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Running
on
Zero
Running
on
Zero
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app.py
CHANGED
@@ -51,15 +51,7 @@ MAX_SEED = 2**32-1
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# https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux
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#@spaces.GPU()
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def change_base_model(repo_id: str, cn_on: bool, disable_model_cache: bool, progress=gr.Progress(track_tqdm=True)):
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global pipe
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global pipe_i2i
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global taef1
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global good_vae
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global controlnet_union
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global controlnet
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global last_model
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global last_cn_on
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global dtype
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try:
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if not disable_model_cache and (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True)
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pipe.to("cpu")
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@@ -138,12 +130,9 @@ def update_selection(evt: gr.SelectData, width, height):
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)
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@spaces.GPU(duration=70)
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def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)):
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global pipe
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global taef1
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global good_vae
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global controlnet
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global controlnet_union
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try:
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good_vae.to("cuda")
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taef1.to("cuda")
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@@ -194,11 +183,9 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
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raise gr.Error(f"Inference Error: {e}") from e
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@spaces.GPU(duration=70)
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def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed, cn_on, progress=gr.Progress(track_tqdm=True)):
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global pipe_i2i
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global good_vae
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global controlnet
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global controlnet_union
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try:
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good_vae.to("cuda")
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generator = torch.Generator(device="cuda").manual_seed(int(float(seed)))
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# https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux
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#@spaces.GPU()
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def change_base_model(repo_id: str, cn_on: bool, disable_model_cache: bool, progress=gr.Progress(track_tqdm=True)):
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global pipe, pipe_i2i, taef1, good_vae, controlnet_union, controlnet, last_model, last_cn_on, dtype
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try:
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if not disable_model_cache and (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True)
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pipe.to("cpu")
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)
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@spaces.GPU(duration=70)
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@torch.inference_mode()
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def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)):
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global pipe, taef1, good_vae, controlnet, controlnet_union
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try:
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good_vae.to("cuda")
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taef1.to("cuda")
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raise gr.Error(f"Inference Error: {e}") from e
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@spaces.GPU(duration=70)
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@torch.inference_mode()
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def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed, cn_on, progress=gr.Progress(track_tqdm=True)):
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global pipe_i2i, good_vae, controlnet, controlnet_union
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try:
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good_vae.to("cuda")
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generator = torch.Generator(device="cuda").manual_seed(int(float(seed)))
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