpms", "flaxpndms"
+                               ]
         scheduler_name = st.selectbox("Scheduler:", scheduler_name_list,
                                       index=scheduler_name_list.index(st.session_state['defaults'].txt2vid.scheduler_name), help="Scheduler to use. Default: klms")